Compare commits
@@ -0,0 +1,75 @@
|
||||
{
|
||||
"env": {
|
||||
"PQL_VAULT": "/mnt/media/Projects/tatlock"
|
||||
},
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||||
"permissions": {
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||||
"allow": [
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||||
"Bash(pql)",
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||||
"Bash(pql *)",
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||||
"Bash(/home/jpmschweitzer/.local/bin/pql:*)",
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||||
"Bash(git status:*)",
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||||
"Bash(git log:*)",
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||||
"Bash(git diff:*)",
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||||
"Bash(git branch:*)",
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||||
"Bash(make test:*)",
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||||
"Bash(make test-unit:*)",
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||||
"Bash(make test-contracts:*)",
|
||||
"Bash(make lint:*)",
|
||||
"Bash(make typecheck:*)",
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||||
"Bash(.venv/bin/python -m pytest:*)",
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||||
"Bash(.venv/bin/pytest:*)",
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||||
"Bash(pytest:*)",
|
||||
"Bash(ruff check:*)",
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||||
"Bash(mypy:*)",
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||||
"Bash(docker logs tatlock:*)",
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||||
"Bash(curl -s http://localhost:8000/*)",
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||||
"Bash(curl -s http://localhost:8777/*)"
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||||
],
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||||
"deny": [
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||||
"Bash(/mnt/media/Projects/cladmin/ops/bin/toj)",
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||||
"Bash(/mnt/media/Projects/cladmin/ops/bin/toj:*)",
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||||
"Bash(chmod -R 777 *)",
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||||
"Bash(chmod 777 *)",
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||||
"Bash(dd if=*)",
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||||
"Bash(find * -delete*)",
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||||
"Bash(find * -exec*)",
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||||
"Bash(git * add --all*)",
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"Bash(git * add -A*)",
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||||
"Bash(git * add .)",
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||||
"Bash(git * branch -D *)",
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||||
"Bash(git * checkout -- *)",
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||||
"Bash(git * clean -fd*)",
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"Bash(git * clean -fdx*)",
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||||
"Bash(git * commit --no-verify*)",
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||||
"Bash(git * merge --no-ff*)",
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||||
"Bash(git * push --force*)",
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"Bash(git * push -f*)",
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"Bash(git * reset --hard*)",
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"Bash(git * restore .*)",
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"Bash(git add --all*)",
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"Bash(git add -A*)",
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"Bash(git add .)",
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"Bash(git branch -D *)",
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||||
"Bash(git checkout -- *)",
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"Bash(git clean -fd*)",
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||||
"Bash(git clean -fdx*)",
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||||
"Bash(git commit --no-verify*)",
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||||
"Bash(git merge --no-ff*)",
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||||
"Bash(git push --force*)",
|
||||
"Bash(git push -f*)",
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||||
"Bash(git reset --hard*)",
|
||||
"Bash(git restore .*)",
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||||
"Bash(mkfs*)",
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||||
"Bash(ollama rm *)",
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||||
"Bash(redis-cli * FLUSHALL*)",
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||||
"Bash(redis-cli * FLUSHDB*)",
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||||
"Bash(rm -rf $HOME)",
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||||
"Bash(rm -rf /)",
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||||
"Bash(rm -rf ~)",
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||||
"Bash(su *)",
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"Bash(sudo *)",
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||||
"Bash(toj)",
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"Bash(toj:*)"
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]
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}
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}
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+11
-9
@@ -8,17 +8,19 @@ API_HOST=0.0.0.0
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API_PORT=8000
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API_PREFIX=/v1
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# Anthropic Configuration (Claude - preferred backend)
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# Set ANTHROPIC_API_KEY to enable Claude as the default backend
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# Without an API key, Tatlock uses Ollama exclusively
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# ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
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ANTHROPIC_MODEL=claude-sonnet-4-20250514
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||||
PREFER_CLOUD_BACKEND=true
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# Ollama Configuration (local fallback when Claude unavailable)
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# Ollama Configuration (local - primary backend)
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OLLAMA_HOST=http://localhost:11434
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OLLAMA_DEFAULT_MODEL=mistral-nemo:latest
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OLLAMA_DEFAULT_MODEL=gemma4:e2b
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OLLAMA_TIMEOUT=120
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STEWARD_TIMEOUT=60
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||||
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||||
# Anthropic Configuration (Claude - cloud fallback)
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# Set ANTHROPIC_API_KEY to keep the Claude fallback available: it is used
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# automatically when Ollama is down, or exclusively when PREFER_CLOUD_BACKEND=true
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# Without an API key, Tatlock uses Ollama only
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||||
# ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
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||||
ANTHROPIC_MODEL=claude-sonnet-5
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||||
PREFER_CLOUD_BACKEND=false
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||||
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||||
# SearXNG Configuration
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||||
SEARXNG_HOST=http://localhost:8087
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||||
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@@ -0,0 +1 @@
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||||
.pql/changelog/*.sql merge=union
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@@ -1,8 +1,9 @@
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name: Build and Push
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||||
on:
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||||
release:
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||||
types: [published]
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||||
push:
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||||
tags:
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||||
- 'v[0-9]*'
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||||
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||||
jobs:
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||||
release:
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||||
@@ -15,7 +16,7 @@ jobs:
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||||
-H "Content-Type: application/json" \
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-d '{"tag_name": "${{ github.ref_name }}", "name": "Release ${{ github.ref_name }}", "body": "Automated release for ${{ github.ref_name }}"}' \
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||||
"${{ github.server_url }}/api/v1/repos/${{ github.repository }}/releases"
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||||
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||||
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||||
build:
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||||
runs-on: ubuntu-latest
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||||
steps:
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||||
@@ -24,7 +25,7 @@ jobs:
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||||
- name: Login to Gitea Registry
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||||
uses: docker/login-action@v3
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||||
with:
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||||
registry: git.schweitz.internal
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||||
registry: git.schweitz.net
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||||
username: ${{ secrets.REGISTRY_USER }}
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password: ${{ secrets.REGISTRY_PASSWORD }}
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||||
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||||
@@ -36,8 +37,8 @@ jobs:
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provenance: false
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sbom: false
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tags: |
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||||
git.schweitz.internal/jpmschweitzer/tatlock:latest
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||||
git.schweitz.internal/jpmschweitzer/tatlock:${{ github.ref_name }}
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||||
git.schweitz.net/jpmschweitzer/tatlock:latest
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||||
git.schweitz.net/jpmschweitzer/tatlock:${{ github.ref_name }}
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||||
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||||
- name: Trigger Watchtower update
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if: success()
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||||
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||||
Executable
+13
@@ -0,0 +1,13 @@
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||||
#!/usr/bin/env bash
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# Trigger only. The checks live in the Makefile, where they can be read, run by
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||||
# hand (`make pre-push`), and changed under review.
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||||
#
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||||
# This file is identical in every repo in this workspace, deliberately: the call
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||||
# surface is the same everywhere even though what each gate runs is not, so
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||||
# nobody has to read a repo to find out how to check it (D-27).
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||||
#
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||||
# Enable per clone with: git config core.hooksPath .githooks
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||||
# Never bypass with --no-verify. Suppress a specific finding deliberately
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||||
# instead, with a reason — see `make pre-push`.
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||||
set -euo pipefail
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||||
exec make -C "$(git rev-parse --show-toplevel)" pre-push
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||||
+24
-6
@@ -46,27 +46,32 @@ ENV/
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||||
.ipynb_checkpoints/
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||||
*.ipynb
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||||
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||||
# Testing & Coverage
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||||
# Caches (pytest, mypy, ruff)
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||||
.cache/
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||||
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||||
# Build output (coverage, logs)
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||||
build/
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||||
|
||||
# Legacy cache/output locations (in case tools fall back)
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||||
.pytest_cache/
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||||
.mypy_cache/
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||||
.ruff_cache/
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||||
.coverage
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||||
.coverage.*
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||||
coverage.xml
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||||
htmlcov/
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||||
|
||||
# Testing
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||||
.tox/
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||||
.nox/
|
||||
*.cover
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||||
.hypothesis/
|
||||
|
||||
# Type checking
|
||||
.mypy_cache/
|
||||
.dmypy.json
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||||
dmypy.json
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||||
.pyre/
|
||||
.pytype/
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||||
|
||||
# Linting
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||||
.ruff_cache/
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||||
|
||||
# Logs
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||||
logs/*
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||||
!logs/traces/
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||||
@@ -98,3 +103,16 @@ ollama_data/
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||||
ehthumbs.db
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||||
Thumbs.db
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||||
Desktop.ini
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||||
|
||||
# Claude Code user-specific settings
|
||||
.claude/settings.local.json
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||||
.pql/*
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||||
!.pql/changelog/
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||||
|
||||
# pql shims planted by `pql init` into the dir core.hooksPath points at.
|
||||
# Per-clone: each embeds the absolute path of the pql binary that planted it.
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||||
# Only .githooks/pre-push is shared.
|
||||
.githooks/pre-commit
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||||
.githooks/post-merge
|
||||
.githooks/post-checkout
|
||||
.githooks/post-rewrite
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||||
|
||||
@@ -0,0 +1,11 @@
|
||||
-- Changelog format marker, written by pql. Comments only: this file
|
||||
-- is never executed — Import descends into the per-table directories
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||||
-- and does not read the changelog root.
|
||||
--
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||||
-- A changelog carrying no marker is format 1, the shape that existed
|
||||
-- before formats were versioned. An older format is migrated forward
|
||||
-- by `pql plan upgrade` (and automatically from the post-merge hook);
|
||||
-- a newer one is refused rather than replayed under rules this binary
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||||
-- does not know. See D-28 and docs/versions.md.
|
||||
-- pql:changelog_format: 2.0.0
|
||||
-- pql:written_by: 2.2.0
|
||||
@@ -0,0 +1,139 @@
|
||||
-- Auto-generated by pql init. CREATE TABLE statements
|
||||
-- for the planning schema; per-table dir keeps the changelog
|
||||
-- self-describing per D-15. CREATE TABLE IF NOT EXISTS is
|
||||
-- idempotent so running schema files from each directory in
|
||||
-- replay order is harmless.
|
||||
--
|
||||
-- Importer parses the markers below to detect schema drift
|
||||
-- between the producing pql version and the local one — a
|
||||
-- bumped canonical_version means projection rules changed
|
||||
-- and replay must refuse rather than silently corrupt state.
|
||||
-- pql:created_by: 2.2.0
|
||||
-- pql:canonical_version: 2
|
||||
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decisions (
|
||||
id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('confirmed','question','rejected')),
|
||||
domain TEXT NOT NULL,
|
||||
title TEXT NOT NULL,
|
||||
status TEXT NOT NULL DEFAULT 'active'
|
||||
CHECK(status IN ('active','superseded','resolved','open')),
|
||||
date TEXT,
|
||||
file_path TEXT NOT NULL,
|
||||
synced_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decision_refs (
|
||||
source_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
target_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
ref_type TEXT NOT NULL
|
||||
CHECK(ref_type IN ('supersedes','references','resolves','depends_on','amends')),
|
||||
note TEXT,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (source_id, target_id, ref_type)
|
||||
);
|
||||
|
||||
-- Identity split (D-26): a ticket's stable, collision-proof identity is its
|
||||
-- record_id (a locally-generated ULID, planning.NewRecordID); the friendly
|
||||
-- T-NNN label lives in ticket_idmap and may be reconciled. Every structural
|
||||
-- reference (parent, deps, history, labels) targets record_id, so a label
|
||||
-- clash never corrupts the graph — only ticket_idmap needs a relabel.
|
||||
CREATE TABLE IF NOT EXISTS tickets (
|
||||
record_id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('initiative','epic','story','task','bug')),
|
||||
parent_record_id TEXT REFERENCES tickets(record_id),
|
||||
title TEXT NOT NULL,
|
||||
description TEXT,
|
||||
-- No CHECK enumeration: the ticket status vocabulary is per-vault
|
||||
-- configurable (ticket_statuses in .pql/config.yaml). Validation lives
|
||||
-- in Go (planning.StatusSet), so adding/renaming statuses needs no
|
||||
-- schema change. The DEFAULT is a harmless fallback — CreateTicket
|
||||
-- always inserts the configured default explicitly.
|
||||
status TEXT NOT NULL DEFAULT 'backlog',
|
||||
priority TEXT DEFAULT 'medium'
|
||||
CHECK(priority IN ('critical','high','medium','low')),
|
||||
assigned_to TEXT,
|
||||
team TEXT,
|
||||
decision_ref TEXT REFERENCES decisions(id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
-- ticket_idmap maps a record_id to its current friendly label (T-NNN).
|
||||
-- ticket_id is intentionally NOT globally unique: two uncoordinated clones
|
||||
-- can mint the same label, which surfaces as a duplicate-label collision
|
||||
-- (detected at replay) and is fixed with "pql ticket relabel".
|
||||
CREATE TABLE IF NOT EXISTS ticket_idmap (
|
||||
record_id TEXT PRIMARY KEY REFERENCES tickets(record_id),
|
||||
ticket_id TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_deps (
|
||||
blocker_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
blocked_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (blocker_record_id, blocked_record_id)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_history (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
field TEXT NOT NULL,
|
||||
old_value TEXT,
|
||||
new_value TEXT,
|
||||
changed_by TEXT,
|
||||
changed_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT UNIQUE,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_labels (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
label TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (ticket_record_id, label)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS meta (
|
||||
key TEXT PRIMARY KEY,
|
||||
value TEXT NOT NULL,
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now'))
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_status ON tickets(status);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_team ON tickets(team);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_decision_ref ON tickets(decision_ref);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_assigned ON tickets(assigned_to);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_parent ON tickets(parent_record_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_ticket_idmap_label ON ticket_idmap(ticket_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_domain ON decisions(domain);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_type ON decisions(type);
|
||||
CREATE INDEX IF NOT EXISTS idx_decision_refs_target ON decision_refs(target_id);
|
||||
@@ -0,0 +1,139 @@
|
||||
-- Auto-generated by pql init. CREATE TABLE statements
|
||||
-- for the planning schema; per-table dir keeps the changelog
|
||||
-- self-describing per D-15. CREATE TABLE IF NOT EXISTS is
|
||||
-- idempotent so running schema files from each directory in
|
||||
-- replay order is harmless.
|
||||
--
|
||||
-- Importer parses the markers below to detect schema drift
|
||||
-- between the producing pql version and the local one — a
|
||||
-- bumped canonical_version means projection rules changed
|
||||
-- and replay must refuse rather than silently corrupt state.
|
||||
-- pql:created_by: 2.2.0
|
||||
-- pql:canonical_version: 2
|
||||
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decisions (
|
||||
id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('confirmed','question','rejected')),
|
||||
domain TEXT NOT NULL,
|
||||
title TEXT NOT NULL,
|
||||
status TEXT NOT NULL DEFAULT 'active'
|
||||
CHECK(status IN ('active','superseded','resolved','open')),
|
||||
date TEXT,
|
||||
file_path TEXT NOT NULL,
|
||||
synced_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decision_refs (
|
||||
source_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
target_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
ref_type TEXT NOT NULL
|
||||
CHECK(ref_type IN ('supersedes','references','resolves','depends_on','amends')),
|
||||
note TEXT,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (source_id, target_id, ref_type)
|
||||
);
|
||||
|
||||
-- Identity split (D-26): a ticket's stable, collision-proof identity is its
|
||||
-- record_id (a locally-generated ULID, planning.NewRecordID); the friendly
|
||||
-- T-NNN label lives in ticket_idmap and may be reconciled. Every structural
|
||||
-- reference (parent, deps, history, labels) targets record_id, so a label
|
||||
-- clash never corrupts the graph — only ticket_idmap needs a relabel.
|
||||
CREATE TABLE IF NOT EXISTS tickets (
|
||||
record_id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('initiative','epic','story','task','bug')),
|
||||
parent_record_id TEXT REFERENCES tickets(record_id),
|
||||
title TEXT NOT NULL,
|
||||
description TEXT,
|
||||
-- No CHECK enumeration: the ticket status vocabulary is per-vault
|
||||
-- configurable (ticket_statuses in .pql/config.yaml). Validation lives
|
||||
-- in Go (planning.StatusSet), so adding/renaming statuses needs no
|
||||
-- schema change. The DEFAULT is a harmless fallback — CreateTicket
|
||||
-- always inserts the configured default explicitly.
|
||||
status TEXT NOT NULL DEFAULT 'backlog',
|
||||
priority TEXT DEFAULT 'medium'
|
||||
CHECK(priority IN ('critical','high','medium','low')),
|
||||
assigned_to TEXT,
|
||||
team TEXT,
|
||||
decision_ref TEXT REFERENCES decisions(id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
-- ticket_idmap maps a record_id to its current friendly label (T-NNN).
|
||||
-- ticket_id is intentionally NOT globally unique: two uncoordinated clones
|
||||
-- can mint the same label, which surfaces as a duplicate-label collision
|
||||
-- (detected at replay) and is fixed with "pql ticket relabel".
|
||||
CREATE TABLE IF NOT EXISTS ticket_idmap (
|
||||
record_id TEXT PRIMARY KEY REFERENCES tickets(record_id),
|
||||
ticket_id TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_deps (
|
||||
blocker_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
blocked_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (blocker_record_id, blocked_record_id)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_history (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
field TEXT NOT NULL,
|
||||
old_value TEXT,
|
||||
new_value TEXT,
|
||||
changed_by TEXT,
|
||||
changed_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT UNIQUE,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_labels (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
label TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (ticket_record_id, label)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS meta (
|
||||
key TEXT PRIMARY KEY,
|
||||
value TEXT NOT NULL,
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now'))
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_status ON tickets(status);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_team ON tickets(team);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_decision_ref ON tickets(decision_ref);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_assigned ON tickets(assigned_to);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_parent ON tickets(parent_record_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_ticket_idmap_label ON ticket_idmap(ticket_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_domain ON decisions(domain);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_type ON decisions(type);
|
||||
CREATE INDEX IF NOT EXISTS idx_decision_refs_target ON decision_refs(target_id);
|
||||
@@ -0,0 +1,34 @@
|
||||
INSERT INTO ticket_history (ticket_record_id, field, old_value, new_value, changed_by, changed_at, created_at, updated_at, deleted_at, hash, canonical_version) VALUES ('06FZ44Z6HSN0RQS0QAYNTPEM5G', 'description', NULL, '`make typecheck` reports 95 errors in 31 files (was 103). This is a dedicated programming pass, not lint tidying, and it is what currently blocks `make pre-push`.
|
||||
|
||||
MEASURED 2026-08-11 so the next session does not re-derive it:
|
||||
|
||||
29 no-untyped-def functions with no annotations — the bulk, and genuine per-function work
|
||||
18 no-any-return mostly downstream of the above
|
||||
12 assignment
|
||||
11 arg-type
|
||||
5 unused-ignore `# type: ignore` comments mypy says are no longer needed
|
||||
5 union-attr
|
||||
4 var-annotated
|
||||
3 override
|
||||
remainder: misc, dict-item, attr-defined, return-value, call-overload
|
||||
|
||||
By file: agents/tatlock.py 15, core/memory_service.py 11, responses/streaming.py 8, responses/service.py 7, core/context.py 6.
|
||||
|
||||
THE TWO SHARED ROOTS ARE ALREADY FIXED (dac259a), so what is left has no lever in it. For reference, they were: five conversation lists declared bare, where mypy infers the element type from the first append (a ModelRequest) and then rejects every ModelResponse; and an agent built as Agent(model, system_prompt=...) with no deps_type, inferred Agent[None, str], while every tool it registers takes RunContext[ToolCallTracker].
|
||||
|
||||
WORTH KNOWING BEFORE STARTING. Annotating partially made mypy count go UP before it went down — declaring `_agent: Agent | None` took agents/tatlock.py from 22 to 24, because resolving the bare Agent to Agent[None, str] surfaced four argument-type errors the Any had been hiding. Expect that shape: a rising count during this work usually means concealment ending, not damage.
|
||||
|
||||
The mypy config is strict — disallow_untyped_defs, disallow_incomplete_defs, warn_return_any, check_untyped_defs, strict_equality — so there is no partial-credit setting to lean on, and weakening it would be the wrong trade for a codebase this central.
|
||||
|
||||
TWO PRE-EXISTING TEST FACTS, both confirmed at HEAD and neither caused by the lint work:
|
||||
- test_tatlock_tool_call_logging_calculator is flaky: failed 2 of 5 full runs, on HEAD and on the lint branch, and fails in isolation at HEAD while passing in isolation after the lint pass. Order- or timing-dependent.
|
||||
- `pytest tests/` cannot collect at all: tests/e2e/test_orchestration_e2e.py uses an `e2e` marker that is not registered and the config is strict about markers. `make test` passes only because it ignores tests/e2e, tests/integration and tests/contracts.', NULL, '2026-08-11 18:26:58', '2026-08-11 18:26:58.523', '2026-08-11 18:26:58.523', NULL, 'c8c9b6a20cf18ca903fc8c720f70e73d', 2) ON CONFLICT(hash) DO NOTHING;
|
||||
INSERT INTO ticket_history (ticket_record_id, field, old_value, new_value, changed_by, changed_at, created_at, updated_at, deleted_at, hash, canonical_version) VALUES ('06FZ4JX9DZB3XAB95EP23YWXMC', 'description', NULL, '`.env` still carries an `ANTHROPIC_API_KEY`. That key was revoked and no longer exists on Anthropic''s side, so this is dead weight rather than an exposure — but it is dead weight that reads exactly like a live credential to anyone who finds it.
|
||||
|
||||
The cost is confusion, not risk. Someone debugging a Claude fallback will find a key present, assume it is configured, and look elsewhere for the failure. The absence of a key is a clear signal; a revoked key is a misleading one.
|
||||
|
||||
`.env` is gitignored here and has never been committed, so nothing needs rewriting — the value simply needs removing from the local file, and the line dropping or blanking in `.env.example` if it appears there too.
|
||||
|
||||
RELATED, and the reason this is filed separately: the workspace vault holds T-13, "Clear the revoked ANTHROPIC_API_KEY from the live Portainer stack". That ticket is scoped to the Portainer stack only. Whoever closes it will reasonably believe the key is gone once the stack is clean, and this copy will survive. The two want doing together even though they commit separately.
|
||||
|
||||
Context on the revocation, since it explains why nobody removed this at the time: the key was revoked on 2026-08-09 after being printed into a transcript by a redaction filter that matched on `KEY` appearing after the `=`. In `ANTHROPIC_API_KEY=...` it appears before, so the filter never fired. The response was rotation, and the leftover copies were not swept.', NULL, '2026-08-11 19:27:52', '2026-08-11 19:27:52.823', '2026-08-11 19:27:52.823', NULL, 'c7834e46268029b74c25a25a83177b64', 2) ON CONFLICT(hash) DO NOTHING;
|
||||
@@ -0,0 +1,139 @@
|
||||
-- Auto-generated by pql init. CREATE TABLE statements
|
||||
-- for the planning schema; per-table dir keeps the changelog
|
||||
-- self-describing per D-15. CREATE TABLE IF NOT EXISTS is
|
||||
-- idempotent so running schema files from each directory in
|
||||
-- replay order is harmless.
|
||||
--
|
||||
-- Importer parses the markers below to detect schema drift
|
||||
-- between the producing pql version and the local one — a
|
||||
-- bumped canonical_version means projection rules changed
|
||||
-- and replay must refuse rather than silently corrupt state.
|
||||
-- pql:created_by: 2.2.0
|
||||
-- pql:canonical_version: 2
|
||||
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decisions (
|
||||
id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('confirmed','question','rejected')),
|
||||
domain TEXT NOT NULL,
|
||||
title TEXT NOT NULL,
|
||||
status TEXT NOT NULL DEFAULT 'active'
|
||||
CHECK(status IN ('active','superseded','resolved','open')),
|
||||
date TEXT,
|
||||
file_path TEXT NOT NULL,
|
||||
synced_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decision_refs (
|
||||
source_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
target_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
ref_type TEXT NOT NULL
|
||||
CHECK(ref_type IN ('supersedes','references','resolves','depends_on','amends')),
|
||||
note TEXT,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (source_id, target_id, ref_type)
|
||||
);
|
||||
|
||||
-- Identity split (D-26): a ticket's stable, collision-proof identity is its
|
||||
-- record_id (a locally-generated ULID, planning.NewRecordID); the friendly
|
||||
-- T-NNN label lives in ticket_idmap and may be reconciled. Every structural
|
||||
-- reference (parent, deps, history, labels) targets record_id, so a label
|
||||
-- clash never corrupts the graph — only ticket_idmap needs a relabel.
|
||||
CREATE TABLE IF NOT EXISTS tickets (
|
||||
record_id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('initiative','epic','story','task','bug')),
|
||||
parent_record_id TEXT REFERENCES tickets(record_id),
|
||||
title TEXT NOT NULL,
|
||||
description TEXT,
|
||||
-- No CHECK enumeration: the ticket status vocabulary is per-vault
|
||||
-- configurable (ticket_statuses in .pql/config.yaml). Validation lives
|
||||
-- in Go (planning.StatusSet), so adding/renaming statuses needs no
|
||||
-- schema change. The DEFAULT is a harmless fallback — CreateTicket
|
||||
-- always inserts the configured default explicitly.
|
||||
status TEXT NOT NULL DEFAULT 'backlog',
|
||||
priority TEXT DEFAULT 'medium'
|
||||
CHECK(priority IN ('critical','high','medium','low')),
|
||||
assigned_to TEXT,
|
||||
team TEXT,
|
||||
decision_ref TEXT REFERENCES decisions(id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
-- ticket_idmap maps a record_id to its current friendly label (T-NNN).
|
||||
-- ticket_id is intentionally NOT globally unique: two uncoordinated clones
|
||||
-- can mint the same label, which surfaces as a duplicate-label collision
|
||||
-- (detected at replay) and is fixed with "pql ticket relabel".
|
||||
CREATE TABLE IF NOT EXISTS ticket_idmap (
|
||||
record_id TEXT PRIMARY KEY REFERENCES tickets(record_id),
|
||||
ticket_id TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_deps (
|
||||
blocker_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
blocked_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (blocker_record_id, blocked_record_id)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_history (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
field TEXT NOT NULL,
|
||||
old_value TEXT,
|
||||
new_value TEXT,
|
||||
changed_by TEXT,
|
||||
changed_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT UNIQUE,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_labels (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
label TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (ticket_record_id, label)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS meta (
|
||||
key TEXT PRIMARY KEY,
|
||||
value TEXT NOT NULL,
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now'))
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_status ON tickets(status);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_team ON tickets(team);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_decision_ref ON tickets(decision_ref);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_assigned ON tickets(assigned_to);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_parent ON tickets(parent_record_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_ticket_idmap_label ON ticket_idmap(ticket_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_domain ON decisions(domain);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_type ON decisions(type);
|
||||
CREATE INDEX IF NOT EXISTS idx_decision_refs_target ON decision_refs(target_id);
|
||||
@@ -0,0 +1,2 @@
|
||||
INSERT INTO ticket_idmap (record_id, ticket_id, created_at, updated_at, deleted_at, hash, canonical_version) VALUES ('06FZ44Z6HSN0RQS0QAYNTPEM5G', 'T-1', '2026-08-11 18:26:58.365', '2026-08-11 18:26:58.365', NULL, 'f1508986553f1ee59145a0d099131a68', 2) ON CONFLICT(record_id) DO UPDATE SET ticket_id=excluded.ticket_id, updated_at=excluded.updated_at, deleted_at=excluded.deleted_at, hash=excluded.hash, canonical_version=excluded.canonical_version WHERE excluded.updated_at >= ticket_idmap.updated_at;
|
||||
INSERT INTO ticket_idmap (record_id, ticket_id, created_at, updated_at, deleted_at, hash, canonical_version) VALUES ('06FZ4JX9DZB3XAB95EP23YWXMC', 'T-2', '2026-08-11 19:27:52.688', '2026-08-11 19:27:52.688', NULL, 'a5b18acb4b9a7e2da8e47b6ee603a5da', 2) ON CONFLICT(record_id) DO UPDATE SET ticket_id=excluded.ticket_id, updated_at=excluded.updated_at, deleted_at=excluded.deleted_at, hash=excluded.hash, canonical_version=excluded.canonical_version WHERE excluded.updated_at >= ticket_idmap.updated_at;
|
||||
@@ -0,0 +1,139 @@
|
||||
-- Auto-generated by pql init. CREATE TABLE statements
|
||||
-- for the planning schema; per-table dir keeps the changelog
|
||||
-- self-describing per D-15. CREATE TABLE IF NOT EXISTS is
|
||||
-- idempotent so running schema files from each directory in
|
||||
-- replay order is harmless.
|
||||
--
|
||||
-- Importer parses the markers below to detect schema drift
|
||||
-- between the producing pql version and the local one — a
|
||||
-- bumped canonical_version means projection rules changed
|
||||
-- and replay must refuse rather than silently corrupt state.
|
||||
-- pql:created_by: 2.2.0
|
||||
-- pql:canonical_version: 2
|
||||
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decisions (
|
||||
id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('confirmed','question','rejected')),
|
||||
domain TEXT NOT NULL,
|
||||
title TEXT NOT NULL,
|
||||
status TEXT NOT NULL DEFAULT 'active'
|
||||
CHECK(status IN ('active','superseded','resolved','open')),
|
||||
date TEXT,
|
||||
file_path TEXT NOT NULL,
|
||||
synced_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decision_refs (
|
||||
source_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
target_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
ref_type TEXT NOT NULL
|
||||
CHECK(ref_type IN ('supersedes','references','resolves','depends_on','amends')),
|
||||
note TEXT,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (source_id, target_id, ref_type)
|
||||
);
|
||||
|
||||
-- Identity split (D-26): a ticket's stable, collision-proof identity is its
|
||||
-- record_id (a locally-generated ULID, planning.NewRecordID); the friendly
|
||||
-- T-NNN label lives in ticket_idmap and may be reconciled. Every structural
|
||||
-- reference (parent, deps, history, labels) targets record_id, so a label
|
||||
-- clash never corrupts the graph — only ticket_idmap needs a relabel.
|
||||
CREATE TABLE IF NOT EXISTS tickets (
|
||||
record_id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('initiative','epic','story','task','bug')),
|
||||
parent_record_id TEXT REFERENCES tickets(record_id),
|
||||
title TEXT NOT NULL,
|
||||
description TEXT,
|
||||
-- No CHECK enumeration: the ticket status vocabulary is per-vault
|
||||
-- configurable (ticket_statuses in .pql/config.yaml). Validation lives
|
||||
-- in Go (planning.StatusSet), so adding/renaming statuses needs no
|
||||
-- schema change. The DEFAULT is a harmless fallback — CreateTicket
|
||||
-- always inserts the configured default explicitly.
|
||||
status TEXT NOT NULL DEFAULT 'backlog',
|
||||
priority TEXT DEFAULT 'medium'
|
||||
CHECK(priority IN ('critical','high','medium','low')),
|
||||
assigned_to TEXT,
|
||||
team TEXT,
|
||||
decision_ref TEXT REFERENCES decisions(id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
-- ticket_idmap maps a record_id to its current friendly label (T-NNN).
|
||||
-- ticket_id is intentionally NOT globally unique: two uncoordinated clones
|
||||
-- can mint the same label, which surfaces as a duplicate-label collision
|
||||
-- (detected at replay) and is fixed with "pql ticket relabel".
|
||||
CREATE TABLE IF NOT EXISTS ticket_idmap (
|
||||
record_id TEXT PRIMARY KEY REFERENCES tickets(record_id),
|
||||
ticket_id TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_deps (
|
||||
blocker_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
blocked_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (blocker_record_id, blocked_record_id)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_history (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
field TEXT NOT NULL,
|
||||
old_value TEXT,
|
||||
new_value TEXT,
|
||||
changed_by TEXT,
|
||||
changed_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT UNIQUE,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_labels (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
label TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (ticket_record_id, label)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS meta (
|
||||
key TEXT PRIMARY KEY,
|
||||
value TEXT NOT NULL,
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now'))
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_status ON tickets(status);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_team ON tickets(team);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_decision_ref ON tickets(decision_ref);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_assigned ON tickets(assigned_to);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_parent ON tickets(parent_record_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_ticket_idmap_label ON ticket_idmap(ticket_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_domain ON decisions(domain);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_type ON decisions(type);
|
||||
CREATE INDEX IF NOT EXISTS idx_decision_refs_target ON decision_refs(target_id);
|
||||
@@ -0,0 +1,139 @@
|
||||
-- Auto-generated by pql init. CREATE TABLE statements
|
||||
-- for the planning schema; per-table dir keeps the changelog
|
||||
-- self-describing per D-15. CREATE TABLE IF NOT EXISTS is
|
||||
-- idempotent so running schema files from each directory in
|
||||
-- replay order is harmless.
|
||||
--
|
||||
-- Importer parses the markers below to detect schema drift
|
||||
-- between the producing pql version and the local one — a
|
||||
-- bumped canonical_version means projection rules changed
|
||||
-- and replay must refuse rather than silently corrupt state.
|
||||
-- pql:created_by: 2.2.0
|
||||
-- pql:canonical_version: 2
|
||||
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decisions (
|
||||
id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('confirmed','question','rejected')),
|
||||
domain TEXT NOT NULL,
|
||||
title TEXT NOT NULL,
|
||||
status TEXT NOT NULL DEFAULT 'active'
|
||||
CHECK(status IN ('active','superseded','resolved','open')),
|
||||
date TEXT,
|
||||
file_path TEXT NOT NULL,
|
||||
synced_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS decision_refs (
|
||||
source_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
target_id TEXT NOT NULL REFERENCES decisions(id) ON DELETE CASCADE,
|
||||
ref_type TEXT NOT NULL
|
||||
CHECK(ref_type IN ('supersedes','references','resolves','depends_on','amends')),
|
||||
note TEXT,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (source_id, target_id, ref_type)
|
||||
);
|
||||
|
||||
-- Identity split (D-26): a ticket's stable, collision-proof identity is its
|
||||
-- record_id (a locally-generated ULID, planning.NewRecordID); the friendly
|
||||
-- T-NNN label lives in ticket_idmap and may be reconciled. Every structural
|
||||
-- reference (parent, deps, history, labels) targets record_id, so a label
|
||||
-- clash never corrupts the graph — only ticket_idmap needs a relabel.
|
||||
CREATE TABLE IF NOT EXISTS tickets (
|
||||
record_id TEXT PRIMARY KEY,
|
||||
type TEXT NOT NULL CHECK(type IN ('initiative','epic','story','task','bug')),
|
||||
parent_record_id TEXT REFERENCES tickets(record_id),
|
||||
title TEXT NOT NULL,
|
||||
description TEXT,
|
||||
-- No CHECK enumeration: the ticket status vocabulary is per-vault
|
||||
-- configurable (ticket_statuses in .pql/config.yaml). Validation lives
|
||||
-- in Go (planning.StatusSet), so adding/renaming statuses needs no
|
||||
-- schema change. The DEFAULT is a harmless fallback — CreateTicket
|
||||
-- always inserts the configured default explicitly.
|
||||
status TEXT NOT NULL DEFAULT 'backlog',
|
||||
priority TEXT DEFAULT 'medium'
|
||||
CHECK(priority IN ('critical','high','medium','low')),
|
||||
assigned_to TEXT,
|
||||
team TEXT,
|
||||
decision_ref TEXT REFERENCES decisions(id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
-- ticket_idmap maps a record_id to its current friendly label (T-NNN).
|
||||
-- ticket_id is intentionally NOT globally unique: two uncoordinated clones
|
||||
-- can mint the same label, which surfaces as a duplicate-label collision
|
||||
-- (detected at replay) and is fixed with "pql ticket relabel".
|
||||
CREATE TABLE IF NOT EXISTS ticket_idmap (
|
||||
record_id TEXT PRIMARY KEY REFERENCES tickets(record_id),
|
||||
ticket_id TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_deps (
|
||||
blocker_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
blocked_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (blocker_record_id, blocked_record_id)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_history (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
field TEXT NOT NULL,
|
||||
old_value TEXT,
|
||||
new_value TEXT,
|
||||
changed_by TEXT,
|
||||
changed_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT UNIQUE,
|
||||
canonical_version INTEGER
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS ticket_labels (
|
||||
ticket_record_id TEXT NOT NULL REFERENCES tickets(record_id),
|
||||
label TEXT NOT NULL,
|
||||
created_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now')),
|
||||
deleted_at TEXT,
|
||||
hash TEXT,
|
||||
canonical_version INTEGER,
|
||||
PRIMARY KEY (ticket_record_id, label)
|
||||
);
|
||||
|
||||
CREATE TABLE IF NOT EXISTS meta (
|
||||
key TEXT PRIMARY KEY,
|
||||
value TEXT NOT NULL,
|
||||
updated_at TEXT NOT NULL DEFAULT (datetime('now'))
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_status ON tickets(status);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_team ON tickets(team);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_decision_ref ON tickets(decision_ref);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_assigned ON tickets(assigned_to);
|
||||
CREATE INDEX IF NOT EXISTS idx_tickets_parent ON tickets(parent_record_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_ticket_idmap_label ON ticket_idmap(ticket_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_domain ON decisions(domain);
|
||||
CREATE INDEX IF NOT EXISTS idx_decisions_type ON decisions(type);
|
||||
CREATE INDEX IF NOT EXISTS idx_decision_refs_target ON decision_refs(target_id);
|
||||
@@ -0,0 +1,36 @@
|
||||
INSERT INTO tickets (record_id, type, parent_record_id, title, description, status, priority, assigned_to, team, decision_ref, created_at, updated_at, deleted_at, hash, canonical_version) VALUES ('06FZ44Z6HSN0RQS0QAYNTPEM5G', 'task', NULL, 'Type the codebase: 95 mypy errors across 31 files', NULL, 'backlog', 'high', NULL, NULL, NULL, '2026-08-11 18:26:58.318', '2026-08-11 18:26:58.318', NULL, 'd881f36d77e58aaa2f94dc08c5b5be3e', 2) ON CONFLICT(record_id) DO UPDATE SET type=excluded.type, parent_record_id=excluded.parent_record_id, title=excluded.title, description=excluded.description, status=excluded.status, priority=excluded.priority, assigned_to=excluded.assigned_to, team=excluded.team, decision_ref=excluded.decision_ref, updated_at=excluded.updated_at, deleted_at=excluded.deleted_at, hash=excluded.hash, canonical_version=excluded.canonical_version WHERE excluded.updated_at >= tickets.updated_at;
|
||||
INSERT INTO tickets (record_id, type, parent_record_id, title, description, status, priority, assigned_to, team, decision_ref, created_at, updated_at, deleted_at, hash, canonical_version) VALUES ('06FZ44Z6HSN0RQS0QAYNTPEM5G', 'task', NULL, 'Type the codebase: 95 mypy errors across 31 files', '`make typecheck` reports 95 errors in 31 files (was 103). This is a dedicated programming pass, not lint tidying, and it is what currently blocks `make pre-push`.
|
||||
|
||||
MEASURED 2026-08-11 so the next session does not re-derive it:
|
||||
|
||||
29 no-untyped-def functions with no annotations — the bulk, and genuine per-function work
|
||||
18 no-any-return mostly downstream of the above
|
||||
12 assignment
|
||||
11 arg-type
|
||||
5 unused-ignore `# type: ignore` comments mypy says are no longer needed
|
||||
5 union-attr
|
||||
4 var-annotated
|
||||
3 override
|
||||
remainder: misc, dict-item, attr-defined, return-value, call-overload
|
||||
|
||||
By file: agents/tatlock.py 15, core/memory_service.py 11, responses/streaming.py 8, responses/service.py 7, core/context.py 6.
|
||||
|
||||
THE TWO SHARED ROOTS ARE ALREADY FIXED (dac259a), so what is left has no lever in it. For reference, they were: five conversation lists declared bare, where mypy infers the element type from the first append (a ModelRequest) and then rejects every ModelResponse; and an agent built as Agent(model, system_prompt=...) with no deps_type, inferred Agent[None, str], while every tool it registers takes RunContext[ToolCallTracker].
|
||||
|
||||
WORTH KNOWING BEFORE STARTING. Annotating partially made mypy count go UP before it went down — declaring `_agent: Agent | None` took agents/tatlock.py from 22 to 24, because resolving the bare Agent to Agent[None, str] surfaced four argument-type errors the Any had been hiding. Expect that shape: a rising count during this work usually means concealment ending, not damage.
|
||||
|
||||
The mypy config is strict — disallow_untyped_defs, disallow_incomplete_defs, warn_return_any, check_untyped_defs, strict_equality — so there is no partial-credit setting to lean on, and weakening it would be the wrong trade for a codebase this central.
|
||||
|
||||
TWO PRE-EXISTING TEST FACTS, both confirmed at HEAD and neither caused by the lint work:
|
||||
- test_tatlock_tool_call_logging_calculator is flaky: failed 2 of 5 full runs, on HEAD and on the lint branch, and fails in isolation at HEAD while passing in isolation after the lint pass. Order- or timing-dependent.
|
||||
- `pytest tests/` cannot collect at all: tests/e2e/test_orchestration_e2e.py uses an `e2e` marker that is not registered and the config is strict about markers. `make test` passes only because it ignores tests/e2e, tests/integration and tests/contracts.', 'backlog', 'high', NULL, NULL, NULL, '2026-08-11 18:26:58.318', '2026-08-11 18:26:58.523', NULL, 'fc047dd080d976c4ca0c551cac8fec93', 2) ON CONFLICT(record_id) DO UPDATE SET type=excluded.type, parent_record_id=excluded.parent_record_id, title=excluded.title, description=excluded.description, status=excluded.status, priority=excluded.priority, assigned_to=excluded.assigned_to, team=excluded.team, decision_ref=excluded.decision_ref, updated_at=excluded.updated_at, deleted_at=excluded.deleted_at, hash=excluded.hash, canonical_version=excluded.canonical_version WHERE excluded.updated_at >= tickets.updated_at;
|
||||
INSERT INTO tickets (record_id, type, parent_record_id, title, description, status, priority, assigned_to, team, decision_ref, created_at, updated_at, deleted_at, hash, canonical_version) VALUES ('06FZ4JX9DZB3XAB95EP23YWXMC', 'bug', NULL, 'A revoked ANTHROPIC_API_KEY is still sitting in .env', NULL, 'backlog', 'medium', NULL, NULL, NULL, '2026-08-11 19:27:52.687', '2026-08-11 19:27:52.687', NULL, '3b00a4b729819e20a6425f971e6cb9da', 2) ON CONFLICT(record_id) DO UPDATE SET type=excluded.type, parent_record_id=excluded.parent_record_id, title=excluded.title, description=excluded.description, status=excluded.status, priority=excluded.priority, assigned_to=excluded.assigned_to, team=excluded.team, decision_ref=excluded.decision_ref, updated_at=excluded.updated_at, deleted_at=excluded.deleted_at, hash=excluded.hash, canonical_version=excluded.canonical_version WHERE excluded.updated_at >= tickets.updated_at;
|
||||
INSERT INTO tickets (record_id, type, parent_record_id, title, description, status, priority, assigned_to, team, decision_ref, created_at, updated_at, deleted_at, hash, canonical_version) VALUES ('06FZ4JX9DZB3XAB95EP23YWXMC', 'bug', NULL, 'A revoked ANTHROPIC_API_KEY is still sitting in .env', '`.env` still carries an `ANTHROPIC_API_KEY`. That key was revoked and no longer exists on Anthropic''s side, so this is dead weight rather than an exposure — but it is dead weight that reads exactly like a live credential to anyone who finds it.
|
||||
|
||||
The cost is confusion, not risk. Someone debugging a Claude fallback will find a key present, assume it is configured, and look elsewhere for the failure. The absence of a key is a clear signal; a revoked key is a misleading one.
|
||||
|
||||
`.env` is gitignored here and has never been committed, so nothing needs rewriting — the value simply needs removing from the local file, and the line dropping or blanking in `.env.example` if it appears there too.
|
||||
|
||||
RELATED, and the reason this is filed separately: the workspace vault holds T-13, "Clear the revoked ANTHROPIC_API_KEY from the live Portainer stack". That ticket is scoped to the Portainer stack only. Whoever closes it will reasonably believe the key is gone once the stack is clean, and this copy will survive. The two want doing together even though they commit separately.
|
||||
|
||||
Context on the revocation, since it explains why nobody removed this at the time: the key was revoked on 2026-08-09 after being printed into a transcript by a redaction filter that matched on `KEY` appearing after the `=`. In `ANTHROPIC_API_KEY=...` it appears before, so the filter never fired. The response was rotation, and the leftover copies were not swept.', 'backlog', 'medium', NULL, NULL, NULL, '2026-08-11 19:27:52.687', '2026-08-11 19:27:52.823', NULL, 'a9c2ca965b3d40a924720c7761c5338d', 2) ON CONFLICT(record_id) DO UPDATE SET type=excluded.type, parent_record_id=excluded.parent_record_id, title=excluded.title, description=excluded.description, status=excluded.status, priority=excluded.priority, assigned_to=excluded.assigned_to, team=excluded.team, decision_ref=excluded.decision_ref, updated_at=excluded.updated_at, deleted_at=excluded.deleted_at, hash=excluded.hash, canonical_version=excluded.canonical_version WHERE excluded.updated_at >= tickets.updated_at;
|
||||
@@ -1,105 +0,0 @@
|
||||
# LLM Agent Instructions
|
||||
|
||||
This document contains instructions and documentation references for AI assistants working with this codebase.
|
||||
|
||||
> **📖 Important**: Before working on this project, read [PHILOSOPHY.md](PHILOSOPHY.md) to understand the system vision, architectural patterns, and design goals. All development should work towards realizing those patterns.
|
||||
# AGENTS.md
|
||||
|
||||
> **Start every session by reading this file.**
|
||||
> This file outlines the operational protocols, coding standards, and architectural decisions for this FastAPI project.
|
||||
|
||||
## 1. Agent Operational Protocols
|
||||
|
||||
### 🧠 Work Patterns (Plan-Act-Reflect)
|
||||
* **Plan:** Before writing code, briefly outline your plan. Identify which files you will touch and what the side effects might be.
|
||||
* **Act:** Execute the changes in small, atomic steps.
|
||||
* **Reflect:** After coding, verify your work. Did you break existing tests? Did you add new tests?
|
||||
|
||||
### 🧪 Local Development Setup
|
||||
* **Always test locally first** before committing and deploying. The build-deploy loop is slow.
|
||||
* **Start the local server** with `./wakeup.sh` - logs are written to `logs/server.log` for easy tailing
|
||||
* **Auto-reload**: The wakeup script runs uvicorn in reload mode - code changes are picked up automatically without restart (except for requirements.txt changes)
|
||||
* **Test REST endpoints** against `http://localhost:8777` using curl or similar tools
|
||||
* **Only deploy** when a phase or feature is complete and tested locally
|
||||
* **Environment**: Copy `.env.example` to `.env` and configure for your local setup (Ollama, Redis, Qdrant hosts)
|
||||
* **Running tests**: Always use the venv explicitly to avoid environment mismatches:
|
||||
```bash
|
||||
.venv/bin/python -m pytest tests/ # All tests
|
||||
.venv/bin/python -m pytest tests/core/ -v # Core tests only
|
||||
```
|
||||
|
||||
### 🌐 Internal Service Access
|
||||
* **git.schweitz.net**: Access via `http://localhost:3002` (direct Gitea) to bypass Authentik SSO
|
||||
* Example: `curl http://localhost:3002/jpmschweitzer/library-desk/raw/branch/main/README.md`
|
||||
* Public repos are readable without authentication
|
||||
* Related repos: `library-desk`, `scheduler`, `core-api`, `portainer-core`
|
||||
|
||||
### 🐳 Deployment & Infrastructure
|
||||
* **Full stack documentation**: Available in the `portainer-core` repo
|
||||
* Access: `curl http://localhost:3002/jpmschweitzer/portainer-core/raw/branch/main/CONTAINERS.md`
|
||||
* Contains: All service ports, URLs, Redis DB allocations, external domains
|
||||
* **Tatlock deployment**:
|
||||
* LAN: `http://192.168.86.149:8000`
|
||||
* External: `tatlock.schweitz.net` (behind Authentik SSO)
|
||||
* Redis DBs: 1 (memory), 6 (benchmarks)
|
||||
* **Health check**: `curl http://192.168.86.149:8000/health`
|
||||
|
||||
### 🛡️ Git Discipline
|
||||
* **NEVER commit to `main` or `master` directly.** Always create a feature branch: `feature/your-feature-name` or `fix/issue-description`.
|
||||
* **Commit Messages:** Use the [Conventional Commits](https://www.conventionalcommits.org/) format.
|
||||
* `feat: add user login endpoint`
|
||||
* `fix: resolve database connection timeout`
|
||||
* `refactor: split monolith dependency file`
|
||||
* **Atomic Commits:** Keep commits small. One logical change = one commit.
|
||||
|
||||
### 📝 Changelog Maintenance
|
||||
* **Update `CHANGELOG.md`** with every user-facing change.
|
||||
* Format: `## [Unreleased] - YYYY-MM-DD` followed by `### Added`, `### Changed`, or `### Fixed`.
|
||||
|
||||
### 🚀 Release Flow
|
||||
When changes are ready for deployment:
|
||||
|
||||
1. **Ask user if deploy cycle is desired**
|
||||
|
||||
2. **Update version** in `pyproject.toml`:
|
||||
- Bug fixes: bump patch version (1.8.3 → 1.8.4)
|
||||
- New features: bump minor version (1.8.4 → 1.9.0)
|
||||
|
||||
3. **Update CHANGELOG.md**:
|
||||
- Move items from `[Unreleased]` to new version section
|
||||
- Add release date: `## [1.8.4] - 2025-12-16`
|
||||
|
||||
4. **Commit and tag**:
|
||||
```bash
|
||||
git add -A
|
||||
git commit -m "fix: description of changes"
|
||||
git tag v1.8.4
|
||||
git push origin main --tags
|
||||
```
|
||||
|
||||
5. **CI/CD triggers automatically**:
|
||||
- Gitea CI builds Docker image on new tag
|
||||
- Watchtower pulls and deploys to production
|
||||
- Verify deployment: `curl http://192.168.86.149:8000/health`
|
||||
|
||||
---
|
||||
|
||||
## 2. FastAPI Architecture & Best Practices
|
||||
*Reference: [FastAPI Best Practices](https://github.com/zhanymkanov/fastapi-best-practices)*
|
||||
|
||||
### 📂 Project Structure (Directory-based, NOT File-type based)
|
||||
Do **not** group files by type (e.g., one huge `routers` folder). Group by **domain/module** inside a `src/` directory.
|
||||
|
||||
**Correct Structure:**
|
||||
```text
|
||||
src/
|
||||
├── auth/
|
||||
│ ├── router.py # Endpoints
|
||||
│ ├── schemas.py # Pydantic models
|
||||
│ ├── service.py # Business logic (CRUD, etc.)
|
||||
│ ├── dependencies.py# Module-specific dependencies
|
||||
│ └── config.py # Module-specific settings
|
||||
├── posts/
|
||||
│ ├── router.py
|
||||
│ └── ...
|
||||
└── main.py # App entry point
|
||||
+128
-1
@@ -7,6 +7,131 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
## [Unreleased]
|
||||
|
||||
### Changed
|
||||
|
||||
- `make setup` now ends with a `pytest --collect-only` pass so a broken environment
|
||||
(missing or mismatched dependency) fails the target itself instead of exiting 0
|
||||
and surfacing later as a confusing test failure (T-47)
|
||||
|
||||
## [2.4.3] - 2026-08-08
|
||||
|
||||
### Fixed
|
||||
|
||||
- Steward routing no longer triggers on words inside its own explanation. Capability
|
||||
extraction reads the declared `DELEGATE:` line instead of substring-matching
|
||||
capability domains across the whole response, where ordinary English routed
|
||||
requests — "description" contains the housekeeper domain "script", "acknowledge"
|
||||
contains "knowledge" and "know". A spurious capability meant a real agent call,
|
||||
including web searches, on queries that needed none.
|
||||
|
||||
## [2.4.2] - 2026-07-19
|
||||
|
||||
### Fixed
|
||||
|
||||
- Container crash-loop on fresh builds: cap `opentelemetry-api` below 1.44,
|
||||
which removed the private `_events` module that pydantic-ai 1.27 imports
|
||||
|
||||
## [2.4.1] - 2026-07-19
|
||||
|
||||
### Changed
|
||||
|
||||
- **Container-name network defaults** - `SEARXNG_HOST`, `LIBRARY_DESK_HOST`, and `CORE_API_HOST` now default to docker container names on the docker-dataplane network (`http://searxng:8080`, `http://library-desk:8089`, `http://core-api:8083`) instead of host `localhost` ports, ahead of the loopback port rebinding; this also fixes `CORE_API_HOST` pointing at port 8090 (the Scheduler's host port) rather than Core-API's 8083. `scripts/test_housekeeper.sh` now reaches Core-API via `localhost:8083` instead of the LAN IP. Local development against host-published ports still works via `.env` overrides
|
||||
|
||||
## [2.4.0] - 2026-07-14
|
||||
|
||||
### Removed
|
||||
|
||||
- **Dead delegation stack** - deleted the duplicate, never-wired coordination layer so exactly ONE delegation implementation remains (`src/agents/delegation.py`): `src/agents/coordination.py` (`CoordinationEngine`, its own `delegate_to_librarian`, `AGENT_EXECUTORS`/`AGENT_STREAM_EXECUTORS`), the broken-by-design `run_librarian_stream` path it used (Ollama streaming + tool call bug), the `stream_delegate_to_*` wrappers with their never-parsed `__DELEGATION_RESULT__` marker, and `HouseholdRegistry.get_streaming_delegation_tools()` (no callers)
|
||||
- **Orphaned agent protocol models** - `src/agents/protocol.py` now contains only the live `AgentError`; the coordination wire protocol it carried (`AgentRequest`, `AgentResponse`, `DelegationIntent`, `CoordinationResult`, `DelegationReason`, `TaskComplexity`, `ToolCallRecord`, `AgentTimeoutError`, `AgentUnavailableError`, `DelegationError`) had no importer left outside its own tests after the coordination stack removal
|
||||
|
||||
### Added
|
||||
|
||||
- **Test-suite tenant guard** - `tests/conftest.py` hard-fails the whole pytest session (exit code 1, zero tests run) if the effective tenant resolves to the production tenant `jpmschweitzer`, mirroring the guard library-desk applies on its side. Suite-level assertions pin that the session runs under `llm_tester` namespaces (Qdrant `memories_llm_tester`, Redis `session:llm_tester:*`), and the e2e isolation constants now derive from the shared `TEST_TENANT`/`PRODUCTION_TENANT` config constants instead of string literals
|
||||
- **Explicit tenant on every library-desk request** - the librarian client now resolves and sends the `user` parameter explicitly on every request (library-desk is removing its server-side default; a missing user would 422). The content extraction endpoints now carry the tenant too, `search_web` no longer falls back to a phantom `tatlock-librarian` user, and a client-level assertion rejects an empty/whitespace tenant before any bytes hit the wire. A parametrized sweep pins the wire contract for all 15 tenant-scoped client methods
|
||||
- **Tenant isolation guard** - non-production environments (development/testing) now FORCE the effective tenant to the reserved test tenant `llm_tester` (only `llm_tester` itself or a `test_`-prefixed override is accepted), regardless of `DEFAULT_USER` misconfiguration, at both config resolution and request-context resolution (`get_user()`). Startup refuses (clear error) when a non-production environment is explicitly configured with the production tenant `jpmschweitzer`, and one loud startup log line states the effective/forced tenant
|
||||
|
||||
- **Conversation context for experts + real-time think messages** - direct delegation (streaming and non-streaming) now passes a trimmed conversation history (last 6 turns) as expert context, so follow-up questions keep their referent; `_stream_direct_delegation` is now an async generator, so butler think messages ("Allow me to consult the archives, sir.") stream BEFORE the research runs instead of after it completes
|
||||
- **Bounded retries and connection reuse for library-desk** - GETs and the read-only `POST /query/*` and `POST /rag/search` endpoints retry once (2 attempts, short backoff) on transport errors and retryable 5xx; wiki writes are never retried. The client now honors `LIBRARY_DESK_TIMEOUT` instead of hardcoded 60s/30s, a librarian run holds one shared HTTP connection instead of constructing a client per tool call, and read tools raise `ModelRetry` on transient HTTP errors so the agent's retry budget engages
|
||||
- **One librarian timeout budget** - new `LIBRARIAN_TIMEOUT` (default 180s) enforced with `asyncio.wait_for` inside `delegate_to_librarian`, capping the previously uncapped live paths (steward direct delegation and streaming). The Ollama provider's AsyncOpenAI client now carries an explicit `OLLAMA_TIMEOUT` instead of the SDK's ~600s default, and the contradictory unused 60s default in `AgentRequest.timeout_seconds` was removed (None defers to the configured budget)
|
||||
- **Search degradation signaling** - The librarian client parses `source_counts` (plus the additive `source_status`/`degraded` fields when a newer library-desk sends them; absence is tolerated), and `hybrid_search` appends a one-line coverage note when a search is degraded or an enabled source leg contributed nothing, so outages are visible to the model and the user. When `source_status` is present it is used exclusively; without it, count-absence is only inferred for the optional legs the request explicitly enabled (web/documents/volatile) - never the always-on vector/graph legs, whose absence from the top-N counts is normal ranking behavior, so healthy searches no longer emit warnings
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Clearing all wiki-page tags is possible again** - the Ollama-safe empty-list sentinel in `update_wiki_page` means "leave unchanged", which made it impossible to remove all tags; passing exactly `["__CLEAR__"]` now sends an empty tag list to library-desk (documented in the tool docstring for the local model)
|
||||
- **Text-delegation fallback pairs results strictly** - the parallel branch now verifies `asyncio.gather` returned one result per parsed delegation (`zip(..., strict=True)`); a count mismatch fails loudly with a curated apology instead of silently attributing outputs to the wrong agent
|
||||
- **Ollama-safe librarian tool schemas** - `update_wiki_page` and `smart_create_wiki_page` no longer use `X | None` parameters (Ollama's OpenAI-compatible API mishandles `anyOf[X, null]`); empty-string/empty-list sentinels are translated to `None` inside the tools, matching the biographer pattern. A snapshot test pins every librarian tool schema to contain no nullable `anyOf`
|
||||
- **Honest expert failures** - `run_librarian` now raises a structured `AgentError` instead of returning error text as if it were research output, so delegation correctly reports `success=False` and the streaming error branch is reachable. Failures surface to the user as curated butler-toned sentences; exception detail (including internal URLs) stays in the logs only. Librarian tool errors no longer leak `str(e)` into synthesis
|
||||
|
||||
- **HybridRAG response mapping** - The librarian client now parses the field names library-desk actually returns (`source_type`/`sources`, `rrf_score`, `context`, per-item `related_dossiers`, synonyms nested in the `keywords` dict); previously every result rendered as "unknown (score: 0.00)". Source icons now key off the per-item `sources` list. Requests no longer send zero limits (the service rejects them with 422); legs are disabled via `enable_*` flags. Pinned by a contract test against a recorded live response (`tests/agents/librarian/fixtures/`)
|
||||
|
||||
## [2.3.0] - 2026-07-13
|
||||
|
||||
### Changed
|
||||
|
||||
- **Local-first backend (claudification rollback)** - Ollama/gemma4 is now the primary backend; Claude remains as fallback. `PREFER_CLOUD_BACKEND` defaults to `false`, Claude is used automatically when the Ollama startup health check fails, and the Steward retries mid-request failures on the other backend in both directions
|
||||
- **Default Claude model `claude-sonnet-5`** - `claude-sonnet-4-20250514` was retired by Anthropic on 2026-06-15 and would 404, leaving the fallback dead
|
||||
- **Dedicated orchestration prompt** - `orchestrate_tool_calls()` now uses a terse tool-execution prompt (`TATLOCK_ORCHESTRATION_PROMPT`); the butler persona prompt suppressed gemma4 tool calling (the model reasoned about the calculator, then answered from memory with wrong arithmetic). Synthesis keeps the persona prompt, so user-visible voice is unchanged
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Startup crash with broken anthropic package** - Anthropic SDK imports in the model selector are now lazy, so an incompatible `anthropic` install degrades to Ollama-only operation instead of crashing the app at import time (root cause of the production outage since April)
|
||||
- **Claude Sonnet 5 rejects sampling parameters** - removed `temperature` from the Steward's direct Claude call and made the Housekeeper's temperature setting backend-conditional via `get_sampling_settings()`
|
||||
- **Pin `anthropic>=0.77,<1.0`** - the April image resolved an anthropic version incompatible with pydantic-ai 1.27
|
||||
- **Steward timeout configurable** - new `STEWARD_TIMEOUT` (default 60s) replaces the hardcoded 30s, which gemma4 chronically exceeded (~35s warm analysis), causing every request to fail or fall back
|
||||
|
||||
### Added
|
||||
|
||||
- **Ollama startup health check** - verifies the server is reachable and `OLLAMA_DEFAULT_MODEL` is pulled; feeds backend resolution and `get_model_info()`
|
||||
- **Contract tests** (`tests/contracts/`, `make test-contracts`) - wire-level tests that send the raw requests the code sends to Ollama (native + OpenAI-compat tool calling), Anthropic (including the pinned temperature-rejection contract), Qdrant, SearXNG, library-desk, and Redis; unreachable services skip, wrong response shapes fail
|
||||
- **Backend resolution unit tests** (`tests/anthropic/`)
|
||||
|
||||
## [2.2.0] - 2026-04-04
|
||||
|
||||
### Changed
|
||||
|
||||
- **Switch default Ollama model to gemma4:e2b** - Replaces mistral-nemo as the local LLM backend; gemma4:e2b has native function calling support, faster tool calling (2-4s vs 15-20s), better parameter accuracy on word problems, and uses less VRAM (8GB vs 9.2GB)
|
||||
|
||||
### Added
|
||||
|
||||
- **Tool calling benchmark script** (`scripts/benchmark_tool_calling.py`) - Compares tool calling accuracy and latency across Ollama models via the Tatlock API
|
||||
|
||||
## [2.1.0] - 2026-02-05
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Streaming SSE compatibility with Open WebUI** - Switch from `exclude_none=True` to `exclude_unset=True` for SSE chunk serialization; `exclude_none` was too aggressive — it stripped `finish_reason: null` from intermediate chunks (which OpenAI includes), while `exclude_unset` correctly omits only fields never passed to the constructor (like `reasoning_content` on content-only chunks) while preserving explicitly-set `finish_reason: null`
|
||||
|
||||
### Changed
|
||||
|
||||
- **Project structure consolidation** - Moved documentation to `docs/`, consolidated all config into `pyproject.toml`, replaced `wakeup.sh`/`pytest.ini`/`requirements*.txt` with `Makefile` + `pyproject.toml`
|
||||
- **CI test gate** - Unit tests now gate release and build jobs in Gitea Actions workflow
|
||||
- **Build output organization** - Tool caches in `.cache/`, generated output (coverage, logs) in `build/`
|
||||
|
||||
## [2.0.5] - 2026-02-05
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Streaming JSON compatibility** - Exclude null fields from streaming chunks using `exclude_none=True`; OpenAI's API omits null fields entirely, and including them (e.g., `content: null`, `reasoning_content: null`) caused parsing issues in Open WebUI
|
||||
|
||||
## [2.0.4] - 2026-02-05
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Open WebUI streaming compatibility** - Replaced `sse_starlette` `EventSourceResponse` with plain `StreamingResponse` for chat completions; `sse_starlette` added `\r\n` line endings and extra SSE fields that Open WebUI couldn't parse
|
||||
|
||||
## [2.0.3] - 2026-02-05
|
||||
|
||||
### Fixed
|
||||
|
||||
- **Steward analysis leaking into responses** - Removed internal routing analysis (`DELEGATE: tatlock_core...`) from user-visible reasoning in both streaming and non-streaming paths
|
||||
|
||||
## [2.0.2] - 2026-02-05
|
||||
|
||||
### Fixed
|
||||
|
||||
- **tool_choice format incompatibility** - Removed `extra_body` tool_choice hack for Claude backend; PydanticAI handles tool_choice natively for Anthropic, preventing infinite tool call loops
|
||||
- **CI trigger** - Changed workflow trigger from `release:published` to `push:tags:v[0-9]*`
|
||||
|
||||
## [2.0.1] - 2026-02-05
|
||||
|
||||
### Fixed
|
||||
@@ -896,7 +1021,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
- CORS middleware
|
||||
- Exception handlers (OpenAI-compatible error format)
|
||||
|
||||
[Unreleased]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v2.0.0...main
|
||||
[Unreleased]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v2.1.0...main
|
||||
[2.1.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v2.0.5...v2.1.0
|
||||
[2.0.5]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v2.0.0...v2.0.5
|
||||
[2.0.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.11.0...v2.0.0
|
||||
[1.11.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.10.0...v1.11.0
|
||||
[1.10.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.9.0...v1.10.0
|
||||
|
||||
@@ -0,0 +1,196 @@
|
||||
# CLAUDE.md — tatlock
|
||||
|
||||
Privacy-first homelab butler. An OpenAI-compatible orchestration API over local models, with
|
||||
household staff agents built on PydanticAI. Python 3.12 / FastAPI, `version = "2.4.3"`.
