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5
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 jpmschweitzer
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214dc4e725 | ||
 jpmschweitzerandClaude Opus 4.5
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acdde99a5c | ||
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jpmschweitzerandClaude Opus 4.5
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4e6f1da4f3 | ||
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jpmschweitzerandClaude Opus 4.5
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7dd2c20e76 | ||
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jpmschweitzerandClaude Opus 4.5
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7426dd1ac3 |
@@ -22,6 +22,7 @@ jobs:
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with:
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context: .
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push: true
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provenance: false
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tags: |
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git.schweitz.net/jpmschweitzer/tatlock:latest
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git.schweitz.net/jpmschweitzer/tatlock:${{ github.ref_name }}
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git.schweitz.internal/jpmschweitzer/tatlock:latest
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git.schweitz.internal/jpmschweitzer/tatlock:${{ github.ref_name }}
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+96
-1
@@ -7,6 +7,100 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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|
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## [Unreleased]
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|
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## [1.2.2] - 2025-12-13
|
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|
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### Fixed
|
||||
|
||||
- **CI**: Add `provenance: false` to docker/build-push-action to fix Gitea registry push
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||||
|
||||
## [1.2.1] - 2025-12-13
|
||||
|
||||
### Changed
|
||||
|
||||
- **Dependency slimming**: Switched from `pydantic-ai` to `pydantic-ai-slim[openai]`
|
||||
- Removes unused LLM provider SDKs (anthropic, boto3, cohere, google-genai, groq, huggingface)
|
||||
- Production packages: 53 (down from ~158)
|
||||
- Production footprint: 178MB
|
||||
- Tatlock uses Ollama via OpenAI-compatible API, so only `openai` extra is needed
|
||||
- See `DEPENDENCY_SLIM.md` for rollback instructions
|
||||
|
||||
## [1.2.0] - 2025-12-13
|
||||
|
||||
### Added
|
||||
|
||||
#### Phase F: Memory System (The Biographer)
|
||||
|
||||
- **Memory Infrastructure** (Phase F.1):
|
||||
- `src/core/context.py`: ContextVar-based request context for async-safe user/conversation tracking
|
||||
- `get_user()`, `get_conversation_id()` helpers
|
||||
- `RequestContext` manager for clean setup/teardown
|
||||
- `src/core/multi_tenancy.py`: User ID sanitization and collection naming
|
||||
- Per-user collection pattern: `memories_{user}`
|
||||
- Redis key patterns: `session:{user}:{conv}`, `entities:{user}:{conv}`
|
||||
- `src/core/embeddings.py`: Ollama embedding client
|
||||
- nomic-embed-text model (768 dimensions)
|
||||
- `embed()`, `embed_batch()`, `health_check()` methods
|
||||
- `src/core/qdrant.py`: Qdrant vector database client
|
||||
- `ensure_collection()`, `upsert_memory()`, `search_memories()`, `delete_memory()`
|
||||
- Type-based filtering for memory queries
|
||||
- `src/core/memory_cache.py`: Redis session memory cache
|
||||
- Session context with 24h TTL (db=2, separate from benchmarks)
|
||||
- Recent entities tracking per conversation
|
||||
|
||||
- **Memory Service** (Phase F.2a):
|
||||
- `src/core/memory_service.py`: Direct access layer for fast, LLM-free memory lookups
|
||||
- Profile methods: `get_profile()`, `set_profile()`
|
||||
- Preference methods: `get_preference()`, `set_preference()`, `get_all_preferences()`
|
||||
- Fact methods: `store_fact()`, `get_fact()`
|
||||
- Session context: `get_session_context()`, `set_session_context()`, `update_session_context()`
|
||||
- Steward integration: `prefetch_context()` for request preprocessing
|
||||
|
||||
- **The Biographer Agent** (Phase F.2b):
|
||||
- `src/agents/biographer/`: Household memory keeper agent
|
||||
- PydanticAI agent with discreet chronicler personality
|
||||
- System prompt emphasizes privacy and accurate recall
|
||||
- **Biographer Tools** (`src/agents/biographer/tools.py`):
|
||||
- `recall_semantic`: Semantic search for memories by meaning
|
||||
- `list_memories`: Browse stored memories by type
|
||||
- `store_insight`: Record new facts from conversation
|
||||
- `update_profile`: Update core profile fields (name, location, timezone)
|
||||
- `update_preference`: Update user preferences (units, theme)
|
||||
- `forget_memory`: Remove specific memories
|
||||
- **Capability Registration**:
|
||||
- `BIOGRAPHER_CAPABILITY` with context domain
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||||
- Automatic registration on startup
|
||||
- Low cost (vector search, minimal LLM)
|
||||
|
||||
- **Delegation Wrapper**:
|
||||
- `delegate_to_biographer()` in `src/agents/delegation.py`
|
||||
- Async delegation with error handling
|
||||
|
||||
- **Steward Memory Integration**:
|
||||
- Memory context pre-fetch during request analysis
|
||||
- Profile and preferences included in Steward's note to Butler
|
||||
- Keyword-based context determination (weather → location, time → timezone)
|
||||
|
||||
- **Configuration**:
|
||||
- `QDRANT_HOST`, `QDRANT_PORT`, `QDRANT_EMBEDDING_DIM` (768)
|
||||
- `OLLAMA_EMBEDDING_MODEL` (nomic-embed-text)
|
||||
- `REDIS_MEMORY_DB` (2), `REDIS_MEMORY_TTL_HOURS` (24)
|
||||
|
||||
- **Test Suite**:
|
||||
- 34 new tests for memory system
|
||||
- Biographer capability tests (15 tests)
|
||||
- Memory service tests (19 tests)
|
||||
|
||||
- **OpenAI Standard `user` Field**:
|
||||
- Added `user` field to `ResponseRequest` schema
|
||||
- Request context set at API entry point
|
||||
- Propagates through async calls via ContextVar
|
||||
|
||||
### Changed
|
||||
- Application startup now registers The Biographer with Household Registry
|
||||
- Steward analysis includes memory context pre-fetch
|
||||
- Librarian client methods now use `get_user()` from context (12 methods updated)
|
||||
- Request router sets user/conversation context at entry
|
||||
|
||||
## [1.1.0] - 2025-12-11
|
||||
|
||||
### Added
|
||||
@@ -390,7 +484,8 @@ 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/v1.1.0...main
|
||||
[Unreleased]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.2.0...main
|
||||
[1.2.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.1.0...v1.2.0
|
||||
[1.1.0]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v1.0.0a...v1.1.0
|
||||
[1.0.0a]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v0.2.5...v1.0.0a
|
||||
[0.2.5]: https://git.schweitz.net/jpmschweitzer/tatlock/compare/v0.2.0...v0.2.5
|
||||
|
||||
@@ -0,0 +1,72 @@
|
||||
# Dependency Slimming: pydantic-ai → pydantic-ai-slim
|
||||
|
||||
**Date**: 2025-12-13
|
||||
**Version**: Post v1.2.0
|
||||
|
||||
## Change
|
||||
|
||||
Switched from `pydantic-ai` to `pydantic-ai-slim[openai]` to reduce container image size.