|
||||
Container `tatlock` on `docker-dataplane`, port **8000**. Redis DB **1** (memory), Qdrant for
|
||||
vectors.
|
||||
|
||||
## Ports
|
||||
|
||||
| | Port | How |
|
||||
|---|---|---|
|
||||
| Local dev | **8777** | `make run` — uvicorn reload, logs to `build/logs/server.log` |
|
||||
| Production | **8000** | container; `http://192.168.86.149:8000/health`, external `tatlock.schweitz.net` behind Authentik |
|
||||
|
||||
Test endpoints against `localhost:8777` while developing. `localhost:8000` is the *container*.
|
||||
|
||||
## Live contract
|
||||
|
||||
`http://localhost:8000/openapi.json` — **5 paths**, `title: OpenAI-Compatible API`, `version:
|
||||
2.4.3` (verified 2026-08-09): `/`, `/health`, `/v1/models`, `/v1/chat/completions`,
|
||||
`/v1/responses`. `/v1/responses` is primary; `/v1/chat/completions` exists for Open WebUI.
|
||||
|
||||
**The spec is the public surface, not the system.** The household capability registry is internal
|
||||
and appears nowhere in those 5 paths. Absence from the spec means "not exposed", not "does not
|
||||
exist".
|
||||
|
||||
## Two traps that make the runtime look like the opposite of what it is
|
||||
|
||||
**1. `src/anthropic` loads at startup; `src/ollama` does not — and Ollama is the primary
|
||||
backend.** A cold `import src.main` inside the container shows `agents, anthropic, chat, core,
|
||||
main, models, responses` — no `ollama`. The only import of it is a *function-body* one at
|
||||
`src/anthropic/model_selector.py:230`. Meanwhile `PREFER_CLOUD_BACKEND=false`, so every request
|
||||
actually goes to Ollama and the Claude path is off (see **workspace D-11**). Reading the module list
|
||||
naively gives you exactly the wrong answer: the package that looks live is the disabled fallback,
|
||||
and the one that looks dead is the hot path. Do not conclude anything about backends from
|
||||
`sys.modules`; read the config.
|
||||
|
||||
**2. In-process singletons are empty outside the app.** `get_household_registry()`
|
||||
(`src/core/household_registry.py:334`) in a fresh `docker exec python` returns **0 members**,
|
||||
while the running app serves 2 models from it — it is populated at startup. Import the
|
||||
module-level definitions or ask the endpoint; never import a singleton and assume it is
|
||||
populated.
|
||||
|
||||
## Stack decisions that bind this repo
|
||||
|
||||
Recorded in the workspace vault, not here. Read before assuming anything about the LLM backend:
|
||||
|
||||
```bash
|
||||
/home/jpmschweitzer/.local/bin/pql --vault /mnt/media/Projects decisions read workspace D-11
|
||||
```
|
||||
|
||||
**workspace D-11 — the Claude migration is abandoned. Tatlock stays on Ollama.** Do not resume it and do
|
||||
not treat its remnants as unfinished work. What you will find, and why none of it is a TODO:
|
||||
`ANTHROPIC_MODEL` is set on the container (`claude-sonnet-4-20250514`) and never used because
|
||||
`PREFER_CLOUD_BACKEND=false`; `ANTHROPIC_API_KEY` is a variable reference whose literal was
|
||||
revoked 2026-08-09; `docs/claude-integration.md` documents a capability that exists but is
|
||||
switched off. The cost is deliberate: reasoning stays at `gemma4:e2b` scale because VRAM is
|
||||
shared with Speaches.
|
||||
|
||||
`REDIS_BENCHMARK_DB=6` is allocated on the container but the benchmarking module was never
|
||||
implemented — see the gotcha below. Vestigial, like the Anthropic settings.
|
||||
|
||||
## Critical gotchas
|
||||
|
||||
**ASGITransport does NOT trigger FastAPI lifespan events.** The session-scoped `_initialize_app`
|
||||
fixture in `tests/conftest.py` calls `initialize_application()` explicitly via `asyncio.run()`.
|
||||
Without it the Ollama/Claude health checks never run: `_ollama_available` stays `None` (treated
|
||||
as available, so requests go to Ollama) and `_claude_available` stays `None` (treated as
|
||||
unavailable, so the Claude fallback never engages).
|
||||
|
||||
**AsyncIO scope mismatch.** `asyncio_default_fixture_loop_scope = function` is set in
|
||||
`pyproject.toml`. Session-scoped async fixtures raise `ScopeMismatch`. Use a sync fixture with
|
||||
`asyncio.run()` for session-scoped initialization.
|
||||
|
||||
**The butler persona prompt suppresses local-model tool calling.** With `TATLOCK_SYSTEM_PROMPT`
|
||||
attached, gemma4 reasons about calling the calculator, then answers from memory with wrong
|
||||
arithmetic — a different wrong product each run. `orchestrate_tool_calls()` therefore uses the
|
||||
terse `TATLOCK_ORCHESTRATION_PROMPT`; the persona is applied in `synthesize_from_results()`. Do
|
||||
not reattach the persona prompt to a tool-phase agent. `tool_choice: "required"` via `extra_body`
|
||||
does **not** force Ollama to call tools — advisory at best.
|
||||
|
||||
**Claude Sonnet 5+ rejects sampling parameters.** `temperature`/`top_p`/`top_k` return 400. Use
|
||||
`get_sampling_settings()` from the model selector rather than passing `ModelSettings(temperature=…)`
|
||||
to agents that can run on the Claude fallback. `make test-contracts` pins this.
|
||||
|
||||
**Integration test timeouts** are 120s to match `OLLAMA_TIMEOUT` (300s for the pure-Ollama
|
||||
fallback test, which Claude cannot rescue). GPU-resident numbers measured 2026-08-07 with
|
||||
gemma4:e2b at ~95 tok/s: full Steward → orchestrate → synthesize ~10–13s for simple turns;
|
||||
librarian-routed ~20–25s (not re-measured). **A single turn costs 3 sequential Ollama calls and
|
||||
~710 generated tokens even for "what is 61 plus 12?"** — mostly the model's own reasoning, paid
|
||||
three times. Cold model load is ~36s, avoided while pinned with `keep_alive: -1`; the
|
||||
`OLLAMA_KEEP_ALIVE=2h` default reintroduces it. Older "~35s steward / ~2 min flow" and "11–25s"
|
||||
figures are superseded — do not plan against them. `STEWARD_TIMEOUT` defaults to 60s.
|
||||
|
||||
**`get_benchmark_store` does not exist.** `src/core/benchmarks.py` was never implemented, and
|
||||
`scripts/benchmark_analysis.py` references it and is broken. Do not add mocks for it in tests.
|
||||
|
||||
**Steward tests need the household registry.** Use `register_household_members()` (sync) in
|
||||
fixtures, not `initialize_application()` (async). The steward extracts capabilities from the
|
||||
registry.
|
||||
|
||||
## Commands
|
||||
|
||||
```bash
|
||||
make setup # venv + all dependencies
|
||||
make run # dev server on 8777, reload, logs to build/logs/server.log
|
||||
make test # unit tests, no external services
|
||||
make test-integration # needs Ollama (and Claude, if enabled)
|
||||
make test-contracts # wire-level contract tests against live service boundaries
|
||||
make lint # ruff linter + formatter check
|
||||
make typecheck # mypy
|
||||
make clean # remove caches and build artifacts
|
||||
```
|
||||
|
||||
Always run pytest through the venv explicitly, to avoid environment mismatch:
|
||||
|
||||
```bash
|
||||
.venv/bin/python -m pytest tests/
|
||||
.venv/bin/python -m pytest tests/core/ -v
|
||||
```
|
||||
|
||||
Dependencies live in `pyproject.toml` (`[project.dependencies]`, `[project.optional-dependencies.dev]`).
|
||||
Copy `.env.example` to `.env` and configure Ollama, Redis and Qdrant hosts.
|
||||
|
||||
**Contract tests before code review.** When the question is "do these two services still agree?",
|
||||
`make test-contracts` answers it by observing the live boundary; reading both codebases only tells
|
||||
you what should happen. Semantics: unreachable → skip, reachable-but-wrong-shape → fail.
|
||||
|
||||
## Architecture
|
||||
|
||||
Domain-first under `src/`: `agents/` (steward, librarian, biographer, housekeeper, tatlock_core),
|
||||
`core/`, `chat/`, `responses/`, `models/`, `ollama/`, `anthropic/`. Two tiers — the Steward routes,
|
||||
Tatlock coordinates. Group new work by domain, not by file type.
|
||||
|
||||
## Internal service access
|
||||
|
||||
`http://localhost:3002` reaches Gitea directly, bypassing Authentik SSO — verified returning
|
||||
`{"version":"1.27.1"}`. Useful for reading a sibling repo's raw files:
|
||||
|
||||
```bash
|
||||
curl http://localhost:3002/jpmschweitzer/library-desk/raw/branch/main/README.md
|
||||
```
|
||||
|
||||
The old AGENTS.md pointed at **`portainer-core`** for full-stack documentation. That repo is
|
||||
**deprecated** and must not be used as a source of infra facts; it was merged into
|
||||
`system-admin-toj/containers/`, where `CONTAINERS.md` is the live inventory.
|
||||
|
||||
## Work tracking
|
||||
|
||||
Work lives in **pql**, not a markdown TODO. **This repo's vault is standalone** — its tickets and
|
||||
internal decisions live here in `.pql/` and `governance/`, and travel with a clone, because
|
||||
`.pql/changelog/` is committed and replayed by the git hooks (workspace D-15). The databases are gitignored
|
||||
and rebuildable with `pql plan rebuild`.
|
||||
|
||||
`pql` is **not** on the non-interactive `PATH` — invoke it as `/home/jpmschweitzer/.local/bin/pql`.
|
||||
From inside this repo no `--vault` is needed; pql anchors at the nearest `.git/` ancestor.
|
||||
|
||||
```bash
|
||||
/home/jpmschweitzer/.local/bin/pql ticket list # this repo's open work
|
||||
/home/jpmschweitzer/.local/bin/pql plan whatsnext # next unblocked item, with context
|
||||
/home/jpmschweitzer/.local/bin/pql decisions list # this repo's own decisions
|
||||
```
|
||||
|
||||
Stack decisions that constrain this service need the flag:
|
||||
|
||||
```bash
|
||||
/home/jpmschweitzer/.local/bin/pql --vault /mnt/media/Projects decisions list --domain tatlock-api
|
||||
```
|
||||
|
||||
The workspace domain is `tatlock-api`, not `tatlock` — pql rejects a domain stem that prefixes
|
||||
another, and `tatlock` prefixes `tatlock-ui`. A `tatlock-api -> tatlock` symlink at the workspace
|
||||
root makes the directory answer to both (workspace D-15).
|
||||
|
||||
Note `ticket new --decision D-N` resolves ids within **one** vault, so a ticket here cannot link
|
||||
to a workspace decision. Cite the id in the ticket body instead.
|
||||
|
||||
## Git
|
||||
|
||||
- **History is linear — no merge commits.** Work on `main`, or a short-lived branch that is
|
||||
fast-forwarded and deleted. This repo's AGENTS.md mandated a feature branch for every change;
|
||||
that rule was retired workspace-wide on 2026-08-08 and does not apply.
|
||||
- **Conventional Commits**: `feat:`, `fix:`, `refactor:`, `docs:`, `chore:`.
|
||||
- **Stage explicitly. Never `git add -A`** — denied by policy, and it sweeps in whatever else is
|
||||
dirty, including secrets.
|
||||
- Update `CHANGELOG.md` for every user-facing change, under `[Unreleased]`.
|
||||
|
||||
## Releasing
|
||||
|
||||
Test locally first — the build-deploy loop is slow. Deploy only when a feature is complete.
|
||||
|
||||
1. Ask whether a deploy is wanted; it is not automatic.
|
||||
2. Bump `version` in `pyproject.toml` (patch for fixes, minor for features).
|
||||
3. Move `[Unreleased]` entries into a dated section in `CHANGELOG.md`.
|
||||
4. Stage the changed files by name, commit, tag `vX.Y.Z`, `git push origin main --tags`.
|
||||
5. Gitea CI builds and pushes on the tag; Watchtower deploys.
|
||||
6. Verify: `curl http://192.168.86.149:8000/health`.
|
||||
+2
-2
@@ -5,8 +5,8 @@ WORKDIR /app
|
||||
RUN apt-get update && apt-get install -y curl \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
COPY requirements.txt pyproject.toml ./
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
COPY pyproject.toml ./
|
||||
RUN pip install --no-cache-dir .
|
||||
|
||||
COPY src/ ./src/
|
||||
|
||||
|
||||
@@ -1,920 +0,0 @@
|
||||
# Tatlock Implementation Roadmap
|
||||
|
||||
> **Reference**: See [PHILOSOPHY.md](PHILOSOPHY.md) for the target architecture and vision
|
||||
|
||||
This document outlines the phased implementation plan to transform the current OpenAI-compatible API into the full Tatlock household butler system.
|
||||
|
||||
## Current State (v1.2.0 - Phase F Complete)
|
||||
|
||||
**What we have**:
|
||||
- ✅ **The Orchestrator** - FastAPI infrastructure layer
|
||||
- OpenAI-compatible API endpoints (Responses API + Chat Completions)
|
||||
- Streaming coordination and conversation management
|
||||
- Response format with reasoning support
|
||||
- Test infrastructure (~400 tests)
|
||||
- ✅ **Two-Tier Architecture**
|
||||
- The Steward analyzes requests and recommends capabilities
|
||||
- Tatlock coordinates execution with scoped tools
|
||||
- Real-time streaming of analysis and reasoning
|
||||
- ✅ **Household Staff**
|
||||
- **Tatlock** (Butler): Primary interface with witty personality
|
||||
- **The Steward**: Request analysis and capability recommendation
|
||||
- **The Librarian**: Research via library-desk HybridRAG + wiki
|
||||
- **The Biographer**: User memory, profiles, preferences, semantic recall
|
||||
- ✅ **Core Tools**
|
||||
- Calculator, Date/Time toolkit, Web search (SearXNG)
|
||||
- ✅ **Memory System**
|
||||
- Direct access layer (memory_service) for fast lookups
|
||||
- Vector storage (Qdrant) for semantic recall
|
||||
- Session cache (Redis) with 24h TTL
|
||||
- Multi-tenancy via ContextVar
|
||||
- ✅ Mock agent (lorem-tester for testing)
|
||||
|
||||
**What we need**:
|
||||
- More household staff (Developer, Secretary, Handyman, Housekeeper)
|
||||
- MCP (Model Context Protocol) integration
|
||||
- Dynamic model switching for specialized tasks
|
||||
- Full multi-tenant database (PostgreSQL)
|
||||
|
||||
---
|
||||
|
||||
## Phase 1: Real LLM Integration - PydanticAI + Tools
|
||||
|
||||
**Goal**: Connect to actual language models and establish the base plumbing
|
||||
|
||||
**Note**: Ollama is an external service dependency (already running separately)
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **PydanticAI Integration** ✅
|
||||
- PydanticAI → Ollama connection ✅
|
||||
- Agent creation patterns ✅
|
||||
- Streaming response handling ✅
|
||||
- Error handling and retries ✅
|
||||
|
||||
2. **Convert Tatlock Agent** ✅
|
||||
- Convert Tatlock agent from mock to PydanticAI ✅
|
||||
- British butler personality prompt ✅
|
||||
- Research-oriented mindset ✅
|
||||
- Streaming to reasoning output ✅
|
||||
- Tool calling framework setup ✅
|
||||
|
||||
3. **Permanent Tools** ✅
|
||||
- Calculator: Safe mathematical expression evaluation ✅
|
||||
- Date/Time toolkit: Current time, relative dates, time differences ✅
|
||||
- Web search: SearXNG integration (external service) ✅
|
||||
- Tool registration with PydanticAI ✅
|
||||
|
||||
4. **Testing Infrastructure** ✅
|
||||
- Integration tests with real LLM ✅
|
||||
- Tool functionality tests ✅
|
||||
- Response quality validation ✅
|
||||
- 131 tests, 81.78% coverage ✅
|
||||
|
||||
### Success Criteria
|
||||
- [x] **PydanticAI agents can call Ollama** (mistral-nemo:latest)
|
||||
- [x] **Streaming works end-to-end**
|
||||
- [x] **Tool calling framework functional**
|
||||
- [x] **Permanent tools working** (calculator, date/time, search)
|
||||
- [x] **Tests pass with real LLM**
|
||||
- [ ] Can switch models dynamically (e.g., Codestral for code)
|
||||
|
||||
### Status
|
||||
**✅ MOSTLY COMPLETE** - Tatlock agent functional with permanent tools
|
||||
|
||||
### Remaining Work
|
||||
- Dynamic model switching for specialized tasks (e.g., Codestral for coding)
|
||||
|
||||
### Why First?
|
||||
Without real LLM integration, we can't meaningfully implement the Steward/Butler pattern. Everything else depends on having actual AI agents working.
|
||||
|
||||
---
|
||||
|
||||
## Phase 2: Orchestration Layer - The Steward
|
||||
|
||||
**Goal**: Implement the first-tier LLM call for tool/agent selection
|
||||
|
||||
**Purpose**: The Steward performs crucial preparatory work before Tatlock engages with a request. By analyzing incoming requests and determining which tools, services, and household staff members will be needed, the Steward creates a curated recommendation that streamlines Tatlock's work and prevents cognitive overload.
|
||||
|
||||
### Core Architecture
|
||||
|
||||
The Steward operates as the first tier in the two-tier request flow:
|
||||
|
||||
```
|
||||
User Request → Orchestrator → Steward Analysis → Recommendations → Tatlock (with scoped tools/agents)
|
||||
```
|
||||
|
||||
**Key Principle**: The Steward narrows the scope to only relevant capabilities, making Tatlock's decision-making cleaner and more focused.
|
||||
|
||||
### Deliverables
|
||||
|
||||
#### 1. Tool & Agent Registry System
|
||||
|
||||
**Purpose**: Centralized catalog of all available capabilities for the Steward to recommend
|
||||
|
||||
**Implementation Details**:
|
||||
- **Registry Module** (`src/core/registry.py`)
|
||||
- Tool registration decorator pattern
|
||||
- Agent registration with capability metadata
|
||||
- Category-based organization (computation, information, automation, communication)
|
||||
- Dynamic tool/agent discovery and loading
|
||||
|
||||
- **Tool Metadata Schema**
|
||||
```python
|
||||
{
|
||||
"name": "calculator",
|
||||
"category": "computation",
|
||||
"description": "Safe mathematical expression evaluation",
|
||||
"capabilities": ["arithmetic", "algebra", "trigonometry"],
|
||||
"cost": "low", # computational cost indicator
|
||||
"requires_network": false
|
||||
}
|
||||
```
|
||||
|
||||
- **Agent Metadata Schema**
|
||||
```python
|
||||
{
|
||||
"name": "developer",
|
||||
"role": "The Developer",
|
||||
"category": "technical",
|
||||
"description": "Software development assistance",
|
||||
"domains": ["code_generation", "debugging", "architecture"],
|
||||
"specialized_model": "codestral", # optional
|
||||
"cost": "high"
|
||||
}
|
||||
```
|
||||
|
||||
- **Registry API**
|
||||
- `get_all_tools()` - List all available tools
|
||||
- `get_all_agents()` - List all expert agents
|
||||
- `get_by_category(category)` - Filter by category
|
||||
- `search_by_capability(query)` - Semantic search (future: vector search)
|
||||
|
||||
**Testing**:
|
||||
- Unit tests for registration and retrieval
|
||||
- Test dynamic loading of new tools/agents
|
||||
- Validate metadata schemas
|
||||
|
||||
#### 2. Steward PydanticAI Agent
|
||||
|
||||
**Purpose**: First-tier LLM that analyzes requests and recommends relevant tools/agents
|
||||
|
||||
**Implementation Details**:
|
||||
|
||||
- **Agent Module** (`src/agents/steward.py`)
|
||||
```python
|
||||
from pydantic_ai import Agent, RunContext
|
||||
from pydantic import BaseModel
|
||||
|
||||
class StewardRecommendation(BaseModel):
|
||||
"""Structured output from Steward analysis"""
|
||||
recommended_tools: list[str]
|
||||
recommended_agents: list[str]
|
||||
reasoning: str
|
||||
estimated_complexity: str # "simple", "moderate", "complex"
|
||||
requires_multi_step: bool
|
||||
|
||||
steward = Agent(
|
||||
'ollama:mistral-nemo', # Same base model as Tatlock
|
||||
result_type=StewardRecommendation,
|
||||
system_prompt="""..."""
|
||||
)
|
||||
```
|
||||
|
||||
- **System Prompt Engineering**
|
||||
- Role: Estate steward responsible for efficient household coordination
|
||||
- Task: Analyze requests to determine needed resources
|
||||
- Output: Structured recommendations with reasoning
|
||||
- Constraints: Be conservative (recommend only truly relevant capabilities)
|
||||
- Context: Full registry of available tools and agents
|
||||
|
||||
- **Steward Tools**
|
||||
```python
|
||||
@steward.tool
|
||||
def get_available_capabilities(ctx: RunContext) -> dict:
|
||||
"""Get catalog of all available tools and agents."""
|
||||
return {
|
||||
"tools": registry.get_all_tools(),
|
||||
"agents": registry.get_all_agents()
|
||||
}
|
||||
```
|
||||
|
||||
- **Request Analysis Flow**
|
||||
1. Receive user request
|
||||
2. Query capability registry via tool
|
||||
3. Analyze request for required capabilities
|
||||
4. Generate structured recommendation
|
||||
5. Format as note to Tatlock
|
||||
|
||||
**Testing**:
|
||||
- Test various request types (simple, complex, multi-domain)
|
||||
- Verify recommendations are relevant and not over-inclusive
|
||||
- Test structured output parsing
|
||||
- Validate reasoning quality
|
||||
|
||||
#### 3. Request Preprocessing Pipeline
|
||||
|
||||
**Purpose**: Integration layer that routes requests through Steward before Tatlock
|
||||
|
||||
**Implementation Details**:
|
||||
|
||||
- **Preprocessing Module** (`src/core/preprocessing.py`)
|
||||
```python
|
||||
async def preprocess_request(user_request: str) -> EnrichedRequest:
|
||||
"""
|
||||
1. Call Steward for analysis
|
||||
2. Get recommendations
|
||||
3. Enrich original request
|
||||
4. Return scoped context for Tatlock
|
||||
"""
|
||||
# Get Steward analysis
|
||||
steward_result = await steward.run(user_request)
|
||||
recommendations = steward_result.data
|
||||
|
||||
# Create note to Tatlock
|
||||
steward_note = format_steward_note(recommendations)
|
||||
|
||||
# Build scoped tool/agent list
|
||||
scoped_tools = get_scoped_tools(recommendations.recommended_tools)
|
||||
scoped_agents = get_scoped_agents(recommendations.recommended_agents)
|
||||
|
||||
return EnrichedRequest(
|
||||
original_request=user_request,
|
||||
steward_note=steward_note,
|
||||
available_tools=scoped_tools,
|
||||
available_agents=scoped_agents,
|
||||
metadata=recommendations
|
||||
)
|
||||
```
|
||||
|
||||
- **Note Formatting**
|
||||
```
|
||||
=== Internal Note from the Steward ===
|
||||
|
||||
Request Analysis:
|
||||
{steward reasoning}
|
||||
|
||||
Recommended Tools:
|
||||
- calculator: For mathematical computations
|
||||
- web_search: To find current information
|
||||
|
||||
Recommended Household Staff:
|
||||
- The Developer: For code generation assistance
|
||||
|
||||
Estimated Complexity: moderate
|
||||
===================================
|
||||
|
||||
[Original User Request]
|
||||
```
|
||||
|
||||
- **Orchestrator Integration**
|
||||
- Modify `src/responses/service.py` to call preprocessing
|
||||
- Prepend Steward note to request before sending to Tatlock
|
||||
- Limit Tatlock's tool access to recommended tools only
|
||||
- Stream Steward's reasoning to output
|
||||
|
||||
**Testing**:
|
||||
- Integration tests for full preprocessing flow
|
||||
- Test request enrichment format
|
||||
- Verify tool scoping works correctly
|
||||
- Test streaming of Steward reasoning
|
||||
|
||||
#### 4. Real-Time Transparency
|
||||
|
||||
**Purpose**: Stream Steward's analysis to user's reasoning output
|
||||
|
||||
**Implementation Details**:
|
||||
|
||||
- **Streaming Integration** (`src/responses/streaming.py`)
|
||||
- Add Steward analysis phase to stream
|
||||
- Format as reasoning item
|
||||
- Include recommendation summary
|
||||
|
||||
- **Example Output to User**:
|
||||
```
|
||||
[Reasoning]
|
||||
Consulting the Steward for resource planning...
|
||||
|
||||
The Steward's Analysis:
|
||||
- Request requires mathematical computation
|
||||
- Need to verify current information via web search
|
||||
- May benefit from Developer's code expertise
|
||||
|
||||
Recommended: calculator, web_search, The Developer
|
||||
|
||||
Proceeding with scoped resources...
|
||||
```
|
||||
|
||||
**Testing**:
|
||||
- Test streaming of Steward analysis
|
||||
- Verify formatting in Open WebUI
|
||||
- Test error handling if Steward fails
|
||||
|
||||
#### 5. Model Efficiency Optimization
|
||||
|
||||
**Purpose**: Ensure the base model stays loaded in VRAM
|
||||
|
||||
**Implementation Details**:
|
||||
|
||||
- **Shared Model Configuration**
|
||||
- Both Steward and Tatlock use `ollama:mistral-nemo` by default
|
||||
- Sequential calls (Steward → Tatlock) keep model hot
|
||||
- No reload delays between tiers
|
||||
|
||||
- **Performance Monitoring**
|
||||
- Log response times for Steward calls
|
||||
- Track total request latency (Steward + Tatlock)
|
||||
- Identify optimization opportunities
|
||||
|
||||
**Testing**:
|
||||
- Benchmark Steward → Tatlock call latency
|
||||
- Verify model stays loaded between calls
|
||||
- Test performance under load
|
||||
|
||||
### Implementation Strategy
|
||||
|
||||
#### Week 1-2: Foundation
|
||||
- [ ] Design and implement registry system
|
||||
- [ ] Create tool/agent metadata schemas
|
||||
- [ ] Build registry API with tests
|
||||
- [ ] Migrate existing tools to registry
|
||||
|
||||
#### Week 3-4: Steward Agent
|
||||
- [ ] Create Steward PydanticAI agent
|
||||
- [ ] Engineer system prompt for analysis
|
||||
- [ ] Implement structured recommendation output
|
||||
- [ ] Add registry query tool
|
||||
- [ ] Test with various request types
|
||||
|
||||
#### Week 5-6: Integration
|
||||
- [ ] Build request preprocessing pipeline
|
||||
- [ ] Implement note formatting
|
||||
- [ ] Integrate with Orchestrator
|
||||
- [ ] Add streaming transparency
|
||||
- [ ] Tool scoping for Tatlock
|
||||
|
||||
#### Week 7: Testing & Refinement
|
||||
- [ ] End-to-end integration tests
|
||||
- [ ] Performance optimization
|
||||
- [ ] Prompt refinement based on results
|
||||
- [ ] Documentation and examples
|
||||
|
||||
### Success Criteria
|
||||
|
||||
- [x] **Steward analyzes incoming requests** using PydanticAI agent
|
||||
- [x] **Produces structured recommendations** (tools, agents, reasoning)
|
||||
- [x] **Recommendations formatted as prepended note** to Tatlock
|
||||
- [x] **Tool registry is queryable and extensible** via clean API
|
||||
- [x] **Steward output visible in reasoning stream** for transparency
|
||||
- [x] **Only recommended tools available** to Tatlock (scoped context)
|
||||
- [x] **Base model stays loaded** between Steward and Tatlock calls
|
||||
- [x] **Recommendations are accurate** (not over/under-inclusive)
|
||||
- [x] **Integration tests pass** for full Steward → Tatlock flow
|
||||
|
||||
### Status
|
||||
**✅ COMPLETE** (v0.2.5)
|
||||
|
||||
### Performance Targets
|
||||
|
||||
- **Steward Analysis Time**: < 2 seconds for typical requests
|
||||
- **Total Added Latency**: < 3 seconds including streaming
|
||||
- **Recommendation Accuracy**: > 90% relevance (manual evaluation)
|
||||
- **Model Reload Delay**: 0 seconds (model stays hot)
|
||||
|
||||
### Risk Mitigation
|
||||
|
||||
**Risk**: Steward recommendations too broad (defeats purpose)
|
||||
- Mitigation: Conservative prompt engineering, test with diverse requests, iterate
|
||||
|
||||
**Risk**: Added latency unacceptable to users
|
||||
- Mitigation: Stream Steward reasoning for transparency, optimize prompt, parallel processing where possible
|
||||
|
||||
**Risk**: Tool registry becomes unwieldy
|
||||
- Mitigation: Good categorization, semantic search (future), regular pruning
|
||||
|
||||
**Risk**: Steward and Tatlock models compete for VRAM
|
||||
- Mitigation: Use same base model, sequential calls, monitor memory
|
||||
|
||||
### Future Enhancements (Post-Phase 2)
|
||||
|
||||
- **Semantic Search**: Vector-based capability search instead of metadata lookup
|
||||
- **Learning from Usage**: Track which recommendations work well, adjust over time
|
||||
- **Confidence Scores**: Steward provides confidence for each recommendation
|
||||
- **Request Classification**: Cache classifications for similar requests
|
||||
- **Multi-Model Support**: Allow Steward to recommend specialized models for specific tasks
|
||||
|
||||
### Estimated Effort
|
||||
|
||||
**7-8 weeks** - Core intelligence routing with comprehensive implementation
|
||||
|
||||
### Why Second?
|
||||
|
||||
The Steward is the foundation of the household architecture. Without it, we'd need to expose all tools/agents to Tatlock, creating cognitive overload and poor decision-making. The Steward enables the focused expertise pattern that makes the whole system work.
|
||||
|
||||
---
|
||||
|
||||
## Phase 3: The Butler - Tatlock Agent
|
||||
|
||||
**Goal**: Implement the second-tier coordinator with personality within the existing Orchestrator infrastructure
|
||||
|
||||
**Context**: The Orchestrator (FastAPI infrastructure) already exists. This phase implements the real Tatlock PydanticAI agent to replace the current mock agent.
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **Butler Agent (Tatlock)**
|
||||
- PydanticAI agent implementation within Orchestrator
|
||||
- Personality prompt engineering (witty British butler)
|
||||
- Tool calling framework
|
||||
- Multi-agent coordination logic
|
||||
|
||||
2. **Scoped Tool Access**
|
||||
- Filter tools based on Steward recommendations
|
||||
- Dynamic tool loading for Butler context
|
||||
- Tool execution framework
|
||||
- Result aggregation
|
||||
|
||||
3. **Real-Time Reasoning Output**
|
||||
- Stream all Butler activities to reasoning output
|
||||
- Tool call progress indicators
|
||||
- Expert agent consultation messages
|
||||
- Wait time transparency
|
||||
|
||||
### Success Criteria
|
||||
- [x] Tatlock receives enriched requests (user + Steward notes)
|
||||
- [x] Only recommended tools are available
|
||||
- [x] Tatlock coordinates multiple tool calls
|
||||
- [x] All actions streamed to reasoning output
|
||||
- [x] Responses have consistent personality
|
||||
- [x] Synthesizes multi-source results coherently
|
||||
|
||||
### Status
|
||||
**✅ COMPLETE** (v1.1.0)
|
||||
|
||||
### Estimated Effort
|
||||
**4-5 weeks** - Complex coordination logic
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Expert Household Staff - Core Agents
|
||||
|
||||
**Goal**: Implement the initial set of domain-specific expert agents
|
||||
|
||||
### Priority Expert Agents
|
||||
|
||||
1. **The Librarian** (Research & Knowledge Management) ✅ **COMPLETE** (v1.1.0)
|
||||
- Research assistance via library-desk HybridRAG
|
||||
- Wiki page management (search, create, update)
|
||||
- Semantic vector search
|
||||
- Knowledge graph queries
|
||||
- Dossier browsing
|
||||
|
||||
2. **The Biographer** (User Memory) ✅ **COMPLETE** (v1.2.0)
|
||||
- User profile management (name, location, timezone)
|
||||
- Preference storage (units, theme)
|
||||
- Semantic memory recall ("What car do I drive?")
|
||||
- Fact storage from conversations
|
||||
- Session context caching
|
||||
|
||||
3. **The Developer** (Software Development) 🔜 **Planned**
|
||||
- Code generation assistance
|
||||
- Debugging support
|
||||
- Documentation generation
|
||||
- Architecture guidance
|
||||
- *Rationale: Directly supports building the system itself*
|
||||
|
||||
4. **The Handyman** (System Maintenance) 🔜 **Planned**
|
||||
- System status queries
|
||||
- Log analysis
|
||||
- Basic troubleshooting
|
||||
- Infrastructure monitoring
|
||||
|
||||
5. **The Secretary** (Scheduling & Organization) 🔜 **Planned**
|
||||
- Calendar integration
|
||||
- Task management
|
||||
- Reminder system
|
||||
- Schedule conflict detection
|
||||
|
||||
6. **The Housekeeper** (Home Automation) 🔜 **Planned**
|
||||
- Home Assistant integration
|
||||
- Device control interface
|
||||
- Status queries
|
||||
- Automation triggers
|
||||
|
||||
### Each Agent Includes
|
||||
- Specialized prompt and personality
|
||||
- Domain-specific tools
|
||||
- MCP integration points (where applicable)
|
||||
- Integration with Butler orchestration
|
||||
|
||||
### Success Criteria
|
||||
- [x] Each agent implemented as separate module
|
||||
- [x] Agents callable via tool framework
|
||||
- [x] Agents use specialized prompts
|
||||
- [x] Results integrate cleanly with Butler
|
||||
- [ ] Can invoke specialized models (e.g., Codestral for Developer)
|
||||
|
||||
### Status
|
||||
**🔶 PARTIAL** - Librarian and Biographer complete, others planned
|
||||
|
||||
### Estimated Effort
|
||||
**6-8 weeks** - Parallel development possible
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: Persistence Layer - Database & Multi-Tenancy
|
||||
|
||||
**Goal**: Add persistent storage and multi-user support when needed
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **PostgreSQL Integration**
|
||||
- Docker compose configuration for PostgreSQL
|
||||
- Database schema design with tenant isolation
|
||||
- Alembic migrations setup
|
||||
- SQLAlchemy models
|
||||
|
||||
2. **Multi-Tenant Architecture**
|
||||
- Tenant identification middleware
|
||||
- Tenant-scoped database sessions
|
||||
- User authentication system (basic)
|
||||
- Per-tenant data isolation
|
||||
|
||||
3. **Core Data Models**
|
||||
- Users and tenants
|
||||
- Conversations and messages (migrate from in-memory)
|
||||
- Agent interactions log
|
||||
- System configuration and preferences
|
||||
|
||||
4. **Migration Strategy**
|
||||
- Gradual migration from in-memory to database
|
||||
- Backward compatibility during transition
|
||||
- Data export/import utilities
|
||||
|
||||
### Success Criteria
|
||||
- [ ] PostgreSQL container running
|
||||
- [ ] Multiple users can authenticate separately
|
||||
- [ ] Each user sees only their own data
|
||||
- [ ] Conversations persist across restarts
|
||||
- [ ] Database migrations work correctly
|
||||
- [ ] Tests verify tenant isolation
|
||||
|
||||
### Estimated Effort
|
||||
**3-4 weeks** - Data layer foundation
|
||||
|
||||
### Why Later?
|
||||
The core orchestration (Steward → Butler → Experts) can work entirely with in-memory state. We only need database persistence when we want conversations to survive restarts and multiple users to have isolated experiences.
|
||||
|
||||
---
|
||||
|
||||
## Phase 6: Extended Services Integration
|
||||
|
||||
**Goal**: Connect to additional supporting services
|
||||
|
||||
### Services to Integrate
|
||||
|
||||
1. **Redis (Memory & Caching)** ✅ **COMPLETE** (v1.2.0)
|
||||
- Benchmark storage (db=1)
|
||||
- Memory cache for sessions (db=2)
|
||||
- 24h TTL for session context
|
||||
- Recent entities tracking
|
||||
|
||||
2. **Qdrant (Vector Storage)** ✅ **COMPLETE** (v1.2.0)
|
||||
- Per-user memory collections
|
||||
- 768-dim nomic-embed-text vectors
|
||||
- Semantic search for recall
|
||||
- Type-based filtering
|
||||
|
||||
3. **SearxNG (Web Search)** ✅ **COMPLETE** (v0.2.0)
|
||||
- Search tool integration
|
||||
- Result processing
|
||||
- Privacy-preserving queries
|
||||
|
||||
4. **library-desk (Research API)** ✅ **COMPLETE** (v1.1.0)
|
||||
- HybridRAG search
|
||||
- Wiki management
|
||||
- Knowledge graph queries
|
||||
|
||||
### Success Criteria
|
||||
- [x] Services communicate correctly
|
||||
- [x] Tatlock can invoke web search
|
||||
- [x] Redis used for session data
|
||||
- [x] Qdrant stores user memories
|
||||
- [x] Ollama serves the base model
|
||||
|
||||
### Status
|
||||
**✅ COMPLETE** - All core services integrated
|
||||
|
||||
### Estimated Effort
|
||||
**3-4 weeks** - Infrastructure setup
|
||||
|
||||
---
|
||||
|
||||
## Phase 7: MCP (Model Context Protocol) Integration
|
||||
|
||||
**Goal**: Enable rich tool integrations via MCP
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **MCP Server Framework**
|
||||
- MCP server implementation
|
||||
- Tool registration via MCP
|
||||
- Schema validation
|
||||
- Error handling
|
||||
|
||||
2. **MCP Client in Agents**
|
||||
- PydanticAI MCP integration
|
||||
- Tool discovery from MCP servers
|
||||
- Dynamic tool loading
|
||||
- Result processing
|
||||
|
||||
3. **Initial MCP Tools**
|
||||
- File system operations
|
||||
- Database queries
|
||||
- API integrations
|
||||
- System commands
|
||||
|
||||
### Success Criteria
|
||||
- [ ] MCP server running
|
||||
- [ ] Tools exposed via MCP protocol
|
||||
- [ ] Agents can discover and use MCP tools
|
||||
- [ ] New tools addable without code changes
|
||||
- [ ] MCP tools visible in Steward recommendations
|
||||
|
||||
### Estimated Effort
|
||||
**3-4 weeks** - Standards-based integration
|
||||
|
||||
---
|
||||
|
||||
## Phase 8: Advanced Memory & Context
|
||||
|
||||
**Goal**: Implement sophisticated memory and context management
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **Long-Term Memory** ✅ **COMPLETE** (v1.2.0 - Phase F)
|
||||
- Memory service for direct key-based access
|
||||
- Qdrant vector storage for semantic recall
|
||||
- Embedding via nomic-embed-text
|
||||
- The Biographer agent for memory management
|
||||
|
||||
2. **Session Memory** ✅ **COMPLETE** (v1.2.0)
|
||||
- Redis session cache with 24h TTL
|
||||
- Recent entities tracking
|
||||
- Conversation context preservation
|
||||
- Multi-tenancy via ContextVar
|
||||
|
||||
3. **Steward Integration** ✅ **COMPLETE** (v1.2.0)
|
||||
- Memory pre-fetch during request analysis
|
||||
- Profile/preferences included in context
|
||||
- Keyword-based context determination
|
||||
|
||||
4. **Context Management** 🔜 **Future**
|
||||
- Smart context window trimming
|
||||
- Conversation branching
|
||||
- Topic tracking
|
||||
- Memory retrieval integration
|
||||
|
||||
5. **Personalization** 🔜 **Future**
|
||||
- User preference learning
|
||||
- Interaction pattern analysis
|
||||
- Adaptive responses
|
||||
- Custom agent personalities per user
|
||||
|
||||
### Success Criteria
|
||||
- [x] User facts stored in Qdrant with semantic search
|
||||
- [x] Profile and preferences accessible via memory_service
|
||||
- [x] Session context cached in Redis
|
||||
- [x] User preferences affect responses (via Steward pre-fetch)
|
||||
- [ ] Conversations automatically embedded to Qdrant
|
||||
- [ ] Memory improves over time (learning from interactions)
|
||||
|
||||
### Status
|
||||
**🔶 PARTIAL** - Core memory system complete, advanced features planned
|
||||
|
||||
### Estimated Effort
|
||||
**4-5 weeks** - AI/ML heavy (remaining work)
|
||||
|
||||
---
|
||||
|
||||
## Phase 9: Extended Household Staff
|
||||
|
||||
**Goal**: Add specialized agents for additional domains
|
||||
|
||||
### Future Agents
|
||||
|
||||
1. **The Librarian** (Knowledge Management)
|
||||
- Personal documentation indexing
|
||||
- Research assistance
|
||||
- Knowledge base queries
|
||||
- Reference management
|
||||
|
||||
2. **The Accountant** (Financial Tracking)
|
||||
- Expense tracking
|
||||
- Budget monitoring
|
||||
- Financial reports
|
||||
- Transaction categorization
|
||||
|
||||
3. **The Chef** (Meal Planning)
|
||||
- Recipe management
|
||||
- Meal planning
|
||||
- Nutrition tracking
|
||||
- Grocery lists
|
||||
|
||||
4. **Others as Needed**
|
||||
- Domain-specific as requirements emerge
|
||||
|
||||
### Success Criteria
|
||||
- [ ] Each new agent follows household pattern
|
||||
- [ ] Integrates with Steward/Butler flow
|
||||
- [ ] Has appropriate specialized tools
|
||||
- [ ] Documented in PHILOSOPHY.md updates
|
||||
|
||||
### Estimated Effort
|
||||
**Ongoing** - Add as needed
|
||||
|
||||
---
|
||||
|
||||
## Phase 10: User Experience Refinement
|
||||
|
||||
**Goal**: Polish the interaction experience
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **Personality Tuning**
|
||||
- Refine Tatlock's wit and tone
|
||||
- Consistent household character
|
||||
- Cultural references appropriate
|
||||
- Humor that doesn't annoy
|
||||
|
||||
2. **Transparency Improvements**
|
||||
- Better progress indicators
|
||||
- Clearer reasoning explanations
|
||||
- Informative wait messages
|
||||
- Error message clarity
|
||||
|
||||
3. **Performance Optimization**
|
||||
- Response time improvements
|
||||
- Model loading optimization
|
||||
- Caching strategies
|
||||
- Streaming smoothness
|
||||
|
||||
### Success Criteria
|
||||
- [ ] Users find Tatlock engaging
|
||||
- [ ] Wait times feel reasonable
|
||||
- [ ] Errors are understandable
|
||||
- [ ] System feels responsive
|
||||
|
||||
### Estimated Effort
|
||||
**Ongoing** - Continuous improvement
|
||||
|
||||
---
|
||||
|
||||
## Phase 11: Production Hardening
|
||||
|
||||
**Goal**: Make the system production-ready for homelab deployment
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **Deployment**
|
||||
- Complete docker-compose stack
|
||||
- Environment configuration
|
||||
- Backup strategies
|
||||
- Update procedures
|
||||
|
||||
2. **Monitoring**
|
||||
- Health checks
|
||||
- Performance metrics
|
||||
- Error tracking
|
||||
- Usage analytics
|
||||
|
||||
3. **Security**
|
||||
- Authentication hardening
|
||||
- Rate limiting
|
||||
- Input validation
|
||||
- Audit logging
|
||||
|
||||
4. **Documentation**
|
||||
- Installation guide
|
||||
- Configuration reference
|
||||
- Troubleshooting guide
|
||||
- Architecture documentation
|
||||
|
||||
### Success Criteria
|
||||
- [ ] One-command deployment
|
||||
- [ ] System health is monitorable
|
||||
- [ ] Secure for homelab use
|
||||
- [ ] Well documented
|
||||
|
||||
### Estimated Effort
|
||||
**3-4 weeks** - Production polish
|
||||
|
||||
---
|
||||
|
||||
## Dependencies Between Phases
|
||||
|
||||
```
|
||||
Phase 1 (Ollama + PydanticAI) ← Foundation for all AI
|
||||
↓
|
||||
Phase 2 (Steward)
|
||||
↓
|
||||
Phase 3 (Butler/Tatlock)
|
||||
↓
|
||||
Phase 4 (Expert Agents) ← Phase 7 (MCP) can enhance
|
||||
↓
|
||||
Phase 5 (Database/Multi-Tenancy) ← Can be deferred
|
||||
↓
|
||||
Phase 6 (Extended Services) → Phase 8 (Advanced Memory)
|
||||
↓
|
||||
Phase 9 (Extended Staff) → Phase 10 (UX) → Phase 11 (Production)
|
||||
```
|
||||
|
||||
**Critical Path**: Phases 1 → 2 → 3 → 4 must be sequential
|
||||
**Can Be Deferred**: Phase 5 (Database) until you need persistence
|
||||
**Parallel Opportunities**: Phase 6 and 7 can overlap; Phase 9 and 10 ongoing
|
||||
|
||||
---
|
||||
|
||||
## Overall Timeline Estimate
|
||||
|
||||
**Minimum Viable Household** (Phases 1-4): **15-20 weeks**
|
||||
- Working Steward → Butler → Expert Agents with real LLM
|
||||
- In-memory state (no persistence needed yet)
|
||||
- Core household functional
|
||||
|
||||
**With Persistence** (Phases 1-5): **18-24 weeks**
|
||||
- Add database and multi-tenancy
|
||||
- Conversations survive restarts
|
||||
- Multiple users supported
|
||||
|
||||
**Full-Featured System** (Phases 1-9): **35-45 weeks**
|
||||
- All services integrated
|
||||
- Advanced memory and context
|
||||
- Extended household staff
|
||||
|
||||
**Production-Ready** (All phases): **40-50 weeks**
|
||||
- Polished UX
|
||||
- Hardened for homelab deployment
|
||||
- Fully documented
|
||||
|
||||
*Note: Timeline assumes consistent part-time development effort*
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Technical
|
||||
- System implements PHILOSOPHY.md patterns
|
||||
- All household roles functional
|
||||
- Multi-tenant isolation verified
|
||||
- Real-time reasoning transparency working
|
||||
- MCP integration complete
|
||||
|
||||
### User Experience
|
||||
- Tatlock feels like interacting with a butler
|
||||
- Wait times are transparent and acceptable
|
||||
- Expert agents provide value in their domains
|
||||
- System is reliable and trustworthy
|
||||
|
||||
### Architecture
|
||||
- Clean separation between household roles
|
||||
- Easy to add new agents/tools
|
||||
- Model efficiency (base model stays loaded)
|
||||
- Scales to household + friends usage
|
||||
|
||||
---
|
||||
|
||||
## Risk Management
|
||||
|
||||
### High Risk Items
|
||||
1. **PydanticAI + Ollama integration complexity**
|
||||
- Mitigation: Prototype early, iterate on connection layer
|
||||
|
||||
2. **Multi-agent coordination complexity**
|
||||
- Mitigation: Start simple, add coordination gradually
|
||||
|
||||
3. **Model performance on homelab hardware**
|
||||
- Mitigation: Model selection, quantization, optimization
|
||||
|
||||
4. **Prompt engineering for personality consistency**
|
||||
- Mitigation: Extensive testing, user feedback, iteration
|
||||
|
||||
### Medium Risk Items
|
||||
- MCP protocol adoption and tooling maturity
|
||||
- Vector embedding quality for memory
|
||||
- Home automation integration variability
|
||||
- User authentication security
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Priority**: Implement The Developer agent for code assistance
|
||||
2. **Integration**: Add Home Assistant integration for The Housekeeper
|
||||
3. **Calendar**: Integrate scheduling service for The Secretary
|
||||
4. **Ongoing**: Add more household staff as needed
|
||||
|
||||
---
|
||||
|
||||
**Document Status**: Active planning document
|
||||
**Created**: 2025-12-06
|
||||
**Last Updated**: 2025-12-13
|
||||
@@ -0,0 +1,89 @@
|
||||
.PHONY: help setup run test test-unit test-integration test-contracts lint typecheck clean
|
||||
|
||||
VENV := .venv
|
||||
PYTHON := $(VENV)/bin/python
|
||||
PIP := $(VENV)/bin/pip
|
||||
PYTEST := $(VENV)/bin/pytest
|
||||
RUFF := $(VENV)/bin/ruff
|
||||
MYPY := $(VENV)/bin/mypy
|
||||
UVICORN := $(VENV)/bin/uvicorn
|
||||
|
||||
HOST := 0.0.0.0
|
||||
PORT := 8777
|
||||
|
||||
help: ## Show this help
|
||||
@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-20s\033[0m %s\n", $$1, $$2}'
|
||||
|
||||
setup: ## Create venv and install all dependencies
|
||||
python3 -m venv $(VENV)
|
||||
$(PIP) install --upgrade pip
|
||||
$(PIP) install -e ".[dev]"
|
||||
# Exit 0 from pip install is not evidence the environment works (D-24) - the
|
||||
# 2026-08-09 core-api incident was exactly this: a venv that "installed fine"
|
||||
# but was missing a declared dependency, surfacing as 11 collection errors
|
||||
# that read like broken imports rather than an environment problem. Collection
|
||||
# is the right cheap check here for that same reason: it imports every test
|
||||
# module (and everything they import) without running the suite, so a missing
|
||||
# or mismatched dependency fails setup itself instead of showing up later as a
|
||||
# mysterious test failure. Scoped like `make test` (excludes e2e/integration/
|
||||
# contracts, which need external services) and --no-cov since coverage
|
||||
# instrumentation is irrelevant to "does this collect".
|
||||
$(PYTEST) --collect-only -q --ignore=tests/e2e --ignore=tests/integration --ignore=tests/contracts --no-cov
|
||||
|
||||
run: ## Start the development server on port 8777
|
||||
@mkdir -p build/logs
|
||||
@if lsof -Pi :$(PORT) -sTCP:LISTEN -t >/dev/null 2>&1; then \
|
||||
echo "Error: Port $(PORT) is already in use"; \
|
||||
echo "Run: lsof -i :$(PORT) to see what's using it"; \
|
||||
exit 1; \
|
||||
fi
|
||||
$(UVICORN) src.main:app --reload --host $(HOST) --port $(PORT) 2>&1 | tee build/logs/server.log
|
||||
|
||||
test: ## Run unit tests (no external services needed)
|
||||
$(PYTEST) --ignore=tests/e2e --ignore=tests/integration --ignore=tests/contracts
|
||||
|
||||
test-unit: test ## Alias for test
|
||||
|
||||
test-integration: ## Run integration tests (needs Claude/Ollama)
|
||||
$(PYTEST) tests/agents/test_tatlock_agent.py -v
|
||||
|
||||
test-contracts: ## Wire-level contract tests against live service boundaries
|
||||
$(PYTEST) tests/contracts -v --no-cov
|
||||
|
||||
lint: ## Run ruff linter and formatter check
|
||||
$(RUFF) check src tests
|
||||
$(RUFF) format --check src tests
|
||||
|
||||
typecheck: ## Run mypy type checking
|
||||
$(MYPY) src
|
||||
|
||||
clean: ## Remove build artifacts, caches, and coverage reports
|
||||
rm -rf .cache build
|
||||
find . -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true
|
||||
|
||||
# git hands a hook a non-login shell, which never sees ~/.local/bin — where
|
||||
# gitleaks lands. Without this the scan reports "not installed" on every push,
|
||||
# which is a check that fails open (D-24).
|
||||
export PATH := $(HOME)/.local/bin:/usr/local/bin:$(PATH)
|
||||
|
||||
.PHONY: secrets
|
||||
secrets: ## Scan the commits about to be pushed for credentials
|
||||
@ci/secrets.sh
|
||||
|
||||
# The call surface is identical in every repo; what it runs is not.
|
||||
#
|
||||
# `secrets` runs first, deliberately: it is the only failure here that cannot be
|
||||
# undone by fixing it afterwards. A failed lint costs another commit; a pushed
|
||||
# credential is cached and indexed whether or not it is later deleted.
|
||||
#
|
||||
# Some of these fail today, and are left wired anyway. The state was measured
|
||||
# once and written down in T-56 rather than being worked around here — a gate
|
||||
# quietly narrowed to what already passes is a gate that reports success for
|
||||
# doing nothing, which is the failure this workspace keeps rediscovering.
|
||||
.PHONY: pre-push
|
||||
pre-push: secrets lint ## Everything the pre-push hook runs
|
||||
@echo " -- not gated here yet: typecheck (T-1), test (T-56)"
|
||||
@echo " typecheck reports 95 errors in 31 files and has never passed, so"
|
||||
@echo " gating on it blocked every push to this repo — including the commit"
|
||||
@echo " that added the gate. Run 'make typecheck' before pushing anything"
|
||||
@echo " that touches types; T-1 is the pass that earns this line's removal."
|
||||
@@ -1,401 +0,0 @@
|
||||
# Tatlock Enhancement Plan: Bidirectional Claude Integration
|
||||
|
||||
## Executive Summary
|
||||
|
||||
Implement a **bidirectional architecture** that:
|
||||
1. **Superpowers Tatlock** by swapping Ollama→Claude backend (200k context, better reasoning, same butler personality)
|
||||
2. **Exposes Tatlock as MCP server** for Claude instances on any device (phone, browser, desktop)
|
||||
|
||||
This gives you the flexibility to use whichever AI is best/most accessible at any moment.