|
||||
|
||||
### Before
|
||||
```
|
||||
pydantic-ai>=1.27,<1.28
|
||||
```
|
||||
|
||||
This installs SDKs for ALL LLM providers:
|
||||
- anthropic
|
||||
- boto3 + botocore (AWS Bedrock)
|
||||
- cohere
|
||||
- google-genai + google-auth
|
||||
- groq
|
||||
- huggingface-hub
|
||||
|
||||
Total packages: ~158
|
||||
|
||||
### After
|
||||
```
|
||||
pydantic-ai-slim[openai]>=1.27,<1.28
|
||||
```
|
||||
|
||||
Only installs the OpenAI-compatible SDK. Ollama works through this interface.
|
||||
|
||||
Expected packages: ~80-90 (significant reduction)
|
||||
|
||||
## Why This Works
|
||||
|
||||
Tatlock uses Ollama exclusively, which implements the OpenAI-compatible API. The code uses:
|
||||
```python
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
|
||||
model = OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
provider=OllamaProvider(base_url=f"{config.OLLAMA_HOST}/v1")
|
||||
)
|
||||
```
|
||||
|
||||
This pattern only requires the `openai` extra, not the full pydantic-ai package.
|
||||
|
||||
## Rollback Instructions
|
||||
|
||||
If this change breaks things:
|
||||
|
||||
1. Revert requirements.txt:
|
||||
```diff
|
||||
- pydantic-ai-slim[openai]>=1.27,<1.28
|
||||
+ pydantic-ai>=1.27,<1.28
|
||||
```
|
||||
|
||||
2. Reinstall dependencies:
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
3. Delete this file once confirmed stable.
|
||||
|
||||
## Testing Checklist
|
||||
|
||||
- [ ] Unit tests pass
|
||||
- [ ] Integration tests pass (with Ollama running)
|
||||
- [ ] Wakeup script e2e test passes
|
||||
- [ ] Container builds successfully
|
||||
- [ ] Container runs correctly
|
||||
+124
-87
@@ -4,35 +4,37 @@
|
||||
|
||||
This document outlines the phased implementation plan to transform the current OpenAI-compatible API into the full Tatlock household butler system.
|
||||
|
||||
## Current State (v0.1.1+ - Phase 1 Mostly Complete)
|
||||
## 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 (131 tests, 81.78% coverage)
|
||||
- ✅ **Tatlock Agent** - Real PydanticAI integration
|
||||
- Connected to Ollama (mistral-nemo:latest)
|
||||
- British butler personality with research mindset
|
||||
- Streaming responses with reasoning
|
||||
- Tool calling framework functional
|
||||
- ✅ **Permanent Tools**
|
||||
- Calculator (safe mathematical expressions)
|
||||
- Date/Time toolkit (current time, relative dates, time differences)
|
||||
- Web search (SearXNG integration)
|
||||
- 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)
|
||||
- ✅ Agent interface abstraction
|
||||
|
||||
**What we need**:
|
||||
- **The Household** - Full multi-agent coordination:
|
||||
- The Steward (first-tier request analysis)
|
||||
- Tatlock coordination layer (expert agent delegation)
|
||||
- Expert household staff agents (Librarian, Developer, Handyman, etc.)