|
||||
|
||||
## Key Insight: Blanket Backend Swap (Simpler Than Sidecar)
|
||||
|
||||
Instead of adding a Claude "Analyst" sidecar agent, **swap the underlying model for ALL agents**:
|
||||
|
||||
```
|
||||
CURRENT: TatlockAgent → OpenAIChatModel → OllamaProvider → Ollama (mistral-nemo)
|
||||
PROPOSED: TatlockAgent → AnthropicModel → AnthropicProvider → Claude API
|
||||
↘ (fallback when offline) → OllamaProvider → Ollama
|
||||
```
|
||||
|
||||
**Why this works:**
|
||||
- PydanticAI natively supports Anthropic via `AnthropicModel` + `AnthropicProvider`
|
||||
- The same `TATLOCK_SYSTEM_PROMPT` is passed to Claude - butler personality preserved
|
||||
- Claude is **better** at following system prompts than mistral-nemo
|
||||
- 200k context for ALL queries, not just "complex" ones
|
||||
- Simpler architecture: no routing logic, no sidecar delegation
|
||||
|
||||
---
|
||||
|
||||
## Research Findings
|
||||
|
||||
### Industry Best Practices (2025-2026)
|
||||
|
||||
**MCP Protocol Updates** ([MCP Spec Updates June 2025](https://auth0.com/blog/mcp-specs-update-all-about-auth/)):
|
||||
- Streamable HTTP replaced SSE (March 2025) - better for cloud deployment
|
||||
- OAuth 2.0 required for remote servers - MCP servers are OAuth Resource Servers
|
||||
- Tool Output Schemas now available - better structured data handling
|
||||
- MCP Registry launched (Sept 2025) - community server discovery
|
||||
|
||||
**Community Patterns** ([Claude Code Router](https://github.com/musistudio/claude-code-router)):
|
||||
- Task-based routing is becoming standard: route simple→local, complex→cloud
|
||||
- Translation proxies bridge Anthropic Messages API ↔ OpenAI format
|
||||
- Cost savings of up to 98% reported with smart routing
|
||||
|
||||
**Home Automation MCP** ([ha-mcp](https://github.com/homeassistant-ai/ha-mcp)):
|
||||
- Production-ready MCP servers exist for Home Assistant
|
||||
- Support Claude Code, Gemini CLI, Open WebUI, VSCode, Cursor
|
||||
- Pattern: expose local tools securely to remote AI clients
|
||||
|
||||
**Remote MCP Access** ([mcp-remote](https://www.npmjs.com/package/mcp-remote)):
|
||||
- Bridge local MCP servers to Claude Desktop/Browser via proxy
|
||||
- Supports authentication headers for security
|
||||
- Works with ngrok/Cloudflare Tunnel for HTTPS
|
||||
|
||||
---
|
||||
|
||||
## Recommended Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────────────────┐
|
||||
│ BIDIRECTIONAL TATLOCK-CLAUDE ARCHITECTURE │
|
||||
├─────────────────────────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ ╔═══════════════════════════════════════════════════════════════════════════════╗ │
|
||||
│ ║ SCENARIO A: Using Tatlock (Open WebUI, local apps) ║ │
|
||||
│ ║ ───────────────────────────────────────────────── ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Request → Steward → Tatlock → Tools + Expert Delegation ║ │
|
||||
│ ║ │ ║ │
|
||||
│ ║ ├─→ Librarian (Claude) → research, wiki, RAG ║ │
|
||||
│ ║ ├─→ Biographer (Claude) → memory, preferences ║ │
|
||||
│ ║ ├─→ Housekeeper (Claude) → home automation ║ │
|
||||
│ ║ └─→ All powered by Claude with Ollama fallback ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Butler personality preserved, 200k context for all queries ║ │
|
||||
│ ╚═══════════════════════════════════════════════════════════════════════════════╝ │
|
||||
│ │
|
||||
│ ╔═══════════════════════════════════════════════════════════════════════════════╗ │
|
||||
│ ║ SCENARIO B: Using Claude.ai / Claude Desktop / Phone ║ │
|
||||
│ ║ ──────────────────────────────────────────────────── ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Claude ──[MCP over HTTPS]──► Tatlock MCP Server → Household Tools ║ │
|
||||
│ ║ │ ║ │
|
||||
│ ║ ├─→ calculator, datetime ║ │
|
||||
│ ║ ├─→ web_search, wiki_search ║ │
|
||||
│ ║ ├─→ hybrid_search (RAG) ║ │
|
||||
│ ║ ├─→ memory_recall, store_insight ║ │
|
||||
│ ║ └─→ home_control (lights, climate) ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Full 200k context, your local tools accessible from anywhere ║ │
|
||||
│ ╚═══════════════════════════════════════════════════════════════════════════════╝ │
|
||||
│ │
|
||||
│ ╔═══════════════════════════════════════════════════════════════════════════════╗ │
|
||||
│ ║ SCENARIO C: Offline (internet down) ║ │
|
||||
│ ║ ────────────────────────────────── ║ │
|
||||
│ ║ ║ │
|
||||
│ ║ Tatlock operates fully locally with Ollama ║ │
|
||||
│ ║ • All tools work (except web search) ║ │
|
||||
│ ║ • Graceful degradation with same butler personality ║ │
|
||||
│ ╚═══════════════════════════════════════════════════════════════════════════════╝ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Implementation Plan
|
||||
|
||||
### Phase 1: Blanket Backend Swap (Claude for All Agents)
|
||||
|
||||
Replace Ollama with Claude as the default backend for all PydanticAI agents, with automatic offline fallback.
|
||||
|
||||
**New Files:**
|
||||
```
|
||||
src/anthropic/
|
||||
├── __init__.py
|
||||
├── provider.py # Claude provider with health check
|
||||
└── model_selector.py # Chooses Claude or Ollama based on availability
|
||||
```
|
||||
|
||||
**Key Implementation (`src/anthropic/provider.py`):**
|
||||
```python
|
||||
from pydantic_ai.models.anthropic import AnthropicModel
|
||||
from pydantic_ai.providers.anthropic import AnthropicProvider
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from src.ollama.provider import get_ollama_provider
|
||||
from src.core.config import config
|
||||
|
||||
_anthropic_available: bool | None = None
|
||||
|
||||
async def check_anthropic_health() -> bool:
|
||||
"""Check if Anthropic API is reachable."""
|
||||
global _anthropic_available
|
||||
try:
|
||||
from anthropic import AsyncAnthropic
|
||||
client = AsyncAnthropic(api_key=config.ANTHROPIC_API_KEY)
|
||||
await client.messages.create(
|
||||
model=config.ANTHROPIC_MODEL,
|
||||
max_tokens=1,
|
||||
messages=[{"role": "user", "content": "hi"}]
|
||||
)
|
||||
_anthropic_available = True
|
||||
except Exception:
|
||||
_anthropic_available = False
|
||||
return _anthropic_available
|
||||
|
||||
def get_model(prefer_cloud: bool = True):
|
||||
"""Get the best available model. Returns Claude if available, otherwise Ollama."""
|
||||
if prefer_cloud and config.ANTHROPIC_API_KEY and _anthropic_available:
|
||||
provider = AnthropicProvider(api_key=config.ANTHROPIC_API_KEY)
|
||||
return AnthropicModel(
|
||||
model_name=config.ANTHROPIC_MODEL,
|
||||
provider=provider,
|
||||
)
|
||||
else:
|
||||
return OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
provider=get_ollama_provider()
|
||||
)
|
||||
```
|
||||
|
||||
**Modify TatlockAgent (`src/agents/tatlock.py`):**
|
||||
```python
|
||||
def _ensure_agent(self):
|
||||
if self._agent is not None:
|
||||
return
|
||||
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
model = get_model(prefer_cloud=True)
|
||||
|
||||
self._agent = Agent(
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT, # Same butler personality!
|
||||
)
|
||||
self._register_tools()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Phase 2: MCP Server (Expose Tools to Claude)
|
||||
|
||||
Create an MCP server that exposes Tatlock's household tools to external Claude instances.
|
||||
|
||||
**New Files:**
|
||||
```
|
||||
src/mcp/
|
||||
├── __init__.py
|
||||
├── server.py # MCP server using mcp Python SDK
|
||||
├── tool_adapters.py # Convert PydanticAI tools → MCP schemas
|
||||
├── auth.py # API key authentication
|
||||
└── transport.py # Streamable HTTP transport
|
||||
```
|
||||
|
||||
**Docker Stack Addition (`stacks/agents.yml`):**
|
||||
```yaml
|
||||
tatlock-mcp:
|
||||
image: git.schweitz.internal/jpmschweitzer/tatlock:latest
|
||||
command: ["python", "-m", "src.mcp.server"]
|
||||
ports:
|
||||
- "8002:8002"
|
||||
environment:
|
||||
- MCP_AUTH_TOKEN=${MCP_AUTH_TOKEN}
|
||||
networks:
|
||||
- docker-dataplane
|
||||
```
|
||||
|
||||
**Claude Desktop Configuration:**
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"tatlock": {
|
||||
"command": "npx",
|
||||
"args": ["mcp-remote", "https://mcp.schweitz.net/sse", "--header", "Authorization: Bearer ${MCP_AUTH_TOKEN}"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Files to Modify
|
||||
|
||||
### Phase 1 - Backend Swap
|
||||
|
||||
**New Files:**
|
||||
| File | Purpose |
|
||||
|------|---------|
|
||||
| `src/anthropic/__init__.py` | Package init |
|
||||
| `src/anthropic/provider.py` | Claude provider with health check |
|
||||
| `src/anthropic/model_selector.py` | Choose Claude or Ollama based on availability |
|
||||
|
||||
**Modified Files:**
|
||||
| File | Changes |
|
||||
|------|---------|
|
||||
| `src/core/config.py` | Add `ANTHROPIC_API_KEY`, `ANTHROPIC_MODEL`, `PREFER_CLOUD_BACKEND` |
|
||||
| `src/agents/tatlock.py` | Use `get_model()` instead of hardcoded Ollama |
|
||||
| `src/agents/librarian/agent.py` | Use `get_model()` instead of hardcoded Ollama |
|
||||
| `src/agents/biographer/agent.py` | Use `get_model()` instead of hardcoded Ollama |
|
||||
| `src/agents/steward/agent.py` | Convert to PydanticAI or add Anthropic API support |
|
||||
| `src/core/startup.py` | Add Anthropic health check on startup |
|
||||
| `requirements.txt` | Add `anthropic>=0.40.0` |
|
||||
| `.env.example` | Document new environment variables |
|
||||
|
||||
### Phase 2 - MCP Server
|
||||
|
||||
**New Files:**
|
||||
| File | Purpose |
|
||||
|------|---------|
|
||||
| `src/mcp/__init__.py` | Package init |
|
||||
| `src/mcp/server.py` | MCP server implementation |
|
||||
| `src/mcp/tool_adapters.py` | PydanticAI → MCP schema conversion |
|
||||
| `src/mcp/auth.py` | Token-based authentication |
|
||||
|
||||
---
|
||||
|
||||
## Cost Analysis
|
||||
|
||||
- **Claude API**: $5-30/month (10-50 calls/day, ~2k input + 1k output tokens/call)
|
||||
- **MCP via Claude Pro**: Included in subscription
|
||||
- **Total**: ~$10-80/month for full bidirectional integration
|
||||
|
||||
---
|
||||
|
||||
## Verification Plan
|
||||
|
||||
### Phase 1 Testing
|
||||
```bash
|
||||
# 1. Run with Claude backend
|
||||
ANTHROPIC_API_KEY=your-key docker-compose up -d tatlock
|
||||
|
||||
# 2. Verify Claude is being used
|
||||
docker logs tatlock 2>&1 | grep -i "anthropic\|claude"
|
||||
|
||||
# 3. Test butler personality
|
||||
curl -X POST http://tatlock.schweitz.internal:8000/v1/responses \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"model": "Tatlock", "input": "Hello, who are you?"}'
|
||||
|
||||
# 4. Test offline fallback
|
||||
ANTHROPIC_API_KEY="" docker-compose up -d tatlock
|
||||
docker logs tatlock 2>&1 | grep -i "ollama\|fallback"
|
||||
```
|
||||
|
||||
### Phase 2 Testing
|
||||
```bash
|
||||
# 1. Start MCP server
|
||||
docker-compose up -d tatlock-mcp
|
||||
|
||||
# 2. Test MCP endpoint
|
||||
curl -X POST https://mcp.schweitz.net/tools/list \
|
||||
-H "Authorization: Bearer $MCP_AUTH_TOKEN"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Implementation Priority
|
||||
|
||||
1. **Phase 1: Backend Swap** (~1 week)
|
||||
- Immediate value: 200k context for ALL queries
|
||||
- Low risk: provider abstraction, graceful offline fallback
|
||||
|
||||
2. **Phase 2: MCP Server** (~2-3 weeks)
|
||||
- Enables cross-device access
|
||||
- Bidirectional: Tatlock superpowered by Claude AND accessible to Claude
|
||||
|
||||
---
|
||||
|
||||
## Future Phases (Optional)
|
||||
|
||||
- **Phase 3: LiteLLM Gateway** - Unified endpoint for all models, config-driven routing
|
||||
- **Phase 4: Multi-Provider** - Add OpenAI, Vertex AI, etc.
|
||||
- **Phase 5: Smart Routing** - Context-aware model selection, cost ceiling enforcement
|
||||
|
||||
---
|
||||
|
||||
## Offline Behavior
|
||||
|
||||
| Scenario | Behavior |
|
||||
|----------|----------|
|
||||
| No API key | Use Ollama exclusively |
|
||||
| API unreachable | Use Ollama, log warning |
|
||||
| API rate limited | Fallback to Ollama |
|
||||
|
||||
| Aspect | Claude | Ollama |
|
||||
|--------|--------|--------|
|
||||
| Context | 200k tokens | ~8k tokens |
|
||||
| Latency | 1-3s (network) | 0.5-1s (local) |
|
||||
| Personality | Preserved | Preserved |
|
||||
| Tools | All work | All work |
|
||||
| Cost | API charges | Free |
|
||||
|
||||
---
|
||||
|
||||
## Implementation Status
|
||||
|
||||
### Phase 1: Backend Swap - CODE COMPLETE (awaiting API access)
|
||||
|
||||
- [x] Add Anthropic config settings to `src/core/config.py`
|
||||
- [x] Add `pydantic-ai-slim[openai,anthropic]` to requirements.txt
|
||||
- [x] Create `src/anthropic/` module (model_selector.py)
|
||||
- [x] Add Claude health check to startup.py
|
||||
- [x] Refactor all PydanticAI agents to use `get_model()`
|
||||
- [x] Librarian
|
||||
- [x] Biographer
|
||||
- [x] Housekeeper
|
||||
- [x] Tatlock (6 locations)
|
||||
- [x] Add Claude API path to Steward agent (direct API calls)
|
||||
- [x] Update `.env.example` with new variables
|
||||
- [x] Test Ollama fallback (working)
|
||||
- [ ] Test with Claude API key (blocked: no API access currently)
|
||||
|
||||
**Note:** Implementation complete. Currently runs in Ollama-only mode. Will automatically use Claude when `ANTHROPIC_API_KEY` is configured.
|
||||
|
||||
### Phase 2: MCP Server - NOT STARTED
|
||||
|
||||
- [ ] Create `src/mcp/` module
|
||||
- [ ] Tool adapters (PydanticAI → MCP schema)
|
||||
- [ ] Authentication middleware
|
||||
- [ ] Streamable HTTP transport
|
||||
- [ ] Docker stack configuration
|
||||
|
||||
---
|
||||
|
||||
## Related Repository Handovers
|
||||
|
||||
Handover documents created in each repo: `PROJECT_CLAUDIFICATION_HANDOVER.md`
|
||||
|
||||
### library-desk - HANDOVER CREATED
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Review HybridRAG response size limits
|
||||
- [ ] Review smart_create endpoint for Claude optimization
|
||||
- [ ] Evaluate response formats for LLM consumption
|
||||
|
||||
### core-api - HANDOVER CREATED
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Review list_devices response format
|
||||
- [ ] Review error messages for LLM consumption
|
||||
- [ ] Evaluate rate limiting for faster Claude processing
|
||||
|
||||
### portainer-core - HANDOVER CREATED (blocking for production)
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Update stack with new environment variables
|
||||
- [ ] Configure secrets management for API key
|
||||
- [ ] Update CONTAINERS.md documentation
|
||||
|
||||
### webber - HANDOVER CREATED
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Review content truncation limits
|
||||
- [ ] Evaluate extraction quality for LLM consumption
|
||||
|
||||
### tatlock-ui - HANDOVER CREATED
|
||||
|
||||
- [x] Write handover document
|
||||
- [ ] Test streaming responses with Claude backend
|
||||
- [ ] Test conversation history with larger context
|
||||
- [ ] Verify tool call display and reasoning rendering
|
||||
@@ -1,6 +1,6 @@
|
||||
# Tatlock - Your Homelab Butler
|
||||
|
||||
> **📖 For the complete system vision and architectural philosophy, see [PHILOSOPHY.md](PHILOSOPHY.md)**
|
||||
> **📖 For the complete system vision and architectural philosophy, see [docs/philosophy.md](docs/philosophy.md)**
|
||||
|
||||
A privacy-first, offline-capable personal assistant system that coordinates specialized AI agents to help with research, development, home automation, and daily organization.
|
||||
|
||||
@@ -58,7 +58,7 @@ A privacy-first, offline-capable personal assistant system that coordinates spec
|
||||
- Error triggers for testing (rate_limit, context_overflow)
|
||||
|
||||
- **Tatlock**: Real PydanticAI agent with butler personality
|
||||
- **LLM Backend**: Ollama (mistral-nemo:latest by default)
|
||||
- **LLM Backend**: Ollama (gemma4:e2b by default, local-first) with optional Claude fallback
|
||||
- **Personality**: Witty British butler, research-oriented
|
||||
- **Core Tools**:
|
||||
- **Calculator**: Safe mathematical expression evaluation
|
||||
@@ -74,7 +74,7 @@ A privacy-first, offline-capable personal assistant system that coordinates spec
|
||||
|
||||
- Python 3.12+ (Python 3.12.11 recommended)
|
||||
- **External Services** (must be running separately):
|
||||
- **Ollama**: LLM inference (mistral-nemo:latest, nomic-embed-text)
|
||||
- **Ollama**: LLM inference (gemma4:e2b, nomic-embed-text)
|
||||
- **Redis**: Caching and session memory
|
||||
- **Qdrant**: Vector storage for The Biographer's memory
|
||||
- **SearXNG**: Web search (optional)
|
||||
@@ -89,12 +89,8 @@ A privacy-first, offline-capable personal assistant system that coordinates spec
|
||||
git clone https://git.schweitz.net/jpmschweitzer/tatlock.git
|
||||
cd tatlock
|
||||
|
||||
# Create virtual environment
|
||||
python -m venv .venv
|
||||
source .venv/bin/activate # Windows: .venv\Scripts\activate
|
||||
|
||||
# Install dependencies
|
||||
pip install -r requirements.txt
|
||||
make setup
|
||||
```
|
||||
|
||||
### Run the Server
|
||||
@@ -268,7 +264,10 @@ Interactive documentation available at:
|
||||
pytest
|
||||
|
||||
# Run unit tests only (no external services needed)
|
||||
pytest --ignore=tests/e2e --ignore=tests/integration
|
||||
pytest --ignore=tests/e2e --ignore=tests/integration --ignore=tests/contracts
|
||||
|
||||
# Wire-level contract tests against live service boundaries
|
||||
make test-contracts
|
||||
|
||||
# Run with coverage
|
||||
pytest --cov=src --cov-report=term-missing
|
||||
@@ -307,12 +306,17 @@ Create a `.env` file for custom configuration:
|
||||
API_HOST=0.0.0.0
|
||||
API_PORT=8000
|
||||
|
||||
# Ollama Configuration
|
||||
# Ollama Configuration (primary backend)
|
||||
OLLAMA_HOST=http://localhost:11434
|
||||
OLLAMA_DEFAULT_MODEL=mistral-nemo:latest
|
||||
OLLAMA_DEFAULT_MODEL=gemma4:e2b
|
||||
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
|
||||
OLLAMA_TIMEOUT=120
|
||||
|
||||
# Claude fallback (optional; used when Ollama is down or PREFER_CLOUD_BACKEND=true)
|
||||
# ANTHROPIC_API_KEY=sk-ant-api03-your-key-here
|
||||
ANTHROPIC_MODEL=claude-sonnet-5
|
||||
PREFER_CLOUD_BACKEND=false
|
||||
|
||||
# Redis Configuration
|
||||
REDIS_HOST=localhost
|
||||
REDIS_PORT=6379
|
||||
@@ -380,9 +384,8 @@ tatlock/
|
||||
│ │ ├── steward/ # The Steward - request analysis
|
||||
│ │ ├── tatlock_core/ # Core butler tools
|
||||
│ │ ├── tatlock.py # Tatlock PydanticAI agent
|
||||
│ │ ├── coordination.py # Multi-agent coordination
|
||||
│ │ ├── delegation.py # Expert delegation wrappers
|
||||
│ │ └── protocol.py # Agent communication protocol
|
||||
│ │ └── protocol.py # Agent error protocol
|
||||
│ ├── responses/ # Responses API (primary endpoint)
|
||||
│ ├── chat/ # Chat Completions wrapper
|
||||
│ ├── models/ # Models listing
|
||||
@@ -396,15 +399,14 @@ tatlock/
|
||||
│ │ └── multi_tenancy.py # User isolation utilities
|
||||
│ └── main.py # Application entry point
|
||||
├── tests/ # Comprehensive test suite
|
||||
├── PHILOSOPHY.md # System vision and architecture
|
||||
├── IMPLEMENTATION_ROADMAP.md # Development phases
|
||||
├── docs/ # Project documentation
|
||||
├── CHANGELOG.md # Version history
|
||||
└── README.md # This file
|
||||
```
|
||||
|
||||
## Development
|
||||
|
||||
For LLM agent development guidelines and architectural decisions, see [AGENTS.md](AGENTS.md).
|
||||
For LLM agent development guidelines and architectural decisions, see [CLAUDE.md](CLAUDE.md).
|
||||
|
||||
## Contributing
|
||||
|
||||
@@ -416,9 +418,9 @@ For LLM agent development guidelines and architectural decisions, see [AGENTS.md
|
||||
|
||||
## Documentation
|
||||
|
||||
- **System Philosophy**: [PHILOSOPHY.md](PHILOSOPHY.md) - Vision, goals, and architectural patterns
|
||||
- **User Guide**: This file - Installation, usage, and examples
|
||||
- **Developer Guidelines**: [AGENTS.md](AGENTS.md) - LLM agent development patterns
|
||||
- **System Philosophy**: [docs/philosophy.md](docs/philosophy.md) - Vision, goals, and architectural patterns
|
||||
- **Development Roadmap**: [docs/roadmap.md](docs/roadmap.md) - Open work and planned phases
|
||||
- **Developer Guidelines**: [CLAUDE.md](CLAUDE.md) - LLM agent development patterns
|
||||
- **Version History**: [CHANGELOG.md](CHANGELOG.md) - Changes and releases
|
||||
|
||||
### External References
|
||||
@@ -432,8 +434,8 @@ For LLM agent development guidelines and architectural decisions, see [AGENTS.md
|
||||
|
||||
## Version
|
||||
|
||||
Current version: **1.3.2** - Biographer tool type hints fix
|
||||
Current version: see [CHANGELOG.md](CHANGELOG.md)
|
||||
|
||||
---
|
||||
|
||||
**Note**: Tatlock is a production-ready homelab butler. All household staff use PydanticAI with Ollama for local LLM inference.
|
||||
**Note**: Tatlock is a production-ready homelab butler. All household staff use PydanticAI with local Ollama inference (gemma4), with an optional Claude cloud fallback.
|
||||
|
||||
Executable
+50
@@ -0,0 +1,50 @@
|
||||
#!/usr/bin/env bash
|
||||
# Secret scan over the commits about to be pushed.
|
||||
#
|
||||
# Lives here rather than inside .githooks/pre-push so it can be read, run by
|
||||
# hand (`make secrets`), and changed under review. A hook is a trigger; it is
|
||||
# not a home for logic. Identical in every repo in this workspace (D-27).
|
||||
set -euo pipefail
|
||||
|
||||
cd "$(git rev-parse --show-toplevel)"
|
||||
|
||||
# A non-login shell — which is what git gives a hook — skips /etc/profile.d
|
||||
# and never sees ~/.local/bin, where the gitleaks release tarball lands.
|
||||
# Without this the scan reports "not installed" on every push.
|
||||
[ -d "$HOME/.local/bin" ] && PATH="$HOME/.local/bin:$PATH"
|
||||
|
||||
if ! command -v gitleaks >/dev/null 2>&1; then
|
||||
echo "FAIL secrets — gitleaks not installed, so this check would be a no-op pretending to pass." >&2
|
||||
echo " https://github.com/gitleaks/gitleaks/releases → ~/.local/bin/gitleaks" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Scan the outgoing range, not full history. History here carries findings
|
||||
# that are settled — test fixtures and vendored third-party code — and a gate
|
||||
# that fails on something unfixable gets bypassed within a week. What matters
|
||||
# is what is about to leave this machine.
|
||||
if upstream=$(git rev-parse --abbrev-ref --symbolic-full-name '@{u}' 2>/dev/null); then
|
||||
range="$upstream..HEAD"
|
||||
elif git rev-parse --verify --quiet origin/main >/dev/null; then
|
||||
range="origin/main..HEAD"
|
||||
else
|
||||
range=""
|
||||
fi
|
||||
|
||||
if [ -z "$range" ]; then
|
||||
gitleaks dir . --redact --no-banner --exit-code 1 || {
|
||||
echo "FAIL secrets — gitleaks found a credential in the working tree." >&2; exit 1; }
|
||||
exit 0
|
||||
fi
|
||||
|
||||
[ -n "$(git log --oneline "$range" 2>/dev/null)" ] || exit 0
|
||||
|
||||
gitleaks git . --log-opts="$range" --redact --no-banner --exit-code 1 >/dev/null 2>&1 || {
|
||||
echo "FAIL secrets — gitleaks found a credential in the commits being pushed." >&2
|
||||
echo " inspect (values redacted): gitleaks git . --log-opts=\"$range\" --redact" >&2
|
||||
echo " then remove and rotate it, or suppress deliberately:" >&2
|
||||
echo " inline '# gitleaks:allow <reason>'" >&2
|
||||
echo " or add the fingerprint to .gitleaksignore WITH a reason" >&2
|
||||
exit 1
|
||||
}
|
||||
echo " ok secrets"
|
||||
@@ -0,0 +1,100 @@
|
||||
# Claude Integration Plan
|
||||
|
||||
## Overview
|
||||
|
||||
Tatlock uses a bidirectional Claude architecture:
|
||||
- **Scenario A**: Tatlock powered by Claude backend (with Ollama fallback) — **COMPLETE**, then **rolled back to local-first**: Ollama/gemma4 is primary, Claude is retained as fallback (`PREFER_CLOUD_BACKEND=false`)
|
||||
- **Scenario B**: Tatlock exposed as MCP server for external Claude instances — **OPEN**
|
||||
- **Scenario C**: Offline operation via Ollama — **COMPLETE**
|
||||
|
||||
---
|
||||
|
||||
## MCP Server (Expose Tools to Claude) — NOT STARTED
|
||||
|
||||
Create an MCP server that exposes Tatlock's household tools to external Claude instances.
|
||||
|
||||
### New Files
|
||||
|
||||
```
|
||||
src/mcp/
|
||||
├── __init__.py
|
||||
├── server.py # MCP server using mcp Python SDK
|
||||
├── tool_adapters.py # Convert PydanticAI tools → MCP schemas
|
||||
├── auth.py # API key authentication
|
||||
└── transport.py # Streamable HTTP transport
|
||||
```
|
||||
|
||||
### Docker Stack Addition
|
||||
|
||||
```yaml
|
||||
tatlock-mcp:
|
||||
image: git.schweitz.net/jpmschweitzer/tatlock:latest
|
||||
command: ["python", "-m", "src.mcp.server"]
|
||||
ports:
|
||||
- "8002:8002"
|
||||
environment:
|
||||
- MCP_AUTH_TOKEN=${MCP_AUTH_TOKEN}
|
||||
networks:
|
||||
- docker-dataplane
|
||||
```
|
||||
|
||||
### Claude Desktop Configuration
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"tatlock": {
|
||||
"command": "npx",
|
||||
"args": ["mcp-remote", "https://mcp.schweitz.net/sse", "--header", "Authorization: Bearer ${MCP_AUTH_TOKEN}"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Checklist
|
||||
|
||||
- [ ] Create `src/mcp/` module
|
||||
- [ ] Tool adapters (PydanticAI → MCP schema)
|
||||
- [ ] Authentication middleware
|
||||
- [ ] Streamable HTTP transport
|
||||
- [ ] Docker stack configuration
|
||||
|
||||
---
|
||||
|
||||
## Future Phases
|
||||
|
||||
- **LiteLLM Gateway** — Unified endpoint for all models, config-driven routing
|
||||
- **Multi-Provider** — Add OpenAI, Vertex AI, etc.
|
||||
- **Smart Routing** — Context-aware model selection, cost ceiling enforcement
|
||||
|
||||
---
|
||||
|
||||
## Offline Behavior
|
||||
|
||||
| Scenario | Behavior |
|
||||
|----------|----------|
|
||||
| No API key | Use Ollama exclusively |
|
||||
| API unreachable | Use Ollama, log warning |
|
||||
| API rate limited | Fallback to Ollama |
|
||||
|
||||
| Aspect | Claude | Ollama |
|
||||
|--------|--------|--------|
|
||||
| Context | 200k tokens | ~8k tokens |
|
||||
| Latency | 1-3s (network) | 0.5-1s (local) |
|
||||
| Personality | Preserved | Preserved |
|
||||
| Tools | All work | All work |
|
||||
| Cost | API charges | Free |
|
||||
|
||||
---
|
||||
|
||||
## Related Repo Handovers
|
||||
|
||||
Handover documents created in each repo: `PROJECT_CLAUDIFICATION_HANDOVER.md`
|
||||
|
||||
### Open Items
|
||||
|
||||
- **library-desk**: Review HybridRAG response size limits, smart_create endpoint, response formats
|
||||
- **core-api**: Review list_devices response format, error messages, rate limiting
|
||||
- **portainer-core**: Update stack with new env vars, configure secrets, update CONTAINERS.md
|
||||
- **webber**: Review content truncation limits, extraction quality
|
||||
- **tatlock-ui**: Test streaming with Claude backend, conversation history, tool call display
|
||||
@@ -10,7 +10,7 @@ This document establishes the foundational philosophy and architectural patterns
|
||||
- When new architectural insights require rethinking core principles
|
||||
|
||||
**When NOT to modify this document**:
|
||||
- During implementation of these patterns (use README.md, AGENTS.md, or code comments for technical details)
|
||||
- During implementation of these patterns (use README.md, CLAUDE.md, or code comments for technical details)
|
||||
- For adding new household members or capabilities within the existing pattern
|
||||
- For tactical decisions about specific technologies or tools
|
||||
|
||||
@@ -270,7 +270,7 @@ The user never directly interacts with the Steward or individual expert agents
|
||||
|
||||
**Related Documents**:
|
||||
- **README.md**: User-facing documentation and usage guide
|
||||
- **AGENTS.md**: LLM agent development guidelines and technical patterns
|
||||
- **CLAUDE.md**: LLM agent development guidelines and technical patterns
|
||||
- **CHANGELOG.md**: Version history and implemented features
|
||||
|
||||
---
|
||||
+208
@@ -0,0 +1,208 @@
|
||||
# Tatlock Implementation Roadmap
|
||||
|
||||
> **Reference**: See [philosophy.md](philosophy.md) for the target architecture and vision
|
||||
|
||||
This document tracks open/planned work. Completed phases have been removed.
|
||||
|
||||
## Current State (v2.0.5)
|
||||
|
||||
**What we have**:
|
||||
- OpenAI-compatible API (Responses API + Chat Completions)
|
||||
- Two-tier architecture (Steward → Tatlock)
|
||||
- Household staff: Tatlock (Butler), Steward, Librarian, Biographer
|
||||
- Core tools: Calculator, Date/Time, Web search (SearXNG)
|
||||
- Memory system: Qdrant (vector), Redis (session cache), multi-tenancy via ContextVar
|
||||
- Dual backend: Ollama/gemma4 (primary) + Claude (fallback)
|
||||
- 439 tests with good coverage
|
||||
|
||||
---
|
||||
|
||||
## Phase 4: Expert Household Staff — Remaining Agents
|
||||
|
||||
**Goal**: Implement remaining domain-specific expert agents
|
||||
|
||||
### Planned Agents
|
||||
|
||||
1. **The Developer** (Software Development)
|
||||
- Code generation assistance
|
||||
- Debugging support
|
||||
- Documentation generation
|
||||
- Architecture guidance
|
||||
|
||||
2. **The Handyman** (System Maintenance)
|
||||
- System status queries
|
||||
- Log analysis
|
||||
- Basic troubleshooting
|
||||
- Infrastructure monitoring
|
||||
|
||||
3. **The Secretary** (Scheduling & Organization)
|
||||
- Calendar integration
|
||||
- Task management
|
||||
- Reminder system
|
||||
- Schedule conflict detection
|
||||
|
||||
4. **The Housekeeper** (Home Automation)
|
||||
- Home Assistant integration
|
||||
- Device control interface
|
||||
- Status queries
|
||||
- Automation triggers
|
||||
|
||||
### Each Agent Includes
|
||||
- Specialized prompt and personality
|
||||
- Domain-specific tools
|
||||
- MCP integration points (where applicable)
|
||||
- Integration with Butler orchestration
|
||||
|
||||
### Success Criteria
|
||||
- [ ] Each agent implemented as separate module
|
||||
- [ ] Agents callable via tool framework
|
||||
- [ ] Can invoke specialized models (e.g., Codestral for Developer)
|
||||
|
||||
---
|
||||
|
||||
## Phase 5: Persistence Layer — Database & Multi-Tenancy
|
||||
|
||||
**Goal**: Add persistent storage and multi-user support
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **PostgreSQL Integration**
|
||||
- Docker compose configuration
|
||||
- Database schema with tenant isolation
|
||||
- Alembic migrations
|
||||
- SQLAlchemy models
|
||||
|
||||
2. **Multi-Tenant Architecture**
|
||||
- Tenant identification middleware
|
||||
- Tenant-scoped database sessions
|
||||
- User authentication system
|
||||
- Per-tenant data isolation
|
||||
|
||||
3. **Core Data Models**
|
||||
- Users and tenants
|
||||
- Conversations and messages (migrate from in-memory)
|
||||
- Agent interactions log
|
||||
- System configuration and preferences
|
||||
|
||||
### Success Criteria
|
||||
- [ ] PostgreSQL container running
|
||||
- [ ] Multiple users authenticate separately
|
||||
- [ ] Each user sees only their own data
|
||||
- [ ] Conversations persist across restarts
|
||||
- [ ] Database migrations work correctly
|
||||
|
||||
---
|
||||
|
||||
## Phase 7: MCP (Model Context Protocol) Integration
|
||||
|
||||
**Goal**: Enable rich tool integrations via MCP
|
||||
|
||||
See also [claude-integration.md](claude-integration.md) for MCP server implementation details.
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **MCP Server Framework**
|
||||
- MCP server implementation
|
||||
- Tool registration via MCP
|
||||
- Schema validation
|
||||
- Error handling
|
||||
|
||||
2. **MCP Client in Agents**
|
||||
- PydanticAI MCP integration
|
||||
- Tool discovery from MCP servers
|
||||
- Dynamic tool loading
|
||||
|
||||
3. **Initial MCP Tools**
|
||||
- File system operations
|
||||
- Database queries
|
||||
- API integrations
|
||||
- System commands
|
||||
|
||||
### Success Criteria
|
||||
- [ ] MCP server running
|
||||
- [ ] Tools exposed via MCP protocol
|
||||
- [ ] Agents can discover and use MCP tools
|
||||
- [ ] New tools addable without code changes
|
||||
- [ ] MCP tools visible in Steward recommendations
|
||||
|
||||
---
|
||||
|
||||
## Phase 8: Advanced Memory & Context — Remaining Work
|
||||
|
||||
**Goal**: Implement sophisticated context management and personalization
|
||||
|
||||
### Open Deliverables
|
||||
|
||||
1. **Context Management**
|
||||
- Smart context window trimming
|
||||
- Conversation branching
|
||||
- Topic tracking
|
||||
|
||||
2. **Personalization**
|
||||
- User preference learning
|
||||
- Interaction pattern analysis
|
||||
- Adaptive responses
|
||||
- Custom agent personalities per user
|
||||
|
||||
### Success Criteria
|
||||
- [ ] Conversations automatically embedded to Qdrant
|
||||
- [ ] Memory improves over time (learning from interactions)
|
||||
|
||||
---
|
||||
|
||||
## Phase 9: Extended Household Staff
|
||||
|
||||
**Goal**: Add specialized agents for additional domains
|
||||
|
||||
### Future Agents
|
||||
- **The Accountant** — Expense tracking, budgets, financial reports
|
||||
- **The Chef** — Meal planning, recipes, nutrition tracking
|
||||
- Others as needs emerge
|
||||
|
||||
---
|
||||
|
||||
## Phase 10: User Experience Refinement
|
||||
|
||||
**Goal**: Polish the interaction experience
|
||||
|
||||
- Personality tuning and consistency
|
||||
- Better progress indicators
|
||||
- Response time improvements
|
||||
- Streaming smoothness
|
||||
|
||||
---
|
||||
|
||||
## Phase 11: Production Hardening
|
||||
|
||||
**Goal**: Make the system production-ready for homelab deployment
|
||||
|
||||
- Complete docker-compose stack
|
||||
- Health checks and monitoring
|
||||
- Authentication hardening and rate limiting
|
||||
- Installation and troubleshooting documentation
|
||||
|
||||
---
|
||||
|
||||
## Dependencies
|
||||
|
||||
```
|
||||
Phase 4 (Remaining Agents)
|
||||
↓
|
||||
Phase 5 (Database/Multi-Tenancy) ← Can be deferred
|
||||
↓
|
||||
Phase 7 (MCP) → Phase 8 (Advanced Memory)
|
||||
↓
|
||||
Phase 9 (Extended Staff) → Phase 10 (UX) → Phase 11 (Production)
|
||||
```
|
||||
|
||||
**Can Be Deferred**: Phase 5 until you need persistence
|
||||
**Parallel Opportunities**: Phases 7 and 8 can overlap; 9 and 10 ongoing
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. Implement The Developer agent for code assistance
|
||||
2. Add Home Assistant integration for The Housekeeper
|
||||
3. Integrate scheduling service for The Secretary
|
||||
4. MCP server for external Claude access
|
||||
@@ -0,0 +1,110 @@
|
||||
# Steward Routing & Thinking — Findings
|
||||
|
||||
**Outcome: no change shipped.** The Steward stays on `gemma4:e2b` with model
|
||||
thinking left at its default (on). Every alternative was measured and every one
|
||||
loses. This document exists so the experiment is not repeated on the same
|
||||
premise.
|
||||
|
||||
Run 2026-08-08 with `scripts/benchmark_routing.py` and
|
||||
`scripts/fixtures/routing_fixtures.py` (40 labelled queries, one repeat per
|
||||
cell, temperature 0.3 as production sends).
|
||||
|
||||
---
|
||||
|
||||
## The premise was wrong
|
||||
|
||||
The experiment was designed around an observation that the Steward pays ~300
|
||||
tokens per turn for reasoning that is generated and thrown away: it calls
|
||||
`/api/generate`, gemma4 reasons by default, and **no `thinking` field comes back
|
||||
in the response**. Disabling thinking therefore looked close to free.
|
||||
|
||||
It is not. The reasoning is not discarded — it is emitted inline in `response`,
|
||||
and it is what produces a correct `DELEGATE:` line. Those tokens are the work,
|
||||
not waste. Suppressing them costs 12.5 points of routing accuracy.
|
||||
|
||||
## Results
|
||||
|
||||
| config | exact | under | over | tokens | latency | resident | predicted | co-resident with nomic |
|
||||
|---|---|---|---|---|---|---|---|---|
|
||||
| **e2b, thinking** *(production)* | **97.5%** | 2.5% | 0% | 361 | 5179 ms | 1778 MB | 7.8 GiB | yes |
|
||||
| e2b, `think: false` | 85.0% | 12.5% | 5.0% | 48 | 1435 ms | 1778 MB | 7.8 GiB | yes |
|
||||
| e4b, thinking | 100% | 0% | 0% | 192 | 4726 ms | 3089 MB | 10.6 GiB | **no** |
|
||||
| e4b, `think: false` | 97.5% | 2.5% | 0% | 52 | 2269 ms | 3089 MB | 10.6 GiB | **no** |
|
||||
|
||||
`think: true` was also measured and landed within one fixture of the default on
|
||||
both models, so production's implicit thinking is the same thing as asking for
|
||||
it explicitly. Format compliance was 100% in every cell — a `DELEGATE:` line is
|
||||
always emitted.
|
||||
|
||||
With 40 fixtures and one repeat, each result is worth 2.5 points, so the
|
||||
97.5-vs-100 gaps are single fixtures and inside the noise. The latency and token
|
||||
medians (40 calls each) and the e2b `think: false` degradation (6 failures with a
|
||||
consistent mechanism) are the parts worth trusting.
|
||||
|
||||
## Why each alternative loses
|
||||
|
||||
**`think: false` on e2b** — 85% exact, and the failures are not random. All three
|
||||
multi-capability fixtures under-route, each missing a second capability. Without
|
||||
reasoning the model names one capability and stops decomposing. It is not
|
||||
degraded across the board; it specifically stops handling compound requests,
|
||||
which is where a user would most notice the Butler quietly doing half the job.
|
||||
|
||||
**e4b, either setting** — disqualified by memory, not by quality. Ollama predicts
|
||||
**10.6 GiB** for it at 16k context. Maximum available on this card is ~7.9 GiB
|
||||
(10.4 free − 2.0 GPU overhead − 0.46 minimum), so e4b *always* exceeds the budget
|
||||
and evicts every co-resident before loading. Observed directly: loading it threw
|
||||
out both `gemma4:e2b` and `nomic-embed-text`. Losing nomic means Tatlock memory
|
||||
and library-desk thrash on every embedding call. Note this is not caused by the
|
||||
2 GiB reservation — without it, available would be ~9.7 GiB, still under 10.6.
|
||||
|
||||
**Lower `OLLAMA_CONTEXT_LENGTH`** — the obvious way to free headroom, and it does
|
||||
not work. Dropping 16384 → 2048, an 8× reduction, moved the prediction only from
|
||||
7.8 to 6.7 GiB. The prediction is dominated by weights and batch size, not KV
|
||||
cache. It would also truncate the Librarian's retrieved passages and webber's
|
||||
code context for a 14% saving that funds nothing.
|
||||
|
||||
**Per-request `num_ctx`** — worse. A single request with a different `num_ctx`
|
||||
reloads the shared runner, which **drops the `keep_alive: -1` pin** (expiry fell
|
||||
from year-2318 to a 2-hour default) and evicts nomic. Three services share this
|
||||
Ollama, so mixed context sizes are a thrash generator, and it fails silently.
|
||||
|
||||
**`OLLAMA_NUM_PARALLEL > 1`** — never viable here. e2b already predicts 7.8 GiB
|
||||
against ~7.9 available, so there is no room for a second slot at any context
|
||||
length. It is also set to 1 deliberately, to avoid batch overflow panics.
|
||||
|
||||
## What the two axes actually control
|
||||
|
||||
They do not interact, which is the useful part:
|
||||
|
||||
- **Model choice** governs VRAM and co-residency. e2b 1778 MB, e4b 3089 MB.
|
||||
- **Think setting** governs tokens, latency and routing quality — and costs
|
||||
**nothing** in VRAM. Verified: e2b is resident at 1778 MB with `think` unset,
|
||||
true and false alike, because the KV cache is allocated for the full context at
|
||||
load time and `think` is a per-request generation parameter.
|
||||
|
||||
So the only real question is whether 313 tokens and 3.7 seconds are worth 12.5
|
||||
points of compound-query routing. On a turn that is already three sequential
|
||||
Ollama calls, they are.
|
||||
|
||||
## Prerequisite: the extraction fix
|
||||
|
||||
These numbers are only meaningful because `_extract_capabilities` was fixed first
|
||||
(commit `a905363`). It previously substring-matched capability *domains* across
|
||||
the Steward's entire response, so ordinary English in the `REASON:` line selected
|
||||
agents — "description" contains the housekeeper domain "script", "acknowledge"
|
||||
contains "knowledge" and "know".
|
||||
|
||||
That made **prose length a routing input**. Benchmarking against it would have
|
||||
shown `think: false` improving routing purely because shorter output produces
|
||||
fewer accidental substring hits — a thinking policy derived from a parsing
|
||||
artefact. The `adversarial` fixture group is regression coverage for exactly this.
|
||||
|
||||
## If this is revisited
|
||||
|
||||
The constraint is the single 11 GB card, not the model. A second inference host
|
||||
(*forge*) removes it entirely, and e4b's 100% routing becomes reachable without
|
||||
evicting anything. Re-run then; on this card the answer is settled.
|
||||
|
||||
`scripts/benchmark_routing.py` takes `--models`, `--think` and `--repeats`, and
|
||||
restores GPU residency on exit — including on SIGTERM, which the first version
|
||||
did not.
|
||||
@@ -0,0 +1,54 @@
|
||||
# Decisions, Questions, Rejected
|
||||
|
||||
This directory holds structured planning records that pql parses
|
||||
into pql.db. Each record is a `### [DQR]-N: Title` heading inside
|
||||
a markdown file. Files live in three per-type subdirectories:
|
||||
|
||||
- `decisions/<domain>.md` — confirmed design decisions
|
||||
- `questions/<domain>.md` — open questions that may resolve into
|
||||
decisions or rejected proposals
|
||||
- `rejected/<domain>.md` — rejected proposals (kept for the audit
|
||||
trail)
|
||||
|
||||
The parser infers domain from the filename stem and record type
|
||||
from the parent subdirectory.
|
||||
|
||||
D-records that propose implementation work link to `initiative`-type
|
||||
tickets via `decision_ref`. Run `pql decisions show <id>
|
||||
--with-tickets` to inspect implementation status.
|
||||
|
||||
## Recommended domains
|
||||
|
||||
Start with this canonical set; create files as records land in
|
||||
each domain:
|
||||
|
||||
- **architecture** — structural commitments (storage, layering,
|
||||
languages, libraries)
|
||||
- **process** — team workflow (commits, branches, releases, reviews)
|
||||
- **design** — user-facing surface (UX, UI, public APIs)
|
||||
- **coding-conventions** — team-internal code shape (style, lint,
|
||||
file layout)
|
||||
- **testing** — quality strategy (coverage, layers, gates)
|
||||
|
||||
You might also want, project-permitting:
|
||||
|
||||
- `accessibility` — if you ship user-facing software
|
||||
- `security` — if you handle user data or network surfaces
|
||||
- `licensing` — if you release open-source or commercial
|
||||
- `documentation` — if user-docs are non-trivial
|
||||
- `deployment` — if shipping is non-trivial
|
||||
- `performance` — if you have perf budgets / SLOs
|
||||
|
||||
<!-- pql:records (auto-generated; do not edit manually) -->
|
||||
|
||||
## Decisions
|
||||
|
||||
- _(none)_
|
||||
|
||||
## Open questions
|
||||
|
||||
- _(none)_
|
||||
|
||||
## Rejected
|
||||
|
||||
- _(none)_
|
||||
+63
-3
@@ -4,17 +4,72 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "tatlock"
|
||||
version = "2.0.1"
|
||||
version = "2.4.3"
|
||||
description = "OpenAI-compatible API with Ollama backend"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = []
|
||||
dependencies = [
|
||||
"fastapi>=0.123,<0.124",
|
||||
"uvicorn[standard]>=0.38,<0.39",
|
||||
"pydantic>=2.11,<2.13",
|
||||
"pydantic-settings>=2.12,<2.13",
|
||||
"pydantic-ai-slim[openai,anthropic]>=1.27,<1.28",
|
||||
# pydantic-ai 1.27 imports the private opentelemetry._events module,
|
||||
# removed in opentelemetry-api 1.44 — cap until pydantic-ai is bumped
|
||||
"opentelemetry-api>=1.30,<1.44",
|
||||
"anthropic>=0.77,<1.0",
|
||||
"httpx>=0.28,<0.29",
|
||||
"sse-starlette>=3.0,<3.1",
|
||||
"python-dotenv>=1.2,<1.3",
|
||||
"starlette>=0.45,<0.46",
|
||||
"redis[hiredis]>=5.2,<6.0",
|
||||
"qdrant-client>=1.12,<2.0",
|
||||
"structlog>=24.1,<25.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = [
|
||||
"pytest>=8.3,<8.4",
|
||||
"pytest-asyncio>=0.25,<0.26",
|
||||
"pytest-cov>=6.0,<6.1",
|
||||
"pytest-mock>=3.14,<3.15",
|
||||
"ruff>=0.8,<0.9",
|
||||
"mypy>=1.14,<1.15",
|
||||
"faker>=34.0,<35.0",
|
||||
"coverage[toml]>=7.7,<7.8",
|
||||
]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
python_files = ["test_*.py"]
|
||||
python_classes = ["Test*"]
|
||||
python_functions = ["test_*"]
|
||||
asyncio_mode = "auto"
|
||||
asyncio_default_fixture_loop_scope = "function"
|
||||
cache_dir = ".cache/pytest"
|
||||
markers = [
|
||||
"unit: Unit tests",
|
||||
"integration: Integration tests",
|
||||
"slow: Slow running tests",
|
||||
"contract: Wire-level contract tests against live service boundaries",
|
||||
]
|
||||
addopts = [
|
||||
"--verbose",
|
||||
"--strict-markers",
|
||||
"--tb=short",
|
||||
"--cov=src",
|
||||
"--cov-report=term-missing",
|
||||
"--cov-report=html:build/coverage/html",
|
||||
"--cov-report=xml:build/coverage/coverage.xml",
|
||||
"--cov-branch",
|
||||
]
|
||||
filterwarnings = [
|
||||
"ignore::DeprecationWarning",
|
||||
]
|
||||
|
||||
[tool.coverage.run]
|
||||
source = ["src"]
|
||||
branch = true
|
||||
data_file = "build/coverage/.coverage"
|
||||
omit = [
|
||||
"*/tests/*",
|
||||
"*/__pycache__/*",
|
||||
@@ -37,11 +92,15 @@ exclude_lines = [
|
||||
]
|
||||
|
||||
[tool.coverage.html]
|
||||
directory = "htmlcov"
|
||||
directory = "build/coverage/html"
|
||||
|
||||
[tool.coverage.xml]
|
||||
output = "build/coverage/coverage.xml"
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
target-version = "py312"
|
||||
cache-dir = ".cache/ruff"
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = [
|
||||
@@ -64,6 +123,7 @@ ignore = [
|
||||
|
||||
[tool.mypy]
|
||||
python_version = "3.12"
|
||||
cache_dir = ".cache/mypy"
|
||||
warn_return_any = true
|
||||
warn_unused_configs = true
|
||||
disallow_untyped_defs = true
|
||||
|
||||
-28
@@ -1,28 +0,0 @@
|
||||
[pytest]
|
||||
testpaths = tests
|
||||
python_files = test_*.py
|
||||
python_classes = Test*
|
||||
python_functions = test_*
|
||||
asyncio_mode = auto
|
||||
asyncio_default_fixture_loop_scope = function
|
||||
|
||||
# Markers
|
||||
markers =
|
||||
unit: Unit tests
|
||||
integration: Integration tests
|
||||
slow: Slow running tests
|
||||
|
||||
# Coverage options (overridden by pyproject.toml)
|
||||
addopts =
|
||||
--verbose
|
||||
--strict-markers
|
||||
--tb=short
|
||||
--cov=src
|
||||
--cov-report=term-missing
|
||||
--cov-report=html
|
||||
--cov-report=xml
|
||||
--cov-branch
|
||||
|
||||
# Ignore warnings from dependencies
|
||||
filterwarnings =
|
||||
ignore::DeprecationWarning
|
||||
@@ -1,25 +0,0 @@
|
||||
# Development and Testing Dependencies
|
||||
# Install with: pip install -r requirements.txt -r requirements-dev.txt
|
||||
|
||||
# Testing Framework
|
||||
# Latest pytest with async support
|
||||
pytest>=8.3,<8.4
|
||||
pytest-asyncio>=0.25,<0.26
|
||||
pytest-cov>=6.0,<6.1
|
||||
|
||||
# Test client for FastAPI
|
||||
httpx>=0.28,<0.29 # Already in requirements.txt but needed for test client
|
||||
|
||||
# Code Quality
|
||||
# Linting and formatting
|
||||
ruff>=0.8,<0.9
|
||||
|
||||
# Type checking
|
||||
mypy>=1.14,<1.15
|
||||
|
||||
# Testing utilities
|
||||
pytest-mock>=3.14,<3.15
|
||||
faker>=34.0,<35.0
|
||||
|
||||
# Coverage reporting
|
||||
coverage[toml]>=7.7,<7.8
|
||||
@@ -1,60 +0,0 @@
|
||||
# Core FastAPI framework and server
|
||||
# FastAPI: Modern, fast web framework for building APIs
|
||||
# Latest: 0.123.9 (Dec 4, 2025) - No known CVEs
|
||||
fastapi>=0.123,<0.124
|
||||
|
||||
# ASGI server for running FastAPI
|
||||
# Latest: 0.38.0 (Oct 18, 2025) - No known CVEs
|
||||
# Note: Old versions had CVE-2020-7694/7695, but 0.38.0 is secure
|
||||
uvicorn[standard]>=0.38,<0.39
|
||||
|
||||
# Additional dependencies
|
||||
# Pydantic for data validation (comes with pydantic-ai but pinning explicitly)
|
||||
# Updated to >=2.11 due to ag-ui-protocol dependency requirement
|
||||
# Latest: 2.12.4 (Nov 5, 2025) - No known CVEs
|
||||
pydantic>=2.11,<2.13
|
||||
|
||||
# Pydantic settings for configuration management
|
||||
# Required explicitly since pydantic-ai-slim doesn't include it
|
||||
# Latest: 2.12.0 (Dec 2025) - No known CVEs
|
||||
pydantic-settings>=2.12,<2.13
|
||||
|
||||
# AI/LLM integration
|
||||
# PydanticAI: Agent framework for using Pydantic with LLMs
|
||||
# Using slim version with openai (Ollama) and anthropic (Claude) extras
|
||||
# See DEPENDENCY_SLIM.md for rollback instructions if this breaks
|
||||
pydantic-ai-slim[openai,anthropic]>=1.27,<1.28
|
||||
|
||||
# HTTP client for Ollama communication
|
||||
# Latest: 0.28.1 - No known CVEs
|
||||
httpx>=0.28,<0.29
|
||||
|
||||
# Server-Sent Events for streaming responses
|
||||
# Required for OpenAI-compatible streaming endpoints
|
||||
# Latest: 3.0.2 (Oct 30, 2025) - No known CVEs
|
||||
sse-starlette>=3.0,<3.1
|
||||
|
||||
# Configuration management
|
||||
# Latest: 1.2.1 (Oct 26, 2025) - No known CVEs
|
||||
python-dotenv>=1.2,<1.3
|
||||
|
||||
# ASGI toolkit (dependency of FastAPI, pinning for security)
|
||||
starlette>=0.45,<0.46
|
||||
|
||||
# Redis for performance benchmarking and caching
|
||||
# Latest: 5.2.1 (Dec 5, 2025) - No known CVEs
|
||||
# hiredis: C parser for better performance
|
||||
redis[hiredis]>=5.2,<6.0
|
||||
|
||||
# Qdrant vector database client for memory storage
|
||||
# Latest: 1.12.1 (Dec 2025) - No known CVEs
|
||||
qdrant-client>=1.12,<2.0
|
||||
|
||||
# Structured logging for observability
|
||||
# Latest: 24.4.0 (Aug 22, 2024) - No known CVEs
|
||||
structlog>=24.1,<25.0
|
||||
|
||||
# Note on version locking strategy:
|
||||
# Using >=X.Y,<X.(Y+1) format to lock to minor versions
|
||||
# This protects against supply chain attacks while allowing patch updates
|
||||
# Update regularly and review changelogs before upgrading minor versions
|
||||
@@ -0,0 +1,235 @@
|
||||
"""
|
||||
Benchmark Steward routing quality against model and thinking settings.
|
||||
|
||||
Talks to Ollama directly. No Tatlock server, no agents, no tools, nothing is
|
||||
executed — the mutating fixtures ("turn on the lights", "update the wiki") only
|
||||
ever produce a routing decision. That makes this cheap and repeatable, and it
|
||||
isolates the question: does the Steward still pick the right capabilities when
|
||||
the model reasons less?
|
||||
|
||||
The request body is byte-identical to StewardAgent._call_ollama, plus the
|
||||
`think` flag under test, so a cell labelled `unset` is exactly what production
|
||||
sends today.
|
||||
|
||||
Three thinking settings, because "on vs off" hides the interesting case:
|
||||
|
||||
unset what production sends now. gemma4 reasons by default, and the
|
||||
response carries no `thinking` field, so those tokens are generated
|
||||
and discarded.
|
||||
true reasoning requested explicitly and returned in `thinking`.
|
||||
false reasoning suppressed.
|
||||
|
||||
Scoring is deliberately asymmetric. A missing capability under-routes and the
|
||||
Butler answers without a tool it needed; a spurious one over-routes, and that is
|
||||
a real agent call — a stray librarian is a multi-second web search on a query
|
||||
that asked for arithmetic. Over-routing is the predicted failure when thinking
|
||||
is off, so `forbid` violations are reported separately rather than folded into
|
||||
one accuracy number.
|
||||
|
||||
Usage:
|
||||
.venv/bin/python scripts/benchmark_routing.py
|
||||
.venv/bin/python scripts/benchmark_routing.py --models gemma4:e2b
|
||||
.venv/bin/python scripts/benchmark_routing.py --think false --repeats 3
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
|
||||
PROJECT_ROOT = Path(__file__).resolve().parents[1]
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
|
||||
from scripts.fixtures.routing_fixtures import FIXTURES # noqa: E402
|
||||
from scripts.ollama_residency import ( # noqa: E402
|
||||
install_sigterm_handler,
|
||||
residency_guard,
|
||||
)
|
||||
from src.agents.steward.agent import build_steward_prompt # noqa: E402
|
||||
from src.agents.steward.service import _DELEGATE_LINE_RE, _extract_capabilities # noqa: E402
|
||||
from src.core.startup import register_household_members # noqa: E402
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
DEFAULT_MODELS = ["gemma4:e2b", "gemma4:e4b"]
|
||||
DEFAULT_THINK = ["unset", "true", "false"]
|
||||
RESULTS_DIR = PROJECT_ROOT / "logs"
|
||||
|
||||
|
||||
def build_body(model: str, prompt: str, think: str) -> dict[str, Any]:
|
||||
"""Mirror StewardAgent._call_ollama exactly, then add the flag under test."""
|
||||
body: dict[str, Any] = {
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": 0.3, # Lower = more consistent
|
||||
"top_p": 0.9,
|
||||
},
|
||||
}
|
||||
if think != "unset":
|
||||
body["think"] = think == "true"
|
||||
return body
|
||||
|
||||
|
||||
def call(client: httpx.Client, body: dict[str, Any]) -> dict[str, Any] | None:
|
||||
try:
|
||||
response = client.post(f"{OLLAMA_URL}/api/generate", json=body)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except Exception as exc: # noqa: BLE001 - a failed cell must not abort the run
|
||||
print(f" ! {exc}", file=sys.stderr)
|
||||
return None
|
||||
|
||||
|
||||
def score(fixture: dict, found: list[str]) -> dict[str, Any]:
|
||||
expected = set(fixture["expect"])
|
||||
forbidden = set(fixture["forbid"])
|
||||
got = set(found)
|
||||
missing = sorted(expected - got)
|
||||
spurious = sorted(got & forbidden)
|
||||
return {
|
||||
"found": found,
|
||||
"missing": missing,
|
||||
"spurious": spurious,
|
||||
# Exact only when everything expected arrived and nothing forbidden did.
|
||||
"exact": not missing and not spurious,
|
||||
"under_routed": bool(missing),
|
||||
"over_routed": bool(spurious),
|
||||
}
|
||||
|
||||
|
||||
def run_cell(client: httpx.Client, model: str, think: str, repeats: int) -> list[dict[str, Any]]:
|
||||
rows: list[dict[str, Any]] = []
|
||||
for fixture in FIXTURES:
|
||||
prompt = build_steward_prompt(fixture["query"], [])
|
||||
body = build_body(model, prompt, think)
|
||||
for rep in range(repeats):
|
||||
started = time.perf_counter()
|
||||
data = call(client, body)
|
||||
elapsed_ms = (time.perf_counter() - started) * 1000
|
||||
if data is None:
|
||||
rows.append({
|
||||
"id": fixture["id"], "group": fixture["group"], "rep": rep,
|
||||
"error": True, "exact": False, "under_routed": False, "over_routed": False,
|
||||
})
|
||||
continue
|
||||
|
||||
text = data.get("response", "") or ""
|
||||
found = _extract_capabilities(text)
|
||||
rows.append({
|
||||
"id": fixture["id"],
|
||||
"group": fixture["group"],
|
||||
"rep": rep,
|
||||
"error": False,
|
||||
"latency_ms": round(elapsed_ms, 1),
|
||||
"eval_tokens": data.get("eval_count"),
|
||||
"prompt_tokens": data.get("prompt_eval_count"),
|
||||
# Did the model obey the documented output shape at all?
|
||||
"has_delegate_line": bool(_DELEGATE_LINE_RE.search(text)),
|
||||
# Whether reasoning came back, as opposed to being generated and dropped.
|
||||
"thinking_returned": bool(data.get("thinking")),
|
||||
"response_chars": len(text),
|
||||
**score(fixture, found),
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def summarise(rows: list[dict[str, Any]]) -> dict[str, Any]:
|
||||
ok = [r for r in rows if not r["error"]]
|
||||
if not ok:
|
||||
return {"n": 0, "errors": len(rows)}
|
||||
latencies = [r["latency_ms"] for r in ok]
|
||||
tokens = [r["eval_tokens"] for r in ok if r["eval_tokens"] is not None]
|
||||
return {
|
||||
"n": len(ok),
|
||||
"errors": len(rows) - len(ok),
|
||||
"exact_pct": round(100 * sum(r["exact"] for r in ok) / len(ok), 1),
|
||||
"under_routed_pct": round(100 * sum(r["under_routed"] for r in ok) / len(ok), 1),
|
||||
"over_routed_pct": round(100 * sum(r["over_routed"] for r in ok) / len(ok), 1),
|
||||
"format_ok_pct": round(100 * sum(r["has_delegate_line"] for r in ok) / len(ok), 1),
|
||||
"thinking_returned_pct": round(100 * sum(r["thinking_returned"] for r in ok) / len(ok), 1),
|
||||
"latency_ms_median": round(statistics.median(latencies), 1),
|
||||
"latency_ms_mean": round(statistics.fmean(latencies), 1),
|
||||
"eval_tokens_median": round(statistics.median(tokens), 1) if tokens else None,
|
||||
"eval_tokens_total": sum(tokens) if tokens else None,
|
||||
}
|
||||
|
||||
|
||||
def main() -> int:
|
||||
install_sigterm_handler()
|
||||
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("--models", default=",".join(DEFAULT_MODELS))
|
||||
parser.add_argument("--think", default=",".join(DEFAULT_THINK),
|
||||
help="comma-separated subset of unset,true,false")
|
||||
parser.add_argument("--repeats", type=int, default=1)
|
||||
parser.add_argument("--timeout", type=float, default=180.0)
|
||||
args = parser.parse_args()
|
||||
|
||||
models = [m.strip() for m in args.models.split(",") if m.strip()]
|
||||
think_modes = [t.strip() for t in args.think.split(",") if t.strip()]
|
||||
|
||||
# build_steward_prompt reads the registry, and the registry is populated at
|
||||
# application startup. Without this the prompt lists no capabilities and every
|
||||
# cell scores zero for reasons that have nothing to do with the model.
|
||||
register_household_members()
|
||||
|
||||
print(f"{len(FIXTURES)} fixtures x {len(models)} models x {len(think_modes)} think "
|
||||
f"x {args.repeats} repeats = {len(FIXTURES) * len(models) * len(think_modes) * args.repeats} calls\n")
|
||||
|
||||
cells: dict[str, Any] = {}
|
||||
# The guard restores production's pinned models however this exits — a
|
||||
# finished run, a failed cell, Ctrl-C or SIGTERM.
|
||||
with residency_guard(models_used=models), httpx.Client(timeout=args.timeout) as client:
|
||||
for model in models:
|
||||
# Absorb the cold load (~36s) outside the measurements.
|
||||
print(f"warming {model} ...", flush=True)
|
||||
call(client, build_body(model, "hi", "false"))
|
||||
for think in think_modes:
|
||||
key = f"{model}|think={think}"
|
||||
print(f" {key} ...", end=" ", flush=True)
|
||||
started = time.perf_counter()
|
||||
rows = run_cell(client, model, think, args.repeats)
|
||||
summary = summarise(rows)
|
||||
cells[key] = {"summary": summary, "rows": rows}
|
||||
print(f"exact={summary.get('exact_pct')}% "
|
||||
f"over={summary.get('over_routed_pct')}% "
|
||||
f"median={summary.get('latency_ms_median')}ms "
|
||||
f"({time.perf_counter() - started:.0f}s)")
|
||||
|
||||
RESULTS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
stamp = datetime.now(UTC).strftime("%Y%m%dT%H%M%SZ")
|
||||
out = RESULTS_DIR / f"routing-bench-{stamp}.json"
|
||||
out.write_text(json.dumps({
|
||||
"generated_at": datetime.now(UTC).isoformat(),
|
||||
"fixtures": len(FIXTURES),
|
||||
"repeats": args.repeats,
|
||||
"cells": cells,
|
||||
}, indent=2))
|
||||
|
||||
print(f"\n{'cell':28} {'exact':>7} {'under':>7} {'over':>7} {'fmt':>6} {'tok':>7} {'ms':>8}")
|
||||
print("-" * 76)
|
||||
for key, cell in cells.items():
|
||||
s = cell["summary"]
|
||||
print(f"{key:28} {s.get('exact_pct'):>6}% {s.get('under_routed_pct'):>6}% "
|
||||
f"{s.get('over_routed_pct'):>6}% {s.get('format_ok_pct'):>5}% "
|
||||
f"{str(s.get('eval_tokens_median')):>7} {s.get('latency_ms_median'):>8}")
|
||||
print(f"\nwritten to {out}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
sys.exit(main())
|
||||
except KeyboardInterrupt:
|
||||
# The residency guard has already run by the time this is caught;
|
||||
# a traceback here would just bury its output.
|
||||
print("\ninterrupted", file=sys.stderr)
|
||||
sys.exit(130)
|
||||
@@ -268,7 +268,7 @@ async def run_benchmarks(iterations: int = 10, verbose: bool = False):
|
||||
print(f" Max: {overall_max:.3f}s (target: ≤5.0s)")
|
||||
print(f" Avg: {overall_avg:.3f}s (target: ≤1.67s)")
|
||||
print(f"\n Recommendations:")
|
||||
print(f" - Switch to a faster model (current: mistral-nemo)")
|
||||
print(f" - Switch to a faster model (current: gemma4:e2b)")
|
||||
print(f" - Reduce system prompt complexity")
|
||||
print(f" - Limit tool calls (currently limited to 3)")
|
||||
print(f" - Consider caching household registry responses")
|
||||
|
||||
@@ -0,0 +1,559 @@
|
||||
"""
|
||||
Benchmark tool calling across different Ollama models via Tatlock API.
|
||||
|
||||
Sends test prompts through the full Tatlock pipeline (Steward -> Orchestration
|
||||
-> Synthesis) and records tool selection accuracy, latency, and response quality.
|
||||
|
||||
Between models, swaps OLLAMA_DEFAULT_MODEL in .env and waits for uvicorn
|
||||
auto-reload. Requires the server to be running via ./wakeup.sh.
|
||||
|
||||
Usage:
|
||||
.venv/bin/python scripts/benchmark_tool_calling.py
|
||||
.venv/bin/python scripts/benchmark_tool_calling.py --models "gemma4:e4b,gemma4:e2b"
|
||||
.venv/bin/python scripts/benchmark_tool_calling.py --iterations 3
|
||||
"""
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import re
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
|
||||
import httpx
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||
from scripts.ollama_residency import install_sigterm_handler, residency_guard
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Configuration
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
API_BASE = "http://localhost:8777"
|
||||
CHAT_URL = f"{API_BASE}/v1/chat/completions"
|
||||
HEALTH_URL = f"{API_BASE}/health"
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
ENV_PATH = Path(__file__).parent.parent / ".env"
|
||||
|
||||
DEFAULT_MODELS = ["mistral-nemo-large:latest", "gemma4:e4b", "gemma4:e2b"]
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Test scenarios
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass
|
||||
class Scenario:
|
||||
name: str
|
||||
prompt: str
|
||||
expected_tool: str | None # None = no tool expected
|
||||
# Patterns to check in the response text for indirect tool-use evidence
|
||||
success_patterns: list[str] = field(default_factory=list)
|
||||
category: str = "basic"
|
||||
|
||||
|
||||
SCENARIOS = [
|
||||
# --- Should call calculate_math ---
|
||||
Scenario(
|
||||
name="Simple arithmetic",
|
||||
prompt="What is 144 divided by 12?",
|
||||
expected_tool="calculate_math",
|
||||
success_patterns=["12"],
|
||||
category="calculator",
|
||||
),
|
||||
Scenario(
|
||||
name="Square root",
|
||||
prompt="What's the square root of 256?",
|
||||
expected_tool="calculate_math",
|
||||
success_patterns=["16"],
|
||||
category="calculator",
|
||||
),
|
||||
Scenario(
|
||||
name="Complex math",
|
||||
prompt="Calculate pi times the square of 5",
|
||||
expected_tool="calculate_math",
|
||||
success_patterns=["78.5"], # pi * 25 ≈ 78.54
|
||||
category="calculator",
|
||||
),
|
||||
Scenario(
|
||||
name="Word problem",
|
||||
prompt="If I have 3 bags with 17 apples each and I eat 4, how many apples do I have?",
|
||||
expected_tool="calculate_math",
|
||||
success_patterns=["47"],
|
||||
category="calculator",
|
||||
),
|
||||
|
||||
# --- Should call get_current_time ---
|
||||
Scenario(
|
||||
name="Current date",
|
||||
prompt="What's today's date?",
|
||||
expected_tool="get_current_time",
|
||||
success_patterns=["2026"], # Should contain current year
|
||||
category="datetime",
|
||||
),
|
||||
Scenario(
|
||||
name="Current time",
|
||||
prompt="What time is it right now?",
|
||||
expected_tool="get_current_time",
|
||||
success_patterns=[":"], # Time format contains colons
|
||||
category="datetime",
|
||||
),
|
||||
|
||||
# --- Should call calculate_date_offset ---
|
||||
Scenario(
|
||||
name="Relative date past",
|
||||
prompt="What was the date 2 weeks ago?",
|
||||
expected_tool="calculate_date_offset",
|
||||
success_patterns=["2026"],
|
||||
category="datetime",
|
||||
),
|
||||
|
||||
# --- Should call calculate_time_difference ---
|
||||
Scenario(
|
||||
name="Date difference",
|
||||
prompt="How many days between January 1st 2025 and March 15th 2025?",
|
||||
expected_tool="calculate_time_difference",
|
||||
success_patterns=["73", "74"], # 73 or 74 days
|
||||
category="datetime",
|
||||
),
|
||||
|
||||
# --- Should NOT call any tool ---
|
||||
Scenario(
|
||||
name="Greeting",
|
||||
prompt="Hello! How are you?",
|
||||
expected_tool=None,
|
||||
success_patterns=["sir"], # Butler personality
|
||||
category="no_tool",
|
||||
),
|
||||
Scenario(
|
||||
name="Knowledge question",
|
||||
prompt="What is the capital of France?",
|
||||
expected_tool=None,
|
||||
success_patterns=["Paris"],
|
||||
category="no_tool",
|
||||
),
|
||||
Scenario(
|
||||
name="Opinion request",
|
||||
prompt="What do you think about rainy days?",
|
||||
expected_tool=None,
|
||||
category="no_tool",
|
||||
),
|
||||
]
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Result tracking
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@dataclass
|
||||
class RunResult:
|
||||
scenario: str
|
||||
model: str
|
||||
iteration: int
|
||||
latency: float
|
||||
response_text: str
|
||||
has_correct_answer: bool
|
||||
error: str | None = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelStats:
|
||||
model: str
|
||||
results: list[RunResult] = field(default_factory=list)
|
||||
|
||||
@property
|
||||
def total(self) -> int:
|
||||
return len(self.results)
|
||||
|
||||
@property
|
||||
def errors(self) -> int:
|
||||
return sum(1 for r in self.results if r.error)
|
||||
|
||||
@property
|
||||
def accuracy(self) -> float:
|
||||
valid = [r for r in self.results if not r.error]
|
||||
if not valid:
|
||||
return 0
|
||||
return sum(1 for r in valid if r.has_correct_answer) / len(valid) * 100
|
||||
|
||||
@property
|
||||
def avg_latency(self) -> float:
|
||||
lats = [r.latency for r in self.results if not r.error]
|
||||
return statistics.mean(lats) if lats else 0
|
||||
|
||||
@property
|
||||
def p95_latency(self) -> float:
|
||||
lats = sorted(r.latency for r in self.results if not r.error)
|
||||
if not lats:
|
||||
return 0
|
||||
return lats[min(int(len(lats) * 0.95), len(lats) - 1)]
|
||||
|
||||
@property
|
||||
def max_latency(self) -> float:
|
||||
lats = [r.latency for r in self.results if not r.error]
|
||||
return max(lats) if lats else 0
|
||||
|
||||
def category_accuracy(self, category: str) -> float:
|
||||
cat_scenarios = {s.name for s in SCENARIOS if s.category == category}
|
||||
valid = [r for r in self.results if not r.error and r.scenario in cat_scenarios]
|
||||
if not valid:
|
||||
return 0
|
||||
return sum(1 for r in valid if r.has_correct_answer) / len(valid) * 100
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# .env manipulation
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def swap_model_in_env(model_name: str):
|
||||
"""Swap OLLAMA_DEFAULT_MODEL in .env file."""
|
||||
content = ENV_PATH.read_text()
|
||||
content = re.sub(
|
||||
r'^OLLAMA_DEFAULT_MODEL=.*$',
|
||||
f'OLLAMA_DEFAULT_MODEL={model_name}',
|
||||
content,
|
||||
flags=re.MULTILINE,
|
||||
)
|
||||
ENV_PATH.write_text(content)
|
||||
print(f" .env updated: OLLAMA_DEFAULT_MODEL={model_name}")
|
||||
|
||||
|
||||
async def wait_for_server_reload(client: httpx.AsyncClient, timeout: float = 30):
|
||||
"""Wait for uvicorn to auto-reload after .env change."""
|
||||
# Give uvicorn a moment to detect the file change
|
||||
await asyncio.sleep(3)
|
||||
|
||||
# Poll health endpoint
|
||||
deadline = time.monotonic() + timeout
|
||||
while time.monotonic() < deadline:
|
||||
try:
|
||||
r = await client.get(HEALTH_URL, timeout=5)
|
||||
if r.status_code == 200:
|
||||
return
|
||||
except Exception:
|
||||
pass
|
||||
await asyncio.sleep(1)
|
||||
|
||||
raise TimeoutError("Server did not come back after reload")
|
||||
|
||||
|
||||
async def warm_up_ollama_model(client: httpx.AsyncClient, model_name: str):
|
||||
"""Send a throwaway request to load the model into VRAM."""
|
||||
print(f" Warming up {model_name} in Ollama...", end=" ", flush=True)
|
||||
try:
|
||||
r = await client.post(
|
||||
f"{OLLAMA_URL}/api/generate",
|
||||
json={"model": model_name, "prompt": "hi", "stream": False},
|
||||
timeout=120,
|
||||
)
|
||||
r.raise_for_status()
|
||||
duration = r.json().get("total_duration", 0) / 1e9
|
||||
print(f"OK ({duration:.1f}s)")
|
||||
except Exception as e:
|
||||
print(f"WARN: {e}")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Core benchmark logic
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def run_scenario(
|
||||
client: httpx.AsyncClient,
|
||||
scenario: Scenario,
|
||||
model: str,
|
||||
iteration: int,
|
||||
) -> RunResult:
|
||||
"""Run a single scenario through the Tatlock API."""
|
||||
payload = {
|
||||
"model": "Tatlock",
|
||||
"messages": [{"role": "user", "content": scenario.prompt}],
|
||||
}
|
||||
|
||||
start = time.monotonic()
|
||||
try:
|
||||
r = await client.post(CHAT_URL, json=payload, timeout=120)
|
||||
latency = time.monotonic() - start
|
||||
|
||||
if r.status_code != 200:
|
||||
return RunResult(
|
||||
scenario=scenario.name,
|
||||
model=model,
|
||||
iteration=iteration,
|
||||
latency=latency,
|
||||
response_text="",
|
||||
has_correct_answer=False,
|
||||
error=f"HTTP {r.status_code}: {r.text[:100]}",
|
||||
)
|
||||
|
||||
data = r.json()
|
||||
response_text = data["choices"][0]["message"]["content"]
|
||||
|
||||
# Check if the response contains expected patterns
|
||||
has_correct = True
|
||||
if scenario.success_patterns:
|
||||
has_correct = any(
|
||||
p.lower() in response_text.lower()
|
||||
for p in scenario.success_patterns
|
||||
)
|
||||
|
||||
return RunResult(
|
||||
scenario=scenario.name,
|
||||
model=model,
|
||||
iteration=iteration,
|
||||
latency=latency,
|
||||
response_text=response_text,
|
||||
has_correct_answer=has_correct,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
latency = time.monotonic() - start
|
||||
return RunResult(
|
||||
scenario=scenario.name,
|
||||
model=model,
|
||||
iteration=iteration,
|
||||
latency=latency,
|
||||
response_text="",
|
||||
has_correct_answer=False,
|
||||
error=str(e)[:200],
|
||||
)
|
||||
|
||||
|
||||
async def benchmark_model(
|
||||
client: httpx.AsyncClient,
|
||||
model_name: str,
|
||||
iterations: int,
|
||||
) -> ModelStats:
|
||||
"""Run all scenarios for a single model."""
|
||||
stats = ModelStats(model=model_name)
|
||||
|
||||
print(f"\n{'=' * 70}")
|
||||
print(f" Model: {model_name}")
|
||||
print(f"{'=' * 70}")
|
||||
|
||||
# Swap model in .env
|
||||
swap_model_in_env(model_name)
|
||||
|
||||
# Warm up model in Ollama BEFORE server reload picks it up
|
||||
await warm_up_ollama_model(client, model_name)
|
||||
|
||||
# Wait for server to reload with new model
|
||||
print(" Waiting for server reload...", end=" ", flush=True)
|
||||
await wait_for_server_reload(client)
|
||||
print("OK")
|
||||
|
||||
# Run a throwaway request through the full pipeline to warm up
|
||||
print(" Warming up pipeline...", end=" ", flush=True)
|
||||
try:
|
||||
await client.post(
|
||||
CHAT_URL,
|
||||
json={"model": "Tatlock", "messages": [{"role": "user", "content": "hi"}]},
|
||||
timeout=120,
|
||||
)
|
||||
print("OK")
|
||||
except Exception as e:
|
||||
print(f"WARN: {e}")
|
||||
|
||||
for iteration in range(iterations):
|
||||
if iterations > 1:
|
||||
print(f"\n --- Iteration {iteration + 1}/{iterations} ---")
|
||||
|
||||
for scenario in SCENARIOS:
|
||||
result = await run_scenario(client, scenario, model_name, iteration)
|
||||
stats.results.append(result)
|
||||
|
||||
# Display
|
||||
if result.error:
|
||||
print(
|
||||
f" [ERR ] {scenario.name:30s} {result.latency:5.1f}s "
|
||||
f"{result.error[:60]}"
|
||||
)
|
||||
elif result.has_correct_answer:
|
||||
preview = result.response_text[:60].replace("\n", " ")
|
||||
print(f" [OK ] {scenario.name:30s} {result.latency:5.1f}s {preview}")
|
||||
else:
|
||||
preview = result.response_text[:60].replace("\n", " ")
|
||||
print(f" [MISS] {scenario.name:30s} {result.latency:5.1f}s {preview}")
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
def print_comparison(all_stats: list[ModelStats]):
|
||||
"""Print side-by-side comparison table."""
|
||||
print("\n" + "=" * 80)
|
||||
print(" COMPARISON SUMMARY")
|
||||
print("=" * 80)
|
||||
|
||||
col_width = max(len(s.model) for s in all_stats) + 2
|
||||
label_width = 32
|
||||
|
||||
header = f"{'Metric':<{label_width}}"
|
||||
for s in all_stats:
|
||||
header += f" {s.model:>{col_width}}"
|
||||
print(f"\n{header}")
|
||||
print("-" * (label_width + (col_width + 2) * len(all_stats)))
|
||||
|
||||
# Answer accuracy
|
||||
row = f"{'Correct answer rate':<{label_width}}"
|
||||
for s in all_stats:
|
||||
row += f" {s.accuracy:>{col_width - 1}.1f}%"
|
||||
print(row)
|
||||
|
||||
# Latency
|
||||
row = f"{'Avg latency':<{label_width}}"
|
||||
for s in all_stats:
|
||||
row += f" {s.avg_latency:>{col_width - 1}.1f}s"
|
||||
print(row)
|
||||
|
||||
row = f"{'P95 latency':<{label_width}}"
|
||||
for s in all_stats:
|
||||
row += f" {s.p95_latency:>{col_width - 1}.1f}s"
|
||||
print(row)
|
||||
|
||||
row = f"{'Max latency':<{label_width}}"
|
||||
for s in all_stats:
|
||||
row += f" {s.max_latency:>{col_width - 1}.1f}s"
|
||||
print(row)
|
||||
|
||||
# Errors
|
||||
row = f"{'Errors':<{label_width}}"
|
||||
for s in all_stats:
|
||||
row += f" {s.errors:>{col_width}}"
|
||||
print(row)
|
||||
|
||||
# Per-category
|
||||
categories = sorted(set(sc.category for sc in SCENARIOS))
|
||||
print(f"\n{'Per-category accuracy':<{label_width}}")
|
||||
print("-" * (label_width + (col_width + 2) * len(all_stats)))
|
||||
for cat in categories:
|
||||
row = f" {cat:<{label_width - 2}}"
|
||||
for s in all_stats:
|
||||
row += f" {s.category_accuracy(cat):>{col_width - 1}.1f}%"
|
||||
print(row)
|
||||
|
||||
# Mismatches
|
||||
print(f"\n{'Missed answers':<50}")
|
||||
print("-" * 80)
|
||||
any_miss = False
|
||||
for scenario in SCENARIOS:
|
||||
misses = []
|
||||
for s in all_stats:
|
||||
sc_results = [r for r in s.results if r.scenario == scenario.name]
|
||||
fails = [r for r in sc_results if not r.has_correct_answer and not r.error]
|
||||
if fails:
|
||||
preview = fails[0].response_text[:50].replace("\n", " ")
|
||||
misses.append(f"{s.model}: \"{preview}\"")
|
||||
if misses:
|
||||
any_miss = True
|
||||
print(f" {scenario.name}")
|
||||
for m in misses:
|
||||
print(f" {m}")
|
||||
|
||||
if not any_miss:
|
||||
print(" (none)")
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
|
||||
|
||||
def save_results(all_stats: list[ModelStats], output_path: Path):
|
||||
"""Save detailed results to JSON."""
|
||||
data = {}
|
||||
for stats in all_stats:
|
||||
data[stats.model] = {
|
||||
"summary": {
|
||||
"accuracy": stats.accuracy,
|
||||
"avg_latency": round(stats.avg_latency, 2),
|
||||
"p95_latency": round(stats.p95_latency, 2),
|
||||
"max_latency": round(stats.max_latency, 2),
|
||||
"errors": stats.errors,
|
||||
"total_runs": stats.total,
|
||||
},
|
||||
"runs": [
|
||||
{
|
||||
"scenario": r.scenario,
|
||||
"iteration": r.iteration,
|
||||
"latency": round(r.latency, 3),
|
||||
"has_correct_answer": r.has_correct_answer,
|
||||
"response_text": r.response_text,
|
||||
"error": r.error,
|
||||
}
|
||||
for r in stats.results
|
||||
],
|
||||
}
|
||||
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
output_path.write_text(json.dumps(data, indent=2))
|
||||
print(f"\nDetailed results saved to: {output_path}")
|
||||
|
||||
|
||||
async def main():
|
||||
parser = argparse.ArgumentParser(description="Benchmark tool calling across Ollama models via Tatlock API")
|
||||
parser.add_argument(
|
||||
"--iterations", type=int, default=1,
|
||||
help="Iterations per model (default: 1)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--models", type=str, default=",".join(DEFAULT_MODELS),
|
||||
help=f"Comma-separated models (default: {','.join(DEFAULT_MODELS)})",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output", type=str, default="logs/benchmark_results.json",
|
||||
help="JSON output path (default: logs/benchmark_results.json)",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
models = [m.strip() for m in args.models.split(",")]
|
||||
|
||||
# Verify server is running
|
||||
async with httpx.AsyncClient() as client:
|
||||
try:
|
||||
r = await client.get(HEALTH_URL, timeout=5)
|
||||
r.raise_for_status()
|
||||
print("Server is running.")
|
||||
except Exception:
|
||||
print("ERROR: Server not running. Start it with ./wakeup.sh first.")
|
||||
return
|
||||
|
||||
print("=" * 70)
|
||||
print(" Tool Calling Benchmark (via Tatlock API)")
|
||||
print("=" * 70)
|
||||
print(f" Models: {', '.join(models)}")
|
||||
print(f" Scenarios: {len(SCENARIOS)}")
|
||||
print(f" Iterations: {args.iterations}")
|
||||
print(f" Total runs: {len(SCENARIOS) * args.iterations * len(models)}")
|
||||
|
||||
# Remember original model to restore after benchmark
|
||||
original_env = ENV_PATH.read_text()
|
||||
|
||||
all_stats = []
|
||||
# Both restores must survive a crash or an interrupt. The .env one especially:
|
||||
# this script rewrites OLLAMA_DEFAULT_MODEL and lets uvicorn reload onto it,
|
||||
# so bailing out mid-run used to leave the *running server* pointed at the
|
||||
# benchmark model — and DEFAULT_MODELS starts at mistral-nemo-large, the 9.2G
|
||||
# model implicated in the 2026-08-07 VRAM outage.
|
||||
install_sigterm_handler()
|
||||
try:
|
||||
with residency_guard(models_used=models):
|
||||
async with httpx.AsyncClient() as client:
|
||||
for model in models:
|
||||
stats = await benchmark_model(client, model, args.iterations)
|
||||
all_stats.append(stats)
|
||||
finally:
|
||||
ENV_PATH.write_text(original_env)
|
||||
print("\n .env restored to original")
|
||||
|
||||
print_comparison(all_stats)
|
||||
save_results(all_stats, Path(args.output))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
asyncio.run(main())
|
||||
except KeyboardInterrupt:
|
||||
# .env and GPU residency are both restored by now; do not bury that
|
||||
# output under a traceback.
|
||||
print("\ninterrupted", file=sys.stderr)
|
||||
raise SystemExit(130) from None
|
||||
@@ -0,0 +1,159 @@
|
||||
"""
|
||||
Labelled queries for the Steward routing benchmark.
|
||||
|
||||
Each fixture carries both `expect` and `forbid`:
|
||||
|
||||
expect capabilities that must appear. Missing one is under-routing — the
|
||||
Butler answers without a tool it needed.
|
||||
forbid capabilities that must not appear. Over-routing is not cosmetic: a
|
||||
spurious librarian is a real multi-second web call, and a spurious
|
||||
housekeeper can actuate hardware.
|
||||
|
||||
`forbid` matters more than `expect` here, because over-recommendation is the
|
||||
predicted failure when model thinking is disabled and the Steward has less room
|
||||
to discriminate.
|
||||
|
||||
The `adversarial` group deserves explanation. Until 2026-08-08 the extractor
|
||||
substring-matched capability *domains* across the Steward's whole response, so
|
||||
ordinary English in its REASON line selected agents: "description" contains the
|
||||
housekeeper domain "script", "acknowledge" contains "knowledge" and "know",
|
||||
"economy" contains the biographer domain "my". Those queries invite exactly that
|
||||
vocabulary. They now serve as an end-to-end regression: routing must depend on
|
||||
what the Steward *decided*, not on the words it happened to use while explaining.
|
||||
|
||||
Expectations follow the routing rules stated in the Steward prompt itself
|
||||
(src/agents/steward/agent.py), not on what a capability could plausibly cover.
|
||||
"""
|
||||
|
||||
CORE = "tatlock_core"
|
||||
LIB = "librarian"
|
||||
BIO = "biographer"
|
||||
HOUSE = "housekeeper"
|
||||
ALL = [CORE, LIB, BIO, HOUSE]
|
||||
|
||||
|
||||
def _others(*keep: str) -> list[str]:
|
||||
return [c for c in ALL if c not in keep]
|
||||
|
||||
|
||||
FIXTURES: list[dict] = [
|
||||
# --- arithmetic and computation -> tatlock_core --------------------------
|
||||
{"id": "math_add", "group": "math", "query": "What is 61 plus 12?",
|
||||
"expect": [CORE], "forbid": _others(CORE)},
|
||||
{"id": "math_percent", "group": "math", "query": "What is 15% of 240?",
|
||||
"expect": [CORE], "forbid": _others(CORE)},
|
||||
{"id": "math_compound", "group": "math", "query": "If I save 200 a month for 3 years, how much is that?",
|
||||
"expect": [CORE], "forbid": _others(CORE)},
|
||||
{"id": "math_sqrt", "group": "math", "query": "What is the square root of 1764?",
|
||||
"expect": [CORE], "forbid": _others(CORE)},
|
||||
|
||||
# --- date and time -> tatlock_core ---------------------------------------
|
||||
{"id": "time_now", "group": "datetime", "query": "What time is it?",
|
||||
"expect": [CORE], "forbid": _others(CORE)},
|
||||
{"id": "time_date", "group": "datetime", "query": "What is today's date?",
|
||||
"expect": [CORE], "forbid": _others(CORE)},
|
||||
{"id": "time_delta", "group": "datetime", "query": "How many days until Christmas?",
|
||||
"expect": [CORE], "forbid": _others(CORE)},
|
||||
|
||||
# --- personal memory -> biographer ---------------------------------------
|
||||
{"id": "bio_location", "group": "biographer", "query": "Where do I live?",
|
||||
"expect": [BIO], "forbid": [LIB, HOUSE]},
|
||||
{"id": "bio_name", "group": "biographer", "query": "What's my name?",
|
||||
"expect": [BIO], "forbid": [LIB, HOUSE]},
|
||||
{"id": "bio_car", "group": "biographer", "query": "What car do I drive?",
|
||||
"expect": [BIO], "forbid": [LIB, HOUSE]},
|
||||
{"id": "bio_store", "group": "biographer", "query": "Remember that I prefer my coffee black.",
|
||||
"expect": [BIO], "forbid": [LIB, HOUSE]},
|
||||
{"id": "bio_list", "group": "biographer", "query": "What do you know about me?",
|
||||
"expect": [BIO], "forbid": [LIB, HOUSE]},
|
||||
{"id": "bio_forget", "group": "biographer", "query": "Forget my old address.",
|
||||
"expect": [BIO], "forbid": [LIB, HOUSE]},
|
||||
|
||||
# --- research and current information -> librarian ------------------------
|
||||
{"id": "lib_weather", "group": "librarian", "query": "What's the weather in Rotterdam tomorrow?",
|
||||
"expect": [LIB], "forbid": [HOUSE]},
|
||||
{"id": "lib_news", "group": "librarian", "query": "What's in the news today?",
|
||||
"expect": [LIB], "forbid": [HOUSE, BIO]},
|
||||
{"id": "lib_url", "group": "librarian", "query": "Read https://example.com/article and summarise it.",
|
||||
"expect": [LIB], "forbid": [HOUSE, BIO]},
|
||||
{"id": "lib_research", "group": "librarian", "query": "Research how tidal power stations work.",
|
||||
"expect": [LIB], "forbid": [HOUSE, BIO]},
|
||||
{"id": "lib_wiki_create", "group": "librarian", "query": "Create a wiki page about our network topology.",
|
||||
"expect": [LIB], "forbid": [HOUSE, BIO]},
|
||||
|
||||
# --- home automation -> housekeeper --------------------------------------
|
||||
{"id": "house_lights_on", "group": "housekeeper", "query": "Turn on the kitchen lights.",
|
||||
"expect": [HOUSE], "forbid": [LIB, BIO, CORE]},
|
||||
{"id": "house_lights_off", "group": "housekeeper", "query": "Switch off all the lights downstairs.",
|
||||
"expect": [HOUSE], "forbid": [LIB, BIO, CORE]},
|
||||
{"id": "house_thermostat", "group": "housekeeper", "query": "Set the thermostat to 20 degrees.",
|
||||
"expect": [HOUSE], "forbid": [LIB, BIO]},
|
||||
{"id": "house_blinds", "group": "housekeeper", "query": "Close the blinds in the living room.",
|
||||
"expect": [HOUSE], "forbid": [LIB, BIO, CORE]},
|
||||
|
||||
# --- conversational -> nothing at all -------------------------------------
|
||||
# The expensive failure mode: a greeting that triggers a web search.
|
||||
{"id": "chat_greeting", "group": "conversational", "query": "Hello!",
|
||||
"expect": [], "forbid": ALL},
|
||||
{"id": "chat_thanks", "group": "conversational", "query": "Thanks, that's helpful.",
|
||||
"expect": [], "forbid": ALL},
|
||||
{"id": "chat_joke", "group": "conversational", "query": "Tell me a joke.",
|
||||
"expect": [], "forbid": ALL},
|
||||
{"id": "chat_howareyou", "group": "conversational", "query": "How are you doing today?",
|
||||
"expect": [], "forbid": ALL},
|
||||
{"id": "chat_prior_turn", "group": "conversational", "query": "What did I just say?",
|
||||
"expect": [], "forbid": ALL},
|
||||
|
||||
# --- genuinely multi-capability -------------------------------------------
|
||||
{"id": "multi_weather_home", "group": "multi",
|
||||
"query": "What's the weather here, and remember that I like it warm?",
|
||||
"expect": [LIB, BIO], "forbid": []},
|
||||
{"id": "multi_recall_search", "group": "multi",
|
||||
"query": "Look up the best route from my home address to Utrecht.",
|
||||
"expect": [BIO, LIB], "forbid": []},
|
||||
{"id": "multi_math_memory", "group": "multi",
|
||||
"query": "Remember that my budget is 500 euro, then work out 12% of it.",
|
||||
"expect": [BIO, CORE], "forbid": [LIB, HOUSE]},
|
||||
|
||||
# --- adversarial: vocabulary that used to select agents by substring ------
|
||||
# "temperature" is a housekeeper domain, but this is a unit conversion.
|
||||
{"id": "adv_temperature", "group": "adversarial", "query": "Convert 98.6 Fahrenheit to Celsius.",
|
||||
"expect": [CORE], "forbid": [HOUSE, LIB, BIO]},
|
||||
# "description" contains "script"; "discover" contains "cover".
|
||||
{"id": "adv_description", "group": "adversarial",
|
||||
"query": "Give me a short description of what 17 times 23 comes to.",
|
||||
"expect": [CORE], "forbid": [HOUSE, LIB]},
|
||||
# "acknowledge" contains "knowledge" and "know".
|
||||
{"id": "adv_acknowledge", "group": "adversarial",
|
||||
"query": "Just acknowledge this and add 5 and 6 for me.",
|
||||
"expect": [CORE], "forbid": [LIB, BIO]},
|
||||
# "my" appears inside "economy".
|
||||
{"id": "adv_economy", "group": "adversarial",
|
||||
"query": "How many zeros are in one trillion?",
|
||||
"expect": [CORE], "forbid": [BIO, HOUSE]},
|
||||
# "fan" inside "fantastic"; also a climate word without a home-control intent.
|
||||
{"id": "adv_fantastic", "group": "adversarial",
|
||||
"query": "That's fantastic. What is 8 squared?",
|
||||
"expect": [CORE], "forbid": [HOUSE, LIB]},
|
||||
# "home" without any actuation intent.
|
||||
{"id": "adv_home_word", "group": "adversarial", "query": "What time do I usually get home?",
|
||||
"expect": [BIO], "forbid": [HOUSE]},
|
||||
# "search" as ordinary English, not a web-search request.
|
||||
{"id": "adv_search_word", "group": "adversarial",
|
||||
"query": "No need to search anything, just tell me what 9 times 9 is.",
|
||||
"expect": [CORE], "forbid": [LIB]},
|
||||
# "create"/"write" are librarian domains but this is conversational.
|
||||
{"id": "adv_write_word", "group": "adversarial", "query": "Can you write that more simply?",
|
||||
"expect": [], "forbid": [LIB, HOUSE]},
|
||||
|
||||
# --- mutating intents: routing only, nothing is ever executed -------------
|
||||
{"id": "mutate_wiki_update", "group": "mutating", "query": "Update the dossier page with today's findings.",
|
||||
"expect": [LIB], "forbid": [HOUSE, CORE]},
|
||||
{"id": "mutate_scene", "group": "mutating", "query": "Run the movie night scene.",
|
||||
"expect": [HOUSE], "forbid": [LIB, BIO, CORE]},
|
||||
]
|
||||
|
||||
|
||||
GROUPS = sorted({f["group"] for f in FIXTURES})
|
||||
|
||||
assert len({f["id"] for f in FIXTURES}) == len(FIXTURES), "duplicate fixture id"
|
||||
@@ -0,0 +1,121 @@
|
||||
"""
|
||||
Guard production's GPU residency across a benchmark run.
|
||||
|
||||
Benchmarks swap models on the card production is serving from. Ollama evicts to
|
||||
make room, so a run leaves its own models resident and the production one gone:
|
||||
the next voice turn pays a ~36s cold load, and the pin that prevented it is
|
||||
silently lost. That happened on 2026-08-08 — a routing benchmark evicted
|
||||
gemma4:e2b and left gemma4:e4b behind, and only the monitoring noticing
|
||||
`unexpected_models` caught it.
|
||||
|
||||
Snapshot before, restore after, and wire the restore to SIGTERM as well as the
|
||||
normal path. Python runs `finally` for SIGINT, which arrives as
|
||||
KeyboardInterrupt, but the default SIGTERM action terminates outright — so
|
||||
`timeout`, a systemd stop or a plain `kill` would skip the guard entirely.
|
||||
|
||||
from scripts.ollama_residency import residency_guard, install_sigterm_handler
|
||||
|
||||
install_sigterm_handler()
|
||||
with residency_guard(models_used=["gemma4:e4b"]):
|
||||
...
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import signal
|
||||
from collections.abc import Iterator
|
||||
from contextlib import contextmanager
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
|
||||
OLLAMA_URL = "http://localhost:11434"
|
||||
|
||||
# keep_alive:-1 yields a year-2318 expiry, so "pinned" is simply "expires more
|
||||
# than a day out". Matches check-ai-pipeline.sh in system-admin-toj.
|
||||
PINNED_THRESHOLD_SECONDS = 86400
|
||||
|
||||
|
||||
def install_sigterm_handler() -> None:
|
||||
"""Make SIGTERM raise, so `finally` blocks and context managers still run."""
|
||||
def _raise(signum, _frame):
|
||||
raise KeyboardInterrupt(f"signal {signum}")
|
||||
|
||||
signal.signal(signal.SIGTERM, _raise)
|
||||
|
||||
|
||||
def snapshot_residency(client: httpx.Client | None = None) -> dict[str, bool]:
|
||||
"""Resident models mapped to whether each is pinned."""
|
||||
owns = client is None
|
||||
client = client or httpx.Client(timeout=30)
|
||||
try:
|
||||
data = client.get(f"{OLLAMA_URL}/api/ps", timeout=10).json()
|
||||
except Exception: # noqa: BLE001 - a missing snapshot must not abort the run
|
||||
return {}
|
||||
finally:
|
||||
if owns:
|
||||
client.close()
|
||||
|
||||
resident: dict[str, bool] = {}
|
||||
now = datetime.now(UTC)
|
||||
for model in data.get("models", []):
|
||||
pinned = False
|
||||
try:
|
||||
expires = datetime.fromisoformat(model.get("expires_at", "").replace("Z", "+00:00"))
|
||||
pinned = (expires - now).total_seconds() > PINNED_THRESHOLD_SECONDS
|
||||
except ValueError:
|
||||
pass
|
||||
resident[model["name"]] = pinned
|
||||
return resident
|
||||
|
||||
|
||||
def set_keep_alive(model: str, keep_alive: Any, client: httpx.Client | None = None) -> bool:
|
||||
"""Load, unload or pin a model. Embedding models reject /api/generate."""
|
||||
owns = client is None
|
||||
client = client or httpx.Client(timeout=180)
|
||||
payload = {"model": model, "keep_alive": keep_alive}
|
||||
try:
|
||||
for endpoint in ("generate", "embed"):
|
||||
try:
|
||||
response = client.post(f"{OLLAMA_URL}/api/{endpoint}", json=payload, timeout=180)
|
||||
except Exception: # noqa: BLE001
|
||||
return False
|
||||
if response.status_code == 200:
|
||||
return True
|
||||
if response.status_code == 400 and "does not support generate" in response.text:
|
||||
continue # embedding-only model; try /api/embed
|
||||
return False
|
||||
return False
|
||||
finally:
|
||||
if owns:
|
||||
client.close()
|
||||
|
||||
|
||||
def restore_residency(before: dict[str, bool], used: list[str]) -> None:
|
||||
"""Evict what the benchmark loaded, then re-pin what was pinned before."""
|
||||
base = {name.split(":")[0] for name in before}
|
||||
with httpx.Client(timeout=180) as client:
|
||||
for model in used:
|
||||
if model not in before and model.split(":")[0] not in base:
|
||||
print(f" residency: unloading benchmark model {model}")
|
||||
set_keep_alive(model, 0, client)
|
||||
for name, pinned in before.items():
|
||||
if not pinned:
|
||||
continue
|
||||
ok = set_keep_alive(name, -1, client)
|
||||
print(f" residency: re-pinned {name}" if ok
|
||||
else f" residency: FAILED to re-pin {name} -- run warmup-ollama.sh")
|
||||
|
||||
|
||||
@contextmanager
|
||||
def residency_guard(models_used: list[str]) -> Iterator[dict[str, bool]]:
|
||||
"""Snapshot residency on entry, restore it on exit however that happens."""
|
||||
before = snapshot_residency()
|
||||
pinned = [n for n, p in before.items() if p]
|
||||
print(f" residency: resident before {sorted(before)}"
|
||||
f"{f' (pinned: {pinned})' if pinned else ''}")
|
||||
try:
|
||||
yield before
|
||||
finally:
|
||||
print(" residency: restoring ...")
|
||||
restore_residency(before, models_used)
|
||||
@@ -3,7 +3,7 @@
|
||||
# Verifies room groups are controlled by checking actual state changes
|
||||
|
||||
API_URL="http://localhost:8777/v1/chat/completions"
|
||||
CORE_API="http://192.168.86.149:8083"
|
||||
CORE_API="http://localhost:8083"
|
||||
RESULTS_FILE="/tmp/housekeeper_test_results.txt"
|
||||
|
||||
GREEN='\033[0;32m'
|
||||
|
||||
+5
-9
@@ -6,7 +6,8 @@ must implement. The interface is designed around the Responses API format.
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import AsyncGenerator, Any
|
||||
from collections.abc import AsyncGenerator
|
||||
from typing import Any
|
||||
|
||||
|
||||
class OutputItem:
|
||||
@@ -19,12 +20,7 @@ class OutputItem:
|
||||
- message: Assistant response message
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
type: str,
|
||||
id: str,
|
||||
**kwargs: Any
|
||||
):
|
||||
def __init__(self, type: str, id: str, **kwargs: Any):
|
||||
self.type = type
|
||||
self.id = id
|
||||
self.data = kwargs
|
||||
@@ -40,7 +36,7 @@ class AgentInterface(ABC):
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
async def generate_response(
|
||||
def generate_response(
|
||||
self,
|
||||
messages: list[dict],
|
||||
reasoning: dict | None = None,
|
||||
@@ -48,7 +44,7 @@ class AgentInterface(ABC):
|
||||
temperature: float = 1.0,
|
||||
max_tokens: int | None = None,
|
||||
stop: list[str] | None = None,
|
||||
**kwargs: Any
|
||||
**kwargs: Any,
|
||||
) -> AsyncGenerator[OutputItem, None]:
|
||||
"""
|
||||
Generate streaming response as output items.
|
||||
|
||||
@@ -11,6 +11,7 @@ For direct key-based lookups (location, timezone, preferences),
|
||||
use the memory_service instead - it's faster and doesn't require LLM.
|
||||
The Biographer handles semantic, fuzzy queries.
|
||||
"""
|
||||
|
||||
from src.agents.biographer.agent import (
|
||||
get_biographer_agent,
|
||||
run_biographer,
|
||||
|
||||
@@ -7,7 +7,8 @@ A PydanticAI agent that serves as the household's memory keeper:
|
||||
- Manages user profile and preferences
|
||||
- Forgets information when requested
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
|
||||
from typing import Any
|
||||
|
||||
from pydantic_ai import Agent
|
||||
|
||||
@@ -19,7 +20,6 @@ from src.agents.biographer.tools import (
|
||||
update_preference,
|
||||
update_profile,
|
||||
)
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
@@ -97,7 +97,7 @@ When recalling:
|
||||
"""
|
||||
|
||||
# Lazy initialization to avoid connection issues during imports
|
||||
_biographer_agent: Optional[Agent[None, str]] = None
|
||||
_biographer_agent: Agent[None, str] | None = None
|
||||
|
||||
|
||||
def _create_biographer_agent() -> Agent[None, str]:
|
||||
@@ -126,6 +126,7 @@ def _create_biographer_agent() -> Agent[None, str]:
|
||||
agent.tool_plain(forget_memory)
|
||||
|
||||
from src.anthropic.model_selector import get_model_info
|
||||
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"biographer_agent_created",
|
||||
@@ -153,7 +154,7 @@ def get_biographer_agent() -> Agent[None, str]:
|
||||
async def run_biographer(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
message_history: list[Any] | None = None,
|
||||
) -> str:
|
||||
"""
|
||||
Execute a memory task with The Biographer.
|
||||
@@ -216,7 +217,7 @@ async def run_biographer(
|
||||
async def run_biographer_stream(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
message_history: list[Any] | None = None,
|
||||
):
|
||||
"""
|
||||
Execute a memory task with streaming output.
|
||||
|
||||
@@ -4,6 +4,7 @@ Biographer capability registration for the Household Registry.
|
||||
Defines The Biographer's capabilities and registers it as a
|
||||
household member for coordination by the Steward and Tatlock.
|
||||
"""
|
||||
|
||||
from src.agents.biographer.agent import get_biographer_agent
|
||||
from src.agents.biographer.tools import BIOGRAPHER_TOOLS
|
||||
from src.core.household_registry import (
|
||||
|
||||
@@ -10,6 +10,7 @@ These tools enable The Biographer to record and recall the user's story:
|
||||
For direct key-based access (get/set profile, preferences),
|
||||
use memory_service directly - these tools are for semantic queries.
|
||||
"""
|
||||
|
||||
from src.core.context import get_user
|
||||
from src.core.embeddings import get_embedding_client
|
||||
from src.core.logging_config import get_logger
|
||||
@@ -23,6 +24,7 @@ logger = get_logger(__name__)
|
||||
# Semantic Recall
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def recall_semantic(
|
||||
query: str,
|
||||
memory_type: str = "",
|
||||
@@ -108,6 +110,7 @@ async def recall_semantic(
|
||||
# Store Memory
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def store_insight(
|
||||
key: str,
|
||||
value: str,
|
||||
@@ -158,7 +161,7 @@ async def store_insight(
|
||||
f"**Keywords:** {', '.join(keywords)}",
|
||||
f"**Importance:** {importance:.1f}",
|
||||
"",
|
||||
"_Memory is now searchable via semantic recall._"
|
||||
"_Memory is now searchable via semantic recall._",
|
||||
]
|
||||
|
||||
logger.info(
|
||||
@@ -216,7 +219,7 @@ async def update_profile(
|
||||
"## Profile Updated",
|
||||
f"**{key}:** {value}",
|
||||
"",
|
||||
"_Profile data is automatically included in context._"
|
||||
"_Profile data is automatically included in context._",
|
||||
]
|
||||
|
||||
logger.info(
|
||||
@@ -271,7 +274,7 @@ async def update_preference(
|
||||
"## Preference Updated",
|
||||
f"**{key}:** {value}",
|
||||
"",
|
||||
"_Preference will be applied to future responses._"
|
||||
"_Preference will be applied to future responses._",
|
||||
]
|
||||
|
||||
logger.info(
|
||||
@@ -293,6 +296,7 @@ async def update_preference(
|
||||
# List Memories
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def list_memories(
|
||||
memory_type: str = "learned_fact",
|
||||
limit: int = 20,
|
||||
@@ -321,7 +325,7 @@ async def list_memories(
|
||||
|
||||
# Convert string to MemoryType
|
||||
try:
|
||||
mem_type = MemoryType(memory_type)
|
||||
MemoryType(memory_type) # validated for its ValueError; the value is unused
|
||||
except ValueError:
|
||||
return f"Invalid memory type '{memory_type}'. Use: user_profile, preference, or learned_fact"
|
||||
|
||||
@@ -378,6 +382,7 @@ async def list_memories(
|
||||
# Forget Memory
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def forget_memory(
|
||||
key: str,
|
||||
memory_type: str = "learned_fact",
|
||||
@@ -420,7 +425,7 @@ async def forget_memory(
|
||||
f"**Key:** {key}",
|
||||
f"**Type:** {memory_type}",
|
||||
"",
|
||||
"_Memory has been removed._"
|
||||
"_Memory has been removed._",
|
||||
]
|
||||
|
||||
logger.info(
|
||||
|
||||
@@ -1,407 +0,0 @@
|
||||
"""
|
||||
Multi-agent coordination engine.
|
||||
|
||||
Orchestrates delegation from Tatlock to expert agents (Librarian, etc.)
|
||||
based on Steward recommendations. Handles:
|
||||
- Routing tasks to appropriate agents
|
||||
- Parallel and sequential execution
|
||||
- Result aggregation
|
||||
- Error handling and graceful degradation
|
||||
"""
|
||||
import asyncio
|
||||
import time
|
||||
from typing import Any, AsyncGenerator, Optional
|
||||
|
||||
from src.agents.librarian import run_librarian, run_librarian_stream
|
||||
from src.agents.protocol import (
|
||||
AgentError,
|
||||
AgentRequest,
|
||||
AgentResponse,
|
||||
AgentTimeoutError,
|
||||
AgentUnavailableError,
|
||||
CoordinationResult,
|
||||
DelegationIntent,
|
||||
DelegationReason,
|
||||
ToolCallRecord,
|
||||
)
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
# Agent execution functions registry
|
||||
AGENT_EXECUTORS: dict[str, Any] = {
|
||||
"librarian": run_librarian,
|
||||
}
|
||||
|
||||
AGENT_STREAM_EXECUTORS: dict[str, Any] = {
|
||||
"librarian": run_librarian_stream,
|
||||
}
|
||||
|
||||
|
||||
class CoordinationEngine:
|
||||
"""
|
||||
Coordinates multi-agent task execution.
|
||||
|
||||
Routes tasks from Tatlock to appropriate expert agents,
|
||||
handles execution, and aggregates results.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the coordination engine."""
|
||||
self.registry = get_household_registry()
|
||||
logger.info("coordination_engine_initialized")
|
||||
|
||||
def get_available_agents(self) -> list[str]:
|
||||
"""
|
||||
Get list of available expert agents.
|
||||
|
||||
Returns:
|
||||
List of agent names that can accept delegations
|
||||
"""
|
||||
available = []
|
||||
for name in self.registry.list_members():
|
||||
member = self.registry.get_member(name)
|
||||
if member and member.agent is not None:
|
||||
available.append(name)
|
||||
return available
|
||||
|
||||
def can_delegate_to(self, agent_name: str) -> bool:
|
||||
"""
|
||||
Check if delegation to an agent is possible.
|
||||
|
||||
Args:
|
||||
agent_name: Name of the target agent
|
||||
|
||||
Returns:
|
||||
True if agent is available and can accept tasks
|
||||
"""
|
||||
if agent_name not in AGENT_EXECUTORS:
|
||||
return False
|
||||
|
||||
member = self.registry.get_member(agent_name)
|
||||
return member is not None and member.agent is not None
|
||||
|
||||
async def execute_delegation(
|
||||
self,
|
||||
intent: DelegationIntent,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
) -> AgentResponse:
|
||||
"""
|
||||
Execute a single delegation to an expert agent.
|
||||
|
||||
Args:
|
||||
intent: The delegation intent with task details
|
||||
context: Additional context for the agent
|
||||
message_history: Optional conversation history
|
||||
|
||||
Returns:
|
||||
AgentResponse with results
|
||||
|
||||
Raises:
|
||||
AgentUnavailableError: If agent is not available
|
||||
AgentTimeoutError: If execution times out
|
||||
AgentError: For other execution errors
|
||||
"""
|
||||
start_time = time.time()
|
||||
agent_name = intent.target_agent
|
||||
|
||||
logger.info(
|
||||
"delegation_started",
|
||||
agent=agent_name,
|
||||
task=intent.task[:100],
|
||||
reason=intent.reason.value,
|
||||
)
|
||||
|
||||
# Check if agent is available
|
||||
if not self.can_delegate_to(agent_name):
|
||||
raise AgentUnavailableError(
|
||||
f"Agent '{agent_name}' is not available for delegation",
|
||||
agent_name=agent_name,
|
||||
)
|
||||
|
||||
# Get the executor
|
||||
executor = AGENT_EXECUTORS.get(agent_name)
|
||||
if not executor:
|
||||
raise AgentUnavailableError(
|
||||
f"No executor found for agent '{agent_name}'",
|
||||
agent_name=agent_name,
|
||||
)
|
||||
|
||||
try:
|
||||
# Build the request
|
||||
request = AgentRequest(
|
||||
task=intent.task,
|
||||
context=context,
|
||||
delegation_reason=intent.reason,
|
||||
)
|
||||
|
||||
# Execute with timeout
|
||||
timeout = request.timeout_seconds or 60
|
||||
|
||||
result = await asyncio.wait_for(
|
||||
executor(
|
||||
task=request.task,
|
||||
context=request.context,
|
||||
message_history=message_history,
|
||||
),
|
||||
timeout=timeout,
|
||||
)
|
||||
|
||||
duration_ms = int((time.time() - start_time) * 1000)
|
||||
|
||||
logger.info(
|
||||
"delegation_completed",
|
||||
agent=agent_name,
|
||||
duration_ms=duration_ms,
|
||||
output_length=len(result),
|
||||
)
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
result=result,
|
||||
reasoning=f"Delegated to {agent_name}: {intent.expected_outcome}",
|
||||
duration_ms=duration_ms,
|
||||
)
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
duration_ms = int((time.time() - start_time) * 1000)
|
||||
logger.error(
|
||||
"delegation_timeout",
|
||||
agent=agent_name,
|
||||
duration_ms=duration_ms,
|
||||
)
|
||||
raise AgentTimeoutError(
|
||||
f"Agent '{agent_name}' timed out after {duration_ms}ms",
|
||||
agent_name=agent_name,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
duration_ms = int((time.time() - start_time) * 1000)
|
||||
logger.error(
|
||||
"delegation_error",
|
||||
agent=agent_name,
|
||||
error=str(e),
|
||||
duration_ms=duration_ms,
|
||||
exc_info=True,
|
||||
)
|
||||
return AgentResponse(
|
||||
success=False,
|
||||
result="",
|
||||
error_message=str(e),
|
||||
duration_ms=duration_ms,
|
||||
)
|
||||
|
||||
async def execute_delegation_stream(
|
||||
self,
|
||||
intent: DelegationIntent,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Execute a delegation with streaming output.
|
||||
|
||||
Args:
|
||||
intent: The delegation intent with task details
|
||||
context: Additional context for the agent
|
||||
message_history: Optional conversation history
|
||||
|
||||
Yields:
|
||||
Text deltas from the agent
|
||||
|
||||
Raises:
|
||||
AgentUnavailableError: If agent is not available
|
||||
"""
|
||||
agent_name = intent.target_agent
|
||||
|
||||
logger.info(
|
||||
"delegation_stream_started",
|
||||
agent=agent_name,
|
||||
task=intent.task[:100],
|
||||
)
|
||||
|
||||
# Check if agent is available
|
||||
if agent_name not in AGENT_STREAM_EXECUTORS:
|
||||
raise AgentUnavailableError(
|
||||
f"Agent '{agent_name}' does not support streaming",
|
||||
agent_name=agent_name,
|
||||
)
|
||||
|
||||
executor = AGENT_STREAM_EXECUTORS[agent_name]
|
||||
|
||||
try:
|
||||
async for delta in executor(
|
||||
task=intent.task,
|
||||
context=context,
|
||||
message_history=message_history,
|
||||
):
|
||||
yield delta
|
||||
|
||||
logger.info("delegation_stream_completed", agent=agent_name)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_stream_error",
|
||||
agent=agent_name,
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
yield f"\n\n[Error from {agent_name}: {str(e)}]"
|
||||
|
||||
async def coordinate(
|
||||
self,
|
||||
intents: list[DelegationIntent],
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
) -> CoordinationResult:
|
||||
"""
|
||||
Coordinate execution of multiple delegations.
|
||||
|
||||
Handles parallel execution for independent tasks and
|
||||
sequential execution for dependent tasks.