|
||||
- Multi-tenant database architecture
|
||||
- Containerized service ecosystem
|
||||
- More household staff (Developer, Secretary, Handyman, Housekeeper)
|
||||
- MCP (Model Context Protocol) integration
|
||||
- Dynamic model switching for specialized tasks
|
||||
- Full multi-tenant database (PostgreSQL)
|
||||
|
||||
---
|
||||
|
||||
@@ -359,15 +361,18 @@ User Request → Orchestrator → Steward Analysis → Recommendations → Tatlo
|
||||
|
||||
### Success Criteria
|
||||
|
||||
- [ ] **Steward analyzes incoming requests** using PydanticAI agent
|
||||
- [ ] **Produces structured recommendations** (tools, agents, reasoning)
|
||||
- [ ] **Recommendations formatted as prepended note** to Tatlock
|
||||
- [ ] **Tool registry is queryable and extensible** via clean API
|
||||
- [ ] **Steward output visible in reasoning stream** for transparency
|
||||
- [ ] **Only recommended tools available** to Tatlock (scoped context)
|
||||
- [ ] **Base model stays loaded** between Steward and Tatlock calls
|
||||
- [ ] **Recommendations are accurate** (not over/under-inclusive)
|
||||
- [ ] **Integration tests pass** for full Steward → Tatlock flow
|
||||
- [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
|
||||
|
||||
@@ -435,12 +440,15 @@ The Steward is the foundation of the household architecture. Without it, we'd ne
|
||||
- Wait time transparency
|
||||
|
||||
### Success Criteria
|
||||
- [ ] Tatlock receives enriched requests (user + Steward notes)
|
||||
- [ ] Only recommended tools are available
|
||||
- [ ] Tatlock coordinates multiple tool calls
|
||||
- [ ] All actions streamed to reasoning output
|
||||
- [ ] Responses have consistent personality
|
||||
- [ ] Synthesizes multi-source results coherently
|
||||
- [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
|
||||
@@ -453,39 +461,44 @@ The Steward is the foundation of the household architecture. Without it, we'd ne
|
||||
|
||||
### Priority Expert Agents
|
||||
|
||||
1. **The Librarian** (Research & Knowledge Management) ⭐ **Priority**
|
||||
- Research assistance and synthesis
|
||||
- Automatic research dossier generation
|
||||
- Knowledge base queries and organization
|
||||
- Reference management
|
||||
- Wiki integration (future: dedicated wiki container)
|
||||
- Mind map maintenance (future)
|
||||
- *Rationale: Helps guide development priorities through better research*
|
||||
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 Developer** (Software Development)
|
||||
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*
|
||||
|
||||
3. **The Handyman** (System Maintenance)
|
||||
4. **The Handyman** (System Maintenance) 🔜 **Planned**
|
||||
- System status queries
|
||||
- Log analysis
|
||||
- Basic troubleshooting
|
||||
- Infrastructure monitoring
|
||||
|
||||
4. **The Secretary** (Scheduling & Organization)
|
||||
- Calendar integration (placeholder)
|
||||
- Task management (placeholder)
|
||||
5. **The Secretary** (Scheduling & Organization) 🔜 **Planned**
|
||||
- Calendar integration
|
||||
- Task management
|
||||
- Reminder system
|
||||
- Schedule conflict detection
|
||||
|
||||
5. **The Housekeeper** (Home Automation)
|
||||
6. **The Housekeeper** (Home Automation) 🔜 **Planned**
|
||||
- Home Assistant integration
|
||||
- Device control interface
|
||||
- Status queries
|
||||
- Automation triggers
|
||||
- Environmental monitoring
|
||||
|
||||
### Each Agent Includes
|
||||
- Specialized prompt and personality
|
||||
@@ -494,12 +507,15 @@ The Steward is the foundation of the household architecture. Without it, we'd ne
|
||||
- Integration with Butler orchestration
|
||||
|
||||
### Success Criteria
|
||||
- [ ] Each agent implemented as separate module
|
||||
- [ ] Agents callable via tool framework
|
||||
- [ ] Agents use specialized prompts
|
||||
- [ ] Results integrate cleanly with Butler
|
||||
- [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
|
||||
|
||||
@@ -556,31 +572,37 @@ The core orchestration (Steward → Butler → Experts) can work entirely with i
|
||||
|
||||
### Services to Integrate
|
||||
|
||||
1. **Redis (Memory & Caching)**
|
||||
- Docker compose setup
|
||||
- Conversation cache
|
||||
- Short-term memory
|
||||
- Session management
|
||||
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
|
||||
|
||||
3. **Qdrant (Vector Storage)**
|
||||
- Docker compose setup
|
||||
- Long-term memory embeddings
|
||||
- Semantic search
|
||||
- Conversation history vectors
|
||||
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
|
||||
|
||||
4. **SearxNG (Web Search)**
|
||||
- Docker compose setup
|
||||
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
|
||||
- [ ] All services defined in docker-compose.yml
|
||||
- [ ] Services communicate correctly
|
||||
- [ ] Tatlock can invoke web search
|
||||
- [ ] Redis used for session data
|
||||
- [ ] Qdrant stores conversation embeddings
|
||||
- [ ] Ollama serves the base model
|
||||
- [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
|
||||
@@ -629,33 +651,48 @@ The core orchestration (Steward → Butler → Experts) can work entirely with i
|
||||
|
||||
### Deliverables
|
||||
|
||||
1. **Long-Term Memory**
|
||||
- Conversation embedding pipeline
|
||||
- Semantic search over history
|
||||
- Memory consolidation
|
||||
- Relevance ranking
|
||||
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. **Context 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
|
||||
|
||||
3. **Personalization**
|
||||
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
|
||||
- [ ] Relevant history retrieved for new requests
|
||||
- [ ] Context stays within model limits
|
||||
- [ ] User preferences affect responses
|
||||
- [ ] Memory improves over time
|
||||
- [ ] Memory improves over time (learning from interactions)
|
||||
|
||||
### Status
|
||||
**🔶 PARTIAL** - Core memory system complete, advanced features planned
|
||||
|
||||
### Estimated Effort
|
||||
**4-5 weeks** - AI/ML heavy
|
||||
**4-5 weeks** - AI/ML heavy (remaining work)
|
||||
|
||||
---
|
||||
|
||||
@@ -871,13 +908,13 @@ Phase 9 (Extended Staff) → Phase 10 (UX) → Phase 11 (Production)
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. **Immediate**: Commit model name fix (Tatlock)
|
||||
2. **Week 1-2**: Begin Phase 1 (PostgreSQL + multi-tenancy design)
|
||||
3. **Week 3**: Parallel prototype of Steward agent
|
||||
4. **Ongoing**: Update this roadmap as we learn
|
||||
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**: 2025-12-06
|
||||
**Last Updated**: 2025-12-13
|
||||
|
||||
@@ -1,535 +0,0 @@
|
||||
# Phase 2 Completion Summary: The Steward
|
||||
|
||||
**Status**: ✅ COMPLETE
|
||||
**Completed**: 2025-12-07
|
||||
**Duration**: 1 day (accelerated from 7-week plan)
|
||||
**Test Coverage**: 223 passing tests (99.5% pass rate)
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
Phase 2 successfully implements **The Steward** - a first-tier LLM agent that creates a two-tier architecture for intelligent request routing. The Steward analyzes incoming requests, identifies relevant household capabilities, and provides scoped tool recommendations to Tatlock (the Butler).
|
||||
|
||||
This architecture prevents cognitive overload by ensuring Tatlock only sees tools relevant to each specific request, while maintaining full conversation context awareness and providing complete observability through benchmarking and logging.
|
||||
|
||||
---
|
||||
|
||||
## Delivered Features
|
||||
|
||||
### 1. The Steward Agent ✅
|
||||
**Location**: `src/agents/steward/`
|
||||
|
||||
- **Request Analysis**: Analyzes user requests with full conversation history
|
||||
- **Capability Recommendation**: Recommends relevant household tools/capabilities
|
||||
- **Context Awareness**: Identifies references to previous conversation turns
|
||||
- **Complexity Assessment**: Estimates request complexity (simple/moderate/complex)
|
||||
- **Missing Capability Detection**: Explicitly states when needed tools are unavailable
|
||||
- **VRAM Efficiency**: Uses same Ollama model as Tatlock (mistral-nemo:latest)
|
||||
|
||||
**Key Files**:
|
||||
- `agent.py`: Steward PydanticAI agent implementation
|
||||
- `schemas.py`: `StewardRecommendation` and `ConversationContext` structures
|
||||
- `service.py`: Service layer with logging and benchmarking
|
||||
|
||||
### 2. Household Registry ✅
|
||||
**Location**: `src/core/household_registry.py`
|
||||
|
||||
- **Centralized Capability Management**: Single source of truth for household tools
|
||||
- **Executive Summaries**: High-level capability descriptions for Steward/Butler coordination
|
||||
- **PydanticAI Toolsets**: Native toolset composition and scoping
|
||||
- **Domain Organization**: Tools organized by household member (e.g., `tatlock_core`)
|
||||
- **Dynamic Tool Scoping**: Creates combined toolsets based on recommendations
|
||||
|
||||
**Architecture**:
|
||||
```
|
||||
HouseholdRegistry
|
||||
├─ HouseholdMember (tatlock_core)
|
||||
│ ├─ HouseholdCapability (summary)
|
||||
│ └─ FunctionToolset (calculator, datetime, search)
|
||||
├─ Future: HouseholdMember (librarian)
|
||||
└─ Future: HouseholdMember (developer)
|
||||
```
|
||||
|
||||
### 3. Request Preprocessing Pipeline ✅
|
||||
**Location**: `src/core/preprocessing.py`
|
||||
|
||||
**4-Phase Flow**:
|
||||
1. **Steward Analysis**: Analyzes request with full conversation history
|
||||
2. **Tool Scoping**: Creates combined toolset from recommendations
|
||||
3. **Note Formatting**: Prepares Steward note for Butler (invisible to user)
|
||||
4. **Enrichment**: Returns `EnrichedRequest` with all context
|
||||
|
||||
**Integration**: Fully integrated with Responses API via `create_response_with_steward()`
|
||||
|
||||
### 4. Tool Usage Tracking ✅
|
||||
**Location**: `src/core/tool_tracking.py`
|
||||
|
||||
**Capabilities**:
|
||||
- Tracks recommended vs. actual tool usage
|
||||
- Logs unexpected tool calls (not recommended but used)
|
||||
- Logs unused recommendations (recommended but not used)
|
||||
- Records timing data for each tool call
|
||||
- Stores benchmarks to Redis for analysis
|
||||
|
||||
**Metrics Supported**:
|
||||
- Precision: Recommended and used / All recommendations
|
||||
- Recall: Recommended and used / All tool calls
|
||||
- F1 Score: Harmonic mean of precision and recall
|
||||
|
||||
### 5. Streaming Transparency ✅
|
||||
**Location**: `src/responses/streaming.py`
|
||||
|
||||
**Features**:
|
||||
- Streams Steward's analysis first (reasoning summary deltas)
|
||||
- Streams Tatlock's response second (output text deltas)
|
||||
- Full SSE support with proper event types
|
||||
- Conversation context visible in stream
|
||||
- Missing capabilities warnings included
|
||||
|
||||
**Event Sequence**:
|
||||
```
|
||||
1. response.reasoning_summary_text.delta (Steward analysis)
|
||||
2. response.reasoning_summary_text.done
|
||||
3. response.output_text.delta (Tatlock response)
|
||||
4. response.output_text.done
|
||||
5. response.done (final response)
|
||||
```
|
||||
|
||||
### 6. Structured Logging ✅
|
||||
**Location**: `src/core/logging_config.py`
|
||||
|
||||
**Features**:
|
||||
- JSON-formatted structured logging via `structlog`
|
||||
- Operation timing via context managers (`log_operation`)
|
||||
- Metadata enrichment for debugging
|
||||
- Integrated with benchmark recording
|
||||
- Machine-parseable output for analysis
|
||||
|
||||
### 7. Redis Benchmark Storage ✅
|
||||
**Location**: `src/core/benchmarks.py`
|
||||
|
||||
**Features**:
|
||||
- Cross-session performance metrics storage
|
||||
- Time-series data with 30-day automatic expiry
|
||||
- Operations tracked: `steward_analysis`, `tool_call`
|
||||
- Queryable by operation type, time range, metadata
|
||||
- Supports accuracy analysis (recommended vs. used)
|
||||
|
||||
**Benchmark Schema**:
|
||||
- Timestamp, operation, duration, success/failure
|
||||
- Steward-specific: recommendation_count, complexity
|
||||
- Tool-specific: tool_name, was_recommended, was_actually_used
|
||||
- Context: conversation_id, metadata dict
|
||||
|
||||
### 8. Benchmark Analysis Tools ✅
|
||||
**Location**: `scripts/benchmark_analysis.py`
|
||||
|
||||
**CLI Features**:
|
||||
```bash
|
||||
# Steward performance over last 24 hours
|
||||
python scripts/benchmark_analysis.py --operation steward_analysis --hours 24
|
||||
|
||||
# Tool recommendation accuracy over last 7 days
|
||||
python scripts/benchmark_analysis.py --tool-accuracy --days 7
|
||||
|
||||
# Summary of all operations
|
||||
python scripts/benchmark_analysis.py --summary --hours 1
|
||||
```
|
||||
|
||||
**Metrics Provided**:
|
||||
- Average Steward latency (target: < 2s)
|
||||
- Success rate percentage
|
||||
- Recommendation count distribution
|
||||
- Complexity distribution
|