|
||||
|
||||
Args:
|
||||
intents: List of delegation intents to execute
|
||||
context: Shared context for all agents
|
||||
message_history: Optional conversation history
|
||||
|
||||
Returns:
|
||||
CoordinationResult with aggregated results
|
||||
"""
|
||||
start_time = time.time()
|
||||
agent_responses: dict[str, AgentResponse] = {}
|
||||
agents_consulted: list[str] = []
|
||||
|
||||
logger.info(
|
||||
"coordination_started",
|
||||
intent_count=len(intents),
|
||||
agents=[i.target_agent for i in intents],
|
||||
)
|
||||
|
||||
# Sort by priority
|
||||
sorted_intents = sorted(intents, key=lambda x: x.priority)
|
||||
|
||||
# Group by dependencies (simple version: sequential for now)
|
||||
# TODO: Implement parallel execution for independent tasks
|
||||
for intent in sorted_intents:
|
||||
try:
|
||||
response = await self.execute_delegation(
|
||||
intent=intent,
|
||||
context=context,
|
||||
message_history=message_history,
|
||||
)
|
||||
agent_responses[intent.target_agent] = response
|
||||
if response.success:
|
||||
agents_consulted.append(intent.target_agent)
|
||||
|
||||
except AgentError as e:
|
||||
agent_responses[intent.target_agent] = AgentResponse(
|
||||
success=False,
|
||||
result="",
|
||||
error_message=str(e),
|
||||
)
|
||||
|
||||
# Aggregate results
|
||||
successful_results = [
|
||||
r.result for r in agent_responses.values() if r.success and r.result
|
||||
]
|
||||
|
||||
final_response = "\n\n---\n\n".join(successful_results) if successful_results else ""
|
||||
|
||||
total_duration = int((time.time() - start_time) * 1000)
|
||||
|
||||
logger.info(
|
||||
"coordination_completed",
|
||||
total_duration_ms=total_duration,
|
||||
agents_consulted=agents_consulted,
|
||||
success_count=len(successful_results),
|
||||
)
|
||||
|
||||
return CoordinationResult(
|
||||
final_response=final_response,
|
||||
agent_responses=agent_responses,
|
||||
delegation_intents=intents,
|
||||
total_duration_ms=total_duration,
|
||||
agents_consulted=agents_consulted,
|
||||
)
|
||||
|
||||
|
||||
# Global coordination engine instance
|
||||
_coordination_engine: Optional[CoordinationEngine] = None
|
||||
|
||||
|
||||
def get_coordination_engine() -> CoordinationEngine:
|
||||
"""Get the global coordination engine instance."""
|
||||
global _coordination_engine
|
||||
if _coordination_engine is None:
|
||||
_coordination_engine = CoordinationEngine()
|
||||
return _coordination_engine
|
||||
|
||||
|
||||
async def delegate_to_librarian(
|
||||
task: str,
|
||||
context: str = "",
|
||||
reason: DelegationReason = DelegationReason.DOMAIN_EXPERTISE,
|
||||
message_history: Optional[list[Any]] = None,
|
||||
) -> AgentResponse:
|
||||
"""
|
||||
Convenience function to delegate a task to The Librarian.
|
||||
|
||||
Args:
|
||||
task: Research task description
|
||||
context: Additional context
|
||||
reason: Why delegating to Librarian
|
||||
message_history: Optional conversation history
|
||||
|
||||
Returns:
|
||||
AgentResponse with research results
|
||||
"""
|
||||
engine = get_coordination_engine()
|
||||
|
||||
intent = DelegationIntent(
|
||||
target_agent="librarian",
|
||||
task=task,
|
||||
reason=reason,
|
||||
expected_outcome="Research findings and relevant information",
|
||||
)
|
||||
|
||||
return await engine.execute_delegation(
|
||||
intent=intent,
|
||||
context=context,
|
||||
message_history=message_history,
|
||||
)
|
||||
|
||||
|
||||
async def delegate_to_librarian_stream(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Convenience function to delegate to Librarian with streaming.
|
||||
|
||||
Args:
|
||||
task: Research task description
|
||||
context: Additional context
|
||||
message_history: Optional conversation history
|
||||
|
||||
Yields:
|
||||
Text deltas from The Librarian
|
||||
"""
|
||||
engine = get_coordination_engine()
|
||||
|
||||
intent = DelegationIntent(
|
||||
target_agent="librarian",
|
||||
task=task,
|
||||
reason=DelegationReason.DOMAIN_EXPERTISE,
|
||||
expected_outcome="Research findings",
|
||||
)
|
||||
|
||||
async for delta in engine.execute_delegation_stream(
|
||||
intent=intent,
|
||||
context=context,
|
||||
message_history=message_history,
|
||||
):
|
||||
yield delta
|
||||
+105
-117
@@ -8,12 +8,14 @@ returns a structured result for synthesis.
|
||||
This implements the agent-as-tool pattern recommended by PydanticAI:
|
||||
agents call other agents via tool wrappers, keeping each agent focused.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import AsyncGenerator, Callable, Optional, Any
|
||||
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
from src.core.tracing import trace_span, SpanType
|
||||
from src.core.tracing import SpanType, trace_span
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
@@ -22,6 +24,7 @@ logger = get_logger(__name__)
|
||||
# Action Types for Think Slug Selection
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class ActionType(Enum):
|
||||
"""
|
||||
Categories of actions for selecting appropriate think messages.
|
||||
@@ -29,11 +32,12 @@ class ActionType(Enum):
|
||||
Each expert has different action types that warrant different
|
||||
butler-perspective messages to the user.
|
||||
"""
|
||||
RETRIEVE = "retrieve" # Looking up existing information
|
||||
RESEARCH = "research" # Conducting new research (web search, etc.)
|
||||
CREATE = "create" # Creating new content (pages, notes)
|
||||
CONTROL = "control" # Controlling devices/automations
|
||||
RECORD = "record" # Recording memories/notes
|
||||
|
||||
RETRIEVE = "retrieve" # Looking up existing information
|
||||
RESEARCH = "research" # Conducting new research (web search, etc.)
|
||||
CREATE = "create" # Creating new content (pages, notes)
|
||||
CONTROL = "control" # Controlling devices/automations
|
||||
RECORD = "record" # Recording memories/notes
|
||||
|
||||
|
||||
# =============================================================================
|
||||
@@ -126,6 +130,49 @@ def _detect_action_type(expert: str, task: str) -> ActionType:
|
||||
return ActionType.RETRIEVE
|
||||
|
||||
|
||||
def build_delegation_context(
|
||||
conversation_history: list[dict] | None,
|
||||
max_turns: int = 6,
|
||||
max_chars_per_turn: int = 500,
|
||||
) -> str:
|
||||
"""
|
||||
Format the most recent conversation turns as delegation context.
|
||||
|
||||
Experts accept a context string but the live paths never passed the
|
||||
in-scope conversation history; this trims it to the last few turns
|
||||
so follow-up questions ("and what about X?") keep their referent.
|
||||
|
||||
Args:
|
||||
conversation_history: Prior messages as {"role", "content"} dicts
|
||||
max_turns: How many trailing turns to include
|
||||
max_chars_per_turn: Truncation limit per turn
|
||||
|
||||
Returns:
|
||||
str: Newline-joined "role: content" lines ("" when no history)
|
||||
"""
|
||||
if not conversation_history:
|
||||
return ""
|
||||
|
||||
lines = []
|
||||
for msg in conversation_history[-max_turns:]:
|
||||
if not isinstance(msg, dict):
|
||||
continue
|
||||
role = msg.get("role", "user")
|
||||
content = msg.get("content", "")
|
||||
if isinstance(content, list):
|
||||
# Tolerate structured content parts
|
||||
content = " ".join(
|
||||
part.get("text", "") if isinstance(part, dict) else str(part) for part in content
|
||||
)
|
||||
content = str(content).strip()
|
||||
if content:
|
||||
lines.append(f"{role}: {content[:max_chars_per_turn]}")
|
||||
|
||||
if not lines:
|
||||
return ""
|
||||
return "Recent conversation:\n" + "\n".join(lines)
|
||||
|
||||
|
||||
def get_think_message(expert: str, task: str, phase: str) -> str:
|
||||
"""
|
||||
Get the appropriate think message for an expert delegation.
|
||||
@@ -161,19 +208,21 @@ class DelegationTask:
|
||||
depends_on: List of task IDs this task depends on
|
||||
result: Result from expert after execution
|
||||
"""
|
||||
|
||||
expert_name: str
|
||||
task: str
|
||||
context: str = ""
|
||||
action: str = ""
|
||||
priority: int = 0
|
||||
depends_on: list[str] = field(default_factory=list)
|
||||
result: Optional[str] = None
|
||||
result: str | None = None
|
||||
task_id: str = ""
|
||||
|
||||
def __post_init__(self):
|
||||
def __post_init__(self) -> None:
|
||||
"""Generate task ID if not provided."""
|
||||
if not self.task_id:
|
||||
import uuid
|
||||
|
||||
self.task_id = f"{self.expert_name}_{uuid.uuid4().hex[:8]}"
|
||||
|
||||
|
||||
@@ -186,14 +235,17 @@ class DelegationResult:
|
||||
expert_name: Which expert handled the task
|
||||
task: Original task description
|
||||
success: Whether the delegation succeeded
|
||||
output: Expert's response/findings
|
||||
error: Error message if failed
|
||||
output: Expert's response/findings. On failure this holds a
|
||||
curated, user-safe butler sentence (never exception detail)
|
||||
error: Short user-safe error label if failed. Exception detail
|
||||
stays in the logs only
|
||||
"""
|
||||
|
||||
expert_name: str
|
||||
task: str
|
||||
success: bool
|
||||
output: str
|
||||
error: Optional[str] = None
|
||||
error: str | None = None
|
||||
|
||||
|
||||
async def delegate_to_librarian(
|
||||
@@ -250,8 +302,14 @@ async def delegate_to_librarian(
|
||||
},
|
||||
) as span:
|
||||
try:
|
||||
# Use run() not run_stream() - avoids Ollama bug
|
||||
output = await run_librarian(task=task, context=context)
|
||||
# Use run() not run_stream() - avoids Ollama bug.
|
||||
# One timeout budget for the whole delegation - covers both
|
||||
# live paths (steward direct delegation and streaming), which
|
||||
# previously had no cap at all (SDK default ~600s per LLM call).
|
||||
output = await asyncio.wait_for(
|
||||
run_librarian(task=task, context=context),
|
||||
timeout=config.LIBRARIAN_TIMEOUT,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"delegation_to_librarian_completed",
|
||||
@@ -273,6 +331,28 @@ async def delegate_to_librarian(
|
||||
output=output,
|
||||
)
|
||||
|
||||
except TimeoutError:
|
||||
logger.error(
|
||||
"delegation_to_librarian_timeout",
|
||||
task=task[:50],
|
||||
timeout_seconds=config.LIBRARIAN_TIMEOUT,
|
||||
)
|
||||
|
||||
if span:
|
||||
span.metadata["success"] = False
|
||||
span.details["error"] = f"timed out after {config.LIBRARIAN_TIMEOUT}s"
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="librarian",
|
||||
task=task,
|
||||
success=False,
|
||||
output=(
|
||||
"I'm afraid the research took longer than expected "
|
||||
"and had to be abandoned, sir."
|
||||
),
|
||||
error="The Librarian did not respond within the time budget.",
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_to_librarian_error",
|
||||
@@ -285,12 +365,15 @@ async def delegate_to_librarian(
|
||||
span.metadata["success"] = False
|
||||
span.details["error"] = str(e)
|
||||
|
||||
# Exception detail stays in the logs; the user-facing output
|
||||
# is a curated butler sentence so internals never leak into
|
||||
# synthesis.
|
||||
return DelegationResult(
|
||||
expert_name="librarian",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
output=get_think_message("librarian", task, "error"),
|
||||
error="The Librarian was unable to complete the task.",
|
||||
)
|
||||
|
||||
|
||||
@@ -383,12 +466,13 @@ async def delegate_to_biographer(
|
||||
span.metadata["success"] = False
|
||||
span.details["error"] = str(e)
|
||||
|
||||
# Exception detail stays in the logs only.
|
||||
return DelegationResult(
|
||||
expert_name="biographer",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
output=get_think_message("biographer", task, "error"),
|
||||
error="The Biographer was unable to complete the task.",
|
||||
)
|
||||
|
||||
|
||||
@@ -480,112 +564,16 @@ async def delegate_to_housekeeper(
|
||||
span.metadata["success"] = False
|
||||
span.details["error"] = str(e)
|
||||
|
||||
# Exception detail stays in the logs only.
|
||||
return DelegationResult(
|
||||
expert_name="housekeeper",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
output=get_think_message("housekeeper", task, "error"),
|
||||
error="The Housekeeper was unable to complete the task.",
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Streaming Delegation Wrappers (with Think Messages)
|
||||
# =============================================================================
|
||||
|
||||
async def stream_delegate_to_librarian(
|
||||
task: str,
|
||||
context: str = "",
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Stream delegation to Librarian with automatic think messages.
|
||||
|
||||
Yields butler-perspective think messages before and after the delegation,
|
||||
allowing the UI to show progress to the user.
|
||||
|
||||
Args:
|
||||
task: Task description
|
||||
context: Additional context
|
||||
|
||||
Yields:
|
||||
str: Think messages and final result marker
|
||||
"""
|
||||
# Yield start message (deterministic)
|
||||
yield get_think_message("librarian", task, "start") + "\n"
|
||||
|
||||
# Execute delegation
|
||||
result = await delegate_to_librarian(task, context)
|
||||
|
||||
# Yield completion message (deterministic)
|
||||
if result.success:
|
||||
yield get_think_message("librarian", task, "success") + "\n"
|
||||
else:
|
||||
yield get_think_message("librarian", task, "error") + "\n"
|
||||
|
||||
# Yield result marker for extraction
|
||||
yield f"__DELEGATION_RESULT__:librarian:{result.output}"
|
||||
|
||||
|
||||
async def stream_delegate_to_biographer(
|
||||
task: str,
|
||||
context: str = "",
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Stream delegation to Biographer with automatic think messages.
|
||||
|
||||
Args:
|
||||
task: Task description
|
||||
context: Additional context
|
||||
|
||||
Yields:
|
||||
str: Think messages and final result marker
|
||||
"""
|
||||
yield get_think_message("biographer", task, "start") + "\n"
|
||||
|
||||
result = await delegate_to_biographer(task, context)
|
||||
|
||||
if result.success:
|
||||
yield get_think_message("biographer", task, "success") + "\n"
|
||||
else:
|
||||
yield get_think_message("biographer", task, "error") + "\n"
|
||||
|
||||
yield f"__DELEGATION_RESULT__:biographer:{result.output}"
|
||||
|
||||
|
||||
async def stream_delegate_to_housekeeper(
|
||||
task: str,
|
||||
context: str = "",
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Stream delegation to Housekeeper with automatic think messages.
|
||||
|
||||
Args:
|
||||
task: Task description
|
||||
context: Additional context
|
||||
|
||||
Yields:
|
||||
str: Think messages and final result marker
|
||||
"""
|
||||
yield get_think_message("housekeeper", task, "start") + "\n"
|
||||
|
||||
result = await delegate_to_housekeeper(task, context)
|
||||
|
||||
if result.success:
|
||||
yield get_think_message("housekeeper", task, "success") + "\n"
|
||||
else:
|
||||
yield get_think_message("housekeeper", task, "error") + "\n"
|
||||
|
||||
yield f"__DELEGATION_RESULT__:housekeeper:{result.output}"
|
||||
|
||||
|
||||
# Mapping of streaming delegation wrappers
|
||||
STREAMING_DELEGATION_WRAPPERS = {
|
||||
"librarian": stream_delegate_to_librarian,
|
||||
"biographer": stream_delegate_to_biographer,
|
||||
"housekeeper": stream_delegate_to_housekeeper,
|
||||
}
|
||||
|
||||
|
||||
# Future expert delegation wrappers will be added here:
|
||||
# - delegate_to_developer(task, context) -> DelegationResult
|
||||
# - delegate_to_secretary(task, context) -> DelegationResult
|
||||
|
||||
@@ -4,6 +4,7 @@ The Housekeeper - Home Automation Agent.
|
||||
Provides home automation capabilities through the core-api service,
|
||||
which wraps the Home Assistant REST API into LLM-friendly endpoints.
|
||||
"""
|
||||
|
||||
from src.agents.housekeeper.agent import run_housekeeper, run_housekeeper_stream
|
||||
from src.agents.housekeeper.capability import (
|
||||
HOUSEKEEPER_CAPABILITY,
|
||||
|
||||
@@ -8,7 +8,8 @@ the core-api service, which wraps Home Assistant REST API, offering:
|
||||
- Script execution
|
||||
- Automation management
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
|
||||
from typing import Any
|
||||
|
||||
from pydantic_ai import Agent
|
||||
|
||||
@@ -27,7 +28,6 @@ from src.agents.housekeeper.tools import (
|
||||
turn_off,
|
||||
turn_on,
|
||||
)
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
@@ -98,7 +98,7 @@ After completing actions, briefly confirm:
|
||||
"""
|
||||
|
||||
# Lazy initialization to avoid connection issues during imports
|
||||
_housekeeper_agent: Optional[Agent[None, str]] = None
|
||||
_housekeeper_agent: Agent[None, str] | None = None
|
||||
|
||||
|
||||
def _create_housekeeper_agent() -> Agent[None, str]:
|
||||
@@ -140,6 +140,7 @@ def _create_housekeeper_agent() -> Agent[None, str]:
|
||||
agent.tool_plain(get_history)
|
||||
|
||||
from src.anthropic.model_selector import get_model_info
|
||||
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"housekeeper_agent_created",
|
||||
@@ -167,7 +168,7 @@ def get_housekeeper_agent() -> Agent[None, str]:
|
||||
async def run_housekeeper(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
message_history: list[Any] | None = None,
|
||||
) -> str:
|
||||
"""
|
||||
Execute a home automation task with The Housekeeper.
|
||||
@@ -204,13 +205,13 @@ async def run_housekeeper(
|
||||
)
|
||||
|
||||
try:
|
||||
# Use temperature 0.1 for slight exploration
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
# Temperature 0.1 for slight exploration (skipped on Claude backend)
|
||||
from src.anthropic.model_selector import get_sampling_settings
|
||||
|
||||
result = await agent.run(
|
||||
prompt,
|
||||
message_history=message_history,
|
||||
model_settings=ModelSettings(temperature=0.1),
|
||||
model_settings=get_sampling_settings(0.1),
|
||||
)
|
||||
|
||||
logger.info(
|
||||
@@ -234,7 +235,7 @@ async def run_housekeeper(
|
||||
async def run_housekeeper_stream(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
message_history: list[Any] | None = None,
|
||||
):
|
||||
"""
|
||||
Execute a home automation task with streaming output.
|
||||
@@ -266,13 +267,13 @@ async def run_housekeeper_stream(
|
||||
)
|
||||
|
||||
try:
|
||||
# Use temperature 0.1 for slight exploration
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
# Temperature 0.1 for slight exploration (skipped on Claude backend)
|
||||
from src.anthropic.model_selector import get_sampling_settings
|
||||
|
||||
async with agent.run_stream(
|
||||
prompt,
|
||||
message_history=message_history,
|
||||
model_settings=ModelSettings(temperature=0.1),
|
||||
model_settings=get_sampling_settings(0.1),
|
||||
) as response:
|
||||
async for delta in response.stream_text(delta=True):
|
||||
yield delta
|
||||
|
||||
@@ -4,6 +4,7 @@ Housekeeper capability registration for the Household Registry.
|
||||
Defines The Housekeeper's capabilities and registers it as a
|
||||
household member for coordination by the Steward and Tatlock.
|
||||
"""
|
||||
|
||||
from src.agents.housekeeper.agent import get_housekeeper_agent
|
||||
from src.agents.housekeeper.tools import HOUSEKEEPER_TOOLS
|
||||
from src.core.household_registry import (
|
||||
|
||||
@@ -5,7 +5,8 @@ Provides async methods for home automation operations via Home Assistant.
|
||||
Core-API is a separate service that wraps the Home Assistant REST API
|
||||
into LLM-friendly endpoints.
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
from pydantic import BaseModel, Field
|
||||
@@ -28,7 +29,7 @@ class Device(BaseModel):
|
||||
name: str
|
||||
state: str
|
||||
domain: str
|
||||
area: Optional[str] = None
|
||||
area: str | None = None
|
||||
attributes: dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
|
||||
@@ -38,8 +39,8 @@ class DeviceState(BaseModel):
|
||||
entity_id: str
|
||||
state: str
|
||||
attributes: dict[str, Any] = Field(default_factory=dict)
|
||||
last_changed: Optional[str] = None
|
||||
last_updated: Optional[str] = None
|
||||
last_changed: str | None = None
|
||||
last_updated: str | None = None
|
||||
|
||||
|
||||
class Scene(BaseModel):
|
||||
@@ -47,7 +48,7 @@ class Scene(BaseModel):
|
||||
|
||||
entity_id: str
|
||||
name: str
|
||||
friendly_name: Optional[str] = None
|
||||
friendly_name: str | None = None
|
||||
|
||||
|
||||
class Script(BaseModel):
|
||||
@@ -55,8 +56,8 @@ class Script(BaseModel):
|
||||
|
||||
entity_id: str
|
||||
name: str
|
||||
description: Optional[str] = None
|
||||
last_triggered: Optional[str] = None
|
||||
description: str | None = None
|
||||
last_triggered: str | None = None
|
||||
|
||||
|
||||
class Automation(BaseModel):
|
||||
@@ -64,8 +65,8 @@ class Automation(BaseModel):
|
||||
|
||||
entity_id: str
|
||||
name: str
|
||||
state: str = "on"
|
||||
last_triggered: Optional[str] = None
|
||||
state: str = "on"
|
||||
last_triggered: str | None = None
|
||||
|
||||
|
||||
class HistoryEntry(BaseModel):
|
||||
@@ -109,8 +110,8 @@ class CoreAPIClient:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_url: Optional[str] = None,
|
||||
api_key: Optional[str] = None,
|
||||
base_url: str | None = None,
|
||||
api_key: str | None = None,
|
||||
timeout: int = 30,
|
||||
):
|
||||
"""
|
||||
@@ -124,7 +125,7 @@ class CoreAPIClient:
|
||||
self.base_url = base_url or str(config.CORE_API_HOST)
|
||||
self.api_key = api_key or config.CORE_API_KEY
|
||||
self.timeout = timeout
|
||||
self._client: Optional[httpx.AsyncClient] = None
|
||||
self._client: httpx.AsyncClient | None = None
|
||||
|
||||
async def __aenter__(self) -> "CoreAPIClient":
|
||||
"""Create HTTP client on context entry."""
|
||||
@@ -159,8 +160,8 @@ class CoreAPIClient:
|
||||
|
||||
async def list_devices(
|
||||
self,
|
||||
domain: Optional[str] = None,
|
||||
area: Optional[str] = None,
|
||||
domain: str | None = None,
|
||||
area: str | None = None,
|
||||
) -> list[Device]:
|
||||
"""
|
||||
List devices, optionally filtered by domain or area.
|
||||
@@ -231,9 +232,9 @@ class CoreAPIClient:
|
||||
async def turn_on(
|
||||
self,
|
||||
entity_id: str,
|
||||
brightness: Optional[int] = None,
|
||||
color_temp: Optional[int] = None,
|
||||
rgb_color: Optional[tuple[int, int, int]] = None,
|
||||
brightness: int | None = None,
|
||||
color_temp: int | None = None,
|
||||
rgb_color: tuple[int, int, int] | None = None,
|
||||
) -> ControlResult:
|
||||
"""
|
||||
Turn on a device.
|
||||
@@ -399,7 +400,7 @@ class CoreAPIClient:
|
||||
async def run_script(
|
||||
self,
|
||||
script_id: str,
|
||||
variables: Optional[dict[str, Any]] = None,
|
||||
variables: dict[str, Any] | None = None,
|
||||
) -> ControlResult:
|
||||
"""
|
||||
Run a script.
|
||||
|
||||
@@ -4,6 +4,7 @@ Housekeeper tools for PydanticAI agent.
|
||||
These tools wrap the core-api service and are registered with
|
||||
The Housekeeper agent for home automation tasks.
|
||||
"""
|
||||
|
||||
from src.agents.housekeeper.client import CoreAPIClient
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
@@ -72,14 +73,24 @@ async def list_devices(
|
||||
return True
|
||||
return False
|
||||
|
||||
sorted_devices = sorted(dom_devices, key=lambda d: (not is_room_group(d), d.entity_id))
|
||||
sorted_devices = sorted(
|
||||
dom_devices, key=lambda d: (not is_room_group(d), d.entity_id)
|
||||
)
|
||||
|
||||
for device in sorted_devices:
|
||||
state_icon = "on" if device.state == "on" else "off" if device.state == "off" else device.state
|
||||
state_icon = (
|
||||
"on"
|
||||
if device.state == "on"
|
||||
else "off"
|
||||
if device.state == "off"
|
||||
else device.state
|
||||
)
|
||||
area_str = f" ({device.area})" if device.area else ""
|
||||
# Mark room groups clearly using actual HA data
|
||||
group_marker = " [ROOM GROUP]" if is_room_group(device) else ""
|
||||
output_parts.append(f"- **{device.name}**{area_str}{group_marker}: {state_icon}")
|
||||
output_parts.append(
|
||||
f"- **{device.name}**{area_str}{group_marker}: {state_icon}"
|
||||
)
|
||||
output_parts.append(f" ID: `{device.entity_id}`")
|
||||
output_parts.append("")
|
||||
|
||||
|
||||
@@ -7,10 +7,10 @@ Connects to the library-desk API to provide:
|
||||
- Knowledge graph queries
|
||||
- Semantic search
|
||||
"""
|
||||
|
||||
from src.agents.librarian.agent import (
|
||||
get_librarian_agent,
|
||||
run_librarian,
|
||||
run_librarian_stream,
|
||||
)
|
||||
from src.agents.librarian.capability import (
|
||||
LIBRARIAN_CAPABILITY,
|
||||
@@ -26,5 +26,4 @@ __all__ = [
|
||||
"register_librarian",
|
||||
"unregister_librarian",
|
||||
"run_librarian",
|
||||
"run_librarian_stream",
|
||||
]
|
||||
|
||||
@@ -7,10 +7,12 @@ the library-desk API, offering:
|
||||
- Wiki and document management
|
||||
- Semantic search and knowledge graph exploration
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
|
||||
from typing import Any
|
||||
|
||||
from pydantic_ai import Agent
|
||||
|
||||
from src.agents.librarian.client import library_client_session
|
||||
from src.agents.librarian.tools import (
|
||||
create_wiki_page,
|
||||
explore_knowledge_graph,
|
||||
@@ -27,7 +29,7 @@ from src.agents.librarian.tools import (
|
||||
smart_create_wiki_page,
|
||||
update_wiki_page,
|
||||
)
|
||||
from src.core.config import config
|
||||
from src.agents.protocol import AgentError
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
@@ -137,8 +139,32 @@ If a tool fails or you cannot access a data source:
|
||||
- It is better to return no information than to return fabricated information
|
||||
"""
|
||||
|
||||
|
||||
# Tool-phase prompt actually used by the agent. The scholarly persona prompt
|
||||
# above suppresses tool calling on small local models (gemma4 answers in
|
||||
# character - "please provide your request" - without ever calling a tool),
|
||||
# the same pathology TATLOCK_ORCHESTRATION_PROMPT fixed for the butler.
|
||||
# Tatlock's synthesis phase supplies the user-facing voice, so the research
|
||||
# phase only needs tool discipline. Kept: the anti-fabrication rule.
|
||||
LIBRARIAN_TASK_PROMPT = """You are The Librarian, the research executor of the \
|
||||
Tatlock household. Your only job is to gather accurate findings by calling the \
|
||||
provided tools.
|
||||
|
||||
- ALWAYS use tools - never answer a research task from memory alone.
|
||||
- Research or wiki questions: call hybrid_search first; then search_wiki and \
|
||||
get_wiki_page to read specific pages BEFORE summarizing them.
|
||||
- Current or external information (weather, news, live facts): call search_web; \
|
||||
call read_url when given a specific URL.
|
||||
- Wiki writing: smart_create_wiki_page when asked for a page about a topic; \
|
||||
create_wiki_page only for user-provided verbatim content; update_wiki_page for \
|
||||
edits (search_wiki, then get_wiki_page, then update).
|
||||
- Reply with a concise factual summary of what the tools returned, citing page \
|
||||
titles and URLs. A later step writes the polished answer, so no personality.
|
||||
- NEVER fabricate. If a tool fails or returns nothing, state exactly what you \
|
||||
could not retrieve and stop."""
|
||||
|
||||
# Lazy initialization to avoid connection issues during imports
|
||||
_librarian_agent: Optional[Agent[None, str]] = None
|
||||
_librarian_agent: Agent[None, str] | None = None
|
||||
|
||||
|
||||
def _create_librarian_agent() -> Agent[None, str]:
|
||||
@@ -150,7 +176,7 @@ def _create_librarian_agent() -> Agent[None, str]:
|
||||
|
||||
agent: Agent[None, str] = Agent(
|
||||
model=model,
|
||||
system_prompt=LIBRARIAN_SYSTEM_PROMPT,
|
||||
system_prompt=LIBRARIAN_TASK_PROMPT,
|
||||
retries=2,
|
||||
)
|
||||
|
||||
@@ -177,6 +203,7 @@ def _create_librarian_agent() -> Agent[None, str]:
|
||||
agent.tool_plain(smart_create_wiki_page)
|
||||
|
||||
from src.anthropic.model_selector import get_model_info
|
||||
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"librarian_agent_created",
|
||||
@@ -204,7 +231,7 @@ def get_librarian_agent() -> Agent[None, str]:
|
||||
async def run_librarian(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
message_history: list[Any] | None = None,
|
||||
) -> str:
|
||||
"""
|
||||
Execute a research task with The Librarian.
|
||||
@@ -220,6 +247,10 @@ async def run_librarian(
|
||||
Returns:
|
||||
Research results and findings
|
||||
|
||||
Raises:
|
||||
AgentError: If the research task fails. Exception detail is
|
||||
logged here; callers map the failure to a user-safe message.
|
||||
|
||||
Example:
|
||||
result = await run_librarian(
|
||||
task="Find information about Docker networking",
|
||||
@@ -241,10 +272,12 @@ async def run_librarian(
|
||||
)
|
||||
|
||||
try:
|
||||
result = await agent.run(
|
||||
prompt,
|
||||
message_history=message_history,
|
||||
)
|
||||
# One shared library-desk connection for all tool calls in this run
|
||||
async with library_client_session():
|
||||
result = await agent.run(
|
||||
prompt,
|
||||
message_history=message_history,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"librarian_task_completed",
|
||||
@@ -255,64 +288,12 @@ async def run_librarian(
|
||||
return result.output
|
||||
|
||||
except Exception as e:
|
||||
# Full detail stays in the logs; callers receive a structured
|
||||
# failure instead of error text masquerading as research output.
|
||||
logger.error(
|
||||
"librarian_task_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
return f"The Librarian encountered an error: {str(e)}"
|
||||
|
||||
|
||||
async def run_librarian_stream(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
):
|
||||
"""
|
||||
Execute a research task with streaming output.
|
||||
|
||||
Yields text deltas as The Librarian generates the response.
|
||||
|
||||
Args:
|
||||
task: The research task or question
|
||||
context: Additional context from conversation
|
||||
message_history: Optional conversation history
|
||||
|
||||
Yields:
|
||||
str: Text deltas from the response
|
||||
|
||||
Example:
|
||||
async for delta in run_librarian_stream("Find Docker docs"):
|
||||
print(delta, end="", flush=True)
|
||||
"""
|
||||
agent = get_librarian_agent()
|
||||
|
||||
# Build prompt with context if provided
|
||||
prompt = task
|
||||
if context:
|
||||
prompt = f"Context: {context}\n\nTask: {task}"
|
||||
|
||||
logger.info(
|
||||
"librarian_stream_started",
|
||||
task=task[:100],
|
||||
)
|
||||
|
||||
try:
|
||||
async with agent.run_stream(
|
||||
prompt,
|
||||
message_history=message_history,
|
||||
) as response:
|
||||
async for delta in response.stream_text(delta=True):
|
||||
yield delta
|
||||
|
||||
logger.info("librarian_stream_completed", task=task[:50])
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"librarian_stream_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
yield f"\n\nThe Librarian encountered an error: {str(e)}"
|
||||
raise AgentError("Research task failed", agent_name="librarian") from e
|
||||
|
||||
@@ -4,6 +4,7 @@ Librarian capability registration for the Household Registry.
|
||||
Defines The Librarian's capabilities and registers it as a
|
||||
household member for coordination by the Steward and Tatlock.
|
||||
"""
|
||||
|
||||
from src.agents.librarian.agent import get_librarian_agent
|
||||
from src.agents.librarian.tools import LIBRARIAN_TOOLS
|
||||
from src.core.household_registry import (
|
||||
|
||||
+317
-95
@@ -7,45 +7,65 @@ Provides async methods for all relevant library-desk endpoints:
|
||||
- Vector search
|
||||
- Knowledge graph queries
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
|
||||
import asyncio
|
||||
from collections.abc import AsyncIterator, Awaitable, Callable
|
||||
from contextlib import asynccontextmanager
|
||||
from contextvars import ContextVar
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from src.core.config import config
|
||||
from src.core.context import get_user
|
||||
from src.core.context import apply_tenant_guard, get_user
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
# Retry policy for idempotent/read-only requests (GETs, POST /query/*,
|
||||
# POST /rag/search). Writes are never retried.
|
||||
_RETRY_ATTEMPTS = 2
|
||||
_RETRY_BACKOFF_SECONDS = 0.5
|
||||
_RETRYABLE_STATUS_CODES = {502, 503, 504}
|
||||
|
||||
# One shared HTTP connection per librarian run (see library_client_session)
|
||||
_shared_http_client: ContextVar[httpx.AsyncClient | None] = ContextVar(
|
||||
"library_desk_http_client", default=None
|
||||
)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Response Models
|
||||
# ============================================================================
|
||||
|
||||
|
||||
class WikiPage(BaseModel):
|
||||
"""Wiki page from library-desk."""
|
||||
|
||||
id: int
|
||||
path: str
|
||||
title: str
|
||||
description: Optional[str] = None
|
||||
content: Optional[str] = None
|
||||
description: str | None = None
|
||||
content: str | None = None
|
||||
tags: list[str] = Field(default_factory=list)
|
||||
created_at: Optional[str] = None
|
||||
updated_at: Optional[str] = None
|
||||
created_at: str | None = None
|
||||
updated_at: str | None = None
|
||||
|
||||
|
||||
class WikiSearchResult(BaseModel):
|
||||
"""Search result from wiki search."""
|
||||
|
||||
id: int
|
||||
path: str
|
||||
title: str
|
||||
description: Optional[str] = None
|
||||
locale: Optional[str] = None
|
||||
description: str | None = None
|
||||
locale: str | None = None
|
||||
|
||||
|
||||
class VectorSearchResult(BaseModel):
|
||||
"""Result from semantic vector search."""
|
||||
|
||||
page_id: int
|
||||
page_path: str
|
||||
page_title: str
|
||||
@@ -56,28 +76,42 @@ class VectorSearchResult(BaseModel):
|
||||
|
||||
class HybridSearchResult(BaseModel):
|
||||
"""Result from HybridRAG search."""
|
||||
source: str # "vector", "graph", "web"
|
||||
|
||||
source: str # source_type: "wiki", "web", "volatile", "document"
|
||||
sources: list[str] = Field(
|
||||
default_factory=list
|
||||
) # legs that found it: "vector", "graph", "web", ...
|
||||
title: str
|
||||
content: str
|
||||
url: Optional[str] = None
|
||||
score: float
|
||||
page_id: Optional[int] = None
|
||||
url: str | None = None
|
||||
score: float # rrf_score from the live service
|
||||
page_id: int | None = None
|
||||
related_dossiers: list[dict[str, Any]] = Field(default_factory=list)
|
||||
metadata: dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class HybridRAGResponse(BaseModel):
|
||||
"""Full response from HybridRAG query."""
|
||||
|
||||
results: list[HybridSearchResult] = Field(default_factory=list)
|
||||
keywords: list[str] = Field(default_factory=list)
|
||||
synonyms: list[str] = Field(default_factory=list)
|
||||
related_dossiers: list[str] = Field(default_factory=list)
|
||||
formatted_context: str = ""
|
||||
search_id: Optional[str] = None
|
||||
search_id: str | None = None
|
||||
source_counts: dict[str, int] = Field(default_factory=dict)
|
||||
timing: dict[str, float] = Field(default_factory=dict)
|
||||
# Additive degradation contract - only newer library-desk versions
|
||||
# send these; absence means "no status reported", not "healthy".
|
||||
# Maps each leg (vector/graph/web/volatile/documents) to
|
||||
# "ok" | "failed" | "disabled".
|
||||
source_status: dict[str, str] = Field(default_factory=dict)
|
||||
degraded: bool = False
|
||||
|
||||
|
||||
class GraphNode(BaseModel):
|
||||
"""Node from knowledge graph."""
|
||||
|
||||
id: str
|
||||
labels: list[str] = Field(default_factory=list)
|
||||
properties: dict[str, Any] = Field(default_factory=dict)
|
||||
@@ -85,12 +119,14 @@ class GraphNode(BaseModel):
|
||||
|
||||
class Dossier(BaseModel):
|
||||
"""A dossier (tag-based collection)."""
|
||||
|
||||
name: str
|
||||
page_count: int
|
||||
|
||||
|
||||
class ResearchSummary(BaseModel):
|
||||
"""Summary of research performed during smart-create."""
|
||||
|
||||
wiki_results: int = 0
|
||||
web_results: int = 0
|
||||
graph_entities: int = 0
|
||||
@@ -100,16 +136,18 @@ class ResearchSummary(BaseModel):
|
||||
|
||||
class WebSearchResult(BaseModel):
|
||||
"""Result from web search via /rag/search."""
|
||||
|
||||
title: str
|
||||
url: str
|
||||
content: str = "" # Full extracted text via Trafilatura
|
||||
snippet: str = "" # Original search engine snippet
|
||||
source: str = "" # Domain name
|
||||
published_date: Optional[str] = None
|
||||
published_date: str | None = None
|
||||
|
||||
|
||||
class WebSearchResponse(BaseModel):
|
||||
"""Response from /rag/search endpoint."""
|
||||
|
||||
query: str
|
||||
search_type: str
|
||||
results: list[WebSearchResult] = Field(default_factory=list)
|
||||
@@ -120,18 +158,20 @@ class WebSearchResponse(BaseModel):
|
||||
|
||||
class ContentExtractionResult(BaseModel):
|
||||
"""Result from content extraction."""
|
||||
|
||||
url: str
|
||||
title: Optional[str] = None
|
||||
title: str | None = None
|
||||
content: str = ""
|
||||
author: Optional[str] = None
|
||||
date: Optional[str] = None
|
||||
language: Optional[str] = None
|
||||
author: str | None = None
|
||||
date: str | None = None
|
||||
language: str | None = None
|
||||
success: bool = True
|
||||
error: Optional[str] = None
|
||||
error: str | None = None
|
||||
|
||||
|
||||
class BatchExtractionResponse(BaseModel):
|
||||
"""Response from batch content extraction."""
|
||||
|
||||
results: list[ContentExtractionResult] = Field(default_factory=list)
|
||||
total_urls: int = 0
|
||||
successful: int = 0
|
||||
@@ -141,6 +181,7 @@ class BatchExtractionResponse(BaseModel):
|
||||
|
||||
class EntityLinking(BaseModel):
|
||||
"""Entity linking results from smart-create."""
|
||||
|
||||
forward_links: int = 0
|
||||
backward_links: int = 0
|
||||
pages_updated: int = 0
|
||||
@@ -148,10 +189,11 @@ class EntityLinking(BaseModel):
|
||||
|
||||
class SmartCreateResponse(BaseModel):
|
||||
"""Response from smart-create wiki page endpoint."""
|
||||
|
||||
page: WikiPage
|
||||
research_summary: ResearchSummary = Field(default_factory=ResearchSummary)
|
||||
sources_used: int = 0
|
||||
search_id: Optional[str] = None
|
||||
search_id: str | None = None
|
||||
entity_linking: EntityLinking = Field(default_factory=EntityLinking)
|
||||
|
||||
|
||||
@@ -159,6 +201,7 @@ class SmartCreateResponse(BaseModel):
|
||||
# Client
|
||||
# ============================================================================
|
||||
|
||||
|
||||
class LibraryDeskClient:
|
||||
"""
|
||||
Async HTTP client for Library-Desk API.
|
||||
@@ -170,9 +213,9 @@ class LibraryDeskClient:
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_url: Optional[str] = None,
|
||||
api_key: Optional[str] = None,
|
||||
timeout: int = 60,
|
||||
base_url: str | None = None,
|
||||
api_key: str | None = None,
|
||||
timeout: int | None = None,
|
||||
):
|
||||
"""
|
||||
Initialize the client.
|
||||
@@ -181,30 +224,55 @@ class LibraryDeskClient:
|
||||
base_url: Library-desk API URL (defaults to config)
|
||||
api_key: API key for authentication (defaults to config)
|
||||
timeout: Request timeout in seconds
|
||||
(defaults to config.LIBRARY_DESK_TIMEOUT)
|
||||
"""
|
||||
self.base_url = base_url or str(config.LIBRARY_DESK_HOST)
|
||||
self.api_key = api_key or config.LIBRARY_DESK_API_KEY
|
||||
self.timeout = timeout
|
||||
self._client: Optional[httpx.AsyncClient] = None
|
||||
self.timeout = timeout if timeout is not None else config.LIBRARY_DESK_TIMEOUT
|
||||
self._client: httpx.AsyncClient | None = None
|
||||
self._owns_client = False
|
||||
|
||||
async def __aenter__(self) -> "LibraryDeskClient":
|
||||
"""Create HTTP client on context entry."""
|
||||
def _build_http_client(self) -> httpx.AsyncClient:
|
||||
"""Build a configured httpx client."""
|
||||
headers = {}
|
||||
if self.api_key:
|
||||
headers["Authorization"] = f"Bearer {self.api_key}"
|
||||
|
||||
self._client = httpx.AsyncClient(
|
||||
return httpx.AsyncClient(
|
||||
base_url=self.base_url,
|
||||
headers=headers,
|
||||
timeout=self.timeout,
|
||||
)
|
||||
|
||||
def _uses_default_target(self) -> bool:
|
||||
"""Whether this client targets the configured library-desk instance."""
|
||||
return (
|
||||
self.base_url == str(config.LIBRARY_DESK_HOST)
|
||||
and self.api_key == config.LIBRARY_DESK_API_KEY
|
||||
)
|
||||
|
||||
async def __aenter__(self) -> "LibraryDeskClient":
|
||||
"""
|
||||
Acquire an HTTP client on context entry.
|
||||
|
||||
Reuses the run-level shared connection (see library_client_session)
|
||||
when one is active, instead of constructing a new client per call.
|
||||
"""
|
||||
shared = _shared_http_client.get()
|
||||
if shared is not None and not shared.is_closed and self._uses_default_target():
|
||||
self._client = shared
|
||||
self._owns_client = False
|
||||
else:
|
||||
self._client = self._build_http_client()
|
||||
self._owns_client = True
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
|
||||
"""Close HTTP client on context exit."""
|
||||
if self._client:
|
||||
"""Close HTTP client on context exit (only if we own it)."""
|
||||
if self._client and self._owns_client:
|
||||
await self._client.aclose()
|
||||
self._client = None
|
||||
self._client = None
|
||||
self._owns_client = False
|
||||
|
||||
def _ensure_client(self) -> httpx.AsyncClient:
|
||||
"""Ensure client is initialized."""
|
||||
@@ -214,6 +282,69 @@ class LibraryDeskClient:
|
||||
)
|
||||
return self._client
|
||||
|
||||
def _resolve_user(self, user: str | None) -> str:
|
||||
"""
|
||||
Resolve the effective tenant for a request and require it non-empty.
|
||||
|
||||
Library-desk is removing its server-side default user, so every
|
||||
request must carry an explicit tenant (a missing user will 422).
|
||||
An empty tenant is a programming or configuration error - fail
|
||||
loudly here, before any bytes hit the wire.
|
||||
|
||||
Explicit user arguments are stripped and routed through the same
|
||||
tenant guard as context resolution (get_user() already applies
|
||||
it), so a dev environment can never send the production tenant -
|
||||
or a sanitization-collision variant of it - to library-desk.
|
||||
"""
|
||||
effective = (user if user is not None else get_user()).strip()
|
||||
if not effective:
|
||||
raise ValueError(
|
||||
"library-desk request requires a non-empty user (tenant); "
|
||||
"got an empty value from the caller or request context"
|
||||
)
|
||||
return apply_tenant_guard(effective)
|
||||
|
||||
async def _request_with_retry(
|
||||
self,
|
||||
send: Callable[[], Awaitable[httpx.Response]],
|
||||
description: str,
|
||||
) -> httpx.Response:
|
||||
"""
|
||||
Send an idempotent/read-only request with a bounded retry.
|
||||
|
||||
Retries once (2 attempts total) with a short backoff on transport
|
||||
errors and retryable 5xx statuses. Only used for GETs and the
|
||||
read-only POST /query/* and /rag/search endpoints - never for
|
||||
wiki writes.
|
||||
"""
|
||||
for attempt in range(1, _RETRY_ATTEMPTS + 1):
|
||||
try:
|
||||
response = await send()
|
||||
except httpx.TransportError as e:
|
||||
if attempt >= _RETRY_ATTEMPTS:
|
||||
raise
|
||||
logger.warning(
|
||||
"library_desk_retry",
|
||||
request=description,
|
||||
error=str(e),
|
||||
attempt=attempt,
|
||||
)
|
||||
else:
|
||||
if (
|
||||
response.status_code not in _RETRYABLE_STATUS_CODES
|
||||
or attempt >= _RETRY_ATTEMPTS
|
||||
):
|
||||
return response
|
||||
logger.warning(
|
||||
"library_desk_retry",
|
||||
request=description,
|
||||
status_code=response.status_code,
|
||||
attempt=attempt,
|
||||
)
|
||||
await asyncio.sleep(_RETRY_BACKOFF_SECONDS * attempt)
|
||||
|
||||
raise RuntimeError("unreachable") # pragma: no cover
|
||||
|
||||
# ========================================================================
|
||||
# HybridRAG
|
||||
# ========================================================================
|
||||
@@ -247,17 +378,19 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
HybridRAGResponse with ranked results and context
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
# The live service requires all limits >= 1 (422 otherwise);
|
||||
# legs are disabled via the enable_* flags, not a zero limit.
|
||||
payload = {
|
||||
"query": query,
|
||||
"config": {
|
||||
"vector_limit": vector_limit,
|
||||
"graph_limit": graph_limit,
|
||||
"web_limit": web_limit,
|
||||
"document_limit": document_limit,
|
||||
"volatile_limit": volatile_limit,
|
||||
"vector_limit": max(vector_limit, 1),
|
||||
"graph_limit": max(graph_limit, 1),
|
||||
"web_limit": max(web_limit, 1),
|
||||
"document_limit": max(document_limit, 1),
|
||||
"volatile_limit": max(volatile_limit, 1),
|
||||
"enable_documents": document_limit > 0,
|
||||
"enable_volatile": volatile_limit > 0,
|
||||
"enable_web": web_limit > 0,
|
||||
@@ -268,43 +401,71 @@ class LibraryDeskClient:
|
||||
|
||||
logger.info("library_desk_hybrid_search", query=query, user=user)
|
||||
|
||||
response = await client.post(
|
||||
"/query/hybrid",
|
||||
json=payload,
|
||||
params={"user": user},
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.post(
|
||||
"/query/hybrid",
|
||||
json=payload,
|
||||
params={"user": user},
|
||||
),
|
||||
"POST /query/hybrid",
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
|
||||
# Parse results
|
||||
# Parse results (live field names: source_type, sources, rrf_score,
|
||||
# related_dossiers; older names kept as fallbacks)
|
||||
results = []
|
||||
for r in data.get("results", []):
|
||||
results.append(HybridSearchResult(
|
||||
source=r.get("source", "unknown"),
|
||||
title=r.get("title", ""),
|
||||
content=r.get("content", ""),
|
||||
url=r.get("url"),
|
||||
score=r.get("score", 0.0),
|
||||
page_id=r.get("page_id"),
|
||||
metadata=r.get("metadata", {}),
|
||||
))
|
||||
results.append(
|
||||
HybridSearchResult(
|
||||
source=r.get("source_type") or r.get("source", "unknown"),
|
||||
sources=r.get("sources", []),
|
||||
title=r.get("title", ""),
|
||||
content=r.get("content", ""),
|
||||
url=r.get("url"),
|
||||
score=r.get("rrf_score", r.get("score", 0.0)),
|
||||
page_id=r.get("page_id"),
|
||||
related_dossiers=r.get("related_dossiers", []),
|
||||
metadata=r.get("metadata", {}),
|
||||
)
|
||||
)
|
||||
|
||||
# Handle keywords being either a list or a dict with core_keywords
|
||||
# Handle keywords being either a list or a dict with core_keywords;
|
||||
# the live service nests synonyms inside the keywords dict as a
|
||||
# {term: [synonyms]} map.
|
||||
raw_keywords = data.get("keywords", [])
|
||||
raw_synonyms: Any = data.get("synonyms", [])
|
||||
if isinstance(raw_keywords, dict):
|
||||
keywords = raw_keywords.get("core_keywords", [])
|
||||
raw_synonyms = raw_keywords.get("synonyms", {})
|
||||
else:
|
||||
keywords = raw_keywords
|
||||
if isinstance(raw_synonyms, dict):
|
||||
synonyms = [s for values in raw_synonyms.values() for s in values]
|
||||
else:
|
||||
synonyms = raw_synonyms
|
||||
|
||||
# Aggregate per-result related dossiers into unique top-level titles
|
||||
related_dossiers: list[str] = []
|
||||
for result in results:
|
||||
for dossier in result.related_dossiers:
|
||||
title = dossier.get("title", "")
|
||||
if title and title not in related_dossiers:
|
||||
related_dossiers.append(title)
|
||||
|
||||
return HybridRAGResponse(
|
||||
results=results,
|
||||
keywords=keywords,
|
||||
synonyms=data.get("synonyms", []),
|
||||
related_dossiers=data.get("related_dossiers", []),
|
||||
formatted_context=data.get("formatted_context", ""),
|
||||
synonyms=synonyms,
|
||||
related_dossiers=related_dossiers,
|
||||
formatted_context=data.get("context", data.get("formatted_context", "")),
|
||||
search_id=data.get("search_id"),
|
||||
source_counts=data.get("source_counts", {}),
|
||||
timing=data.get("timing", {}),
|
||||
# Additive fields - tolerate absence on older library-desk
|
||||
source_status=data.get("source_status") or {},
|
||||
degraded=bool(data.get("degraded", False)),
|
||||
)
|
||||
|
||||
# ========================================================================
|
||||
@@ -328,14 +489,17 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
List of matching wiki pages
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
logger.debug("library_desk_wiki_search", query=query, user=user)
|
||||
|
||||
response = await client.get(
|
||||
"/wiki/search",
|
||||
params={"q": query, "user": user, "limit": limit},
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.get(
|
||||
"/wiki/search",
|
||||
params={"q": query, "user": user, "limit": limit},
|
||||
),
|
||||
"GET /wiki/search",
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
@@ -357,12 +521,15 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
WikiPage with full content
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
response = await client.get(
|
||||
f"/wiki/pages/{page_id}",
|
||||
params={"user": user},
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.get(
|
||||
f"/wiki/pages/{page_id}",
|
||||
params={"user": user},
|
||||
),
|
||||
f"GET /wiki/pages/{page_id}",
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
@@ -371,7 +538,7 @@ class LibraryDeskClient:
|
||||
async def list_wiki_pages(
|
||||
self,
|
||||
user: str | None = None,
|
||||
tag: Optional[str] = None,
|
||||
tag: str | None = None,
|
||||
limit: int = 50,
|
||||
) -> list[WikiPage]:
|
||||
"""
|
||||
@@ -385,14 +552,17 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
List of wiki pages
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
params: dict[str, Any] = {"user": user, "limit": limit}
|
||||
if tag:
|
||||
params["tag"] = tag
|
||||
|
||||
response = await client.get("/wiki/pages", params=params)
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.get("/wiki/pages", params=params),
|
||||
"GET /wiki/pages",
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -405,7 +575,7 @@ class LibraryDeskClient:
|
||||
content: str,
|
||||
user: str | None = None,
|
||||
description: str = "",
|
||||
tags: Optional[list[str]] = None,
|
||||
tags: list[str] | None = None,
|
||||
) -> WikiPage:
|
||||
"""
|
||||
Create a new wiki page.
|
||||
@@ -421,7 +591,7 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
Created WikiPage
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
@@ -444,10 +614,10 @@ class LibraryDeskClient:
|
||||
self,
|
||||
page_id: int,
|
||||
user: str | None = None,
|
||||
content: Optional[str] = None,
|
||||
title: Optional[str] = None,
|
||||
tags: Optional[list[str]] = None,
|
||||
description: Optional[str] = None,
|
||||
content: str | None = None,
|
||||
title: str | None = None,
|
||||
tags: list[str] | None = None,
|
||||
description: str | None = None,
|
||||
) -> WikiPage:
|
||||
"""
|
||||
Update an existing wiki page.
|
||||
@@ -466,7 +636,7 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
Updated WikiPage
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
# Build update payload with only provided fields
|
||||
@@ -500,7 +670,7 @@ class LibraryDeskClient:
|
||||
topic: str,
|
||||
tags: list[str],
|
||||
user: str | None = None,
|
||||
path: Optional[str] = None,
|
||||
path: str | None = None,
|
||||
include_web_research: bool = True,
|
||||
include_wiki_search: bool = True,
|
||||
) -> SmartCreateResponse:
|
||||
@@ -524,7 +694,7 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
SmartCreateResponse with page and research metadata
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
@@ -575,12 +745,15 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
List of dossiers with page counts
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
response = await client.get(
|
||||
"/wiki/dossiers",
|
||||
params={"user": user},
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.get(
|
||||
"/wiki/dossiers",
|
||||
params={"user": user},
|
||||
),
|
||||
"GET /wiki/dossiers",
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
@@ -610,7 +783,7 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
List of matching document chunks with scores
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
@@ -636,7 +809,7 @@ class LibraryDeskClient:
|
||||
self,
|
||||
cypher_query: str,
|
||||
user: str | None = None,
|
||||
parameters: Optional[dict[str, Any]] = None,
|
||||
parameters: dict[str, Any] | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""
|
||||
Execute a Cypher query on the knowledge graph.
|
||||
@@ -651,7 +824,7 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
List of result records
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
@@ -670,7 +843,7 @@ class LibraryDeskClient:
|
||||
async def list_graph_nodes(
|
||||
self,
|
||||
user: str | None = None,
|
||||
node_type: Optional[str] = None,
|
||||
node_type: str | None = None,
|
||||
limit: int = 100,
|
||||
) -> list[GraphNode]:
|
||||
"""
|
||||
@@ -684,14 +857,17 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
List of graph nodes
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
params: dict[str, Any] = {"user": user, "limit": limit}
|
||||
if node_type:
|
||||
params["node_type"] = node_type
|
||||
|
||||
response = await client.get("/graph/nodes", params=params)
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.get("/graph/nodes", params=params),
|
||||
"GET /graph/nodes",
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -712,12 +888,15 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
Node with relationships and connected nodes
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
response = await client.get(
|
||||
f"/graph/nodes/{node_id}",
|
||||
params={"user": user},
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.get(
|
||||
f"/graph/nodes/{node_id}",
|
||||
params={"user": user},
|
||||
),
|
||||
f"GET /graph/nodes/{node_id}",
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
@@ -736,7 +915,10 @@ class LibraryDeskClient:
|
||||
"""
|
||||
try:
|
||||
client = self._ensure_client()
|
||||
response = await client.get("/health")
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.get("/health"),
|
||||
"GET /health",
|
||||
)
|
||||
return response.status_code == 200
|
||||
except Exception as e:
|
||||
logger.warning("library_desk_health_check_failed", error=str(e))
|
||||
@@ -768,19 +950,22 @@ class LibraryDeskClient:
|
||||
Returns:
|
||||
WebSearchResponse with results and pre-formatted sources
|
||||
"""
|
||||
user = user or get_user()
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
"query": query,
|
||||
"search_type": search_type,
|
||||
"limit": limit,
|
||||
"user": user or "tatlock-librarian",
|
||||
"user": user,
|
||||
}
|
||||
|
||||
logger.info("library_desk_web_search", query=query, limit=limit)
|
||||
|
||||
response = await client.post("/rag/search", json=payload, timeout=30.0)
|
||||
response = await self._request_with_retry(
|
||||
lambda: client.post("/rag/search", json=payload),
|
||||
"POST /rag/search",
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -813,6 +998,7 @@ class LibraryDeskClient:
|
||||
async def extract_content(
|
||||
self,
|
||||
url: str,
|
||||
user: str | None = None,
|
||||
include_metadata: bool = True,
|
||||
max_length: int = 5000,
|
||||
) -> ContentExtractionResult:
|
||||
@@ -826,12 +1012,14 @@ class LibraryDeskClient:
|
||||
|
||||
Args:
|
||||
url: URL to extract content from
|
||||
user: User identifier (defaults to request context)
|
||||
include_metadata: Whether to extract author, date, etc.
|
||||
max_length: Maximum content length
|
||||
|
||||
Returns:
|
||||
ContentExtractionResult (check .success and .error fields)
|
||||
"""
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
@@ -842,7 +1030,11 @@ class LibraryDeskClient:
|
||||
|
||||
logger.debug("library_desk_extract_content", url=url)
|
||||
|
||||
response = await client.post("/content/extract", json=payload, timeout=30.0)
|
||||
response = await client.post(
|
||||
"/content/extract",
|
||||
json=payload,
|
||||
params={"user": user},
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
@@ -862,6 +1054,7 @@ class LibraryDeskClient:
|
||||
async def extract_content_batch(
|
||||
self,
|
||||
urls: list[str],
|
||||
user: str | None = None,
|
||||
include_metadata: bool = True,
|
||||
max_length: int = 2000,
|
||||
) -> BatchExtractionResponse:
|
||||
@@ -875,12 +1068,14 @@ class LibraryDeskClient:
|
||||
|
||||
Args:
|
||||
urls: List of URLs to extract (max 20)
|
||||
user: User identifier (defaults to request context)
|
||||
include_metadata: Whether to extract author, date, etc.
|
||||
max_length: Maximum content length per URL
|
||||
|
||||
Returns:
|
||||
BatchExtractionResponse with results and stats
|
||||
"""
|
||||
user = self._resolve_user(user)
|
||||
client = self._ensure_client()
|
||||
|
||||
payload = {
|
||||
@@ -894,7 +1089,7 @@ class LibraryDeskClient:
|
||||
response = await client.post(
|
||||
"/content/extract/batch",
|
||||
json=payload,
|
||||
timeout=60.0, # Longer timeout for batch
|
||||
params={"user": user},
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
@@ -933,3 +1128,30 @@ async def get_library_client() -> LibraryDeskClient:
|
||||
results = await client.hybrid_search("query")
|
||||
"""
|
||||
return LibraryDeskClient()
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def library_client_session() -> AsyncIterator[None]:
|
||||
"""
|
||||
Hold ONE shared HTTP connection for the duration of a librarian run.
|
||||
|
||||
While the session is active, every LibraryDeskClient targeting the
|
||||
configured library-desk instance reuses the shared httpx client
|
||||
instead of constructing (and tearing down) a connection per tool
|
||||
call. Nested sessions are no-ops.
|
||||
|
||||
Usage:
|
||||
async with library_client_session():
|
||||
... # librarian tools reuse one connection
|
||||
"""
|
||||
if _shared_http_client.get() is not None:
|
||||
yield
|
||||
return
|
||||
|
||||
http_client = LibraryDeskClient()._build_http_client()
|
||||
token = _shared_http_client.set(http_client)
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
_shared_http_client.reset(token)
|
||||
await http_client.aclose()
|
||||
|
||||
+194
-58
@@ -4,16 +4,110 @@ Librarian tools for PydanticAI agent.
|
||||
These tools wrap the library-desk API and are registered with
|
||||
The Librarian agent for research and knowledge management tasks.
|
||||
"""
|
||||
from src.agents.librarian.client import LibraryDeskClient
|
||||
|
||||
import httpx
|
||||
from pydantic_ai import ModelRetry
|
||||
|
||||
from src.agents.librarian.client import HybridRAGResponse, LibraryDeskClient
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
def _retry_if_transient(e: Exception, what: str) -> None:
|
||||
"""
|
||||
Convert transient HTTP errors into ModelRetry so the agent's
|
||||
retry budget (Agent(retries=2)) engages instead of the tool
|
||||
swallowing the failure.
|
||||
|
||||
Only read tools call this - writes are never retried to avoid
|
||||
duplicate wiki pages.
|
||||
"""
|
||||
retryable = isinstance(e, httpx.TransportError)
|
||||
if isinstance(e, httpx.HTTPStatusError):
|
||||
status = e.response.status_code
|
||||
retryable = status >= 500 or status == 429
|
||||
if retryable:
|
||||
raise ModelRetry(f"{what} is temporarily unavailable; please retry.") from e
|
||||
|
||||
|
||||
# Icons keyed by the values library-desk emits in each result's `sources`
|
||||
# list (search legs) and `source_type` (result origin).
|
||||
SOURCE_ICONS = {
|
||||
"vector": "📄",
|
||||
"graph": "🔗",
|
||||
"web": "🌐",
|
||||
"document": "📑",
|
||||
"documents": "📑",
|
||||
"volatile": "⚡",
|
||||
"wiki": "📄",
|
||||
}
|
||||
|
||||
|
||||
def _coverage_note(
|
||||
response: HybridRAGResponse,
|
||||
include_web: bool,
|
||||
include_documents: bool,
|
||||
include_volatile: bool,
|
||||
) -> str:
|
||||
"""
|
||||
Build a one-line coverage note when the search was degraded or an
|
||||
enabled source leg contributed nothing, so outages stay visible to
|
||||
the model and the user instead of silently narrowing results.
|
||||
|
||||
When the additive source_status/degraded contract is present it is
|
||||
authoritative and used EXCLUSIVELY - no count heuristics. Without
|
||||
it, absence from source_counts is only inferred for the optional
|
||||
legs this request explicitly enabled (web/documents/volatile);
|
||||
the always-on wiki legs (vector/graph) are never inferred, because
|
||||
source_counts only tallies the sources of the final top-N fused
|
||||
results, so their absence is normal ranking behavior, not an outage.
|
||||
"""
|
||||
if response.source_status:
|
||||
failed = sorted(leg for leg, status in response.source_status.items() if status == "failed")
|
||||
if failed:
|
||||
return (
|
||||
"⚠️ *Coverage note: results are partial - "
|
||||
f"these sources failed: {', '.join(failed)}.*"
|
||||
)
|
||||
if response.degraded:
|
||||
return (
|
||||
"⚠️ *Coverage note: results are partial - "
|
||||
"one or more sources failed during this search.*"
|
||||
)
|
||||
return ""
|
||||
|
||||
if not response.source_counts:
|
||||
# Older library-desk without per-source reporting - nothing to infer
|
||||
return ""
|
||||
|
||||
# Only legs the request explicitly enabled; never vector/graph (their
|
||||
# absence from the top-N counts is healthy, see docstring)
|
||||
expected = set()
|
||||
if include_web:
|
||||
expected.add("web")
|
||||
if include_documents:
|
||||
expected.add("documents")
|
||||
if include_volatile:
|
||||
expected.add("volatile")
|
||||
|
||||
# Normalize count keys to leg names (document/documents)
|
||||
aliases = {"document": "documents"}
|
||||
reported = {aliases.get(key, key) for key in response.source_counts}
|
||||
missing = sorted(expected - reported)
|
||||
if missing:
|
||||
return (
|
||||
"⚠️ *Coverage note: no results came from: "
|
||||
f"{', '.join(missing)} (source unavailable or nothing found).*"
|
||||
)
|
||||
return ""
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# HybridRAG Search
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def hybrid_search(
|
||||
query: str,
|
||||
include_web: bool = True,
|
||||
@@ -68,21 +162,16 @@ async def hybrid_search(
|
||||
|
||||
# Add related dossiers
|
||||
if response.related_dossiers:
|
||||
output_parts.append(
|
||||
f"**Related Dossiers:** {', '.join(response.related_dossiers)}"
|
||||
)
|
||||
output_parts.append(f"**Related Dossiers:** {', '.join(response.related_dossiers)}")
|
||||
|
||||
output_parts.append("")
|
||||
|
||||
# Add results
|
||||
for i, result in enumerate(response.results, 1):
|
||||
source_icon = {
|
||||
"vector": "📄",
|
||||
"graph": "🔗",
|
||||
"web": "🌐",
|
||||
"document": "📑",
|
||||
"volatile": "⚡",
|
||||
}.get(result.source, "•")
|
||||
source_keys = result.sources or [result.source]
|
||||
source_icon = "".join(
|
||||
dict.fromkeys(SOURCE_ICONS.get(key, "•") for key in source_keys)
|
||||
)
|
||||
|
||||
output_parts.append(
|
||||
f"{i}. {source_icon} **{result.title}** (score: {result.score:.2f})"
|
||||
@@ -92,23 +181,37 @@ async def hybrid_search(
|
||||
output_parts.append(f" {result.content[:300]}...")
|
||||
output_parts.append("")
|
||||
|
||||
# Surface degraded coverage so outages are visible downstream
|
||||
coverage_note = _coverage_note(
|
||||
response,
|
||||
include_web=include_web,
|
||||
include_documents=include_documents,
|
||||
include_volatile=include_volatile,
|
||||
)
|
||||
if coverage_note:
|
||||
output_parts.append(coverage_note)
|
||||
|
||||
logger.info(
|
||||
"librarian_hybrid_search",
|
||||
query=query,
|
||||
result_count=len(response.results),
|
||||
degraded=response.degraded,
|
||||
source_counts=response.source_counts,
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_hybrid_search_error", error=str(e), query=query)
|
||||
return f"Error searching: {str(e)}"
|
||||
_retry_if_transient(e, "The knowledge archive")
|
||||
return "I was unable to search the knowledge archives; the search service did not respond properly."
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Wiki Operations
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def search_wiki(
|
||||
query: str,
|
||||
limit: int = 10,
|
||||
@@ -139,18 +242,22 @@ async def search_wiki(
|
||||
|
||||
output_parts = [f"## Wiki Search: {query}\n"]
|
||||
|
||||
for i, page in enumerate(results, 1):
|
||||
output_parts.append(f"{i}. **{page.title}**")
|
||||
output_parts.append(f" Path: {page.path}")
|
||||
# No ordinal numbering: small models pass the list position to
|
||||
# get_wiki_page instead of the page ID unless the ID is the only
|
||||
# number in sight.
|
||||
for page in results:
|
||||
output_parts.append(f"- **{page.title}** (page_id: {page.id})")
|
||||
output_parts.append(f" Path: {page.path}")
|
||||
if page.description:
|
||||
output_parts.append(f" {page.description}")
|
||||
output_parts.append(f" {page.description}")
|
||||
output_parts.append("")
|
||||
|
||||
return "\n".join(output_parts)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_wiki_search_error", error=str(e))
|
||||
return f"Error searching wiki: {str(e)}"
|
||||
_retry_if_transient(e, "The wiki search")
|
||||
return "I was unable to search the wiki at this time."
|
||||
|
||||
|
||||
async def get_wiki_page(
|
||||
@@ -193,7 +300,8 @@ async def get_wiki_page(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_get_page_error", error=str(e), page_id=page_id)
|
||||
return f"Error getting page {page_id}: {str(e)}"
|
||||
_retry_if_transient(e, "The wiki")
|
||||
return f"I was unable to retrieve wiki page {page_id}."
|
||||
|
||||
|
||||
async def list_dossiers() -> str:
|
||||
@@ -219,15 +327,14 @@ async def list_dossiers() -> str:
|
||||
output_parts = ["## Research Dossiers\n"]
|
||||
|
||||
for dossier in dossiers:
|
||||
output_parts.append(
|
||||
f"- **{dossier.name}** ({dossier.page_count} pages)"
|
||||
)
|
||||
output_parts.append(f"- **{dossier.name}** ({dossier.page_count} pages)")
|
||||
|
||||
return "\n".join(output_parts)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_list_dossiers_error", error=str(e))
|
||||
return f"Error listing dossiers: {str(e)}"
|
||||
_retry_if_transient(e, "The dossier index")
|
||||
return "I was unable to retrieve the list of dossiers."
|
||||
|
||||
|
||||
async def get_dossier_pages(
|
||||
@@ -268,13 +375,15 @@ async def get_dossier_pages(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_get_dossier_error", error=str(e))
|
||||
return f"Error getting dossier: {str(e)}"
|
||||
_retry_if_transient(e, "The dossier index")
|
||||
return f"I was unable to retrieve the dossier '{dossier_name}'."
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Semantic Search
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def semantic_search(
|
||||
query: str,
|
||||
limit: int = 10,
|
||||
@@ -306,9 +415,7 @@ async def semantic_search(
|
||||
output_parts = [f"## Semantic Search: {query}\n"]
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
output_parts.append(
|
||||
f"{i}. **{result.page_title}** (score: {result.score:.2f})"
|
||||
)
|
||||
output_parts.append(f"{i}. **{result.page_title}** (score: {result.score:.2f})")
|
||||
output_parts.append(f" Path: {result.page_path}")
|
||||
output_parts.append(f" {result.chunk_text[:200]}...")
|
||||
output_parts.append("")
|
||||
@@ -317,13 +424,15 @@ async def semantic_search(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_semantic_search_error", error=str(e))
|
||||
return f"Error in semantic search: {str(e)}"
|
||||
_retry_if_transient(e, "The semantic search")
|
||||
return "I was unable to complete the semantic search."
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Knowledge Graph
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def explore_knowledge_graph(
|
||||
entity_type: str = "Document",
|
||||
limit: int = 20,
|
||||
@@ -370,7 +479,8 @@ async def explore_knowledge_graph(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_explore_graph_error", error=str(e))
|
||||
return f"Error exploring knowledge graph: {str(e)}"
|
||||
_retry_if_transient(e, "The knowledge graph")
|
||||
return "I was unable to explore the knowledge graph."
|
||||
|
||||
|
||||
async def find_related_entities(
|
||||
@@ -441,13 +551,15 @@ async def find_related_entities(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_find_related_error", error=str(e))
|
||||
return f"Error finding related entities: {str(e)}"
|
||||
_retry_if_transient(e, "The knowledge graph")
|
||||
return f"I was unable to look up entities related to '{entity_name}'."
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Web Search & Content Extraction
|
||||
# ============================================================================
|
||||
|
||||
|
||||
async def search_web(
|
||||
query: str,
|
||||
limit: int = 10,
|
||||
@@ -488,7 +600,9 @@ async def search_web(
|
||||
return f"No results found for '{query}'"
|
||||
|
||||
output_parts = [f"## Web Search: {query}\n"]
|
||||
output_parts.append(f"*Found {response.total_results} results in {response.search_time_ms}ms*\n")
|
||||
output_parts.append(
|
||||
f"*Found {response.total_results} results in {response.search_time_ms}ms*\n"
|
||||
)
|
||||
|
||||
for i, result in enumerate(response.results, 1):
|
||||
output_parts.append(f"### {i}. {result.title}")
|
||||
@@ -524,7 +638,8 @@ async def search_web(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_web_search_error", error=str(e), query=query)
|
||||
return f"Error searching web: {str(e)}"
|
||||
_retry_if_transient(e, "The web search")
|
||||
return "I was unable to search the web at this time."
|
||||
|
||||
|
||||
async def read_url(
|
||||
@@ -600,7 +715,8 @@ async def read_url(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_read_url_error", error=str(e), url=url)
|
||||
return f"Error reading URL: {str(e)}"
|
||||
_retry_if_transient(e, "Content extraction")
|
||||
return f"I was unable to read the page at {url}."
|
||||
|
||||
|
||||
async def read_urls_batch(
|
||||
@@ -635,7 +751,7 @@ async def read_urls_batch(
|
||||
)
|
||||
|
||||
output_parts = [
|
||||
f"## Batch Content Extraction",
|
||||
"## Batch Content Extraction",
|
||||
f"*Extracted {response.successful}/{response.total_urls} URLs in {response.extraction_time_ms}ms*\n",
|
||||
]
|
||||
|
||||
@@ -674,19 +790,23 @@ async def read_urls_batch(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_read_urls_batch_error", error=str(e))
|
||||
return f"Error reading URLs: {str(e)}"
|
||||
_retry_if_transient(e, "Content extraction")
|
||||
return "I was unable to read the requested pages."
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Wiki Write Operations
|
||||
# ============================================================================
|
||||
|
||||
CLEAR_TAGS_SENTINEL = "__CLEAR__"
|
||||
|
||||
|
||||
async def update_wiki_page(
|
||||
page_id: int,
|
||||
content: str | None = None,
|
||||
title: str | None = None,
|
||||
tags: list[str] | None = None,
|
||||
description: str | None = None,
|
||||
content: str = "",
|
||||
title: str = "",
|
||||
tags: list[str] = [], # noqa: B006 - sentinel, never mutated
|
||||
description: str = "",
|
||||
) -> str:
|
||||
"""
|
||||
Update an existing wiki page.
|
||||
@@ -700,12 +820,17 @@ async def update_wiki_page(
|
||||
- Updating tags to organize pages into dossiers
|
||||
- Fixing descriptions or titles
|
||||
|
||||
Note: empty values are sentinels for "leave unchanged" (Ollama's
|
||||
OpenAI-compatible API mishandles anyOf[X, null] parameter schemas).
|
||||
|
||||
Args:
|
||||
page_id: ID of the page to update (get from search_wiki results)
|
||||
content: New markdown content (optional - only if changing content)
|
||||
title: New title (optional - only if renaming)
|
||||
tags: New tag list (optional - replaces existing tags)
|
||||
description: New description (optional)
|
||||
content: New markdown content (empty = leave unchanged)
|
||||
title: New title (empty = leave unchanged)
|
||||
tags: New tag list, replaces existing tags (empty = leave unchanged).
|
||||
To remove ALL tags from a page, pass exactly ["__CLEAR__"]
|
||||
(an empty list means "leave unchanged", not "clear")
|
||||
description: New description (empty = leave unchanged)
|
||||
|
||||
Returns:
|
||||
Confirmation with updated page details
|
||||
@@ -713,27 +838,34 @@ async def update_wiki_page(
|
||||
Examples:
|
||||
update_wiki_page(42, content="# Updated Content\\n\\nNew information here")
|
||||
update_wiki_page(42, tags=["projects", "devops"]) # Add to dossiers
|
||||
update_wiki_page(42, tags=["__CLEAR__"]) # Remove all tags
|
||||
update_wiki_page(42, description="Updated description")
|
||||
"""
|
||||
# Empty list = leave unchanged; the explicit clear sentinel sends an
|
||||
# empty tag list to the service, which replaces (clears) all tags.
|
||||
clear_tags = tags == [CLEAR_TAGS_SENTINEL]
|
||||
|
||||
try:
|
||||
async with LibraryDeskClient() as client:
|
||||
page = await client.update_wiki_page(
|
||||
page_id=page_id,
|
||||
content=content,
|
||||
title=title,
|
||||
tags=tags,
|
||||
description=description,
|
||||
content=content if content else None,
|
||||
title=title if title else None,
|
||||
tags=[] if clear_tags else (tags if tags else None),
|
||||
description=description if description else None,
|
||||
)
|
||||
|
||||
# Build update summary
|
||||
updated_fields = []
|
||||
if content is not None:
|
||||
if content:
|
||||
updated_fields.append("content")
|
||||
if title is not None:
|
||||
if title:
|
||||
updated_fields.append("title")
|
||||
if tags is not None:
|
||||
if clear_tags:
|
||||
updated_fields.append("tags (cleared)")
|
||||
elif tags:
|
||||
updated_fields.append("tags")
|
||||
if description is not None:
|
||||
if description:
|
||||
updated_fields.append("description")
|
||||
|
||||
output_parts = [
|
||||
@@ -745,7 +877,9 @@ async def update_wiki_page(
|
||||
if page.tags:
|
||||
output_parts.append(f"**Tags:** {', '.join(page.tags)}")
|
||||
|
||||
output_parts.append("\n*Vector embeddings and knowledge graph will be updated automatically.*")
|
||||
output_parts.append(
|
||||
"\n*Vector embeddings and knowledge graph will be updated automatically.*"
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"librarian_update_page",
|
||||
@@ -757,7 +891,7 @@ async def update_wiki_page(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_update_page_error", error=str(e), page_id=page_id)
|
||||
return f"Error updating page {page_id}: {str(e)}"
|
||||
return f"I was unable to update wiki page {page_id}."
|
||||
|
||||
|
||||
async def create_wiki_page(
|
||||
@@ -819,7 +953,9 @@ async def create_wiki_page(
|
||||
if page.description:
|
||||
output_parts.append(f"**Description:** {page.description}")
|
||||
|
||||
output_parts.append("\n*Vector embeddings and knowledge graph will be updated automatically.*")
|
||||
output_parts.append(
|
||||
"\n*Vector embeddings and knowledge graph will be updated automatically.*"
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"librarian_create_page",
|
||||
@@ -832,13 +968,13 @@ async def create_wiki_page(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_create_page_error", error=str(e), title=title)
|
||||
return f"Error creating page: {str(e)}"
|
||||
return f"I was unable to create the page '{title}'."
|
||||
|
||||
|
||||
async def smart_create_wiki_page(
|
||||
topic: str,
|
||||
tags: list[str],
|
||||
path: str | None = None,
|
||||
path: str = "",
|
||||
include_web_research: bool = True,
|
||||
include_wiki_search: bool = True,
|
||||
) -> str:
|
||||
@@ -860,7 +996,7 @@ async def smart_create_wiki_page(
|
||||
Args:
|
||||
topic: The topic to research and create a page about
|
||||
tags: List of tags/dossiers for categorization
|
||||
path: Optional custom path (auto-generated from topic if not provided)
|
||||
path: Optional custom path (empty = auto-generated from topic)
|
||||
include_web_research: Whether to search the web (default: True)
|
||||
include_wiki_search: Whether to search existing wiki (default: True)
|
||||
|
||||
@@ -876,7 +1012,7 @@ async def smart_create_wiki_page(
|
||||
response = await client.smart_create_wiki_page(
|
||||
topic=topic,
|
||||
tags=tags,
|
||||
path=path,
|
||||
path=path if path else None,
|
||||
include_web_research=include_web_research,
|
||||
include_wiki_search=include_wiki_search,
|
||||
)
|
||||
@@ -921,7 +1057,7 @@ async def smart_create_wiki_page(
|
||||
|
||||
except Exception as e:
|
||||
logger.error("librarian_smart_create_error", error=str(e), topic=topic)
|
||||
return f"Error creating page about '{topic}': {str(e)}"
|
||||
return f"I was unable to create a page about '{topic}'."
|
||||
|
||||
|
||||
# ============================================================================
|
||||
|
||||
+25
-48
@@ -12,16 +12,16 @@ infrastructure is real production code.
|
||||
import asyncio
|
||||
import random
|
||||
import secrets
|
||||
from typing import AsyncGenerator, Any
|
||||
from collections.abc import AsyncGenerator
|
||||
from typing import Any
|
||||
|
||||
from src.agents.base import AgentInterface, OutputItem
|
||||
from src.core.exceptions import (
|
||||
RateLimitError,
|
||||
ContextLengthError,
|
||||
APIError,
|
||||
ContextLengthError,
|
||||
RateLimitError,
|
||||
)
|
||||
|
||||
|
||||
# Mock lorem ipsum content
|
||||
LOREM_PARAGRAPHS = [
|
||||
"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.",
|
||||
@@ -46,33 +46,27 @@ MOCK_TOOLS = [
|
||||
"description": "Search the knowledge base for relevant information",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {"type": "string", "description": "Search query"}
|
||||
},
|
||||
"required": ["query"]
|
||||
}
|
||||
"properties": {"query": {"type": "string", "description": "Search query"}},
|
||||
"required": ["query"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "calculate",
|
||||
"description": "Perform mathematical calculations",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"expression": {"type": "string", "description": "Math expression"}
|
||||
},
|
||||
"required": ["expression"]
|
||||
}
|
||||
"properties": {"expression": {"type": "string", "description": "Math expression"}},
|
||||
"required": ["expression"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "get_weather",
|
||||
"description": "Get current weather for a location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {"type": "string", "description": "City name"}
|
||||
},
|
||||
"required": ["location"]
|
||||
}
|
||||
"properties": {"location": {"type": "string", "description": "City name"}},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
@@ -109,7 +103,7 @@ class LoremTesterAgent(AgentInterface):
|
||||
temperature: float = 1.0,
|
||||
max_tokens: int | None = None,
|
||||
stop: list[str] | None = None,
|
||||
**kwargs: Any
|
||||
**kwargs: Any,
|
||||
) -> AsyncGenerator[OutputItem, None]:
|
||||
"""
|
||||
Generate mock response with reasoning, tools, and content.
|
||||
@@ -123,8 +117,7 @@ class LoremTesterAgent(AgentInterface):
|
||||
# 1. Yield reasoning item if requested
|
||||
if reasoning and reasoning.get("summary") == "auto":
|
||||
yield await self._create_reasoning_item(
|
||||
messages,
|
||||
effort=reasoning.get("effort", "medium")
|
||||
messages, effort=reasoning.get("effort", "medium")
|
||||
)
|
||||
|
||||
# 2. Randomly yield function calls if tools available (30% chance)
|
||||
@@ -150,7 +143,7 @@ class LoremTesterAgent(AgentInterface):
|
||||
"reasoning": True,
|
||||
"tools": True,
|
||||
"vision": False, # Not yet
|
||||
"audio": False, # Not yet
|
||||
"audio": False, # Not yet
|
||||
}
|
||||
|
||||
# Private helper methods
|
||||
@@ -179,9 +172,7 @@ class LoremTesterAgent(AgentInterface):
|
||||
raise APIError("Invalid tool call: tool 'nonexistent' not found (mock trigger)")
|
||||
|
||||
async def _create_reasoning_item(
|
||||
self,
|
||||
messages: list[dict],
|
||||
effort: str = "medium"
|
||||
self, messages: list[dict], effort: str = "medium"
|
||||
) -> OutputItem:
|
||||
"""Create a reasoning output item with mock thinking steps."""
|
||||
|
||||
@@ -200,23 +191,17 @@ class LoremTesterAgent(AgentInterface):
|
||||
steps = random.sample(REASONING_STEPS, min(num_steps, len(REASONING_STEPS)))
|
||||
|
||||
return OutputItem(
|
||||
type="reasoning",
|
||||
id=f"rs_{generate_id()}",
|
||||
summary=steps,
|
||||
status="completed"
|
||||
type="reasoning", id=f"rs_{generate_id()}", summary=steps, status="completed"
|
||||
)
|
||||
|
||||
async def _create_tool_calls(
|
||||
self,
|
||||
tools: list[dict]
|
||||
) -> AsyncGenerator[OutputItem, None]:
|
||||
async def _create_tool_calls(self, tools: list[dict]) -> AsyncGenerator[OutputItem, None]:
|
||||
"""Create mock function call output items."""
|
||||
|
||||
# Randomly select 1-2 tools to "call"
|
||||
num_calls = random.randint(1, 2)
|
||||
selected_tools = random.sample(
|
||||
MOCK_TOOLS[:min(len(MOCK_TOOLS), len(tools))],
|
||||
min(num_calls, len(MOCK_TOOLS), len(tools))
|
||||
MOCK_TOOLS[: min(len(MOCK_TOOLS), len(tools))],
|
||||
min(num_calls, len(MOCK_TOOLS), len(tools)),
|
||||
)
|
||||
|
||||
for tool in selected_tools:
|
||||
@@ -228,7 +213,7 @@ class LoremTesterAgent(AgentInterface):
|
||||
id=f"fc_{generate_id()}",
|
||||
name=tool["name"],
|
||||
arguments=args,
|
||||
status="completed"
|
||||
status="completed",
|
||||
)
|
||||
|
||||
def _generate_mock_args(self, tool: dict) -> str:
|
||||
@@ -254,11 +239,7 @@ class LoremTesterAgent(AgentInterface):
|
||||
# Generic mock arguments
|
||||
return json.dumps({"input": "mock_value"})
|
||||
|
||||
async def _create_message_item(
|
||||
self,
|
||||
messages: list[dict],
|
||||
temperature: float
|
||||
) -> OutputItem:
|
||||
async def _create_message_item(self, messages: list[dict], temperature: float) -> OutputItem:
|
||||
"""Create final message output item with lorem ipsum content."""
|
||||
|
||||
# Select random lorem ipsum paragraphs
|
||||
@@ -270,10 +251,6 @@ class LoremTesterAgent(AgentInterface):
|
||||
type="message",
|
||||
id=f"msg_{generate_id()}",
|
||||
role="assistant",
|
||||
content=[{
|
||||
"type": "output_text",
|
||||
"text": content,
|
||||
"annotations": []
|
||||
}],
|
||||
status="completed"
|
||||
content=[{"type": "output_text", "text": content, "annotations": []}],
|
||||
status="completed",
|
||||
)
|
||||
|
||||
+20
-11
@@ -14,12 +14,13 @@ Supports:
|
||||
- Result aggregation from multiple experts
|
||||
- Partial failure handling
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from collections.abc import AsyncGenerator
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from typing import AsyncGenerator, Optional, Callable, Any
|
||||
|
||||
from src.agents.delegation import DelegationTask, DelegationResult, delegate_to_librarian
|
||||
from src.agents.delegation import DelegationResult, DelegationTask, delegate_to_librarian
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
@@ -27,8 +28,9 @@ logger = get_logger(__name__)
|
||||
|
||||
class ExecutionMode(str, Enum):
|
||||
"""Execution mode for multi-expert coordination."""
|
||||
|
||||
SEQUENTIAL = "sequential" # One at a time, in order
|
||||
PARALLEL = "parallel" # All at once, concurrently
|
||||
PARALLEL = "parallel" # All at once, concurrently
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -38,12 +40,13 @@ class OrchestrationContext:
|
||||
|
||||
Tracks the user's request, delegation tasks, and results.
|
||||
"""
|
||||
|
||||
user_message: str
|
||||
steward_note: str
|
||||
conversation_id: Optional[str] = None
|
||||
conversation_id: str | None = None
|
||||
|
||||
|
||||
def parse_delegation_from_steward_note(steward_note: str) -> Optional[DelegationTask]:
|
||||
def parse_delegation_from_steward_note(steward_note: str) -> DelegationTask | None:
|
||||
"""
|
||||
Parse a delegation task from Steward's note.
|
||||
|
||||
@@ -68,9 +71,9 @@ def parse_delegation_from_steward_note(steward_note: str) -> Optional[Delegation
|
||||
# Look for DELEGATE: pattern
|
||||
# Match: "DELEGATE: expert_name to action description"
|
||||
match = re.search(
|
||||
r'DELEGATE:\s*(\w+)\s+to\s+(.+?)(?:\n|REASON:|COMPLEXITY:|CONTEXT:|$)',
|
||||
r"DELEGATE:\s*(\w+)\s+to\s+(.+?)(?:\n|REASON:|COMPLEXITY:|CONTEXT:|$)",
|
||||
steward_note,
|
||||
re.IGNORECASE | re.MULTILINE
|
||||
re.IGNORECASE | re.MULTILINE,
|
||||
)
|
||||
|
||||
if match:
|
||||
@@ -135,7 +138,7 @@ async def execute_delegation(
|
||||
async def orchestrate_with_think_updates(
|
||||
user_message: str,
|
||||
steward_note: str,
|
||||
delegation_task: Optional[DelegationTask] = None,
|
||||
delegation_task: DelegationTask | None = None,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Orchestrate expert delegation with streaming think updates.
|
||||
@@ -218,17 +221,21 @@ def extract_delegation_context(
|
||||
}
|
||||
|
||||
# Extract REASON:
|
||||
reason_match = re.search(r'REASON:\s*(.+?)(?:\n|COMPLEXITY:|CONTEXT:|$)', steward_note, re.IGNORECASE)
|
||||
reason_match = re.search(
|
||||
r"REASON:\s*(.+?)(?:\n|COMPLEXITY:|CONTEXT:|$)", steward_note, re.IGNORECASE
|
||||
)
|
||||
if reason_match:
|
||||
result["reason"] = reason_match.group(1).strip()
|
||||
|
||||
# Extract COMPLEXITY:
|
||||
complexity_match = re.search(r'COMPLEXITY:\s*(.+?)(?:\n|CONTEXT:|$)', steward_note, re.IGNORECASE)
|
||||
complexity_match = re.search(
|
||||
r"COMPLEXITY:\s*(.+?)(?:\n|CONTEXT:|$)", steward_note, re.IGNORECASE
|
||||
)
|
||||
if complexity_match:
|
||||
result["complexity"] = complexity_match.group(1).strip()
|
||||
|
||||
# Extract CONTEXT:
|
||||
context_match = re.search(r'CONTEXT:\s*(.+?)$', steward_note, re.IGNORECASE | re.MULTILINE)
|
||||
context_match = re.search(r"CONTEXT:\s*(.+?)$", steward_note, re.IGNORECASE | re.MULTILINE)
|
||||
if context_match:
|
||||
result["context"] = context_match.group(1).strip()
|
||||
|
||||
@@ -239,6 +246,7 @@ def extract_delegation_context(
|
||||
# Multi-Expert Coordination
|
||||
# ============================================================================
|
||||
|
||||
|
||||
@dataclass
|
||||
class MultiExpertResult:
|
||||
"""
|
||||
@@ -250,6 +258,7 @@ class MultiExpertResult:
|
||||
failed_experts: List of expert names that failed
|
||||
combined_output: Aggregated output from all successful experts
|
||||
"""
|
||||
|
||||
results: dict[str, DelegationResult] = field(default_factory=dict)
|
||||
all_succeeded: bool = True
|
||||
failed_experts: list[str] = field(default_factory=list)
|
||||
|
||||
+5
-189
@@ -1,180 +1,11 @@
|
||||
"""
|
||||
Agent communication protocol for multi-agent coordination.
|
||||
Agent error protocol.
|
||||
|
||||
Defines standardized request/response formats for communication between:
|
||||
- Steward (request analysis) → Tatlock (coordination)
|
||||
- Tatlock (coordination) → Expert agents (Librarian, Developer, etc.)
|
||||
Structured exceptions raised by expert agents (e.g. The Librarian) so
|
||||
callers - the delegation wrappers in src/agents/delegation.py - can
|
||||
report success=False and map failures to curated user-safe messages
|
||||
while exception detail stays in the logs.
|
||||
"""
|
||||
from enum import Enum
|
||||
from typing import Any, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class DelegationReason(str, Enum):
|
||||
"""Why a task is being delegated to an expert agent."""
|
||||
DOMAIN_EXPERTISE = "domain_expertise" # Expert has specialized knowledge
|
||||
TOOL_ACCESS = "tool_access" # Expert has required tools
|
||||
RESOURCE_EFFICIENCY = "resource_efficiency" # Better handled by specialist
|
||||
USER_PREFERENCE = "user_preference" # User requested specific agent
|
||||
|
||||
|
||||
class TaskComplexity(str, Enum):
|
||||
"""Complexity estimate for task execution."""
|
||||
SIMPLE = "simple" # Single tool call, fast
|
||||
MODERATE = "moderate" # Multiple steps, moderate time
|
||||
COMPLEX = "complex" # Multi-agent, significant processing
|
||||
|
||||
|
||||
class AgentRequest(BaseModel):
|
||||
"""
|
||||
Request to an expert agent.
|
||||
|
||||
Contains everything the agent needs to execute a task,
|
||||
including context from the conversation and delegation intent.
|
||||
"""
|
||||
task: str = Field(
|
||||
...,
|
||||
description="Clear description of what the agent should do"
|
||||
)
|
||||
context: str = Field(
|
||||
default="",
|
||||
description="Relevant context from conversation history"
|
||||
)
|
||||
constraints: list[str] = Field(
|
||||
default_factory=list,
|
||||
description="Any constraints or requirements for the task"
|
||||
)
|
||||
delegation_reason: DelegationReason = Field(
|
||||
default=DelegationReason.DOMAIN_EXPERTISE,
|
||||
description="Why this task was delegated to this agent"
|
||||
)
|
||||
user_id: str = Field(
|
||||
default="default",
|
||||
description="User identifier for multi-tenant operations"
|
||||
)
|
||||
max_tokens: Optional[int] = Field(
|
||||
default=None,
|
||||
description="Optional token limit for response"
|
||||
)
|
||||
timeout_seconds: Optional[int] = Field(
|
||||
default=60,
|
||||
description="Maximum time for task completion"
|
||||
)
|
||||
|
||||
|
||||
class ToolCallRecord(BaseModel):
|
||||
"""Record of a tool call made during execution."""
|
||||
tool_name: str
|
||||
arguments: dict[str, Any]
|
||||
result: str
|
||||
duration_ms: int
|
||||
|
||||
|
||||
class AgentResponse(BaseModel):
|
||||
"""
|
||||
Response from an expert agent.
|
||||
|
||||
Contains the result, reasoning, and metadata about execution.
|
||||
"""
|
||||
success: bool = Field(
|
||||
...,
|
||||
description="Whether the task completed successfully"
|
||||
)
|
||||
result: str = Field(
|
||||
...,
|
||||
description="The main output/answer from the agent"
|
||||
)
|
||||
reasoning: str = Field(
|
||||
default="",
|
||||
description="Agent's reasoning process (for transparency)"
|
||||
)
|
||||
tool_calls: list[ToolCallRecord] = Field(
|
||||
default_factory=list,
|
||||
description="Tools called during execution"
|
||||
)
|
||||
confidence: float = Field(
|
||||
default=1.0,
|
||||
ge=0.0,
|
||||
le=1.0,
|
||||
description="Agent's confidence in the result (0.0-1.0)"
|
||||
)
|
||||
sources: list[str] = Field(
|
||||
default_factory=list,
|
||||
description="Sources or references used"
|
||||
)
|
||||
error_message: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Error details if success=False"
|
||||
)
|
||||
duration_ms: int = Field(
|
||||
default=0,
|
||||
description="Total execution time in milliseconds"
|
||||
)
|
||||
|
||||
|
||||
class DelegationIntent(BaseModel):
|
||||
"""
|
||||
Intent to delegate a task to an expert agent.
|
||||
|
||||
Created by Tatlock when deciding to delegate, based on
|
||||
Steward's recommendations.
|
||||
"""
|
||||
target_agent: str = Field(
|
||||
...,
|
||||
description="Name of the expert agent to delegate to"
|
||||
)
|
||||
task: str = Field(
|
||||
...,
|
||||
description="Task description for the agent"
|
||||
)
|
||||
reason: DelegationReason = Field(
|
||||
default=DelegationReason.DOMAIN_EXPERTISE,
|
||||
description="Why delegating to this agent"
|
||||
)
|
||||
expected_outcome: str = Field(
|
||||
default="",
|
||||
description="What we expect the agent to provide"
|
||||
)
|
||||
priority: int = Field(
|
||||
default=1,
|
||||
ge=1,
|
||||
le=10,
|
||||
description="Priority (1=highest, 10=lowest)"
|
||||
)
|
||||
depends_on: list[str] = Field(
|
||||
default_factory=list,
|
||||
description="Other delegation IDs this depends on (for sequencing)"
|
||||
)
|
||||
|
||||
|
||||
class CoordinationResult(BaseModel):
|
||||
"""
|
||||
Result of multi-agent coordination.
|
||||
|
||||
Aggregates results from multiple expert agents into
|
||||
a single coherent response.
|
||||
"""
|
||||
final_response: str = Field(
|
||||
...,
|
||||
description="Synthesized response from all agents"
|
||||
)
|
||||
agent_responses: dict[str, AgentResponse] = Field(
|
||||
default_factory=dict,
|
||||
description="Individual responses keyed by agent name"
|
||||
)
|
||||
delegation_intents: list[DelegationIntent] = Field(
|
||||
default_factory=list,
|
||||
description="All delegations that were executed"
|
||||
)
|
||||
total_duration_ms: int = Field(
|
||||
default=0,
|
||||
description="Total coordination time"
|
||||
)
|
||||
agents_consulted: list[str] = Field(
|
||||
default_factory=list,
|
||||
description="Names of agents that contributed"
|
||||
)
|
||||
|
||||
|
||||
class AgentError(Exception):
|
||||
@@ -184,18 +15,3 @@ class AgentError(Exception):
|
||||
self.message = message
|
||||
self.agent_name = agent_name
|
||||
super().__init__(f"[{agent_name}] {message}")
|
||||
|
||||
|
||||
class AgentTimeoutError(AgentError):
|
||||
"""Agent execution timed out."""
|
||||
pass
|
||||
|
||||
|
||||
class AgentUnavailableError(AgentError):
|
||||
"""Agent is not available or registered."""
|
||||
pass
|
||||
|
||||
|
||||
class DelegationError(AgentError):
|
||||
"""Error during task delegation."""
|
||||
pass
|
||||
|
||||
+11
-12
@@ -8,9 +8,6 @@ It provides a central place to:
|
||||
- Check model capabilities
|
||||
"""
|
||||
|
||||
import time
|
||||
from typing import Type
|
||||
|
||||
from src.agents.base import AgentInterface
|
||||
from src.agents.lorem_tester import LoremTesterAgent
|
||||
from src.agents.tatlock import TatlockAgent
|
||||
@@ -63,7 +60,7 @@ class ModelRegistry:
|
||||
if model_id not in cls.MODELS:
|
||||
raise ModelNotFoundError(model_id)
|
||||
|
||||
agent_class: Type[AgentInterface] = cls.MODELS[model_id]["agent_class"]
|
||||
agent_class: type[AgentInterface] = cls.MODELS[model_id]["agent_class"]
|
||||
return agent_class()
|
||||
|
||||
@classmethod
|
||||
@@ -106,14 +103,16 @@ class ModelRegistry:
|
||||
agent = cls.get_agent(model_id)
|
||||
capabilities = await agent.get_capabilities()
|
||||
|
||||
models.append({
|
||||
"id": model_id,
|
||||
"object": "model",
|
||||
"created": config["created"],
|
||||
"owned_by": config["owned_by"],
|
||||
"capabilities": capabilities,
|
||||
"description": config["description"],
|
||||
})
|
||||
models.append(
|
||||
{
|
||||
"id": model_id,
|
||||
"object": "model",
|
||||
"created": config["created"],
|
||||
"owned_by": config["owned_by"],
|
||||
"capabilities": capabilities,
|
||||
"description": config["description"],
|
||||
}
|
||||
)
|
||||
|
||||
return models
|
||||
|
||||
|
||||
@@ -4,6 +4,7 @@ Steward agent package.
|
||||
The Steward analyzes incoming requests and recommends relevant household
|
||||
capabilities, creating a two-tier architecture with the Butler.
|
||||
"""
|
||||
|
||||
from .agent import StewardAgent, get_steward_agent
|
||||
from .schemas import ConversationContext, StewardRecommendation
|
||||
from .service import analyze_request, format_steward_note
|
||||
|
||||
+38
-29
@@ -8,10 +8,10 @@ This creates a two-tier architecture that prevents cognitive overload.
|
||||
Uses plain text output (not JSON) for reliability. Supports both Claude
|
||||
(preferred) and Ollama (fallback) backends via direct API calls.
|
||||
"""
|
||||
import httpx
|
||||
from typing import Optional
|
||||
|
||||
from src.anthropic.model_selector import is_claude_available, get_model_info
|
||||
import httpx
|
||||
|
||||
from src.anthropic.model_selector import get_model_info, is_claude_available, resolve_backend
|
||||
from src.core.config import config
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger
|
||||
@@ -29,9 +29,7 @@ def build_steward_prompt(query: str, conversation_history: list[dict]) -> str:
|
||||
|
||||
cap_list = []
|
||||
for cap in capabilities:
|
||||
cap_list.append(
|
||||
f"• {cap.name} - {cap.description} (domains: {', '.join(cap.domains)})"
|
||||
)
|
||||
cap_list.append(f"• {cap.name} - {cap.description} (domains: {', '.join(cap.domains)})")
|
||||
capabilities_text = "\n".join(cap_list)
|
||||
|
||||
# Format conversation history if present
|
||||
@@ -111,20 +109,21 @@ class StewardAgent:
|
||||
(preferred) and Ollama (fallback) backends via direct API calls.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
def __init__(self) -> None:
|
||||
"""Initialize Steward with backend selection based on availability."""
|
||||
# Ollama config (fallback)
|
||||
self.ollama_host = str(config.OLLAMA_HOST).rstrip('/')
|
||||
# Ollama config (primary)
|
||||
self.ollama_host = str(config.OLLAMA_HOST).rstrip("/")
|
||||
self.ollama_model = config.OLLAMA_DEFAULT_MODEL
|
||||
|
||||
# Claude config (preferred)
|
||||
# Claude config (fallback)
|
||||
self.claude_model = config.ANTHROPIC_MODEL
|
||||
self._anthropic_client = None
|
||||
|
||||
# Determine which backend to use
|
||||
self._use_claude = config.PREFER_CLOUD_BACKEND and is_claude_available()
|
||||
# Determine which backend to use (Ollama-first, Claude when
|
||||
# preferred via config or when Ollama is down)
|
||||
self._use_claude = resolve_backend() == "claude"
|
||||
|
||||
self.timeout = 30.0 # 30 second timeout for analysis
|
||||
self.timeout = float(config.STEWARD_TIMEOUT)
|
||||
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
@@ -138,6 +137,7 @@ class StewardAgent:
|
||||
"""Get or create Anthropic client (lazy initialization)."""
|
||||
if self._anthropic_client is None:
|
||||
from anthropic import AsyncAnthropic
|
||||
|
||||
self._anthropic_client = AsyncAnthropic(api_key=config.ANTHROPIC_API_KEY)
|
||||
return self._anthropic_client
|
||||
|
||||
@@ -145,12 +145,12 @@ class StewardAgent:
|
||||
"""Call Claude API directly for plain text generation."""
|
||||
client = self._get_anthropic_client()
|
||||
|
||||
# No temperature: rejected by Claude Sonnet 5+ (sampling params deprecated)
|
||||
response = await client.messages.create(
|
||||
model=self.claude_model,
|
||||
max_tokens=1024,
|
||||
system=system_prompt,
|
||||
messages=[{"role": "user", "content": user_message}],
|
||||
temperature=0.3, # Lower = more consistent
|
||||
)
|
||||
|
||||
return response.content[0].text.strip()
|
||||
@@ -166,20 +166,16 @@ class StewardAgent:
|
||||
"stream": False,
|
||||
"options": {
|
||||
"temperature": 0.3, # Lower = more consistent
|
||||
"top_p": 0.9
|
||||
}
|
||||
}
|
||||
"top_p": 0.9,
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
return result["response"].strip()
|
||||
|
||||
async def analyze(
|
||||
self,
|
||||
query: str,
|
||||
conversation_history: Optional[list[dict]] = None
|
||||
) -> str:
|
||||
async def analyze(self, query: str, conversation_history: list[dict] | None = None) -> str:
|
||||
"""
|
||||
Analyze query and return plain text recommendation.
|
||||
|
||||
@@ -227,20 +223,33 @@ class StewardAgent:
|
||||
return analysis_text
|
||||
|
||||
except Exception as e:
|
||||
# If Claude fails, try Ollama as fallback
|
||||
# Mid-request fallback: retry on the other backend when possible
|
||||
if self._use_claude:
|
||||
logger.warning(
|
||||
"steward_claude_fallback",
|
||||
error=str(e),
|
||||
)
|
||||
analysis_text = await self._call_ollama(prompt)
|
||||
logger.debug(
|
||||
"steward_analysis_received",
|
||||
backend="ollama_fallback",
|
||||
text_preview=analysis_text[:150],
|
||||
fallback_backend = "ollama_fallback"
|
||||
elif is_claude_available():
|
||||
logger.warning(
|
||||
"steward_ollama_fallback",
|
||||
error=str(e),
|
||||
)
|
||||
return analysis_text
|
||||
raise
|
||||
analysis_text = await self._call_claude(
|
||||
system_prompt="You are the Steward of the household, advising the Butler (Tatlock) on which capabilities to use. Be concise and specific.",
|
||||
user_message=prompt,
|
||||
)
|
||||
fallback_backend = "claude_fallback"
|
||||
else:
|
||||
raise
|
||||
|
||||
logger.debug(
|
||||
"steward_analysis_received",
|
||||
backend=fallback_backend,
|
||||
text_preview=analysis_text[:150],
|
||||
)
|
||||
return analysis_text
|
||||
|
||||
|
||||
# Global Steward instance
|
||||
|
||||
@@ -4,7 +4,8 @@ Steward agent schemas.
|
||||
Defines the structured output models for Steward's request analysis
|
||||
and capability recommendations.
|
||||
"""
|
||||
from typing import Any, Literal, Optional
|
||||
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
@@ -16,16 +17,16 @@ class ConversationContext(BaseModel):
|
||||
The Steward analyzes the full conversation to identify references
|
||||
to previous topics, helping the Butler maintain context.
|
||||
"""
|
||||
|
||||
has_previous_context: bool = Field(
|
||||
description="Whether the current request references previous conversation turns"
|
||||
)
|
||||
relevant_turns: list[int] = Field(
|
||||
default_factory=list,
|
||||
description="0-indexed turn numbers that are relevant to the current request"
|
||||
description="0-indexed turn numbers that are relevant to the current request",
|
||||
)
|
||||
context_summary: str = Field(
|
||||
default="",
|
||||
description="Brief summary of relevant context for the Butler"
|
||||
default="", description="Brief summary of relevant context for the Butler"
|
||||
)
|
||||
|
||||
|
||||
@@ -40,29 +41,28 @@ class StewardRecommendation(BaseModel):
|
||||
- Conversation context
|
||||
- Missing capabilities (if any)
|
||||
"""
|
||||
|
||||
recommended_capabilities: list[str] = Field(
|
||||
description="List of household member names to include (e.g., ['tatlock_core'])"
|
||||
)
|
||||
reasoning: str = Field(
|
||||
description="Explanation of why these capabilities were recommended"
|
||||
)
|
||||
reasoning: str = Field(description="Explanation of why these capabilities were recommended")
|
||||
estimated_complexity: Literal["simple", "moderate", "complex"] = Field(
|
||||
description="Complexity assessment: simple (1 tool), moderate (2-3 tools), complex (multiple tools/steps)"
|
||||
)
|
||||
conversation_context: ConversationContext = Field(
|
||||
description="Contextual information from conversation history"
|
||||
)
|
||||
missing_capabilities: Optional[str] = Field(
|
||||
missing_capabilities: str | None = Field(
|
||||
default=None,
|
||||
description="Description of capabilities that would be helpful but aren't available"
|
||||
description="Description of capabilities that would be helpful but aren't available",
|
||||
)
|
||||
memory_context: dict[str, Any] = Field(
|
||||
default_factory=dict,
|
||||
description="Pre-fetched user context from memory (profile, preferences)"
|
||||
description="Pre-fetched user context from memory (profile, preferences)",
|
||||
)
|
||||
enriched_query: str = Field(
|
||||
default="",
|
||||
description="User query with auto-filled context (location, timezone) when not specified"
|
||||
description="User query with auto-filled context (location, timezone) when not specified",
|
||||
)
|
||||
|
||||
def format_for_butler(self) -> str:
|
||||
@@ -114,8 +114,9 @@ class StewardRecommendation(BaseModel):
|
||||
lines.append(f" • preferences: {prefs_str}")
|
||||
|
||||
# Add delegation instructions when expert agents are recommended
|
||||
delegation_agents = [c for c in self.recommended_capabilities
|
||||
if c in ("biographer", "librarian")]
|
||||
delegation_agents = [
|
||||
c for c in self.recommended_capabilities if c in ("biographer", "librarian")
|
||||
]
|
||||
if delegation_agents:
|
||||
lines.append("-" * 40)
|
||||
lines.append("DELEGATION REQUIRED:")
|
||||
|
||||
+150
-59
@@ -7,47 +7,102 @@ and error handling.
|
||||
Parses plain text recommendations into structured data.
|
||||
Includes memory pre-fetch for user context injection.
|
||||
"""
|
||||
|
||||
import re
|
||||
from typing import Any, Optional
|
||||
from typing import Any
|
||||
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger, log_operation
|
||||
from src.core.memory_service import memory_service
|
||||
|
||||
from .agent import get_steward_agent
|
||||
from .schemas import ConversationContext, StewardRecommendation
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
_DELEGATE_LINE_RE = re.compile(r"^[ \t]*DELEGATE:[ \t]*(.+)$", re.IGNORECASE | re.MULTILINE)
|
||||
|
||||
|
||||
def _mentions(needle: str, haystack: str) -> bool:
|
||||
"""Whole-word containment. Substring matching is what made this go wrong."""
|
||||
return re.search(rf"(?<!\w){re.escape(needle)}(?!\w)", haystack) is not None
|
||||
|
||||
|
||||
def _extract_capabilities(text: str) -> list[str]:
|
||||
"""
|
||||
Extract capability names from Steward's text response.
|
||||
Extract capability names from the Steward's declared delegation.
|
||||
|
||||
Uses keyword matching to find mentioned capabilities.
|
||||
The prompt instructs the Steward to answer in a fixed shape::
|
||||
|
||||
DELEGATE: <capability> to <action> <task>
|
||||
REASON: ...
|
||||
COMPLEXITY: ...
|
||||
CONTEXT: ...
|
||||
|
||||
Only the DELEGATE line states intent; the rest is free prose. An earlier
|
||||
version substring-matched capability *domains* across the whole response,
|
||||
which routed on ordinary English: "description" contains "script" and
|
||||
"discover" contains "cover" (both housekeeper domains), "acknowledge"
|
||||
contains "knowledge" and "know" (librarian, biographer), and "economy"
|
||||
contains "my" (biographer). Any REASON line could therefore summon agents
|
||||
the Steward never asked for, and a spurious librarian is a real
|
||||
multi-second web call.
|
||||
|
||||
It also made prose length a routing input, so anything that shortened the
|
||||
Steward's output — such as disabling model thinking — would look like it had
|
||||
improved routing.
|
||||
|
||||
Resolution is layered, most explicit first:
|
||||
1. a DELEGATE line beginning with a capability name — the documented shape
|
||||
2. a capability named anywhere on a DELEGATE line
|
||||
3. a capability *domain* on a DELEGATE line, for a loosely worded answer
|
||||
4. no DELEGATE line: capability names only, never domains
|
||||
|
||||
Args:
|
||||
text: Steward's plain text analysis
|
||||
|
||||
Returns:
|
||||
List of capability names (e.g., ['tatlock_core'])
|
||||
List of capability names (e.g. ['tatlock_core']), de-duplicated.
|
||||
"""
|
||||
text_lower = text.lower()
|
||||
registry = get_household_registry()
|
||||
capabilities = registry.get_all_capabilities()
|
||||
delegate_lines = [line.strip().lower() for line in _DELEGATE_LINE_RE.findall(text or "")]
|
||||
|
||||
found_caps = []
|
||||
found_caps: list[str] = []
|
||||
|
||||
for cap in capabilities:
|
||||
# Check if capability name is mentioned
|
||||
if cap.name.lower() in text_lower:
|
||||
found_caps.append(cap.name)
|
||||
def _add(name: str) -> None:
|
||||
if name not in found_caps:
|
||||
found_caps.append(name)
|
||||
|
||||
if not delegate_lines:
|
||||
# Either the Steward judged no capability necessary — the prompt's
|
||||
# conversational path, whose correct answer is [] — or it ignored the
|
||||
# format. Names only: domain words are ordinary English and would fire
|
||||
# on any prose, which is the bug described above.
|
||||
haystack = (text or "").lower()
|
||||
for cap in capabilities:
|
||||
if _mentions(cap.name.lower(), haystack):
|
||||
_add(cap.name)
|
||||
return found_caps
|
||||
|
||||
for line in delegate_lines:
|
||||
leading = next((c for c in capabilities if line.startswith(c.name.lower())), None)
|
||||
if leading is not None:
|
||||
_add(leading.name)
|
||||
continue
|
||||
|
||||
# Check if any domains are mentioned
|
||||
for domain in cap.domains:
|
||||
if domain.lower() in text_lower:
|
||||
found_caps.append(cap.name)
|
||||
break
|
||||
named = [c for c in capabilities if _mentions(c.name.lower(), line)]
|
||||
if named:
|
||||
for cap in named:
|
||||
_add(cap.name)
|
||||
continue
|
||||
|
||||
# Last resort. Scoped to this line, so the REASON and CONTEXT prose that
|
||||
# caused the original misrouting can no longer reach it.
|
||||
for cap in capabilities:
|
||||
if any(_mentions(domain.lower(), line) for domain in cap.domains):
|
||||
_add(cap.name)
|
||||
|
||||
return found_caps
|
||||
|
||||
@@ -73,8 +128,7 @@ def _extract_complexity(text: str) -> str:
|
||||
|
||||
|
||||
def _extract_conversation_context(
|
||||
text: str,
|
||||
conversation_history: list[dict]
|
||||
text: str, conversation_history: list[dict]
|
||||
) -> ConversationContext:
|
||||
"""
|
||||
Extract conversation context analysis from text.
|
||||
@@ -89,13 +143,15 @@ def _extract_conversation_context(
|
||||
text_lower = text.lower()
|
||||
|
||||
# Check if conversation history is referenced
|
||||
has_context = bool(conversation_history) and any([
|
||||
"previous" in text_lower,
|
||||
"earlier" in text_lower,
|
||||
"context" in text_lower,
|
||||
"turn" in text_lower,
|
||||
"history" in text_lower,
|
||||
])
|
||||
has_context = bool(conversation_history) and any(
|
||||
[
|
||||
"previous" in text_lower,
|
||||
"earlier" in text_lower,
|
||||
"context" in text_lower,
|
||||
"turn" in text_lower,
|
||||
"history" in text_lower,
|
||||
]
|
||||
)
|
||||
|
||||
# Extract turn numbers if mentioned (e.g., "turn 0", "turn 1")
|
||||
relevant_turns = []
|
||||
@@ -107,21 +163,23 @@ def _extract_conversation_context(
|
||||
context_summary = ""
|
||||
if has_context:
|
||||
# Extract sentence(s) mentioning context
|
||||
sentences = text.split('.')
|
||||
context_sentences = [s for s in sentences if any(
|
||||
word in s.lower() for word in ["previous", "earlier", "context", "history"]
|
||||
)]
|
||||
sentences = text.split(".")
|
||||
context_sentences = [
|
||||
s
|
||||
for s in sentences
|
||||
if any(word in s.lower() for word in ["previous", "earlier", "context", "history"])
|
||||
]
|
||||
if context_sentences:
|
||||
context_summary = context_sentences[0].strip()
|
||||
|
||||
return ConversationContext(
|
||||
has_previous_context=has_context,
|
||||
relevant_turns=relevant_turns,
|
||||
context_summary=context_summary
|
||||
context_summary=context_summary,
|
||||
)
|
||||
|
||||
|
||||
def _extract_missing_capabilities(text: str) -> Optional[str]:
|
||||
def _extract_missing_capabilities(text: str) -> str | None:
|
||||
"""
|
||||
Extract missing capability notes from text.
|
||||
|
||||
@@ -134,15 +192,16 @@ def _extract_missing_capabilities(text: str) -> Optional[str]:
|
||||
text_lower = text.lower()
|
||||
|
||||
# Look for indicators of missing capabilities
|
||||
if any(word in text_lower for word in [
|
||||
"missing", "unavailable", "not available", "don't have", "doesn't have"
|
||||
]):
|
||||
if any(
|
||||
word in text_lower
|
||||
for word in ["missing", "unavailable", "not available", "don't have", "doesn't have"]
|
||||
):
|
||||
# Find the sentence mentioning missing capabilities
|
||||
sentences = text.split('.')
|
||||
sentences = text.split(".")
|
||||
for sentence in sentences:
|
||||
if any(word in sentence.lower() for word in [
|
||||
"missing", "unavailable", "not available"
|
||||
]):
|
||||
if any(
|
||||
word in sentence.lower() for word in ["missing", "unavailable", "not available"]
|
||||
):
|
||||
return sentence.strip()
|
||||
|
||||
return None
|
||||
@@ -183,7 +242,7 @@ def _build_enriched_query(user_request: str, memory_context: dict[str, Any]) ->
|
||||
# Check if location is needed and not specified
|
||||
location_keywords = ["weather", "temperature", "forecast", "nearby", "local", "here"]
|
||||
# Use word boundary pattern to avoid false positives like "at" in "what"
|
||||
location_prepositions = [r'\bin\b', r'\bat\b', r'\bnear\b', r'\baround\b', r'\bfor\b']
|
||||
location_prepositions = [r"\bin\b", r"\bat\b", r"\bnear\b", r"\baround\b", r"\bfor\b"]
|
||||
location_specified = any(re.search(p, request_lower) for p in location_prepositions)
|
||||
|
||||
if any(word in request_lower for word in location_keywords):
|
||||
@@ -234,32 +293,65 @@ async def _prefetch_memory_context(user_request: str) -> dict[str, Any]:
|
||||
profile_keys = []
|
||||
|
||||
# Location-related queries
|
||||
if any(word in request_lower for word in [
|
||||
"weather", "temperature", "forecast", "nearby", "local",
|
||||
"directions", "distance", "map", "here",
|
||||
# Direct location questions
|
||||
"live", "where", "home", "reside", "location", "address",
|
||||
]):
|
||||
if any(
|
||||
word in request_lower
|
||||
for word in [
|
||||
"weather",
|
||||
"temperature",
|
||||
"forecast",
|
||||
"nearby",
|
||||
"local",
|
||||
"directions",
|
||||
"distance",
|
||||
"map",
|
||||
"here",
|
||||
# Direct location questions
|
||||
"live",
|
||||
"where",
|
||||
"home",
|
||||
"reside",
|
||||
"location",
|
||||
"address",
|
||||
]
|
||||
):
|
||||
profile_keys.append("location")
|
||||
|
||||
# Time-related queries
|
||||
if any(word in request_lower for word in [
|
||||
"time", "schedule", "meeting", "appointment", "reminder",
|
||||
"alarm", "when", "today", "tomorrow"
|
||||
]):
|
||||
if any(
|
||||
word in request_lower
|
||||
for word in [
|
||||
"time",
|
||||
"schedule",
|
||||
"meeting",
|
||||
"appointment",
|
||||
"reminder",
|
||||
"alarm",
|
||||
"when",
|
||||
"today",
|
||||
"tomorrow",
|
||||
]
|
||||
):
|
||||
profile_keys.append("timezone")
|
||||
|
||||
# Personal queries
|
||||
if any(word in request_lower for word in [
|
||||
"my name", "who am i", "about me"
|
||||
]):
|
||||
if any(word in request_lower for word in ["my name", "who am i", "about me"]):
|
||||
profile_keys.append("name")
|
||||
|
||||
# Always fetch preferences if they might affect response format
|
||||
include_preferences = any(word in request_lower for word in [
|
||||
"temperature", "weather", "convert", "unit", "format",
|
||||
"celsius", "fahrenheit", "metric", "imperial"
|
||||
])
|
||||
include_preferences = any(
|
||||
word in request_lower
|
||||
for word in [
|
||||
"temperature",
|
||||
"weather",
|
||||
"convert",
|
||||
"unit",
|
||||
"format",
|
||||
"celsius",
|
||||
"fahrenheit",
|
||||
"metric",
|
||||
"imperial",
|
||||
]
|
||||
)
|
||||
|
||||
try:
|
||||
return await memory_service.prefetch_context(
|
||||
@@ -278,7 +370,7 @@ async def _prefetch_memory_context(user_request: str) -> dict[str, Any]:
|
||||
async def analyze_request(
|
||||
user_request: str,
|
||||
conversation_history: list[dict],
|
||||
conversation_id: Optional[str] = None,
|
||||
conversation_id: str | None = None,
|
||||
) -> StewardRecommendation:
|
||||
"""
|
||||
Analyze user request with full conversation context.
|
||||
@@ -310,7 +402,7 @@ async def analyze_request(
|
||||
"request_preview": user_request[:100],
|
||||
"conversation_id": conversation_id,
|
||||
"history_length": len(conversation_history),
|
||||
}
|
||||
},
|
||||
) as log_ctx:
|
||||
try:
|
||||
# Pre-fetch user context from memory (fast, no LLM)
|
||||
@@ -329,8 +421,7 @@ async def analyze_request(
|
||||
|
||||
# Get plain text analysis from Steward
|
||||
analysis_text = await steward.analyze(
|
||||
user_request,
|
||||
conversation_history=conversation_history
|
||||
user_request, conversation_history=conversation_history
|
||||
)
|
||||
|
||||
# Parse plain text into structured recommendation
|
||||
|
||||
+138
-92
@@ -6,24 +6,25 @@ The agent embodies a witty, capable British butler personality.
|
||||
"""
|
||||
|
||||
import secrets
|
||||
from typing import AsyncGenerator, Any
|
||||
from collections.abc import AsyncGenerator
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
from pydantic_ai import Agent, RunContext
|
||||
|
||||
from src.agents.base import AgentInterface, OutputItem
|
||||
from src.agents.tatlock_core.tools import (
|
||||
calculate,
|
||||
get_current_datetime,
|
||||
calculate_time_offset,
|
||||
get_current_datetime,
|
||||
time_difference,
|
||||
)
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
from src.core.tracing import (
|
||||
start_span, end_span, get_current_span,
|
||||
SpanType,
|
||||
add_tool_spans_from_messages,
|
||||
SpanType, SpanStatus,
|
||||
end_span,
|
||||
start_span,
|
||||
)
|
||||
|
||||
logger = get_logger(__name__)
|
||||
@@ -32,9 +33,10 @@ logger = get_logger(__name__)
|
||||
@dataclass
|
||||
class ToolCallTracker:
|
||||
"""Tracks tool calls for reporting to reasoning output."""
|
||||
|
||||
calls: list[str] = field(default_factory=list)
|
||||
|
||||
def log_call(self, message: str):
|
||||
def log_call(self, message: str) -> None:
|
||||
"""Log a tool call."""
|
||||
self.calls.append(message)
|
||||
|
||||
@@ -129,6 +131,22 @@ or
|
||||
"""
|
||||
|
||||
|
||||
# Tool-phase prompt for orchestrate_tool_calls(). The butler personality prompt
|
||||
# suppresses tool calling on small local models (gemma4 reasons about the tool,
|
||||
# then answers from memory with wrong arithmetic), so the orchestration phase
|
||||
# uses a terse operator prompt; synthesize_from_results() applies the persona.
|
||||
TATLOCK_ORCHESTRATION_PROMPT = """You are the tool-execution phase of Tatlock, \
|
||||
a butler assistant. Your only job is to gather accurate results by calling the \
|
||||
provided tools.
|
||||
|
||||
- ALWAYS use tools for the task - never answer from memory and never do mental math.
|
||||
- Mathematics: call the calculate tool, even for trivial arithmetic.
|
||||
- Dates and times: call the date/time tools, never guess.
|
||||
- When the instructions say DELEGATE to an agent, call the matching delegate_to_* tool.
|
||||
- After the tool results arrive, reply with a one-line factual summary of the results. \
|
||||
A later step writes the polished reply, so do not add personality."""
|
||||
|
||||
|
||||
class TatlockAgent(AgentInterface):
|
||||
"""
|
||||
Tatlock - The Butler agent using PydanticAI with Ollama.
|
||||
@@ -137,11 +155,14 @@ class TatlockAgent(AgentInterface):
|
||||
currently in Phase 1 (basic LLM integration without expert agents).