||||
- Tool-specific accuracy (precision/recall/F1)
|
||||
- Per-tool usage patterns
|
||||
|
||||
---
|
||||
|
||||
## Architecture
|
||||
|
||||
### Request Flow
|
||||
|
||||
```
|
||||
User Request
|
||||
↓
|
||||
Responses API (FastAPI)
|
||||
↓
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ Preprocessing Pipeline │
|
||||
│ ├─ Steward Agent │
|
||||
│ │ ├─ Receives: Full conversation history │
|
||||
│ │ ├─ Analyzes: Context + requirements │
|
||||
│ │ ├─ Queries: Household registry │
|
||||
│ │ └─ Returns: StewardRecommendation │
|
||||
│ │ │
|
||||
│ ├─ Create Scoped Toolset │
|
||||
│ │ └─ CombinedToolset from capabilities │
|
||||
│ │ │
|
||||
│ └─ Format Steward Note │
|
||||
│ └─ Context summary for Butler │
|
||||
└─────────────────────────────────────────────┘
|
||||
↓
|
||||
Tatlock Agent (Butler)
|
||||
├─ Receives: Enriched request + note
|
||||
├─ Tools: ONLY scoped recommendations
|
||||
├─ Tracking: Tool usage monitored
|
||||
└─ Context: Full conversation history
|
||||
↓
|
||||
Response to User
|
||||
├─ Steward's reasoning (streamed first)
|
||||
└─ Tatlock's response (streamed second)
|
||||
|
||||
Background:
|
||||
└─ Redis: Benchmarks + metrics
|
||||
```
|
||||
|
||||
### Two-Tier Abstraction
|
||||
|
||||
**Tier 1: Executive Summaries (Steward/Butler coordination)**
|
||||
```python
|
||||
HouseholdCapability(
|
||||
name="tatlock_core",
|
||||
role="Butler's Core Tools",
|
||||
category="core",
|
||||
description="Mathematical calculation, date/time operations, web search",
|
||||
domains=["computation", "information", "datetime"],
|
||||
cost="low",
|
||||
requires_network=True
|
||||
)
|
||||
```
|
||||
|
||||
**Tier 2: Implementation Details (Tool execution)**
|
||||
```python
|
||||
FunctionToolset containing:
|
||||
- calculate(expression: str) -> str
|
||||
- get_current_datetime(format_str: str) -> str
|
||||
- calculate_time_offset(offset: str) -> str
|
||||
- time_difference(date1: str, date2: str) -> str
|
||||
- search_web(query: str, num_results: int) -> str
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Test Coverage
|
||||
|
||||
### Test Statistics
|
||||
- **Total Tests**: 223 (219 passing, 1 pre-existing failure unrelated to Phase 2)
|
||||
- **Pass Rate**: 99.5%
|
||||
- **Coverage**: 77.6% overall
|
||||
|
||||
### Test Categories
|
||||
|
||||
#### Unit Tests ✅
|
||||
- **Household Registry** (12 tests): Registration, retrieval, toolset composition
|
||||
- **Steward Schemas** (11 tests): Data structures, formatting
|
||||
- **Steward Service** (9 tests): Request analysis, context detection, capabilities
|
||||
- **Preprocessing** (6 tests via integration): Request enrichment, tool scoping
|
||||
|
||||
#### Integration Tests ✅
|
||||
- **Steward → Tatlock Flow** (6 tests):
|
||||
- Simple math request
|
||||
- Conversation history propagation
|
||||
- No capabilities needed (conversational)
|
||||
- Tool tracker integration
|
||||
- Missing capabilities warning
|
||||
- Conversation ID propagation
|
||||
|
||||
- **Streaming Integration** (4 tests):
|
||||
- Basic streaming with Steward
|
||||
- Conversation history in streaming
|
||||
- Reasoning contains Steward analysis
|
||||
- Missing capabilities in stream
|
||||
|
||||
### Key Test Files
|
||||
- `tests/agents/steward/test_steward_schemas.py`
|
||||
- `tests/agents/steward/test_steward_service.py`
|
||||
- `tests/integration/test_steward_tatlock_integration.py`
|
||||
- `tests/integration/test_steward_streaming.py`
|
||||
|
||||
---
|
||||
|
||||
## Technical Achievements
|
||||
|
||||
### 1. PydanticAI Native Patterns ✅
|
||||
- `FunctionToolset` for tool grouping
|
||||
- `CombinedToolset` for dynamic composition
|
||||
- Decorator-based tool registration (`@agent.tool`)
|
||||
- Structured outputs via Pydantic models (`StewardRecommendation`)
|
||||
- Dependency injection for tracking (`RunContext[ToolCallTracker]`)
|
||||
|
||||
### 2. Tool Scoping Enforcement ✅
|
||||
- Compile-time scoping via toolset creation
|
||||
- Tools not even visible to LLM if not recommended
|
||||
- Fresh agent instances with scoped tools only
|
||||
- No runtime permission checks needed
|
||||
|
||||
### 3. Conversation Context Awareness ✅
|
||||
- Steward sees FULL conversation history
|
||||
- Identifies references to previous turns
|
||||
- Provides contextual notes to Butler
|
||||
- Example: "User mentioned Python debugging in turn 3"
|
||||
|
||||
### 4. Plain Text Approach ✅
|
||||
- Steward returns natural language analysis
|
||||
- Service layer parses for structured data
|
||||
- Keyword extraction for capabilities
|
||||
- Pattern matching for complexity and context
|
||||
|
||||
### 5. Observability ✅
|
||||
- Structured logging for all operations
|
||||
- Benchmark recording to Redis
|
||||
- Tool usage tracking (recommended vs. actual)
|
||||
- Cross-session performance analysis
|
||||
|
||||
---
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Latency (Estimated)
|
||||
- **Steward Analysis**: ~1-2 seconds (single LLM call)
|
||||
- **Tatlock Execution**: ~2-5 seconds (depends on tool usage)
|
||||
- **Total Added Overhead**: ~1-2 seconds vs. direct Tatlock call
|
||||
- **Streaming Transparency**: Steward reasoning visible immediately
|
||||
|
||||
### Resource Usage
|
||||
- **VRAM**: Same model for both agents (mistral-nemo:latest)
|
||||
- **Model Loading**: No additional model loads (efficient!)
|
||||
- **Redis**: Minimal (benchmarks with 30-day expiry)
|
||||
- **Network**: Only when web search tools used
|
||||
|
||||
### Accuracy Targets
|
||||
- **Recommendation Precision**: > 90% (tools recommended and actually used)
|
||||
- **Recommendation Recall**: > 90% (tools used were recommended)
|
||||
- **False Positives**: < 10% (recommended but not used)
|
||||
- **False Negatives**: < 10% (used but not recommended)
|
||||
|
||||
*Note: Actual metrics available via `scripts/benchmark_analysis.py` after production usage*
|
||||
|
||||
---
|
||||
|
||||
## Files Created
|
||||
|
||||
### Core Implementation
|
||||
1. `src/core/household_registry.py` - Capability management
|
||||
2. `src/core/preprocessing.py` - Request preprocessing pipeline
|
||||
3. `src/core/tool_tracking.py` - Tool usage tracking
|
||||
4. `src/core/logging_config.py` - Structured logging (M1)
|
||||
5. `src/core/benchmarks.py` - Redis benchmark storage (M1)
|
||||
|
||||
### Steward Agent
|
||||
6. `src/agents/steward/agent.py` - Steward PydanticAI agent
|
||||
7. `src/agents/steward/schemas.py` - Data structures
|
||||
8. `src/agents/steward/service.py` - Service layer
|
||||
|
||||
### Tatlock Core Organization
|
||||
9. `src/agents/tatlock_core/tools.py` - Tool implementations (reorganized)
|
||||
10. `src/agents/tatlock_core/toolset.py` - PydanticAI toolset
|
||||
11. `src/agents/tatlock_core/capability.py` - Registry integration
|
||||
|
||||
### Tests
|
||||
12. `tests/agents/steward/test_steward_schemas.py` - Schema tests
|
||||
13. `tests/agents/steward/test_steward_service.py` - Service tests
|
||||
14. `tests/integration/test_steward_tatlock_integration.py` - Full flow tests
|
||||
15. `tests/integration/test_steward_streaming.py` - Streaming tests
|
||||
|
||||
### Tools & Documentation
|
||||
16. `scripts/benchmark_analysis.py` - Performance analysis CLI
|
||||
17. `PHASE2_PLAN.md` - Detailed implementation plan
|
||||
18. `PHASE2_COMPLETE.md` - This completion summary
|
||||
|
||||
### Modified Files
|
||||
- `src/agents/tatlock.py` - Added `run_with_scoped_tools()` method
|
||||
- `src/responses/service.py` - Added `create_response_with_steward()`
|
||||
- `src/responses/router.py` - Steward routing logic
|
||||
- `src/responses/streaming.py` - Added `stream_response_with_steward()`
|
||||
- `CHANGELOG.md` - Phase 2 documentation
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Technical ✅
|
||||
- ✅ Household registry operational with executive summaries
|
||||
- ✅ Steward produces structured recommendations
|
||||
- ✅ Steward analyzes full conversation context
|
||||
- ✅ Tool scoping enforced (Tatlock can't use non-recommended tools)
|
||||
- ✅ Model efficiency preserved (no reload delays)
|
||||
- ✅ Performance benchmarks recorded to Redis
|
||||
- ✅ Tool usage tracking (recommended vs. actual)
|
||||
- ✅ Streaming transparency implemented
|
||||
|
||||
### Observability ✅
|
||||
- ✅ Structured logging (JSON format)
|
||||
- ✅ Benchmark analysis tools available
|
||||
- ✅ Tool recommendation accuracy measurable
|
||||
- ✅ Cross-session performance trends visible
|
||||
|
||||
### Architectural ✅
|
||||
- ✅ PydanticAI patterns followed (Toolsets, decorators, structured outputs)
|
||||
- ✅ Clean separation: registry vs. agents vs. tools
|
||||
- ✅ Two-tier abstraction working (summaries vs. details)
|
||||
- ✅ Future-proof for expert agents (Phase 4)
|
||||
|
||||
### Testing ✅
|
||||
- ✅ 223 tests passing (99.5% pass rate)
|
||||
- ✅ Integration tests for full flow
|
||||
- ✅ Streaming integration tests
|
||||
- ✅ 77.6% test coverage maintained
|
||||
|
||||
---
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Non-Streaming Request
|
||||
```python
|
||||
from src.responses.service import create_response_with_steward
|
||||
from src.responses.schemas import ResponseRequest
|
||||
|
||||
request = ResponseRequest(
|
||||
model="tatlock",
|
||||
input=[
|
||||
{"role": "user", "content": "What's sqrt(144)?"}
|
||||
],
|
||||
metadata={"conversation_id": "conv_123"}
|
||||
)
|
||||
|
||||
response = await create_response_with_steward(request)
|
||||
|
||||
# Response includes:
|
||||
# 1. Steward's analysis (reasoning output)
|
||||
# 2. Tatlock's answer (message output)
|
||||
```
|
||||
|
||||
### Streaming Request
|
||||
```python
|
||||
from src.responses.streaming import StreamingCoordinator
|
||||
|
||||
coordinator = StreamingCoordinator()
|
||||
|
||||
async for event in coordinator.stream_response_with_steward(request):
|
||||
if event.event == "response.reasoning_summary_text.delta":
|
||||
print(f"Steward: {event.delta}", end="")
|
||||
elif event.event == "response.output_text.delta":
|
||||
print(f"Tatlock: {event.delta}", end="")
|
||||
elif event.event == "response.done":
|
||||
print(f"\nFinal response: {event.response.id}")
|
||||
```
|
||||
|
||||
### Benchmark Analysis
|
||||
```bash
|
||||
# View Steward performance
|
||||
python scripts/benchmark_analysis.py --operation steward_analysis --hours 24
|
||||
|
||||
# Analyze tool accuracy
|
||||
python scripts/benchmark_analysis.py --tool-accuracy --days 7
|
||||
|
||||
# Get summary
|
||||
python scripts/benchmark_analysis.py --summary --hours 1
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Future-Proofing for Phase 4
|
||||
|
||||
### Expert Agent Pattern (Ready to Use)
|
||||
|
||||
When adding The Librarian, The Developer, or other expert agents:
|
||||
|
||||
```
|
||||
src/agents/librarian/
|
||||
├── agent.py # Librarian PydanticAI agent
|
||||
├── tools.py # Research, wiki, knowledge tools
|
||||
├── toolset.py # PydanticAI toolset
|
||||
└── capability.py # Registry integration
|
||||
```
|
||||
|
||||
**Registration**:
|
||||
```python
|
||||
from src.core.household_registry import get_household_registry
|
||||
|
||||
registry = get_household_registry()
|
||||
registry.register(
|
||||
name="librarian",
|
||||
capability=LIBRARIAN_CAPABILITY,
|
||||
toolset=librarian_toolset,
|
||||
agent=librarian_agent # For delegation
|
||||
)
|
||||
```
|
||||
|
||||
**Delegation from Tatlock** (Phase 4):
|
||||
```python
|
||||
@tatlock_agent.tool
|
||||
async def consult_librarian(
|
||||
ctx: RunContext[None],
|
||||
research_query: str
|
||||
) -> str:
|
||||
"""Consult the Librarian for research assistance."""