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
def __init__(self) -> None:
|
||||
"""Initialize Tatlock (lazy agent creation)."""
|
||||
self._agent = None # Lazy initialization
|
||||
# Deps are a ToolCallTracker: every registered tool takes
|
||||
# RunContext[ToolCallTracker], and run() is called with one. Saying so
|
||||
# is what lets the tool registrations below type-check at all.
|
||||
self._agent: Agent[ToolCallTracker, str] | None = None # Lazy initialization
|
||||
|
||||
def _ensure_agent(self):
|
||||
def _ensure_agent(self) -> None:
|
||||
"""Ensure the PydanticAI agent is initialized (lazy initialization)."""
|
||||
if self._agent is not None:
|
||||
return
|
||||
@@ -162,13 +183,19 @@ class TatlockAgent(AgentInterface):
|
||||
self._agent = Agent(
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT,
|
||||
deps_type=ToolCallTracker,
|
||||
)
|
||||
|
||||
# Register tools with the agent
|
||||
self._register_tools()
|
||||
|
||||
def _register_tools(self):
|
||||
"""Register permanent tools with the PydanticAI agent."""
|
||||
def _register_tools(self) -> None:
|
||||
"""Register permanent tools with the PydanticAI agent.
|
||||
|
||||
Called only from _ensure_agent, immediately after the agent is built, so
|
||||
the assert documents an invariant rather than guarding a real case.
|
||||
"""
|
||||
assert self._agent is not None, "_register_tools called before the agent exists"
|
||||
|
||||
# Calculator tool
|
||||
@self._agent.tool
|
||||
@@ -223,7 +250,9 @@ class TatlockAgent(AgentInterface):
|
||||
|
||||
# Time difference calculator
|
||||
@self._agent.tool
|
||||
def calculate_time_difference(ctx: RunContext[ToolCallTracker], date1_str: str, date2_str: str = "now") -> str:
|
||||
def calculate_time_difference(
|
||||
ctx: RunContext[ToolCallTracker], date1_str: str, date2_str: str = "now"
|
||||
) -> str:
|
||||
"""
|
||||
Calculate the difference between two dates.
|
||||
|
||||
@@ -235,7 +264,9 @@ class TatlockAgent(AgentInterface):
|
||||
Human-readable description of the time difference
|
||||
"""
|
||||
if ctx.deps:
|
||||
ctx.deps.log_call(f"🕐 Calculating time difference between {date1_str} and {date2_str}")
|
||||
ctx.deps.log_call(
|
||||
f"🕐 Calculating time difference between {date1_str} and {date2_str}"
|
||||
)
|
||||
return time_difference(date1_str, date2_str)
|
||||
|
||||
# NOTE: Web search has been moved to The Librarian agent.
|
||||
@@ -255,7 +286,7 @@ class TatlockAgent(AgentInterface):
|
||||
temperature: float = 1.0,
|
||||
max_tokens: int | None = None,
|
||||
stop: list[str] | None = None,
|
||||
**kwargs: Any
|
||||
**kwargs: Any,
|
||||
) -> AsyncGenerator[OutputItem, None]:
|
||||
"""
|
||||
Generate response using PydanticAI with Ollama.
|
||||
@@ -289,20 +320,28 @@ class TatlockAgent(AgentInterface):
|
||||
type="message",
|
||||
id=f"msg_{generate_id()}",
|
||||
role="assistant",
|
||||
content=[{
|
||||
"type": "output_text",
|
||||
"text": "I'm afraid I didn't receive a message, sir. How may I assist you?",
|
||||
"annotations": []
|
||||
}],
|
||||
status="completed"
|
||||
content=[
|
||||
{
|
||||
"type": "output_text",
|
||||
"text": "I'm afraid I didn't receive a message, sir. How may I assist you?",
|
||||
"annotations": [],
|
||||
}
|
||||
],
|
||||
status="completed",
|
||||
)
|
||||
return
|
||||
|
||||
# Build message history (all messages except the last user message)
|
||||
# PydanticAI expects history as list of ModelRequest/ModelResponse objects
|
||||
from pydantic_ai.messages import ModelRequest, ModelResponse, UserPromptPart, TextPart
|
||||
from pydantic_ai.messages import (
|
||||
ModelMessage,
|
||||
ModelRequest,
|
||||
ModelResponse,
|
||||
TextPart,
|
||||
UserPromptPart,
|
||||
)
|
||||
|
||||
message_history = []
|
||||
message_history: list[ModelMessage] = []
|
||||
for i, msg in enumerate(messages[:-1]): # All messages except the last one
|
||||
role = msg.get("role")
|
||||
content = msg.get("content", "")
|
||||
@@ -314,7 +353,9 @@ class TatlockAgent(AgentInterface):
|
||||
|
||||
# Debug: Check for problematic content
|
||||
if '"' in content or "'" in content:
|
||||
logger.debug(f"Message {i} ({role}) contains quotes. Content preview: {content[:100]}...")
|
||||
logger.debug(
|
||||
f"Message {i} ({role}) contains quotes. Content preview: {content[:100]}..."
|
||||
)
|
||||
|
||||
# Convert to PydanticAI message format
|
||||
try:
|
||||
@@ -323,9 +364,7 @@ class TatlockAgent(AgentInterface):
|
||||
ModelRequest(parts=[UserPromptPart(content=content)])
|
||||
)
|
||||
elif role == "assistant":
|
||||
message_history.append(
|
||||
ModelResponse(parts=[TextPart(content=content)])
|
||||
)
|
||||
message_history.append(ModelResponse(parts=[TextPart(content=content)]))
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating message history item {i}: {e}")
|
||||
logger.error(f"Problematic content: {repr(content)}")
|
||||
@@ -336,7 +375,9 @@ class TatlockAgent(AgentInterface):
|
||||
if message_history:
|
||||
for i, hist_msg in enumerate(message_history):
|
||||
msg_type = type(hist_msg).__name__
|
||||
content_preview = str(hist_msg.parts[0].content)[:50] if hist_msg.parts else "no parts"
|
||||
content_preview = (
|
||||
str(hist_msg.parts[0].content)[:50] if hist_msg.parts else "no parts"
|
||||
)
|
||||
logger.info(f" History[{i}]: {msg_type} - {content_preview}...")
|
||||
|
||||
# Generate reasoning output if requested
|
||||
@@ -346,10 +387,10 @@ class TatlockAgent(AgentInterface):
|
||||
id=f"reasoning_{generate_id()}",
|
||||
summary=[
|
||||
"Analyzing your request, sir...",
|
||||
"Formulating response based on available knowledge..."
|
||||
"Formulating response based on available knowledge...",
|
||||
],
|
||||
thinking="", # PydanticAI doesn't expose internal reasoning yet
|
||||
status="completed"
|
||||
status="completed",
|
||||
)
|
||||
|
||||
# Create a tool call tracker for this request
|
||||
@@ -366,7 +407,7 @@ class TatlockAgent(AgentInterface):
|
||||
result = await self.agent.run(
|
||||
user_message,
|
||||
message_history=message_history if message_history else None,
|
||||
deps=tracker
|
||||
deps=tracker,
|
||||
)
|
||||
final_text = result.output
|
||||
|
||||
@@ -377,7 +418,7 @@ class TatlockAgent(AgentInterface):
|
||||
id=f"reasoning_tools_{generate_id()}",
|
||||
summary=tracker.calls,
|
||||
thinking="",
|
||||
status="completed"
|
||||
status="completed",
|
||||
)
|
||||
|
||||
# Yield the complete message
|
||||
@@ -386,12 +427,8 @@ class TatlockAgent(AgentInterface):
|
||||
type="message",
|
||||
id=msg_id,
|
||||
role="assistant",
|
||||
content=[{
|
||||
"type": "output_text",
|
||||
"text": final_text,
|
||||
"annotations": []
|
||||
}],
|
||||
status="completed"
|
||||
content=[{"type": "output_text", "text": final_text, "annotations": []}],
|
||||
status="completed",
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
@@ -400,12 +437,14 @@ class TatlockAgent(AgentInterface):
|
||||
type="message",
|
||||
id=f"msg_{generate_id()}",
|
||||
role="assistant",
|
||||
content=[{
|
||||
"type": "output_text",
|
||||
"text": f"My apologies, sir. I encountered an error: {str(e)}",
|
||||
"annotations": []
|
||||
}],
|
||||
status="failed"
|
||||
content=[
|
||||
{
|
||||
"type": "output_text",
|
||||
"text": f"My apologies, sir. I encountered an error: {str(e)}",
|
||||
"annotations": [],
|
||||
}
|
||||
],
|
||||
status="failed",
|
||||
)
|
||||
|
||||
async def supports_tools(self) -> bool:
|
||||
@@ -474,9 +513,15 @@ class TatlockAgent(AgentInterface):
|
||||
enriched_message = f"{steward_note}\n\n{user_message}"
|
||||
|
||||
# Convert message history to PydanticAI format
|
||||
from pydantic_ai.messages import ModelRequest, ModelResponse, UserPromptPart, TextPart
|
||||
from pydantic_ai.messages import (
|
||||
ModelMessage,
|
||||
ModelRequest,
|
||||
ModelResponse,
|
||||
TextPart,
|
||||
UserPromptPart,
|
||||
)
|
||||
|
||||
pydantic_history = []
|
||||
pydantic_history: list[ModelMessage] = []
|
||||
for msg in message_history:
|
||||
role = msg.get("role")
|
||||
content = msg.get("content", "")
|
||||
@@ -485,22 +530,19 @@ class TatlockAgent(AgentInterface):
|
||||
continue
|
||||
|
||||
if role == "user":
|
||||
pydantic_history.append(
|
||||
ModelRequest(parts=[UserPromptPart(content=content)])
|
||||
)
|
||||
pydantic_history.append(ModelRequest(parts=[UserPromptPart(content=content)]))
|
||||
elif role == "assistant":
|
||||
pydantic_history.append(
|
||||
ModelResponse(parts=[TextPart(content=content)])
|
||||
)
|
||||
pydantic_history.append(ModelResponse(parts=[TextPart(content=content)]))
|
||||
|
||||
# Run with scoped tools and tracker
|
||||
# Force tool_choice: required to make LLM actually call tools
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
# Force tool_choice to make LLM actually call tools
|
||||
from src.anthropic.model_selector import get_tool_choice_settings
|
||||
|
||||
result = await scoped_agent.run(
|
||||
enriched_message,
|
||||
message_history=pydantic_history if pydantic_history else None,
|
||||
deps=tool_tracker,
|
||||
model_settings=ModelSettings(extra_body={"tool_choice": "required"})
|
||||
model_settings=get_tool_choice_settings(),
|
||||
)
|
||||
|
||||
logger.info(
|
||||
@@ -559,9 +601,15 @@ class TatlockAgent(AgentInterface):
|
||||
enriched_message = f"{steward_note}\n\n{user_message}"
|
||||
|
||||
# Convert message history to PydanticAI format
|
||||
from pydantic_ai.messages import ModelRequest, ModelResponse, UserPromptPart, TextPart
|
||||
from pydantic_ai.messages import (
|
||||
ModelMessage,
|
||||
ModelRequest,
|
||||
ModelResponse,
|
||||
TextPart,
|
||||
UserPromptPart,
|
||||
)
|
||||
|
||||
pydantic_history = []
|
||||
pydantic_history: list[ModelMessage] = []
|
||||
for msg in message_history:
|
||||
role = msg.get("role")
|
||||
content = msg.get("content", "")
|
||||
@@ -570,13 +618,9 @@ class TatlockAgent(AgentInterface):
|
||||
continue
|
||||
|
||||
if role == "user":
|
||||
pydantic_history.append(
|
||||
ModelRequest(parts=[UserPromptPart(content=content)])
|
||||
)
|
||||
pydantic_history.append(ModelRequest(parts=[UserPromptPart(content=content)]))
|
||||
elif role == "assistant":
|
||||
pydantic_history.append(
|
||||
ModelResponse(parts=[TextPart(content=content)])
|
||||
)
|
||||
pydantic_history.append(ModelResponse(parts=[TextPart(content=content)]))
|
||||
|
||||
# Use run() instead of run_stream() to avoid Ollama 400 bug
|
||||
# with streaming + tool calls (PydanticAI issues #1292, #2256)
|
||||
@@ -584,7 +628,7 @@ class TatlockAgent(AgentInterface):
|
||||
result = await scoped_agent.run(
|
||||
enriched_message,
|
||||
message_history=pydantic_history if pydantic_history else None,
|
||||
deps=tool_tracker
|
||||
deps=tool_tracker,
|
||||
)
|
||||
|
||||
# Stream the final response in chunks to maintain UX
|
||||
@@ -592,7 +636,7 @@ class TatlockAgent(AgentInterface):
|
||||
chunk_size = 50 # characters per chunk
|
||||
|
||||
for i in range(0, len(response_text), chunk_size):
|
||||
yield response_text[i:i + chunk_size]
|
||||
yield response_text[i : i + chunk_size]
|
||||
|
||||
logger.info("tatlock_scoped_run_complete")
|
||||
|
||||
@@ -624,15 +668,16 @@ class TatlockAgent(AgentInterface):
|
||||
- tool_outputs: Dict mapping tool names to their outputs
|
||||
- raw_output: The agent's raw text output
|
||||
"""
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
from pydantic_ai.messages import (
|
||||
ModelMessage,
|
||||
ModelRequest,
|
||||
ModelResponse,
|
||||
UserPromptPart,
|
||||
TextPart,
|
||||
ToolCallPart,
|
||||
ToolReturnPart,
|
||||
UserPromptPart,
|
||||
)
|
||||
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
logger.info(
|
||||
@@ -648,17 +693,17 @@ class TatlockAgent(AgentInterface):
|
||||
SpanType.TATLOCK,
|
||||
metadata={
|
||||
"scoped_tool_count": len(scoped_tools),
|
||||
"tool_names": [getattr(t, '__name__', str(t)) for t in scoped_tools[:5]],
|
||||
"tool_names": [getattr(t, "__name__", str(t)) for t in scoped_tools[:5]],
|
||||
},
|
||||
)
|
||||
|
||||
# Create a fresh agent instance with scoped tools only
|
||||
model = get_model()
|
||||
|
||||
# Create agent with scoped tools
|
||||
# Create agent with scoped tools, using the tool-phase prompt
|
||||
scoped_agent = Agent(
|
||||
model,
|
||||
system_prompt=TATLOCK_SYSTEM_PROMPT,
|
||||
system_prompt=TATLOCK_ORCHESTRATION_PROMPT,
|
||||
tools=scoped_tools,
|
||||
)
|
||||
|
||||
@@ -666,7 +711,7 @@ class TatlockAgent(AgentInterface):
|
||||
enriched_message = f"{steward_note}\n\n{user_message}"
|
||||
|
||||
# Convert message history to PydanticAI format
|
||||
pydantic_history = []
|
||||
pydantic_history: list[ModelMessage] = []
|
||||
for msg in message_history:
|
||||
role = msg.get("role")
|
||||
content = msg.get("content", "")
|
||||
@@ -675,26 +720,24 @@ class TatlockAgent(AgentInterface):
|
||||
continue
|
||||
|
||||
if role == "user":
|
||||
pydantic_history.append(
|
||||
ModelRequest(parts=[UserPromptPart(content=content)])
|
||||
)
|
||||
pydantic_history.append(ModelRequest(parts=[UserPromptPart(content=content)]))
|
||||
elif role == "assistant":
|
||||
pydantic_history.append(
|
||||
ModelResponse(parts=[TextPart(content=content)])
|
||||
)
|
||||
pydantic_history.append(ModelResponse(parts=[TextPart(content=content)]))
|
||||
|
||||
# Run with scoped tools and tracker
|
||||
from src.anthropic.model_selector import get_tool_choice_settings
|
||||
|
||||
result = await scoped_agent.run(
|
||||
enriched_message,
|
||||
message_history=pydantic_history if pydantic_history else None,
|
||||
deps=tool_tracker,
|
||||
model_settings=ModelSettings(extra_body={"tool_choice": "required"})
|
||||
model_settings=get_tool_choice_settings(),
|
||||
)
|
||||
|
||||
# Extract tool calls and results from the agent's messages
|
||||
tools_called = []
|
||||
expert_results = {}
|
||||
tool_outputs = {}
|
||||
expert_results: dict[str, Any] = {}
|
||||
tool_outputs: dict[str, Any] = {}
|
||||
|
||||
# Parse through new messages to find tool calls and returns
|
||||
for msg in result.new_messages():
|
||||
@@ -766,7 +809,14 @@ class TatlockAgent(AgentInterface):
|
||||
Returns:
|
||||
str: Butler-toned response synthesized from all results
|
||||
"""
|
||||
from pydantic_ai.messages import ModelRequest, ModelResponse, UserPromptPart, TextPart
|
||||
from pydantic_ai.messages import (
|
||||
ModelMessage,
|
||||
ModelRequest,
|
||||
ModelResponse,
|
||||
TextPart,
|
||||
UserPromptPart,
|
||||
)
|
||||
|
||||
from src.anthropic.model_selector import get_model
|
||||
|
||||
logger.info(
|
||||
@@ -824,7 +874,7 @@ class TatlockAgent(AgentInterface):
|
||||
)
|
||||
|
||||
# Convert message history to PydanticAI format
|
||||
pydantic_history = []
|
||||
pydantic_history: list[ModelMessage] = []
|
||||
for msg in message_history:
|
||||
role = msg.get("role")
|
||||
content = msg.get("content", "")
|
||||
@@ -833,13 +883,9 @@ class TatlockAgent(AgentInterface):
|
||||
continue
|
||||
|
||||
if role == "user":
|
||||
pydantic_history.append(
|
||||
ModelRequest(parts=[UserPromptPart(content=content)])
|
||||
)
|
||||
pydantic_history.append(ModelRequest(parts=[UserPromptPart(content=content)]))
|
||||
elif role == "assistant":
|
||||
pydantic_history.append(
|
||||
ModelResponse(parts=[TextPart(content=content)])
|
||||
)
|
||||
pydantic_history.append(ModelResponse(parts=[TextPart(content=content)]))
|
||||
|
||||
# Run synthesis
|
||||
result = await synthesis_agent.run(
|
||||
@@ -869,9 +915,9 @@ class TatlockAgent(AgentInterface):
|
||||
async def get_capabilities(self) -> dict:
|
||||
"""Return current capabilities."""
|
||||
return {
|
||||
"streaming": True, # Streaming implemented
|
||||
"reasoning": True, # Basic reasoning summaries
|
||||
"tools": True, # Permanent tools: calculator, date/time, search
|
||||
"vision": False, # Future
|
||||
"audio": False, # Future
|
||||
"streaming": True, # Streaming implemented
|
||||
"reasoning": True, # Basic reasoning summaries
|
||||
"tools": True, # Permanent tools: calculator, date/time, search
|
||||
"vision": False, # Future
|
||||
"audio": False, # Future
|
||||
}
|
||||
|
||||
@@ -5,14 +5,15 @@ Provides calculator and date/time capabilities.
|
||||
Web search has been moved to The Librarian agent.
|
||||
Organized as a household member with toolset and capability registration.
|
||||
"""
|
||||
|
||||
from .capability import TATLOCK_CORE_CAPABILITY, get_capability
|
||||
from .toolset import get_core_tools, tatlock_core_tools
|
||||
from .tools import (
|
||||
calculate,
|
||||
calculate_time_offset,
|
||||
get_current_datetime,
|
||||
time_difference,
|
||||
)
|
||||
from .toolset import get_core_tools, tatlock_core_tools
|
||||
|
||||
__all__ = [
|
||||
# Tools
|
||||
|
||||
@@ -4,8 +4,8 @@ Household capability definition for Tatlock's core tools.
|
||||
Provides the executive summary that the Steward and Butler see
|
||||
for coordinating household capabilities.
|
||||
"""
|
||||
from src.core.household_registry import HouseholdCapability
|
||||
|
||||
from src.core.household_registry import HouseholdCapability
|
||||
|
||||
TATLOCK_CORE_CAPABILITY = HouseholdCapability(
|
||||
name="tatlock_core",
|
||||
|
||||
@@ -6,13 +6,11 @@ These tools are always available to the butler agent:
|
||||
- Date/Time toolkit: For current time and time calculations
|
||||
- SearXNG search: For searching the web for current information
|
||||
"""
|
||||
|
||||
import math
|
||||
import re
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
import httpx
|
||||
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
@@ -22,6 +20,7 @@ logger = get_logger(__name__)
|
||||
# Calculator Tool
|
||||
# ============================================================================
|
||||
|
||||
|
||||
def calculate(expression: str) -> str:
|
||||
"""
|
||||
Safely evaluate mathematical expressions.
|
||||
@@ -50,33 +49,29 @@ def calculate(expression: str) -> str:
|
||||
# Create safe namespace with math functions
|
||||
safe_dict = {
|
||||
# Basic math functions
|
||||
'sqrt': math.sqrt,
|
||||
'pow': math.pow,
|
||||
'abs': abs,
|
||||
'round': round,
|
||||
|
||||
"sqrt": math.sqrt,
|
||||
"pow": math.pow,
|
||||
"abs": abs,
|
||||
"round": round,
|
||||
# Trigonometric
|
||||
'sin': math.sin,
|
||||
'cos': math.cos,
|
||||
'tan': math.tan,
|
||||
'asin': math.asin,
|
||||
'acos': math.acos,
|
||||
'atan': math.atan,
|
||||
|
||||
"sin": math.sin,
|
||||
"cos": math.cos,
|
||||
"tan": math.tan,
|
||||
"asin": math.asin,
|
||||
"acos": math.acos,
|
||||
"atan": math.atan,
|
||||
# Logarithmic
|
||||
'log': math.log,
|
||||
'log10': math.log10,
|
||||
'log2': math.log2,
|
||||
'exp': math.exp,
|
||||
|
||||
"log": math.log,
|
||||
"log10": math.log10,
|
||||
"log2": math.log2,
|
||||
"exp": math.exp,
|
||||
# Other
|
||||
'ceil': math.ceil,
|
||||
'floor': math.floor,
|
||||
'factorial': math.factorial,
|
||||
|
||||
"ceil": math.ceil,
|
||||
"floor": math.floor,
|
||||
"factorial": math.factorial,
|
||||
# Constants
|
||||
'pi': math.pi,
|
||||
'e': math.e,
|
||||
"pi": math.pi,
|
||||
"e": math.e,
|
||||
}
|
||||
|
||||
# Evaluate the expression safely
|
||||
@@ -101,6 +96,7 @@ def calculate(expression: str) -> str:
|
||||
# Date/Time Toolkit
|
||||
# ============================================================================
|
||||
|
||||
|
||||
def get_current_datetime(format_str: str = "full") -> str:
|
||||
"""
|
||||
Get the current date and time.
|
||||
@@ -161,7 +157,7 @@ def calculate_time_offset(offset_description: str) -> str:
|
||||
|
||||
# Parse the offset description
|
||||
# Pattern: "N unit(s) ago/from now"
|
||||
pattern = r'(\d+)\s+(second|minute|hour|day|week|month|year)s?\s+(ago|from\s+now)'
|
||||
pattern = r"(\d+)\s+(second|minute|hour|day|week|month|year)s?\s+(ago|from\s+now)"
|
||||
match = re.match(pattern, offset_description.lower().strip())
|
||||
|
||||
if not match:
|
||||
|
||||
@@ -4,11 +4,11 @@ PydanticAI toolset for Tatlock's core tools.
|
||||
Converts the core tool functions into PydanticAI tool definitions
|
||||
that can be registered with agents and the household registry.
|
||||
"""
|
||||
|
||||
from pydantic_ai.tools import Tool
|
||||
|
||||
from . import tools
|
||||
|
||||
|
||||
# Create tool definitions for PydanticAI
|
||||
calculator_tool = Tool(
|
||||
function=tools.calculate,
|
||||
|
||||
+22
-25
@@ -13,11 +13,11 @@ import math
|
||||
import re
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Calculator Tool
|
||||
# ============================================================================
|
||||
|
||||
|
||||
def calculate(expression: str) -> str:
|
||||
"""
|
||||
Safely evaluate mathematical expressions.
|
||||
@@ -46,33 +46,29 @@ def calculate(expression: str) -> str:
|
||||
# Create safe namespace with math functions
|
||||
safe_dict = {
|
||||
# Basic math functions
|
||||
'sqrt': math.sqrt,
|
||||
'pow': math.pow,
|
||||
'abs': abs,
|
||||
'round': round,
|
||||
|
||||
"sqrt": math.sqrt,
|
||||
"pow": math.pow,
|
||||
"abs": abs,
|
||||
"round": round,
|
||||
# Trigonometric
|
||||
'sin': math.sin,
|
||||
'cos': math.cos,
|
||||
'tan': math.tan,
|
||||
'asin': math.asin,
|
||||
'acos': math.acos,
|
||||
'atan': math.atan,
|
||||
|
||||
"sin": math.sin,
|
||||
"cos": math.cos,
|
||||
"tan": math.tan,
|
||||
"asin": math.asin,
|
||||
"acos": math.acos,
|
||||
"atan": math.atan,
|
||||
# Logarithmic
|
||||
'log': math.log,
|
||||
'log10': math.log10,
|
||||
'log2': math.log2,
|
||||
'exp': math.exp,
|
||||
|
||||
"log": math.log,
|
||||
"log10": math.log10,
|
||||
"log2": math.log2,
|
||||
"exp": math.exp,
|
||||
# Other
|
||||
'ceil': math.ceil,
|
||||
'floor': math.floor,
|
||||
'factorial': math.factorial,
|
||||
|
||||
"ceil": math.ceil,
|
||||
"floor": math.floor,
|
||||
"factorial": math.factorial,
|
||||
# Constants
|
||||
'pi': math.pi,
|
||||
'e': math.e,
|
||||
"pi": math.pi,
|
||||
"e": math.e,
|
||||
}
|
||||
|
||||
# Evaluate the expression safely
|
||||
@@ -97,6 +93,7 @@ def calculate(expression: str) -> str:
|
||||
# Date/Time Toolkit
|
||||
# ============================================================================
|
||||
|
||||
|
||||
def get_current_datetime(format_str: str = "full") -> str:
|
||||
"""
|
||||
Get the current date and time.
|
||||
@@ -157,7 +154,7 @@ def calculate_time_offset(offset_description: str) -> str:
|
||||
|
||||
# Parse the offset description
|
||||
# Pattern: "N unit(s) ago/from now"
|
||||
pattern = r'(\d+)\s+(second|minute|hour|day|week|month|year)s?\s+(ago|from\s+now)'
|
||||
pattern = r"(\d+)\s+(second|minute|hour|day|week|month|year)s?\s+(ago|from\s+now)"
|
||||
match = re.match(pattern, offset_description.lower().strip())
|
||||
|
||||
if not match:
|
||||
|
||||
@@ -1,17 +1,26 @@
|
||||
"""
|
||||
Anthropic/Claude integration module.
|
||||
|
||||
Provides model selection with automatic fallback between Claude and Ollama.
|
||||
Provides model selection with Ollama as primary backend and Claude
|
||||
as the cloud fallback.
|
||||
"""
|
||||
|
||||
from src.anthropic.model_selector import (
|
||||
check_claude_health,
|
||||
check_ollama_health,
|
||||
get_model,
|
||||
get_tool_choice_settings,
|
||||
is_claude_available,
|
||||
is_ollama_available,
|
||||
resolve_backend,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"check_claude_health",
|
||||
"check_ollama_health",
|
||||
"get_model",
|
||||
"get_tool_choice_settings",
|
||||
"is_claude_available",
|
||||
"is_ollama_available",
|
||||
"resolve_backend",
|
||||
]
|
||||
|
||||
+170
-29
@@ -1,23 +1,84 @@
|
||||
"""
|
||||
Model selector for Claude/Ollama backend switching.
|
||||
Model selector for Ollama/Claude backend switching.
|
||||
|
||||
Provides automatic model selection with Claude as preferred backend
|
||||
and Ollama as offline fallback.
|
||||
Provides automatic model selection with Ollama as the primary local backend
|
||||
and Claude as the cloud fallback. Claude is used when PREFER_CLOUD_BACKEND
|
||||
is enabled, or automatically when Ollama is unavailable at startup.
|
||||
|
||||
The Anthropic SDK is imported lazily so a missing or broken `anthropic`
|
||||
package degrades to Ollama-only operation instead of crashing the app.
|
||||
"""
|
||||
|
||||
from typing import Union
|
||||
from __future__ import annotations
|
||||
|
||||
from pydantic_ai.models.anthropic import AnthropicModel
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.anthropic import AnthropicProvider
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import httpx
|
||||
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pydantic_ai.models.anthropic import AnthropicModel
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
# Cached health check result (set once at startup)
|
||||
# Cached health check results (set once at startup)
|
||||
_claude_available: bool | None = None
|
||||
_ollama_available: bool | None = None
|
||||
|
||||
|
||||
async def check_ollama_health() -> bool:
|
||||
"""
|
||||
Check if the Ollama server is reachable and has the configured model.
|
||||
|
||||
This should be called once at application startup.
|
||||
The result is cached in `_ollama_available`.
|
||||
|
||||
Returns:
|
||||
True if Ollama is reachable and OLLAMA_DEFAULT_MODEL is pulled.
|
||||
"""
|
||||
global _ollama_available
|
||||
|
||||
host = str(config.OLLAMA_HOST).rstrip("/")
|
||||
model = config.OLLAMA_DEFAULT_MODEL
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=5.0) as client:
|
||||
response = await client.get(f"{host}/api/tags")
|
||||
response.raise_for_status()
|
||||
names = [m.get("name", "") for m in response.json().get("models", [])]
|
||||
|
||||
if model in names or f"{model}:latest" in names:
|
||||
_ollama_available = True
|
||||
logger.info(
|
||||
"ollama_health_check_passed",
|
||||
host=host,
|
||||
model=model,
|
||||
)
|
||||
return True
|
||||
|
||||
_ollama_available = False
|
||||
logger.warning(
|
||||
"ollama_health_check_failed",
|
||||
reason="model_not_pulled",
|
||||
host=host,
|
||||
model=model,
|
||||
hint=f"run `ollama pull {model}`",
|
||||
)
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
_ollama_available = False
|
||||
logger.warning(
|
||||
"ollama_health_check_failed",
|
||||
reason="server_unreachable",
|
||||
host=host,
|
||||
error=str(e),
|
||||
)
|
||||
return False
|
||||
|
||||
|
||||
async def check_claude_health() -> bool:
|
||||
@@ -85,46 +146,93 @@ def is_claude_available() -> bool:
|
||||
return _claude_available is True
|
||||
|
||||
|
||||
def get_model(prefer_cloud: bool | None = None) -> Union[AnthropicModel, OpenAIChatModel]:
|
||||
def is_ollama_available() -> bool:
|
||||
"""
|
||||
Check if Ollama is available (from cached health check result).
|
||||
|
||||
Returns:
|
||||
False only if the startup health check confirmed Ollama is down.
|
||||
Unknown (check not run yet) counts as available so that contexts
|
||||
without lifespan events keep the local-first behavior.
|
||||
"""
|
||||
return _ollama_available is not False
|
||||
|
||||
|
||||
def resolve_backend(prefer_cloud: bool | None = None) -> str:
|
||||
"""
|
||||
Resolve which backend should serve requests.
|
||||
|
||||
Ollama is the primary backend. Claude is used when explicitly
|
||||
preferred via PREFER_CLOUD_BACKEND, or as automatic fallback
|
||||
when the startup health check found Ollama down.
|
||||
|
||||
Args:
|
||||
prefer_cloud: Override config.PREFER_CLOUD_BACKEND for this call.
|
||||
|
||||
Returns:
|
||||
"claude" or "ollama".
|
||||
"""
|
||||
use_cloud = prefer_cloud if prefer_cloud is not None else config.PREFER_CLOUD_BACKEND
|
||||
|
||||
if use_cloud and is_claude_available():
|
||||
return "claude"
|
||||
|
||||
if not is_ollama_available() and is_claude_available():
|
||||
logger.warning(
|
||||
"backend_fallback_to_claude",
|
||||
reason="ollama_unavailable",
|
||||
)
|
||||
return "claude"
|
||||
|
||||
return "ollama"
|
||||
|
||||
|
||||
def get_model(prefer_cloud: bool | None = None) -> AnthropicModel | OpenAIChatModel:
|
||||
"""
|
||||
Get the best available model.
|
||||
|
||||
Returns Claude if available and preferred, otherwise Ollama.
|
||||
Returns Ollama unless Claude is preferred (or Ollama is down).
|
||||
|
||||
Args:
|
||||
prefer_cloud: Override config.PREFER_CLOUD_BACKEND for this call.
|
||||
If None, uses the config value.
|
||||
|
||||
Returns:
|
||||
PydanticAI model instance (AnthropicModel or OpenAIChatModel).
|
||||
PydanticAI model instance (OpenAIChatModel or AnthropicModel).
|
||||
|
||||
Example:
|
||||
>>> model = get_model()
|
||||
>>> agent = Agent(model, system_prompt="...")
|
||||
"""
|
||||
# Determine preference
|
||||
use_cloud = prefer_cloud if prefer_cloud is not None else config.PREFER_CLOUD_BACKEND
|
||||
if resolve_backend(prefer_cloud) == "claude":
|
||||
try:
|
||||
from pydantic_ai.models.anthropic import AnthropicModel
|
||||
from pydantic_ai.providers.anthropic import AnthropicProvider
|
||||
|
||||
# Use Claude if available and preferred
|
||||
if use_cloud and is_claude_available():
|
||||
logger.debug(
|
||||
"model_selected",
|
||||
backend="claude",
|
||||
model=config.ANTHROPIC_MODEL,
|
||||
)
|
||||
return AnthropicModel(
|
||||
model_name=config.ANTHROPIC_MODEL,
|
||||
provider=AnthropicProvider(api_key=config.ANTHROPIC_API_KEY),
|
||||
)
|
||||
logger.debug(
|
||||
"model_selected",
|
||||
backend="claude",
|
||||
model=config.ANTHROPIC_MODEL,
|
||||
)
|
||||
return AnthropicModel(
|
||||
model_name=config.ANTHROPIC_MODEL,
|
||||
provider=AnthropicProvider(api_key=config.ANTHROPIC_API_KEY),
|
||||
)
|
||||
except ImportError as e:
|
||||
logger.error(
|
||||
"claude_backend_import_failed",
|
||||
error=str(e),
|
||||
hint="anthropic package missing or incompatible; using Ollama",
|
||||
)
|
||||
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
|
||||
# Fall back to Ollama
|
||||
from src.ollama.provider import get_ollama_provider
|
||||
|
||||
logger.debug(
|
||||
"model_selected",
|
||||
backend="ollama",
|
||||
model=config.OLLAMA_DEFAULT_MODEL,
|
||||
reason="fallback" if use_cloud else "preferred_local",
|
||||
)
|
||||
return OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
@@ -132,6 +240,37 @@ def get_model(prefer_cloud: bool | None = None) -> Union[AnthropicModel, OpenAIC
|
||||
)
|
||||
|
||||
|
||||
def get_tool_choice_settings() -> ModelSettings:
|
||||
"""
|
||||
Get model_settings for forcing tool calls on the first request.
|
||||
|
||||
For Claude: PydanticAI handles tool_choice natively, so no extra_body needed.
|
||||
For Ollama: Pass tool_choice="required" via extra_body to force tool calling.
|
||||
"""
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
|
||||
if resolve_backend() == "claude":
|
||||
# PydanticAI's Anthropic model handles tool_choice internally
|
||||
return ModelSettings()
|
||||
else:
|
||||
# Ollama needs explicit tool_choice via extra_body
|
||||
return ModelSettings(extra_body={"tool_choice": "required"})
|
||||
|
||||
|
||||
def get_sampling_settings(temperature: float) -> ModelSettings:
|
||||
"""
|
||||
Get model_settings with a sampling temperature where the backend allows it.
|
||||
|
||||
Ollama accepts a temperature; Claude Sonnet 5+ rejects sampling
|
||||
parameters, so the Claude backend gets empty settings.
|
||||
"""
|
||||
from pydantic_ai.settings import ModelSettings
|
||||
|
||||
if resolve_backend() == "claude":
|
||||
return ModelSettings()
|
||||
return ModelSettings(temperature=temperature)
|
||||
|
||||
|
||||
def get_model_info() -> dict:
|
||||
"""
|
||||
Get information about the current model configuration.
|
||||
@@ -141,12 +280,14 @@ def get_model_info() -> dict:
|
||||
Returns:
|
||||
Dict with backend, model name, and availability info.
|
||||
"""
|
||||
use_cloud = config.PREFER_CLOUD_BACKEND and is_claude_available()
|
||||
backend = resolve_backend()
|
||||
|
||||
return {
|
||||
"backend": "claude" if use_cloud else "ollama",
|
||||
"model": config.ANTHROPIC_MODEL if use_cloud else config.OLLAMA_DEFAULT_MODEL,
|
||||
"backend": backend,
|
||||
"model": config.ANTHROPIC_MODEL if backend == "claude" else config.OLLAMA_DEFAULT_MODEL,
|
||||
"claude_available": is_claude_available(),
|
||||
"claude_configured": bool(config.ANTHROPIC_API_KEY),
|
||||
"ollama_available": is_ollama_available(),
|
||||
"ollama_model": config.OLLAMA_DEFAULT_MODEL,
|
||||
"prefer_cloud": config.PREFER_CLOUD_BACKEND,
|
||||
}
|
||||
|
||||
+24
-19
@@ -2,12 +2,13 @@
|
||||
Chat completion router.
|
||||
OpenAI-compatible /v1/chat/completions endpoint.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import AsyncGenerator
|
||||
from collections.abc import AsyncGenerator
|
||||
|
||||
from fastapi import APIRouter
|
||||
from sse_starlette.sse import EventSourceResponse
|
||||
from starlette.responses import StreamingResponse
|
||||
|
||||
from src.chat import service
|
||||
from src.chat.schemas import (
|
||||
@@ -22,47 +23,51 @@ router = APIRouter(prefix="/chat", tags=["chat"])
|
||||
|
||||
async def _stream_response(
|
||||
request: ChatCompletionRequest,
|
||||
) -> AsyncGenerator[dict, None]:
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
Generate SSE stream for chat completion.
|
||||
|
||||
EventSourceResponse adds "data: " prefix automatically.
|
||||
We just yield the dict/string content.
|
||||
Yields raw SSE-formatted strings matching OpenAI's format exactly:
|
||||
data: {json}\n\n
|
||||
"""
|
||||
try:
|
||||
async for chunk in service.create_chat_completion_stream(request):
|
||||
# Yield dict - EventSourceResponse will format as SSE
|
||||
yield {"data": chunk.model_dump_json()}
|
||||
yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
|
||||
|
||||
# Send [DONE] message
|
||||
yield {"data": "[DONE]"}
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in streaming response: {e}")
|
||||
error_data = {"error": {"message": str(e), "type": "internal_error"}}
|
||||
yield {"data": json.dumps(error_data)}
|
||||
error_data = json.dumps({"error": {"message": str(e), "type": "internal_error"}})
|
||||
yield f"data: {error_data}\n\n"
|
||||
|
||||
|
||||
@router.post("/completions", response_model=ChatCompletionResponse)
|
||||
async def create_chat_completion(
|
||||
request: ChatCompletionRequest,
|
||||
) -> ChatCompletionResponse | EventSourceResponse:
|
||||
) -> ChatCompletionResponse | StreamingResponse:
|
||||
"""
|
||||
Create chat completion (OpenAI-compatible).
|
||||
|
||||
|
||||
Supports both regular and streaming responses.
|
||||
Currently returns mock lorem ipsum responses.
|
||||
|
||||
|
||||
Args:
|
||||
request: Chat completion request
|
||||
|
||||
|
||||
Returns:
|
||||
Chat completion response or SSE stream
|
||||
"""
|
||||
logger.info(f"Chat completion request for model: {request.model}")
|
||||
|
||||
|
||||
if request.stream:
|
||||
logger.info("Streaming response requested")
|
||||
return EventSourceResponse(_stream_response(request))
|
||||
|
||||
return StreamingResponse(
|
||||
_stream_response(request),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-store",
|
||||
"X-Accel-Buffering": "no",
|
||||
},
|
||||
)
|
||||
|
||||
return await service.create_chat_completion(request)
|
||||
|
||||
@@ -2,6 +2,7 @@
|
||||
OpenAI-compatible chat completion schemas.
|
||||
Following OpenAI API specification for compatibility.
|
||||
"""
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import Field
|
||||
@@ -11,6 +12,7 @@ from src.core.models import CustomBaseModel
|
||||
|
||||
class ChatMessage(CustomBaseModel):
|
||||
"""OpenAI-compatible chat message."""
|
||||
|
||||
role: Literal["system", "user", "assistant"]
|
||||
content: str
|
||||
name: str | None = None
|
||||
@@ -18,6 +20,7 @@ class ChatMessage(CustomBaseModel):
|
||||
|
||||
class ChatCompletionRequest(CustomBaseModel):
|
||||
"""OpenAI-compatible chat completion request."""
|
||||
|
||||
model: str = Field(..., description="Model to use for completion")
|
||||
messages: list[ChatMessage] = Field(..., description="List of messages")
|
||||
temperature: float | None = Field(default=0.7, ge=0.0, le=2.0)
|
||||
@@ -29,6 +32,7 @@ class ChatCompletionRequest(CustomBaseModel):
|
||||
|
||||
class ChatCompletionChoice(CustomBaseModel):
|
||||
"""Choice in chat completion response."""
|
||||
|
||||
index: int
|
||||
message: ChatMessage
|
||||
finish_reason: str | None
|
||||
@@ -36,6 +40,7 @@ class ChatCompletionChoice(CustomBaseModel):
|
||||
|
||||
class ChatCompletionUsage(CustomBaseModel):
|
||||
"""Token usage information."""
|
||||
|
||||
prompt_tokens: int
|
||||
completion_tokens: int
|
||||
total_tokens: int
|
||||
@@ -43,6 +48,7 @@ class ChatCompletionUsage(CustomBaseModel):
|
||||
|
||||
class ChatCompletionResponse(CustomBaseModel):
|
||||
"""OpenAI-compatible chat completion response."""
|
||||
|
||||
id: str
|
||||
object: str = "chat.completion"
|
||||
created: int
|
||||
@@ -53,6 +59,7 @@ class ChatCompletionResponse(CustomBaseModel):
|
||||
|
||||
class ChatCompletionChunkDelta(CustomBaseModel):
|
||||
"""Delta in streaming chunk."""
|
||||
|
||||
role: str | None = None
|
||||
content: str | None = None
|
||||
reasoning_content: str | None = None # For thinking/reasoning (DeepSeek R1 format)
|
||||
@@ -60,6 +67,7 @@ class ChatCompletionChunkDelta(CustomBaseModel):
|
||||
|
||||
class ChatCompletionChunkChoice(CustomBaseModel):
|
||||
"""Choice in streaming chunk."""
|
||||
|
||||
index: int
|
||||
delta: ChatCompletionChunkDelta
|
||||
finish_reason: str | None = None
|
||||
@@ -67,6 +75,7 @@ class ChatCompletionChunkChoice(CustomBaseModel):
|
||||
|
||||
class ChatCompletionChunk(CustomBaseModel):
|
||||
"""OpenAI-compatible streaming chunk."""
|
||||
|
||||
id: str
|
||||
object: str = "chat.completion.chunk"
|
||||
created: int
|
||||
|
||||
+13
-17
@@ -4,17 +4,17 @@ Chat completion service.
|
||||
Wrapper around Responses API that converts to Chat Completions format.
|
||||
Embeds reasoning in <think> tags for Open WebUI compatibility.
|
||||
"""
|
||||
import asyncio
|
||||
|
||||
import time
|
||||
import uuid
|
||||
from typing import AsyncGenerator
|
||||
from collections.abc import AsyncGenerator
|
||||
|
||||
from src.chat import constants
|
||||
from src.chat.schemas import (
|
||||
ChatCompletionChoice,
|
||||
ChatCompletionChunk,
|
||||
ChatCompletionChunkChoice,
|
||||
ChatCompletionChunkDelta,
|
||||
ChatCompletionChoice,
|
||||
ChatCompletionRequest,
|
||||
ChatCompletionResponse,
|
||||
ChatCompletionUsage,
|
||||
@@ -43,10 +43,7 @@ async def create_chat_completion(
|
||||
created_at = int(time.time())
|
||||
|
||||
# Convert Chat request to Responses request
|
||||
input_messages = [
|
||||
{"role": msg.role, "content": msg.content}
|
||||
for msg in request.messages
|
||||
]
|
||||
input_messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]
|
||||
|
||||
response_request = ResponseRequest(
|
||||
model=request.model,
|
||||
@@ -54,7 +51,9 @@ async def create_chat_completion(
|
||||
reasoning={"effort": "medium", "summary": "auto"}, # Enable reasoning
|
||||
temperature=request.temperature or 1.0,
|
||||
max_output_tokens=request.max_tokens,
|
||||
stop=request.stop if isinstance(request.stop, list) else ([request.stop] if request.stop else None),
|
||||
stop=request.stop
|
||||
if isinstance(request.stop, list)
|
||||
else ([request.stop] if request.stop else None),
|
||||
)
|
||||
|
||||
# Call Responses API (will use Steward for Tatlock)
|
||||
@@ -118,16 +117,13 @@ async def create_chat_completion_stream(
|
||||
Yields:
|
||||
Chat completion chunks with reasoning as <think> tags
|
||||
"""
|
||||
from src.responses.streaming import StreamingCoordinator, StreamEventType
|
||||
from src.responses.streaming import StreamEventType, StreamingCoordinator
|
||||
|
||||
completion_id = f"chatcmpl-{uuid.uuid4().hex[:24]}"
|
||||
created_at = int(time.time())
|
||||
|
||||
# Convert Chat request to Responses request
|
||||
input_messages = [
|
||||
{"role": msg.role, "content": msg.content}
|
||||
for msg in request.messages
|
||||
]
|
||||
input_messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]
|
||||
|
||||
response_request = ResponseRequest(
|
||||
model=request.model,
|
||||
@@ -135,7 +131,9 @@ async def create_chat_completion_stream(
|
||||
reasoning={"effort": "medium", "summary": "auto"},
|
||||
temperature=request.temperature or 1.0,
|
||||
max_output_tokens=request.max_tokens,
|
||||
stop=request.stop if isinstance(request.stop, list) else ([request.stop] if request.stop else None),
|
||||
stop=request.stop
|
||||
if isinstance(request.stop, list)
|
||||
else ([request.stop] if request.stop else None),
|
||||
stream=True,
|
||||
)
|
||||
|
||||
@@ -163,7 +161,6 @@ async def create_chat_completion_stream(
|
||||
|
||||
# Stream from Responses API
|
||||
coordinator = StreamingCoordinator()
|
||||
in_reasoning = False
|
||||
|
||||
if use_steward:
|
||||
stream_generator = coordinator.stream_response_with_steward(response_request)
|
||||
@@ -174,7 +171,6 @@ async def create_chat_completion_stream(
|
||||
if event.event == StreamEventType.REASONING_SUMMARY_DELTA:
|
||||
# Stream reasoning via reasoning_content field (DeepSeek R1 format)
|
||||
# Open WebUI renders this as collapsible thinking block
|
||||
in_reasoning = True
|
||||
yield ChatCompletionChunk(
|
||||
id=completion_id,
|
||||
object=constants.CHAT_COMPLETION_CHUNK_OBJECT,
|
||||
@@ -191,7 +187,7 @@ async def create_chat_completion_stream(
|
||||
|
||||
elif event.event == StreamEventType.REASONING_SUMMARY_DONE:
|
||||
# Signal end of reasoning block (no content needed)
|
||||
in_reasoning = False
|
||||
pass # nothing downstream reads this; the event just ends the block
|
||||
|
||||
elif event.event == StreamEventType.OUTPUT_TEXT_DELTA:
|
||||
# Stream message content
|
||||
|
||||
+107
-95
@@ -2,13 +2,22 @@
|
||||
Global application configuration.
|
||||
Following best practice of splitting config across domains.
|
||||
"""
|
||||
|
||||
from enum import Enum
|
||||
from functools import lru_cache
|
||||
from pathlib import Path
|
||||
|
||||
from pydantic import Field, HttpUrl
|
||||
from pydantic import Field, HttpUrl, model_validator
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
|
||||
# Tenant isolation constants (see docs: tenant-based isolation, no separate
|
||||
# test infrastructure). The production tenant owns real data in the shared
|
||||
# services (Qdrant/Neo4j/Wiki.js/Redis); everything non-production must run
|
||||
# under the reserved test tenant or an explicit test_-prefixed namespace.
|
||||
PRODUCTION_TENANT = "jpmschweitzer"
|
||||
TEST_TENANT = "llm_tester"
|
||||
TEST_TENANT_PREFIX = "test_"
|
||||
|
||||
|
||||
def _get_version_from_pyproject() -> str:
|
||||
"""
|
||||
@@ -34,6 +43,7 @@ def _get_version_from_pyproject() -> str:
|
||||
|
||||
class Environment(str, Enum):
|
||||
"""Application environment."""
|
||||
|
||||
DEVELOPMENT = "development"
|
||||
PRODUCTION = "production"
|
||||
TESTING = "testing"
|
||||
@@ -42,163 +52,150 @@ class Environment(str, Enum):
|
||||
class Config(BaseSettings):
|
||||
"""
|
||||
Global application configuration.
|
||||
|
||||
|
||||
Loads from environment variables and .env file.
|
||||
Domain-specific configs should be in their respective modules.
|
||||
"""
|
||||
|
||||
model_config = SettingsConfigDict(
|
||||
env_file=".env",
|
||||
env_file_encoding="utf-8",
|
||||
case_sensitive=True,
|
||||
extra="ignore",
|
||||
)
|
||||
|
||||
|
||||
# Application
|
||||
APP_NAME: str = "OpenAI-Compatible API"
|
||||
APP_VERSION: str = Field(default_factory=_get_version_from_pyproject)
|
||||
ENVIRONMENT: Environment = Environment.DEVELOPMENT
|
||||
DEBUG: bool = Field(default=False, description="Debug mode")
|
||||
|
||||
|
||||
# API Configuration
|
||||
API_HOST: str = Field(default="0.0.0.0", description="API host")
|
||||
API_PORT: int = Field(default=8000, description="API port")
|
||||
API_PREFIX: str = Field(default="/v1", description="API route prefix")
|
||||
|
||||
# Anthropic Configuration (Claude - preferred backend)
|
||||
|
||||
# Anthropic Configuration (Claude - cloud fallback)
|
||||
ANTHROPIC_API_KEY: str | None = Field(
|
||||
default=None,
|
||||
description="Anthropic API key for Claude access"
|
||||
default=None, description="Anthropic API key for the Claude fallback backend"
|
||||
)
|
||||
ANTHROPIC_MODEL: str = Field(
|
||||
default="claude-sonnet-4-20250514",
|
||||
description="Claude model to use"
|
||||
default="claude-sonnet-5", description="Claude model for the fallback backend"
|
||||
)
|
||||
PREFER_CLOUD_BACKEND: bool = Field(
|
||||
default=True,
|
||||
description="Prefer Claude over Ollama when available"
|
||||
default=False, description="Prefer Claude over Ollama (default: local-first)"
|
||||
)
|
||||
|
||||
# Ollama Configuration (local fallback)
|
||||
OLLAMA_HOST: HttpUrl = Field(
|
||||
default="http://localhost:11434",
|
||||
description="Ollama server URL"
|
||||
)
|
||||
OLLAMA_DEFAULT_MODEL: str = Field(
|
||||
default="mistral-nemo:latest",
|
||||
description="Default Ollama model"
|
||||
)
|
||||
OLLAMA_TIMEOUT: int = Field(
|
||||
default=120,
|
||||
description="Ollama request timeout in seconds"
|
||||
# Ollama Configuration (local - primary backend)
|
||||
OLLAMA_HOST: HttpUrl = Field(default="http://localhost:11434", description="Ollama server URL")
|
||||
OLLAMA_DEFAULT_MODEL: str = Field(default="gemma4:e2b", description="Default Ollama model")
|
||||
OLLAMA_TIMEOUT: int = Field(default=120, description="Ollama request timeout in seconds")
|
||||
STEWARD_TIMEOUT: int = Field(
|
||||
default=60, description="Steward analysis timeout in seconds (gemma4 needs ~35s warm)"
|
||||
)
|
||||
STREAM_TIMEOUT: int = Field(
|
||||
default=20,
|
||||
description="Timeout for each streaming turn in seconds"
|
||||
default=20, description="Timeout for each streaming turn in seconds"
|
||||
)
|
||||
|
||||
# SearXNG Configuration
|
||||
SEARXNG_HOST: HttpUrl = Field(
|
||||
default="http://localhost:8087",
|
||||
description="SearXNG server URL"
|
||||
)
|
||||
SEARXNG_TIMEOUT: int = Field(
|
||||
default=30,
|
||||
description="SearXNG request timeout in seconds"
|
||||
default="http://searxng:8080",
|
||||
description="SearXNG server URL (container name; internal port 8080)",
|
||||
)
|
||||
SEARXNG_TIMEOUT: int = Field(default=30, description="SearXNG request timeout in seconds")
|
||||
|
||||
# Redis Configuration
|
||||
REDIS_HOST: str = Field(
|
||||
default="localhost",
|
||||
description="Redis server host"
|
||||
)
|
||||
REDIS_PORT: int = Field(
|
||||
default=6379,
|
||||
description="Redis server port"
|
||||
)
|
||||
REDIS_TIMEOUT: int = Field(
|
||||
default=5,
|
||||
description="Redis connection timeout in seconds"
|
||||
)
|
||||
REDIS_HOST: str = Field(default="localhost", description="Redis server host")
|
||||
REDIS_PORT: int = Field(default=6379, description="Redis server port")
|
||||
REDIS_TIMEOUT: int = Field(default=5, description="Redis connection timeout in seconds")
|
||||
|
||||
# Library-Desk Configuration (The Librarian backend)
|
||||
LIBRARIAN_TIMEOUT: int = Field(
|
||||
default=180, description="Total time budget for a librarian delegation in seconds"
|
||||
)
|
||||
LIBRARY_DESK_HOST: HttpUrl = Field(
|
||||
default="http://localhost:8089",
|
||||
description="Library-Desk API URL"
|
||||
default="http://library-desk:8089",
|
||||
description="Library-Desk API URL (container name; internal port 8089)",
|
||||
)
|
||||
LIBRARY_DESK_API_KEY: str = Field(
|
||||
default="",
|
||||
description="API key for Library-Desk authentication"
|
||||
default="", description="API key for Library-Desk authentication"
|
||||
)
|
||||
LIBRARY_DESK_TIMEOUT: int = Field(
|
||||
default=60,
|
||||
description="Library-Desk request timeout in seconds"
|
||||
default=60, description="Library-Desk request timeout in seconds"
|
||||
)
|
||||
|
||||
# Core-API Configuration (The Housekeeper backend)
|
||||
CORE_API_HOST: HttpUrl = Field(
|
||||
default="http://localhost:8090",
|
||||
description="Core-API URL for Home Assistant integration"
|
||||
)
|
||||
CORE_API_KEY: str = Field(
|
||||
default="",
|
||||
description="API key for Core-API authentication"
|
||||
)
|
||||
CORE_API_TIMEOUT: int = Field(
|
||||
default=30,
|
||||
description="Core-API request timeout in seconds"
|
||||
default="http://core-api:8083",
|
||||
description="Core-API URL for Home Assistant integration (container name; internal port 8083)",
|
||||
)
|
||||
CORE_API_KEY: str = Field(default="", description="API key for Core-API authentication")
|
||||
CORE_API_TIMEOUT: int = Field(default=30, description="Core-API request timeout in seconds")
|
||||
|
||||
# Qdrant Configuration (Memory vector storage)
|
||||
QDRANT_HOST: str = Field(
|
||||
default="localhost",
|
||||
description="Qdrant server host"
|
||||
)
|
||||
QDRANT_PORT: int = Field(
|
||||
default=6333,
|
||||
description="Qdrant server port"
|
||||
)
|
||||
QDRANT_HOST: str = Field(default="localhost", description="Qdrant server host")
|
||||
QDRANT_PORT: int = Field(default=6333, description="Qdrant server port")
|
||||
QDRANT_EMBEDDING_DIM: int = Field(
|
||||
default=768,
|
||||
description="Embedding dimension (768 for nomic-embed-text)"
|
||||
default=768, description="Embedding dimension (768 for nomic-embed-text)"
|
||||
)
|
||||
|
||||
# Ollama Embedding Configuration
|
||||
OLLAMA_EMBEDDING_MODEL: str = Field(
|
||||
default="nomic-embed-text",
|
||||
description="Ollama model for embeddings"
|
||||
default="nomic-embed-text", description="Ollama model for embeddings"
|
||||
)
|
||||
|
||||
# Redis Memory Database
|
||||
REDIS_MEMORY_DB: int = Field(
|
||||
default=1,
|
||||
description="Redis database number for memory cache"
|
||||
)
|
||||
REDIS_MEMORY_TTL_HOURS: int = Field(
|
||||
default=24,
|
||||
description="TTL for session context in hours"
|
||||
)
|
||||
REDIS_MEMORY_DB: int = Field(default=1, description="Redis database number for memory cache")
|
||||
REDIS_MEMORY_TTL_HOURS: int = Field(default=24, description="TTL for session context in hours")
|
||||
|
||||
# Logging
|
||||
LOG_LEVEL: str | None = Field(
|
||||
default=None,
|
||||
description="Logging level (auto-set based on environment if not specified)"
|
||||
default=None, description="Logging level (auto-set based on environment if not specified)"
|
||||
)
|
||||
|
||||
# User Configuration
|
||||
DEFAULT_USER: str | None = Field(
|
||||
default=None,
|
||||
description="Default user for single-user setup (auto-set based on environment if not specified)"
|
||||
description="Default user for single-user setup (auto-set based on environment if not specified)",
|
||||
)
|
||||
|
||||
# CORS
|
||||
CORS_ORIGINS: list[str] = Field(
|
||||
default=["*"],
|
||||
description="Allowed CORS origins"
|
||||
)
|
||||
CORS_ORIGINS: list[str] = Field(default=["*"], description="Allowed CORS origins")
|
||||
CORS_ALLOW_CREDENTIALS: bool = True
|
||||
CORS_ALLOW_METHODS: list[str] = ["*"]
|
||||
CORS_ALLOW_HEADERS: list[str] = ["*"]
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _refuse_production_tenant_outside_production(self) -> "Config":
|
||||
"""
|
||||
Refuse startup when a non-production environment is explicitly
|
||||
configured with the production tenant.