|
||||
return await librarian_agent.run(research_query, usage=ctx.usage)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Lessons Learned
|
||||
|
||||
### What Went Well
|
||||
1. **PydanticAI Integration**: Native toolset patterns work beautifully
|
||||
2. **Two-Tier Architecture**: Clean separation between coordination and execution
|
||||
3. **Plain Text Approach**: More flexible than structured output for Steward
|
||||
4. **Test Coverage**: Comprehensive integration tests caught edge cases early
|
||||
5. **Streaming**: SSE events provide excellent real-time transparency
|
||||
|
||||
### Challenges Overcome
|
||||
1. **Schema vs. Agent OutputItems**: Fixed `_calculate_usage` to handle both types
|
||||
2. **Registry Initialization**: Added fixtures to ensure registry available in tests
|
||||
3. **Plain Text Parsing**: Keyword extraction works well but needs careful test mocking
|
||||
4. **Complexity Substring Matching**: "Complexity:" contains "complex" - fixed test mocks
|
||||
|
||||
### Optimizations
|
||||
1. **Single Model**: Using same Ollama model for both agents saves VRAM
|
||||
2. **Sequential Execution**: No parallel LLM calls needed (Steward → Tatlock)
|
||||
3. **Tool Scoping**: Fresh agent instances more reliable than runtime filtering
|
||||
4. **Benchmark Expiry**: 30-day TTL prevents Redis bloat
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
### Immediate
|
||||
- Monitor Steward accuracy in production
|
||||
- Collect real-world benchmarks
|
||||
- Iterate on Steward prompt based on metrics
|
||||
|
||||
### Phase 3 (Optional)
|
||||
- Web search delegation to The Librarian
|
||||
- Enhanced research capabilities
|
||||
- Multi-source information synthesis
|
||||
|
||||
### Phase 4
|
||||
- Expert agent delegation (Librarian, Developer, etc.)
|
||||
- Dynamic agent selection based on request
|
||||
- Cross-agent collaboration patterns
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
Phase 2 successfully delivers a production-ready two-tier architecture with The Steward managing intelligent request routing and tool scoping. The implementation is:
|
||||
|
||||
- ✅ **Complete**: All planned features delivered
|
||||
- ✅ **Tested**: 223 tests with 99.5% pass rate
|
||||
- ✅ **Observable**: Full logging and benchmarking
|
||||
- ✅ **Efficient**: Single model, minimal overhead
|
||||
- ✅ **Extensible**: Ready for expert agents in Phase 4
|
||||
|
||||
The Steward provides intelligent capability coordination while maintaining conversation context awareness, creating a foundation for scalable multi-agent collaboration in future phases.
|
||||
|
||||
**Phase 2 Status**: ✅ **COMPLETE**
|
||||
|
||||
---
|
||||
|
||||
**Document Version**: 1.0
|
||||
**Created**: 2025-12-07
|
||||
**Author**: Development Team
|
||||
**Reference**: [PHASE2_PLAN.md](PHASE2_PLAN.md)
|
||||
-865
@@ -1,865 +0,0 @@
|
||||
# Phase 2 Implementation Plan: The Steward
|
||||
|
||||
**Status**: Active Planning
|
||||
**Created**: 2025-12-07
|
||||
**Estimated Duration**: 4-5 weeks
|
||||
**Goal**: Implement first-tier request analysis and household capability coordination
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
Phase 2 introduces **The Steward** - a first-tier LLM agent that analyzes incoming requests, identifies relevant household capabilities, and provides focused recommendations to Tatlock (the Butler). This creates a two-tier architecture that prevents cognitive overload and enables efficient tool/agent coordination.
|
||||
|
||||
### Key Deliverables
|
||||
|
||||
1. **Household Registry**: Centralized capability catalog with PydanticAI Toolsets
|
||||
2. **Steward Agent**: Request analyzer with conversation context awareness
|
||||
3. **Tool Scoping**: Dynamic toolset creation based on recommendations
|
||||
4. **Observability**: Performance benchmarking and tool usage tracking via Redis
|
||||
5. **Integration**: Full Steward → Tatlock request flow
|
||||
|
||||
---
|
||||
|
||||
## Core Architectural Principles
|
||||
|
||||
### 1. Household-Based Organization
|
||||
- Each expert agent owns their tools in a domain directory
|
||||
- Tools organized as functional clusters around capabilities
|
||||
- Example: `src/agents/tatlock_core/` contains calculator, datetime, web search
|
||||
|
||||
### 2. Two-Tier Capability Abstraction
|
||||
- **Executive Summary**: High-level capabilities for Steward/Butler coordination
|
||||
- **Implementation Details**: Full tool specifications for household members
|
||||
- Steward sees summaries, household members see full details
|
||||
|
||||
### 3. PydanticAI Native Patterns
|
||||
- Use `FunctionToolset` and `CombinedToolset` for composition
|
||||
- Decorator-based tool registration (`@agent.tool`)
|
||||
- Structured outputs via Pydantic models
|
||||
- Agent delegation pattern for expert agents (Phase 4)
|
||||
|
||||
### 4. Separate Registries
|
||||
- **Household Registry**: Tools + capabilities (new in Phase 2)
|
||||
- **Model Registry**: Agents/models (existing from Phase 1)
|
||||
- Clean separation of concerns
|
||||
|
||||
### 5. Start Minimal
|
||||
- Only 3 core Tatlock tools initially: calculator, datetime, web search
|
||||
- No new tools until expert agents exist (Phase 4)
|
||||
- Prove the pattern before expanding
|
||||
|
||||
---
|
||||
|
||||
## Implementation Milestones
|
||||
|
||||
|
||||
### Milestone 1: Household Registry + Logging Infrastructure (Week 1-2)
|
||||
|
||||
#### Goal
|
||||
Create a registry system that aggregates household capabilities using PydanticAI Toolsets and establish observability infrastructure.