|
||||
|
||||
This is the hard stop of the tenant isolation guard: a dev/test
|
||||
instance must never be able to read or write the production
|
||||
tenant's data in the shared services.
|
||||
|
||||
The comparison is on the sanitized form: namespaces are derived
|
||||
through sanitize_user_id(), so variants like "JPMSchweitzer" or
|
||||
"jpmschweitzer." collide with the production namespaces and are
|
||||
refused just as loudly.
|
||||
"""
|
||||
from src.core.multi_tenancy import sanitize_user_id
|
||||
|
||||
if (
|
||||
self.ENVIRONMENT != Environment.PRODUCTION
|
||||
and self.DEFAULT_USER is not None
|
||||
and sanitize_user_id(self.DEFAULT_USER) == sanitize_user_id(PRODUCTION_TENANT)
|
||||
):
|
||||
raise ValueError(
|
||||
f"Refusing to start: ENVIRONMENT={self.ENVIRONMENT.value} is "
|
||||
f"explicitly configured with the production tenant "
|
||||
f"'{PRODUCTION_TENANT}'. Non-production environments must use "
|
||||
f"'{TEST_TENANT}' or a '{TEST_TENANT_PREFIX}'-prefixed tenant. "
|
||||
f"Unset DEFAULT_USER or set ENVIRONMENT=production."
|
||||
)
|
||||
return self
|
||||
|
||||
@property
|
||||
def redis_memory_url(self) -> str:
|
||||
"""Construct Redis connection URL for memory cache."""
|
||||
@@ -239,23 +236,38 @@ class Config(BaseSettings):
|
||||
@property
|
||||
def effective_default_user(self) -> str:
|
||||
"""
|
||||
Get effective default user, auto-determining from environment if not set.
|
||||
Get effective default user (tenant), enforcing tenant isolation.
|
||||
|
||||
- development/testing: llm_tester (isolated test scope)
|
||||
- production: jpmschweitzer (real user)
|
||||
- production: DEFAULT_USER if set, else the production tenant
|
||||
- development/testing: FORCED to the reserved test tenant
|
||||
("llm_tester") - the only accepted overrides are the test tenant
|
||||
itself or a "test_"-prefixed namespace. Any other DEFAULT_USER
|
||||
value is treated as misconfiguration and ignored.
|
||||
"""
|
||||
if self.DEFAULT_USER is not None:
|
||||
return self.DEFAULT_USER
|
||||
if self.ENVIRONMENT == Environment.PRODUCTION:
|
||||
return "jpmschweitzer"
|
||||
return "llm_tester"
|
||||
return self.DEFAULT_USER or PRODUCTION_TENANT
|
||||
|
||||
if self.DEFAULT_USER is not None and (
|
||||
self.DEFAULT_USER == TEST_TENANT or self.DEFAULT_USER.startswith(TEST_TENANT_PREFIX)
|
||||
):
|
||||
return self.DEFAULT_USER
|
||||
return TEST_TENANT
|
||||
|
||||
@property
|
||||
def tenant_forced(self) -> bool:
|
||||
"""Whether the tenant guard overrode a misconfigured DEFAULT_USER."""
|
||||
return (
|
||||
self.ENVIRONMENT != Environment.PRODUCTION
|
||||
and self.DEFAULT_USER is not None
|
||||
and self.effective_default_user != self.DEFAULT_USER
|
||||
)
|
||||
|
||||
|
||||
@lru_cache
|
||||
def get_config() -> Config:
|
||||
"""
|
||||
Get cached configuration instance.
|
||||
|
||||
|
||||
Uses lru_cache to ensure config is loaded once and reused.
|
||||
"""
|
||||
return Config()
|
||||
|
||||
+53
-6
@@ -16,7 +16,9 @@ Usage:
|
||||
from src.core.context import get_user
|
||||
user = get_user() # Returns current request's user
|
||||
"""
|
||||
|
||||
from contextvars import ContextVar
|
||||
from types import TracebackType
|
||||
|
||||
|
||||
def get_default_user() -> str:
|
||||
@@ -28,6 +30,7 @@ def get_default_user() -> str:
|
||||
"""
|
||||
# Import here to avoid circular dependency
|
||||
from src.core.config import config
|
||||
|
||||
return config.effective_default_user
|
||||
|
||||
|
||||
@@ -36,9 +39,41 @@ def get_default_user() -> str:
|
||||
# and resolve the real default in get_user()
|
||||
_USER_NOT_SET = "__user_not_set__"
|
||||
current_user: ContextVar[str] = ContextVar("current_user", default=_USER_NOT_SET)
|
||||
current_conversation: ContextVar[str | None] = ContextVar(
|
||||
"current_conversation", default=None
|
||||
)
|
||||
current_conversation: ContextVar[str | None] = ContextVar("current_conversation", default=None)
|
||||
|
||||
|
||||
def apply_tenant_guard(user: str) -> str:
|
||||
"""
|
||||
Enforce tenant isolation at request-context resolution.
|
||||
|
||||
In non-production environments the production tenant must never be
|
||||
the effective user - a request that explicitly asks for it is forced
|
||||
to the reserved test tenant instead (with a loud log line).
|
||||
|
||||
Comparison happens on the *sanitized* form of the user: every local
|
||||
namespace (Qdrant collection, Redis key) is derived through
|
||||
sanitize_user_id(), so any raw variant that collides with the
|
||||
production tenant after sanitization ("JPMSchweitzer",
|
||||
"jpmschweitzer.", " jpmschweitzer", ...) would otherwise resolve to
|
||||
the production namespaces. Those variants are forced too.
|
||||
"""
|
||||
# Import here to avoid circular dependency
|
||||
from src.core.config import PRODUCTION_TENANT, TEST_TENANT, Environment, config
|
||||
from src.core.multi_tenancy import sanitize_user_id
|
||||
|
||||
if config.ENVIRONMENT != Environment.PRODUCTION and sanitize_user_id(user) == sanitize_user_id(
|
||||
PRODUCTION_TENANT
|
||||
):
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
get_logger(__name__).warning(
|
||||
"tenant_guard_forced",
|
||||
environment=config.ENVIRONMENT.value,
|
||||
requested_tenant=user,
|
||||
forced_tenant=TEST_TENANT,
|
||||
)
|
||||
return TEST_TENANT
|
||||
return user
|
||||
|
||||
|
||||
def get_user() -> str:
|
||||
@@ -48,6 +83,8 @@ def get_user() -> str:
|
||||
Returns:
|
||||
User identifier for the current request.
|
||||
Falls back to environment-aware default if not set.
|
||||
In non-production environments the production tenant is never
|
||||
returned - the tenant guard forces the reserved test tenant.
|
||||
|
||||
Example:
|
||||
user = get_user() # "llm_tester" (dev) or "jpmschweitzer" (prod)
|
||||
@@ -55,7 +92,7 @@ def get_user() -> str:
|
||||
user = current_user.get()
|
||||
if user == _USER_NOT_SET:
|
||||
return get_default_user()
|
||||
return user
|
||||
return apply_tenant_guard(user)
|
||||
|
||||
|
||||
def get_conversation_id() -> str | None:
|
||||
@@ -106,7 +143,12 @@ class RequestContext:
|
||||
self._conv_token = current_conversation.set(self.conversation_id)
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type, exc_val, exc_tb) -> None:
|
||||
async def __aexit__(
|
||||
self,
|
||||
exc_type: type[BaseException] | None,
|
||||
exc_val: BaseException | None,
|
||||
exc_tb: TracebackType | None,
|
||||
) -> None:
|
||||
"""Reset context variables on exit."""
|
||||
if self._user_token is not None:
|
||||
current_user.reset(self._user_token)
|
||||
@@ -119,7 +161,12 @@ class RequestContext:
|
||||
self._conv_token = current_conversation.set(self.conversation_id)
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb) -> None:
|
||||
def __exit__(
|
||||
self,
|
||||
exc_type: type[BaseException] | None,
|
||||
exc_val: BaseException | None,
|
||||
exc_tb: TracebackType | None,
|
||||
) -> None:
|
||||
"""Sync context manager exit."""
|
||||
if self._user_token is not None:
|
||||
current_user.reset(self._user_token)
|
||||
|
||||
@@ -8,7 +8,8 @@ Provides async embedding operations via Ollama API:
|
||||
|
||||
Adapted from library-desk patterns.
|
||||
"""
|
||||
from typing import Optional
|
||||
|
||||
from types import TracebackType
|
||||
|
||||
import httpx
|
||||
|
||||
@@ -73,7 +74,12 @@ class OllamaEmbeddingClient:
|
||||
await self._get_client()
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type, exc_val, exc_tb) -> None:
|
||||
async def __aexit__(
|
||||
self,
|
||||
exc_type: type[BaseException] | None,
|
||||
exc_val: BaseException | None,
|
||||
exc_tb: TracebackType | None,
|
||||
) -> None:
|
||||
"""Async context manager exit."""
|
||||
await self.close()
|
||||
|
||||
|
||||
@@ -2,12 +2,13 @@
|
||||
Global exception definitions.
|
||||
Domain-specific exceptions should be in their respective modules.
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
|
||||
class AppException(Exception):
|
||||
"""Base exception for all application errors."""
|
||||
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
message: str = "An error occurred",
|
||||
@@ -22,26 +23,26 @@ class AppException(Exception):
|
||||
|
||||
class OllamaConnectionError(AppException):
|
||||
"""Raised when cannot connect to Ollama service."""
|
||||
|
||||
|
||||
def __init__(self, message: str = "Cannot connect to Ollama service"):
|
||||
super().__init__(message=message, status_code=503)
|
||||
|
||||
|
||||
class OllamaTimeoutError(AppException):
|
||||
"""Raised when Ollama request times out."""
|
||||
|
||||
|
||||
def __init__(self, message: str = "Ollama request timed out"):
|
||||
super().__init__(message=message, status_code=504)
|
||||
|
||||
|
||||
class ModelNotFoundError(AppException):
|
||||
"""Raised when requested model is not available."""
|
||||
|
||||
|
||||
def __init__(self, model_name: str):
|
||||
super().__init__(
|
||||
message=f"Model '{model_name}' not found",
|
||||
status_code=404,
|
||||
details={"model": model_name}
|
||||
details={"model": model_name},
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -5,10 +5,10 @@ Provides centralized registry of household members (agents) with their
|
||||
capabilities and tools. Supports two-tier abstraction: executive summaries
|
||||
for coordination and full toolsets for execution.
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel, ConfigDict
|
||||
from pydantic_ai import Agent
|
||||
|
||||
from .logging_config import get_logger
|
||||
|
||||
@@ -22,6 +22,7 @@ class HouseholdCapability(BaseModel):
|
||||
This is what the Steward and Butler see for coordination.
|
||||
High-level description without implementation details.
|
||||
"""
|
||||
|
||||
name: str # Unique identifier: "tatlock_core", "librarian", "developer"
|
||||
role: str # Display name: "Butler's Core Tools", "The Librarian"
|
||||
category: str # "core", "research", "technical", "automation"
|
||||
@@ -38,11 +39,12 @@ class HouseholdMember(BaseModel):
|
||||
Contains both the executive summary (for coordination) and
|
||||
implementation details (tools/agent).
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
capability: HouseholdCapability
|
||||
tools: list[Any] # PydanticAI tool definitions (any type since Tool is a dataclass)
|
||||
agent: Optional[Any] = None # For expert agents (Phase 4)
|
||||
agent: Any | None = None # For expert agents (Phase 4)
|
||||
|
||||
|
||||
class HouseholdRegistry:
|
||||
@@ -55,7 +57,7 @@ class HouseholdRegistry:
|
||||
3. Agent delegation (Phase 4)
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
def __init__(self) -> None:
|
||||
"""Initialize empty registry."""
|
||||
self._members: dict[str, HouseholdMember] = {}
|
||||
logger.info("household_registry_initialized")
|
||||
@@ -65,7 +67,7 @@ class HouseholdRegistry:
|
||||
name: str,
|
||||
capability: HouseholdCapability,
|
||||
tools: list[Any],
|
||||
agent: Optional[Any] = None,
|
||||
agent: Any | None = None,
|
||||
) -> None:
|
||||
"""
|
||||
Register a household member.
|
||||
@@ -95,9 +97,7 @@ class HouseholdRegistry:
|
||||
... )
|
||||
"""
|
||||
if name != capability.name:
|
||||
raise ValueError(
|
||||
f"Name mismatch: '{name}' != '{capability.name}'"
|
||||
)
|
||||
raise ValueError(f"Name mismatch: '{name}' != '{capability.name}'")
|
||||
|
||||
self._members[name] = HouseholdMember(
|
||||
capability=capability,
|
||||
@@ -132,7 +132,7 @@ class HouseholdRegistry:
|
||||
role=member.capability.role,
|
||||
)
|
||||
|
||||
def get_member(self, name: str) -> Optional[HouseholdMember]:
|
||||
def get_member(self, name: str) -> HouseholdMember | None:
|
||||
"""
|
||||
Get full household member specification.
|
||||
|
||||
@@ -273,65 +273,6 @@ class HouseholdRegistry:
|
||||
|
||||
return tools
|
||||
|
||||
def get_streaming_delegation_tools(self, names: list[str]) -> list[Any]:
|
||||
"""
|
||||
Get streaming delegation wrapper tools for specified capabilities.
|
||||
|
||||
Similar to get_delegation_tools() but returns streaming wrappers
|
||||
that yield butler-perspective think messages during execution.
|
||||
|
||||
These wrappers emit think slugs like:
|
||||
- "Allow me to consult the archives, sir."
|
||||
- "The Librarian has compiled the relevant findings."
|
||||
|
||||
Args:
|
||||
names: List of member names to include
|
||||
|
||||
Returns:
|
||||
List of streaming delegation wrappers and/or raw tools
|
||||
|
||||
Example:
|
||||
>>> tools = registry.get_streaming_delegation_tools(["librarian"])
|
||||
>>> async for chunk in tools[0](task="Search for Docker"):
|
||||
... print(chunk) # Yields think messages then result
|
||||
"""
|
||||
from src.agents.delegation import STREAMING_DELEGATION_WRAPPERS
|
||||
|
||||
tools = []
|
||||
for name in names:
|
||||
member = self._members.get(name)
|
||||
if not member:
|
||||
logger.warning(
|
||||
"household_member_not_found",
|
||||
requested_name=name,
|
||||
available_names=list(self._members.keys()),
|
||||
)
|
||||
continue
|
||||
|
||||
# Check if this member has a streaming delegation wrapper
|
||||
if name in STREAMING_DELEGATION_WRAPPERS and member.agent is not None:
|
||||
tools.append(STREAMING_DELEGATION_WRAPPERS[name])
|
||||
logger.debug(
|
||||
"streaming_delegation_wrapper_added",
|
||||
member=name,
|
||||
)
|
||||
else:
|
||||
# No agent = direct tools (e.g., tatlock_core)
|
||||
tools.extend(member.tools)
|
||||
logger.debug(
|
||||
"raw_tools_added",
|
||||
member=name,
|
||||
tool_count=len(member.tools),
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"streaming_delegation_tools_created",
|
||||
requested_members=names,
|
||||
total_tools=len(tools),
|
||||
)
|
||||
|
||||
return tools
|
||||
|
||||
def list_members(self) -> list[str]:
|
||||
"""
|
||||
List all registered member names.
|
||||
|
||||
+33
-13
@@ -4,12 +4,14 @@ Structured logging configuration using structlog.
|
||||
Deeply integrates with FastAPI/uvicorn's built-in logging to provide
|
||||
seamless structured logs across the entire application stack.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import logging.config
|
||||
import sys
|
||||
from collections.abc import AsyncIterator
|
||||
from contextlib import asynccontextmanager
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any, AsyncIterator
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any
|
||||
|
||||
import structlog
|
||||
from structlog.types import EventDict, Processor
|
||||
@@ -19,7 +21,7 @@ from .config import config
|
||||
|
||||
def add_timestamp(logger: Any, method_name: str, event_dict: EventDict) -> EventDict:
|
||||
"""Add ISO 8601 timestamp to log entries."""
|
||||
event_dict["timestamp"] = datetime.now(timezone.utc).isoformat()
|
||||
event_dict["timestamp"] = datetime.now(UTC).isoformat()
|
||||
return event_dict
|
||||
|
||||
|
||||
@@ -41,11 +43,28 @@ def extract_from_record(logger: Any, method_name: str, event_dict: EventDict) ->
|
||||
# Extract custom fields from record
|
||||
for key, value in record.__dict__.items():
|
||||
if key not in {
|
||||
"name", "msg", "args", "created", "filename", "funcName",
|
||||
"levelname", "levelno", "lineno", "module", "msecs",
|
||||
"message", "pathname", "process", "processName", "relativeCreated",
|
||||
"thread", "threadName", "exc_info", "exc_text", "stack_info",
|
||||
"taskName"
|
||||
"name",
|
||||
"msg",
|
||||
"args",
|
||||
"created",
|
||||
"filename",
|
||||
"funcName",
|
||||
"levelname",
|
||||
"levelno",
|
||||
"lineno",
|
||||
"module",
|
||||
"msecs",
|
||||
"message",
|
||||
"pathname",
|
||||
"process",
|
||||
"processName",
|
||||
"relativeCreated",
|
||||
"thread",
|
||||
"threadName",
|
||||
"exc_info",
|
||||
"exc_text",
|
||||
"stack_info",
|
||||
"taskName",
|
||||
}:
|
||||
event_dict[key] = value
|
||||
|
||||
@@ -164,7 +183,7 @@ def get_logger(name: str) -> structlog.stdlib.BoundLogger:
|
||||
async def log_operation(
|
||||
operation: str,
|
||||
initial_context: dict[str, Any] | None = None,
|
||||
logger_name: str = "tatlock.operations"
|
||||
logger_name: str = "tatlock.operations",
|
||||
) -> AsyncIterator[dict[str, Any]]:
|
||||
"""
|
||||
Context manager for automatic operation timing and logging.
|
||||
@@ -187,21 +206,21 @@ async def log_operation(
|
||||
context = initial_context or {}
|
||||
context["operation"] = operation
|
||||
|
||||
start_time = datetime.now(timezone.utc)
|
||||
start_time = datetime.now(UTC)
|
||||
logger.info("operation_started", **context)
|
||||
|
||||
try:
|
||||
yield context
|
||||
|
||||
# Success case
|
||||
duration = (datetime.now(timezone.utc) - start_time).total_seconds()
|
||||
duration = (datetime.now(UTC) - start_time).total_seconds()
|
||||
context["duration_seconds"] = duration
|
||||
context["success"] = True
|
||||
logger.info("operation_completed", **context)
|
||||
|
||||
except Exception as e:
|
||||
# Error case
|
||||
duration = (datetime.now(timezone.utc) - start_time).total_seconds()
|
||||
duration = (datetime.now(UTC) - start_time).total_seconds()
|
||||
context["duration_seconds"] = duration
|
||||
context["success"] = False
|
||||
context["error"] = str(e)
|
||||
@@ -228,7 +247,8 @@ def get_uvicorn_log_config() -> dict[str, Any]:
|
||||
"()": structlog.stdlib.ProcessorFormatter,
|
||||
"processors": [
|
||||
structlog.stdlib.ProcessorFormatter.remove_processors_meta,
|
||||
structlog.processors.JSONRenderer() if config.log_format == "json"
|
||||
structlog.processors.JSONRenderer()
|
||||
if config.log_format == "json"
|
||||
else structlog.dev.ConsoleRenderer(colors=True),
|
||||
],
|
||||
},
|
||||
|
||||
@@ -8,6 +8,7 @@ Provides short-term memory storage with TTL:
|
||||
|
||||
Uses Redis DB 1.
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
@@ -15,7 +16,7 @@ import redis.asyncio as redis
|
||||
|
||||
from .config import config
|
||||
from .logging_config import get_logger
|
||||
from .multi_tenancy import get_session_key, get_entities_key
|
||||
from .multi_tenancy import get_entities_key, get_session_key
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
+16
-14
@@ -21,14 +21,14 @@ Usage:
|
||||
# Get session context
|
||||
ctx = await memory_service.get_session_context(conversation_id)
|
||||
"""
|
||||
from datetime import datetime, timezone
|
||||
|
||||
from datetime import UTC, datetime
|
||||
from enum import Enum
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from .config import config
|
||||
from .context import get_user, get_conversation_id
|
||||
from .context import get_conversation_id, get_user
|
||||
from .embeddings import get_embedding_client
|
||||
from .logging_config import get_logger
|
||||
from .memory_cache import get_memory_cache
|
||||
@@ -40,22 +40,24 @@ logger = get_logger(__name__)
|
||||
|
||||
class MemoryType(str, Enum):
|
||||
"""Types of memories stored in Qdrant."""
|
||||
USER_PROFILE = "user_profile" # Name, location, timezone
|
||||
PREFERENCE = "preference" # Units, language, theme
|
||||
LEARNED_FACT = "learned_fact" # "My car is a Tesla"
|
||||
|
||||
USER_PROFILE = "user_profile" # Name, location, timezone
|
||||
PREFERENCE = "preference" # Units, language, theme
|
||||
LEARNED_FACT = "learned_fact" # "My car is a Tesla"
|
||||
|
||||
|
||||
class MemoryRecord(BaseModel):
|
||||
"""A memory record stored in Qdrant."""
|
||||
|
||||
id: str
|
||||
type: MemoryType
|
||||
key: str # e.g., "location", "timezone", "car"
|
||||
value: str # The actual content
|
||||
key: str # e.g., "location", "timezone", "car"
|
||||
value: str # The actual content
|
||||
keywords: list[str] = Field(default_factory=list)
|
||||
importance: float = 0.5 # 0.0 - 1.0
|
||||
source: str = "explicit" # "explicit" | "inferred" | "conversation"
|
||||
created_at: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
|
||||
updated_at: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
|
||||
importance: float = 0.5 # 0.0 - 1.0
|
||||
source: str = "explicit" # "explicit" | "inferred" | "conversation"
|
||||
created_at: str = Field(default_factory=lambda: datetime.now(UTC).isoformat())
|
||||
updated_at: str = Field(default_factory=lambda: datetime.now(UTC).isoformat())
|
||||
|
||||
|
||||
class MemoryService:
|
||||
@@ -72,7 +74,7 @@ class MemoryService:
|
||||
- Semantic recall: "What did I mention about X?" → Use Memory Agent
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
def __init__(self) -> None:
|
||||
"""Initialize memory service with lazy client loading."""
|
||||
self._qdrant = None
|
||||
self._embedding = None
|
||||
@@ -528,7 +530,7 @@ class MemoryService:
|
||||
"keywords": keywords,
|
||||
"importance": importance,
|
||||
"source": source,
|
||||
"updated_at": datetime.now(timezone.utc).isoformat(),
|
||||
"updated_at": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
|
||||
result = await self.qdrant.upsert_memory(
|
||||
|
||||
+6
-5
@@ -2,6 +2,7 @@
|
||||
Custom Pydantic base models for consistent serialization.
|
||||
Following best practice of having a global base model.
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
@@ -17,12 +18,13 @@ def datetime_to_iso_str(dt: datetime) -> str:
|
||||
class CustomBaseModel(BaseModel):
|
||||
"""
|
||||
Custom base model with consistent configuration.
|
||||
|
||||
|
||||
All domain models should inherit from this for:
|
||||
- Consistent JSON serialization
|
||||
- Timezone-aware datetime handling
|
||||
- Alias population support
|
||||
"""
|
||||
|
||||
model_config = ConfigDict(
|
||||
json_encoders={datetime: datetime_to_iso_str},
|
||||
populate_by_name=True,
|
||||
@@ -30,14 +32,13 @@ class CustomBaseModel(BaseModel):
|
||||
validate_assignment=True,
|
||||
arbitrary_types_allowed=True,
|
||||
)
|
||||
|
||||
|
||||
def serializable_dict(self, **kwargs: Any) -> dict[str, Any]:
|
||||
"""
|
||||
Return dict with only JSON-serializable fields.
|
||||
|
||||
|
||||
Useful for logging and debugging.
|
||||
"""
|
||||
return jsonable_encoder(
|
||||
self.model_dump(**kwargs),
|
||||
custom_encoder={datetime: datetime_to_iso_str}
|
||||
self.model_dump(**kwargs), custom_encoder={datetime: datetime_to_iso_str}
|
||||
)
|
||||
|
||||
@@ -7,6 +7,7 @@ Provides utilities for user namespace management across:
|
||||
|
||||
Adapted from library-desk patterns.
|
||||
"""
|
||||
|
||||
import re
|
||||
|
||||
|
||||
@@ -39,13 +40,13 @@ def sanitize_user_id(user_id: str) -> str:
|
||||
sanitized = sanitized.replace(".", "_")
|
||||
|
||||
# Replace any non-alphanumeric characters with underscores
|
||||
sanitized = re.sub(r'[^a-z0-9_]', '_', sanitized)
|
||||
sanitized = re.sub(r"[^a-z0-9_]", "_", sanitized)
|
||||
|
||||
# Remove consecutive underscores
|
||||
sanitized = re.sub(r'_+', '_', sanitized)
|
||||
sanitized = re.sub(r"_+", "_", sanitized)
|
||||
|
||||
# Remove leading/trailing underscores
|
||||
sanitized = sanitized.strip('_')
|
||||
sanitized = sanitized.strip("_")
|
||||
|
||||
return sanitized
|
||||
|
||||
@@ -141,7 +142,7 @@ def validate_user_id(user_id: str) -> bool:
|
||||
return False
|
||||
|
||||
# Must contain at least one alphanumeric character
|
||||
if not re.search(r'[a-zA-Z0-9]', user_id):
|
||||
if not re.search(r"[a-zA-Z0-9]", user_id):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
+14
-12
@@ -3,15 +3,16 @@ Request preprocessing pipeline.
|
||||
|
||||
Analyzes requests via the Steward and creates scoped toolsets for Tatlock.
|
||||
"""
|
||||
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from typing import Any, Optional
|
||||
from typing import Any
|
||||
|
||||
from src.agents.steward import analyze_request, format_steward_note
|
||||
from src.agents.steward.schemas import StewardRecommendation
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger
|
||||
from src.core.tracing import trace_span, SpanType
|
||||
from src.core.tracing import SpanType, trace_span
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
@@ -45,6 +46,7 @@ class EnrichedRequest:
|
||||
recommendation: Full Steward recommendation
|
||||
steward_reasoning: Plain text reasoning for streaming to user
|
||||
"""
|
||||
|
||||
original_request: str
|
||||
steward_note: str
|
||||
scoped_tools: list[Any] # PydanticAI tool definitions
|
||||
@@ -55,7 +57,7 @@ class EnrichedRequest:
|
||||
async def preprocess_request(
|
||||
user_request: str,
|
||||
conversation_history: list[dict],
|
||||
conversation_id: Optional[str] = None,
|
||||
conversation_id: str | None = None,
|
||||
) -> EnrichedRequest:
|
||||
"""
|
||||
Analyze request via Steward and prepare scoped context for Tatlock.
|
||||
@@ -111,12 +113,14 @@ async def preprocess_request(
|
||||
|
||||
# Update span with results
|
||||
if span:
|
||||
span.metadata.update({
|
||||
"recommended_capabilities": recommendation.recommended_capabilities,
|
||||
"complexity": recommendation.estimated_complexity,
|
||||
"has_memory_context": bool(recommendation.memory_context),
|
||||
"has_conversation_context": recommendation.conversation_context.has_previous_context,
|
||||
})
|
||||
span.metadata.update(
|
||||
{
|
||||
"recommended_capabilities": recommendation.recommended_capabilities,
|
||||
"complexity": recommendation.estimated_complexity,
|
||||
"has_memory_context": bool(recommendation.memory_context),
|
||||
"has_conversation_context": recommendation.conversation_context.has_previous_context,
|
||||
}
|
||||
)
|
||||
span.details["reasoning"] = recommendation.reasoning
|
||||
if recommendation.enriched_query:
|
||||
span.details["enriched_query"] = recommendation.enriched_query
|
||||
@@ -128,9 +132,7 @@ async def preprocess_request(
|
||||
# Uses agent-as-tool pattern: expert agents get delegation wrappers,
|
||||
# core tools are returned directly
|
||||
registry = get_household_registry()
|
||||
scoped_tools = registry.get_delegation_tools(
|
||||
recommendation.recommended_capabilities
|
||||
)
|
||||
scoped_tools = registry.get_delegation_tools(recommendation.recommended_capabilities)
|
||||
|
||||
logger.info(
|
||||
"preprocessing_complete",
|
||||
|
||||
+2
-1
@@ -8,8 +8,9 @@ Provides async operations for storing and retrieving memory embeddings:
|
||||
|
||||
Adapted from library-desk patterns.
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
from uuid import uuid4, uuid5, NAMESPACE_DNS
|
||||
from uuid import NAMESPACE_DNS, uuid4, uuid5
|
||||
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client.http import models as qdrant_models
|
||||
|
||||
+3
-2
@@ -1,6 +1,7 @@
|
||||
"""
|
||||
Core router for health and root endpoints.
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter
|
||||
@@ -16,7 +17,7 @@ router = APIRouter(tags=["core"])
|
||||
async def health_check() -> dict[str, str]:
|
||||
"""
|
||||
Health check endpoint.
|
||||
|
||||
|
||||
Returns:
|
||||
Health status
|
||||
"""
|
||||
@@ -27,7 +28,7 @@ async def health_check() -> dict[str, str]:
|
||||
async def root() -> dict[str, str]:
|
||||
"""
|
||||
Root endpoint.
|
||||
|
||||
|
||||
Returns:
|
||||
API information
|
||||
"""
|
||||
|
||||
+41
-5
@@ -5,18 +5,49 @@ Handles initialization of household registry and other startup tasks.
|
||||
This module should be called during application startup to register
|
||||
all household members.
|
||||
"""
|
||||
|
||||
from src.agents.biographer import register_biographer
|
||||
from src.agents.housekeeper import register_housekeeper
|
||||
from src.agents.librarian import register_librarian
|
||||
from src.agents.tatlock_core import TATLOCK_CORE_CAPABILITY, tatlock_core_tools
|
||||
from src.anthropic.model_selector import check_claude_health, get_model_info
|
||||
from src.anthropic.model_selector import (
|
||||
check_claude_health,
|
||||
check_ollama_health,
|
||||
get_model_info,
|
||||
)
|
||||
from src.core.config import Environment, config
|
||||
from src.core.household_registry import get_household_registry
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
def register_household_members():
|
||||
def log_tenant_guard() -> None:
|
||||
"""
|
||||
Emit one loud startup log line stating the effective tenant.
|
||||
|
||||
In non-production environments the tenant guard forces the reserved
|
||||
test tenant regardless of DEFAULT_USER misconfiguration - this line
|
||||
makes that override visible at startup.
|
||||
"""
|
||||
if config.ENVIRONMENT == Environment.PRODUCTION:
|
||||
logger.info(
|
||||
"tenant_guard_production",
|
||||
environment=config.ENVIRONMENT.value,
|
||||
tenant=config.effective_default_user,
|
||||
)
|
||||
return
|
||||
|
||||
logger.warning(
|
||||
"tenant_guard_active",
|
||||
environment=config.ENVIRONMENT.value,
|
||||
forced_tenant=config.effective_default_user,
|
||||
default_user_overridden=config.tenant_forced,
|
||||
configured_default_user=config.DEFAULT_USER,
|
||||
)
|
||||
|
||||
|
||||
def register_household_members() -> None:
|
||||
"""
|
||||
Register all household members with the registry.
|
||||
|
||||
@@ -82,12 +113,12 @@ def register_household_members():
|
||||
)
|
||||
|
||||
|
||||
async def initialize_application():
|
||||
async def initialize_application() -> None:
|
||||
"""
|
||||
Initialize the application.
|
||||
|
||||
Performs all startup tasks:
|
||||
1. Check Claude API health (for backend selection)
|
||||
1. Check Ollama (primary) and Claude (fallback) health for backend selection
|
||||
2. Register household members
|
||||
3. (Future) Initialize connections
|
||||
|
||||
@@ -95,13 +126,18 @@ async def initialize_application():
|
||||
"""
|
||||
logger.info("application_initialization_starting")
|
||||
|
||||
# Check Claude API health for backend selection
|
||||
# Tenant isolation guard: state the effective tenant loudly
|
||||
log_tenant_guard()
|
||||
|
||||
# Check backend health: Ollama is primary, Claude is the fallback
|
||||
await check_ollama_health()
|
||||
await check_claude_health()
|
||||
model_info = get_model_info()
|
||||
logger.info(
|
||||
"model_backend_configured",
|
||||
backend=model_info["backend"],
|
||||
model=model_info["model"],
|
||||
ollama_available=model_info["ollama_available"],
|
||||
claude_available=model_info["claude_available"],
|
||||
)
|
||||
|
||||
|
||||
@@ -4,7 +4,6 @@ Tool call tracking.
|
||||
Tracks which tools are recommended by the Steward versus which tools
|
||||
are actually used by Tatlock for debugging and analysis.
|
||||
"""
|
||||
from typing import Optional
|
||||
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
@@ -19,11 +18,7 @@ class ToolCallTracker:
|
||||
to measure recommendation accuracy.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
recommended_capabilities: list[str],
|
||||
conversation_id: Optional[str] = None
|
||||
):
|
||||
def __init__(self, recommended_capabilities: list[str], conversation_id: str | None = None):
|
||||
"""
|
||||
Initialize tool call tracker.
|
||||
|
||||
@@ -51,11 +46,11 @@ class ToolCallTracker:
|
||||
return tool_name.replace("delegate_to_", "")
|
||||
return tool_name
|
||||
|
||||
def log_call(self, message: str):
|
||||
def log_call(self, message: str) -> None:
|
||||
"""Log a tool call message (for UI display)."""
|
||||
logger.debug("tool_call_message", message=message)
|
||||
|
||||
async def track_call(self, tool_name: str, duration: float):
|
||||
async def track_call(self, tool_name: str, duration: float) -> None:
|
||||
"""
|
||||
Record a tool call with timing.
|
||||
|
||||
@@ -87,7 +82,7 @@ class ToolCallTracker:
|
||||
was_recommended=was_recommended,
|
||||
)
|
||||
|
||||
async def finalize(self):
|
||||
async def finalize(self) -> None:
|
||||
"""
|
||||
Finalize tracking and log unused recommended tools.
|
||||
|
||||
@@ -95,9 +90,7 @@ class ToolCallTracker:
|
||||
tools that were recommended but never used.
|
||||
"""
|
||||
# Normalize actual tool names to capabilities for comparison
|
||||
used_capabilities = {
|
||||
self._extract_capability(tool) for tool in self.actual_calls.keys()
|
||||
}
|
||||
used_capabilities = {self._extract_capability(tool) for tool in self.actual_calls.keys()}
|
||||
# Find tools that were recommended but not used
|
||||
unused_tools = self.recommended_capabilities - used_capabilities
|
||||
|
||||
@@ -128,9 +121,7 @@ class ToolCallTracker:
|
||||
"""
|
||||
total_calls = sum(len(durations) for durations in self.actual_calls.values())
|
||||
# Normalize actual tool names to capabilities for comparison
|
||||
used_capabilities = {
|
||||
self._extract_capability(tool) for tool in self.actual_calls.keys()
|
||||
}
|
||||
used_capabilities = {self._extract_capability(tool) for tool in self.actual_calls.keys()}
|
||||
unused = self.recommended_capabilities - used_capabilities
|
||||
|
||||
return {
|
||||
@@ -139,12 +130,8 @@ class ToolCallTracker:
|
||||
"tools_unused": list(unused),
|
||||
"total_calls": total_calls,
|
||||
"accuracy": {
|
||||
"recommended_and_used": len(
|
||||
self.recommended_capabilities & used_capabilities
|
||||
),
|
||||
"recommended_and_used": len(self.recommended_capabilities & used_capabilities),
|
||||
"recommended_but_unused": len(unused),
|
||||
"not_recommended_but_used": len(
|
||||
used_capabilities - self.recommended_capabilities
|
||||
),
|
||||
"not_recommended_but_used": len(used_capabilities - self.recommended_capabilities),
|
||||
},
|
||||
}
|
||||
|
||||
+23
-14
@@ -9,16 +9,16 @@ Enable via DEBUG=true environment variable.
|
||||
Traces are written to logs/traces/{trace_id}.json
|
||||
View with logs/traces/viewer.html
|
||||
"""
|
||||
from contextlib import asynccontextmanager
|
||||
from contextvars import ContextVar
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import json
|
||||
import secrets
|
||||
from contextlib import asynccontextmanager
|
||||
from contextvars import ContextVar
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
@@ -27,6 +27,7 @@ logger = get_logger(__name__)
|
||||
|
||||
class SpanType(str, Enum):
|
||||
"""Types of traced operations."""
|
||||
|
||||
ROUTER = "router"
|
||||
STEWARD = "steward"
|
||||
TATLOCK = "tatlock"
|
||||
@@ -36,6 +37,7 @@ class SpanType(str, Enum):
|
||||
|
||||
class SpanStatus(str, Enum):
|
||||
"""Span completion status."""
|
||||
|
||||
OK = "ok"
|
||||
ERROR = "error"
|
||||
|
||||
@@ -43,6 +45,7 @@ class SpanStatus(str, Enum):
|
||||
@dataclass
|
||||
class Span:
|
||||
"""A single traced operation."""
|
||||
|
||||
span_id: str
|
||||
name: str
|
||||
type: SpanType
|
||||
@@ -88,6 +91,7 @@ class Span:
|
||||
@dataclass
|
||||
class Trace:
|
||||
"""Complete trace of a request."""
|
||||
|
||||
trace_id: str
|
||||
conversation_id: str | None
|
||||
user: str
|
||||
@@ -116,7 +120,9 @@ class Trace:
|
||||
"conversation_id": self.conversation_id,
|
||||
"user": self.user,
|
||||
"timestamp": self.timestamp.isoformat(),
|
||||
"total_duration_ms": round(self.total_duration_ms, 2) if self.total_duration_ms else None,
|
||||
"total_duration_ms": round(self.total_duration_ms, 2)
|
||||
if self.total_duration_ms
|
||||
else None,
|
||||
"status": self.status,
|
||||
"request": self.request,
|
||||
"response": self.response,
|
||||
@@ -132,6 +138,7 @@ _current_span: ContextVar[Span | None] = ContextVar("current_span", default=None
|
||||
def tracing_enabled() -> bool:
|
||||
"""Check if tracing is enabled (requires DEBUG=true)."""
|
||||
from src.core.config import config
|
||||
|
||||
return config.DEBUG
|
||||
|
||||
|
||||
@@ -163,7 +170,7 @@ def start_trace(
|
||||
trace_id=_generate_id("trace_"),
|
||||
conversation_id=conversation_id,
|
||||
user=user,
|
||||
timestamp=datetime.now(timezone.utc),
|
||||
timestamp=datetime.now(UTC),
|
||||
request=request,
|
||||
)
|
||||
_current_trace.set(trace)
|
||||
@@ -209,7 +216,7 @@ def start_span(
|
||||
span_id=_generate_id("span_"),
|
||||
name=name,
|
||||
type=span_type,
|
||||
start_time=datetime.now(timezone.utc),
|
||||
start_time=datetime.now(UTC),
|
||||
parent_id=parent.span_id if parent else None,
|
||||
metadata=metadata or {},
|
||||
details=details or {},
|
||||
@@ -254,7 +261,7 @@ def end_span(
|
||||
if not span:
|
||||
return
|
||||
|
||||
span.end_time = datetime.now(timezone.utc)
|
||||
span.end_time = datetime.now(UTC)
|
||||
span.status = status
|
||||
if error:
|
||||
span.error = error
|
||||
@@ -405,7 +412,7 @@ def add_tool_spans_from_messages(messages: list[Any], parent_span: Span | None =
|
||||
if isinstance(part, ToolCallPart):
|
||||
tool_calls[part.tool_call_id] = {
|
||||
"name": part.tool_name,
|
||||
"args": part.args if hasattr(part, 'args') else {},
|
||||
"args": part.args if hasattr(part, "args") else {},
|
||||
}
|
||||
elif isinstance(msg, ModelRequest):
|
||||
for part in msg.parts:
|
||||
@@ -418,7 +425,7 @@ def add_tool_spans_from_messages(messages: list[Any], parent_span: Span | None =
|
||||
name=tool_info["name"],
|
||||
type=SpanType.TOOL,
|
||||
start_time=parent_span.start_time, # Approximate
|
||||
end_time=parent_span.end_time or datetime.now(timezone.utc),
|
||||
end_time=parent_span.end_time or datetime.now(UTC),
|
||||
parent_id=parent_span.span_id,
|
||||
status=SpanStatus.OK,
|
||||
metadata={
|
||||
@@ -427,7 +434,9 @@ def add_tool_spans_from_messages(messages: list[Any], parent_span: Span | None =
|
||||
},
|
||||
details={
|
||||
"args": tool_info.get("args", {}),
|
||||
"result": part.content[:2000] if isinstance(part.content, str) else str(part.content)[:2000],
|
||||
"result": part.content[:2000]
|
||||
if isinstance(part.content, str)
|
||||
else str(part.content)[:2000],
|
||||
},
|
||||
)
|
||||
parent_span.children.append(span.span_id)
|
||||
|
||||
+20
-14
@@ -4,7 +4,10 @@ Trace viewer router.
|
||||
Serves the trace viewer UI and trace files when tracing is enabled.
|
||||
Only available when DEBUG=true.
|
||||
"""
|
||||
|
||||
from datetime import UTC
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from fastapi.responses import HTMLResponse, JSONResponse
|
||||
@@ -66,12 +69,12 @@ async def list_traces(
|
||||
return {"traces": [], "total": 0}
|
||||
|
||||
import json
|
||||
from datetime import datetime, timezone, timedelta
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# Calculate cutoff time if filtering by time
|
||||
cutoff_time = None
|
||||
if since_minutes:
|
||||
cutoff_time = datetime.now(timezone.utc) - timedelta(minutes=since_minutes)
|
||||
cutoff_time = datetime.now(UTC) - timedelta(minutes=since_minutes)
|
||||
|
||||
# Get all trace files, sorted by modification time (newest first)
|
||||
trace_files = sorted(
|
||||
@@ -80,7 +83,7 @@ async def list_traces(
|
||||
reverse=True,
|
||||
)
|
||||
|
||||
traces = []
|
||||
traces: list[dict[str, Any]] = []
|
||||
for path in trace_files:
|
||||
if len(traces) >= limit:
|
||||
break
|
||||
@@ -93,7 +96,7 @@ async def list_traces(
|
||||
trace_timestamp = data.get("timestamp")
|
||||
if cutoff_time and trace_timestamp:
|
||||
try:
|
||||
ts = datetime.fromisoformat(trace_timestamp.replace('Z', '+00:00'))
|
||||
ts = datetime.fromisoformat(trace_timestamp.replace("Z", "+00:00"))
|
||||
if ts < cutoff_time:
|
||||
continue
|
||||
except (ValueError, TypeError):
|
||||
@@ -109,15 +112,17 @@ async def list_traces(
|
||||
if search and search.lower() not in request_preview.lower():
|
||||
continue
|
||||
|
||||
traces.append({
|
||||
"trace_id": data.get("trace_id"),
|
||||
"timestamp": trace_timestamp,
|
||||
"user": data.get("user"),
|
||||
"status": trace_status,
|
||||
"total_duration_ms": data.get("total_duration_ms"),
|
||||
"span_count": len(data.get("spans", [])),
|
||||
"request_preview": request_preview[:100],
|
||||
})
|
||||
traces.append(
|
||||
{
|
||||
"trace_id": data.get("trace_id"),
|
||||
"timestamp": trace_timestamp,
|
||||
"user": data.get("user"),
|
||||
"status": trace_status,
|
||||
"total_duration_ms": data.get("total_duration_ms"),
|
||||
"span_count": len(data.get("spans", [])),
|
||||
"request_preview": request_preview[:100],
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning("trace_list_parse_error", path=str(path), error=str(e))
|
||||
|
||||
@@ -145,9 +150,10 @@ async def get_trace(trace_id: str):
|
||||
|
||||
try:
|
||||
import json
|
||||
|
||||
with open(trace_path) as f:
|
||||
data = json.load(f)
|
||||
return JSONResponse(content=data)
|
||||
except Exception as e:
|
||||
logger.error("trace_read_error", trace_id=trace_id, error=str(e))
|
||||
raise HTTPException(status_code=500, detail="Failed to read trace")
|
||||
raise HTTPException(status_code=500, detail="Failed to read trace") from e
|
||||
|
||||
+8
-7
@@ -9,8 +9,9 @@ Main responsibilities:
|
||||
- Router registration
|
||||
- Lifecycle management
|
||||
"""
|
||||
|
||||
from collections.abc import AsyncGenerator
|
||||
from contextlib import asynccontextmanager
|
||||
from typing import AsyncGenerator
|
||||
|
||||
from fastapi import FastAPI, Request, status
|
||||
from fastapi.exceptions import RequestValidationError
|
||||
@@ -64,7 +65,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
|
||||
def create_application() -> FastAPI:
|
||||
"""
|
||||
Application factory.
|
||||
|
||||
|
||||
Creates and configures the FastAPI application.
|
||||
Following best practice of using factory pattern.
|
||||
"""
|
||||
@@ -75,7 +76,7 @@ def create_application() -> FastAPI:
|
||||
lifespan=lifespan,
|
||||
debug=config.DEBUG,
|
||||
)
|
||||
|
||||
|
||||
# Add middleware
|
||||
application.add_middleware(
|
||||
CORSMiddleware,
|
||||
@@ -84,10 +85,10 @@ def create_application() -> FastAPI:
|
||||
allow_methods=config.CORS_ALLOW_METHODS,
|
||||
allow_headers=config.CORS_ALLOW_HEADERS,
|
||||
)
|
||||
|
||||
|
||||
# Register exception handlers
|
||||
register_exception_handlers(application)
|
||||
|
||||
|
||||
# Include routers
|
||||
application.include_router(core_router) # Health and root endpoints
|
||||
application.include_router(chat_router, prefix=config.API_PREFIX)
|
||||
@@ -105,10 +106,10 @@ def create_application() -> FastAPI:
|
||||
def register_exception_handlers(application: FastAPI) -> None:
|
||||
"""
|
||||
Register global exception handlers.
|
||||
|
||||
|
||||
Provides consistent error responses compatible with OpenAI API.
|
||||
"""
|
||||
|
||||
|
||||
@application.exception_handler(AppException)
|
||||
async def app_exception_handler(
|
||||
request: Request,
|
||||
|
||||
@@ -2,6 +2,7 @@
|
||||
Models router.
|
||||
OpenAI-compatible /v1/models endpoint.
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter
|
||||
@@ -18,9 +19,9 @@ router = APIRouter(prefix="/models", tags=["models"])
|
||||
async def list_models() -> ModelsResponse:
|
||||
"""
|
||||
List available models (OpenAI-compatible).
|
||||
|
||||
|
||||
Currently returns mock model list.
|
||||
|
||||
|
||||
Returns:
|
||||
List of available models
|
||||
"""
|
||||
|
||||
@@ -1,11 +1,13 @@
|
||||
"""
|
||||
OpenAI-compatible models schemas.
|
||||
"""
|
||||
|
||||
from src.core.models import CustomBaseModel
|
||||
|
||||
|
||||
class Model(CustomBaseModel):
|
||||
"""OpenAI-compatible model object."""
|
||||
|
||||
id: str
|
||||
object: str = "model"
|
||||
created: int
|
||||
@@ -14,5 +16,6 @@ class Model(CustomBaseModel):
|
||||
|
||||
class ModelsResponse(CustomBaseModel):
|
||||
"""OpenAI-compatible models list response."""
|
||||
|
||||
object: str = "list"
|
||||
data: list[Model]
|
||||
|
||||
+33
-30
@@ -2,8 +2,10 @@
|
||||
Ollama HTTP client.
|
||||
Handles all communication with the Ollama service.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import Any, AsyncGenerator
|
||||
from collections.abc import AsyncGenerator
|
||||
from typing import Any
|
||||
|
||||
import httpx
|
||||
from httpx import ConnectError, TimeoutException
|
||||
@@ -22,14 +24,14 @@ logger = logging.getLogger(__name__)
|
||||
class OllamaClient:
|
||||
"""
|
||||
Async client for Ollama API.
|
||||
|
||||
|
||||
Follows best practice of using async for I/O operations.
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self, base_url: str | None = None, timeout: int | None = None):
|
||||
"""
|
||||
Initialize Ollama client.
|
||||
|
||||
|
||||
Args:
|
||||
base_url: Ollama server URL (defaults to config)
|
||||
timeout: Request timeout in seconds (defaults to config)
|
||||
@@ -37,7 +39,7 @@ class OllamaClient:
|
||||
self.base_url = base_url or str(config.OLLAMA_HOST)
|
||||
self.timeout = timeout or config.OLLAMA_TIMEOUT
|
||||
self._client: httpx.AsyncClient | None = None
|
||||
|
||||
|
||||
async def __aenter__(self) -> "OllamaClient":
|
||||
"""Async context manager entry."""
|
||||
self._client = httpx.AsyncClient(
|
||||
@@ -45,32 +47,32 @@ class OllamaClient:
|
||||
timeout=self.timeout,
|
||||
)
|
||||
return self
|
||||
|
||||
|
||||
async def __aexit__(self, *args: Any) -> None:
|
||||
"""Async context manager exit."""
|
||||
if self._client:
|
||||
await self._client.aclose()
|
||||
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
request: OllamaChatRequest,
|
||||
) -> OllamaChatResponse:
|
||||
"""
|
||||
Send chat request to Ollama (non-streaming).
|
||||
|
||||
|
||||
Args:
|
||||
request: Chat request with model and messages
|
||||
|
||||
|
||||
Returns:
|
||||
Complete chat response
|
||||
|
||||
|
||||
Raises:
|
||||
OllamaConnectionError: Cannot connect to Ollama
|
||||
OllamaTimeoutError: Request timed out
|
||||
"""
|
||||
if not self._client:
|
||||
raise RuntimeError("Client not initialized. Use async with context.")
|
||||
|
||||
|
||||
try:
|
||||
response = await self._client.post(
|
||||
"/api/chat",
|
||||
@@ -78,38 +80,38 @@ class OllamaClient:
|
||||
)
|
||||
response.raise_for_status()
|
||||
return OllamaChatResponse(**response.json())
|
||||
|
||||
|
||||
except ConnectError as e:
|
||||
logger.error(f"Cannot connect to Ollama at {self.base_url}: {e}")
|
||||
raise OllamaConnectionError() from e
|
||||
|
||||
|
||||
except TimeoutException as e:
|
||||
logger.error(f"Ollama request timed out after {self.timeout}s: {e}")
|
||||
raise OllamaTimeoutError() from e
|
||||
|
||||
|
||||
async def chat_stream(
|
||||
self,
|
||||
request: OllamaChatRequest,
|
||||
) -> AsyncGenerator[dict[str, Any], None]:
|
||||
"""
|
||||
Send streaming chat request to Ollama.
|
||||
|
||||
|
||||
Args:
|
||||
request: Chat request with stream=True
|
||||
|
||||
|
||||
Yields:
|
||||
Streaming response chunks
|
||||
|
||||
|
||||
Raises:
|
||||
OllamaConnectionError: Cannot connect to Ollama
|
||||
OllamaTimeoutError: Request timed out
|
||||
"""
|
||||
if not self._client:
|
||||
raise RuntimeError("Client not initialized. Use async with context.")
|
||||
|
||||
|
||||
# Ensure streaming is enabled
|
||||
request.stream = True
|
||||
|
||||
|
||||
try:
|
||||
async with self._client.stream(
|
||||
"POST",
|
||||
@@ -120,48 +122,49 @@ class OllamaClient:
|
||||
async for line in response.aiter_lines():
|
||||
if line.strip():
|
||||
import json
|
||||
|
||||
yield json.loads(line)
|
||||
|
||||
|
||||
except ConnectError as e:
|
||||
logger.error(f"Cannot connect to Ollama at {self.base_url}: {e}")
|
||||
raise OllamaConnectionError() from e
|
||||
|
||||
|
||||
except TimeoutException as e:
|
||||
logger.error(f"Ollama request timed out after {self.timeout}s: {e}")
|
||||
raise OllamaTimeoutError() from e
|
||||
|
||||
|
||||
async def list_models(self) -> OllamaModelsResponse:
|
||||
"""
|
||||
List available models from Ollama.
|
||||
|
||||
|
||||
Returns:
|
||||
List of available models
|
||||
|
||||
|
||||
Raises:
|
||||
OllamaConnectionError: Cannot connect to Ollama
|
||||
"""
|
||||
if not self._client:
|
||||
raise RuntimeError("Client not initialized. Use async with context.")
|
||||
|
||||
|
||||
try:
|
||||
response = await self._client.get("/api/tags")
|
||||
response.raise_for_status()
|
||||
return OllamaModelsResponse(**response.json())
|
||||
|
||||
|
||||
except ConnectError as e:
|
||||
logger.error(f"Cannot connect to Ollama at {self.base_url}: {e}")
|
||||
raise OllamaConnectionError() from e
|
||||
|
||||
|
||||
async def health_check(self) -> bool:
|
||||
"""
|
||||
Check if Ollama service is healthy.
|
||||
|
||||
|
||||
Returns:
|
||||
True if healthy, False otherwise
|
||||
"""
|
||||
if not self._client:
|
||||
raise RuntimeError("Client not initialized. Use async with context.")
|
||||
|
||||
|
||||
try:
|
||||
response = await self._client.get("/")
|
||||
return response.status_code == 200
|
||||
@@ -174,7 +177,7 @@ class OllamaClient:
|
||||
async def get_ollama_client() -> AsyncGenerator[OllamaClient, None]:
|
||||
"""
|
||||
FastAPI dependency to provide Ollama client.
|
||||
|
||||
|
||||
Follows best practice of dependency injection.
|
||||
"""
|
||||
async with OllamaClient() as client:
|
||||
|
||||
+22
-10
@@ -5,6 +5,7 @@ Ollama's OpenAI-compatible API rejects messages with `content: null`,
|
||||
which PydanticAI sends for assistant messages that only contain tool calls.
|
||||
This provider sanitizes messages to use empty strings instead of null.
|
||||
"""
|
||||
|
||||
from typing import Any
|
||||
|
||||
from openai import AsyncOpenAI
|
||||
@@ -40,14 +41,21 @@ class TatlockOllamaProvider(OllamaProvider):
|
||||
# Override the client with our sanitized version
|
||||
self._openai_client = _SanitizedAsyncOpenAI(base_url=base_url)
|
||||
|
||||
logger.debug("tatlock_ollama_provider_created", base_url=base_url)
|
||||
logger.debug(
|
||||
"tatlock_ollama_provider_created",
|
||||
base_url=base_url,
|
||||
timeout=config.OLLAMA_TIMEOUT,
|
||||
)
|
||||
|
||||
|
||||
class _SanitizedAsyncOpenAI(AsyncOpenAI):
|
||||
"""AsyncOpenAI client that sanitizes messages before sending."""
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
# Ollama doesn't need an API key
|
||||
# Ollama doesn't need an API key. Cap each LLM call at the
|
||||
# configured Ollama timeout instead of the SDK default (~600s),
|
||||
# so one stuck request cannot eat the whole delegation budget.
|
||||
kwargs.setdefault("timeout", float(config.OLLAMA_TIMEOUT))
|
||||
super().__init__(api_key="ollama", **kwargs)
|
||||
|
||||
@property
|
||||
@@ -106,14 +114,18 @@ def _sanitize_messages(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
for msg in messages:
|
||||
msg_copy = dict(msg)
|
||||
|
||||
# Fix null content in assistant messages with tool calls
|
||||
if msg_copy.get("role") == "assistant":
|
||||
if msg_copy.get("content") is None and msg_copy.get("tool_calls"):
|
||||
msg_copy["content"] = ""
|
||||
logger.debug(
|
||||
"sanitized_null_content",
|
||||
tool_call_count=len(msg_copy["tool_calls"]),
|
||||
)
|
||||
# Fix null content in ANY message: Ollama rejects content: null with
|
||||
# "invalid message content type: <nil>". The tool-call-only assistant
|
||||
# case is the common one, but gemma thinking-only turns produce
|
||||
# assistant messages with null content and NO tool_calls, which
|
||||
# previously slipped through and 400'd the whole agent run.
|
||||
if "content" in msg_copy and msg_copy.get("content") is None:
|
||||
msg_copy["content"] = ""
|
||||
logger.debug(
|
||||
"sanitized_null_content",
|
||||
role=msg_copy.get("role"),
|
||||
tool_call_count=len(msg_copy.get("tool_calls") or []),
|
||||
)
|
||||
|
||||
sanitized.append(msg_copy)
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user