|
||||
|
||||
#### Tasks
|
||||
|
||||
**1.1 Create Household Registry Module**
|
||||
|
||||
Location: `src/core/household_registry.py`
|
||||
|
||||
```python
|
||||
from pydantic import BaseModel
|
||||
from pydantic_ai import FunctionToolset, CombinedToolset
|
||||
|
||||
class HouseholdCapability(BaseModel):
|
||||
"""Executive summary of a household member's capabilities."""
|
||||
name: str # "tatlock_core", "librarian", "developer"
|
||||
role: str # "Butler's Core Tools", "The Librarian"
|
||||
category: str # "core", "research", "technical"
|
||||
description: str # One-sentence description
|
||||
domains: list[str] # ["computation", "information", "datetime"]
|
||||
cost: str # "low", "medium", "high"
|
||||
requires_network: bool
|
||||
|
||||
class HouseholdMember(BaseModel):
|
||||
"""Full specification of a household member."""
|
||||
capability: HouseholdCapability
|
||||
toolset: FunctionToolset
|
||||
agent: Agent | None = None # For expert agents in Phase 4
|
||||
|
||||
class HouseholdRegistry:
|
||||
"""Registry of household capabilities and implementations."""
|
||||
|
||||
def __init__(self):
|
||||
self._members: dict[str, HouseholdMember] = {}
|
||||
|
||||
def register(
|
||||
self,
|
||||
name: str,
|
||||
capability: HouseholdCapability,
|
||||
toolset: FunctionToolset,
|
||||
agent: Agent | None = None
|
||||
):
|
||||
"""Register a household member."""
|
||||
self._members[name] = HouseholdMember(
|
||||
capability=capability,
|
||||
toolset=toolset,
|
||||
agent=agent
|
||||
)
|
||||
|
||||
def get_all_capabilities(self) -> list[HouseholdCapability]:
|
||||
"""Get executive summaries for Steward/Butler."""
|
||||
return [m.capability for m in self._members.values()]
|
||||
|
||||
def get_scoped_toolset(self, names: list[str]) -> CombinedToolset:
|
||||
"""Create combined toolset from recommended capabilities."""
|
||||
toolsets = [self._members[name].toolset for name in names]
|
||||
return CombinedToolset(toolsets)
|
||||
|
||||
# Global registry instance
|
||||
household_registry = HouseholdRegistry()
|
||||
```
|
||||
|
||||
|
||||
**1.2 Reorganize Tatlock Core Tools**
|
||||
|
||||
Create domain-based organization:
|
||||
|
||||
```
|
||||
src/agents/tatlock_core/
|
||||
├── __init__.py
|
||||
├── tools.py # Tool implementations (moved from src/agents/tools.py)
|
||||
├── toolset.py # PydanticAI toolset registration
|
||||
└── capability.py # Executive summary for registry
|
||||
```
|
||||
|
||||
**1.3 Create Logging Infrastructure**
|
||||
|
||||
Location: `src/core/logging_config.py`
|
||||
|
||||
- Structured logging with `structlog`
|
||||
- JSON format for machine parsing
|
||||
- Operation timing and metadata tracking
|
||||
- Context manager for automatic timing
|
||||
|
||||
**1.4 Create Redis Benchmark Storage**
|
||||
|
||||
Location: `src/core/benchmarks.py`
|
||||
|
||||
Features:
|
||||
- Performance benchmark recording (Steward analysis, tool calls)
|
||||
- Cross-session persistence via Redis
|
||||
- Time-series storage with automatic expiry (30 days)
|
||||
- Queryable metrics for analysis
|
||||
|
||||
Benchmark schema:
|
||||
```python
|
||||
class PerformanceBenchmark(BaseModel):
|
||||
timestamp: datetime
|
||||
operation: str # "steward_analysis", "tool_call"
|
||||
duration_seconds: float
|
||||
success: bool
|
||||
|
||||
# Steward-specific
|
||||
recommendation_count: Optional[int]
|
||||
confidence: Optional[float]
|
||||
|
||||
# Tool-specific
|
||||
tool_name: Optional[str]
|
||||
was_recommended: Optional[bool]
|
||||
was_actually_used: Optional[bool]
|
||||
|
||||
# Context
|
||||
conversation_id: Optional[str]
|
||||
metadata: dict
|
||||
```
|
||||
|
||||
**1.5 Testing**
|
||||
|
||||
- Test household registry registration and retrieval
|
||||
- Test Toolset composition
|
||||
- Test benchmark recording to Redis
|
||||
- Test structured logging output
|
||||
|
||||
#### Success Criteria
|
||||
- ✅ Household registry operational
|
||||
- ✅ Tatlock core tools organized in domain directory
|
||||
- ✅ Redis benchmarks working
|
||||
- ✅ Structured logging functional
|
||||
- ✅ Tests pass and maintain 80%+ coverage
|
||||
|
||||
---
|
||||
|
||||
|
||||
### Milestone 2: Minimal Steward Agent with Context Analysis (Week 3-4)
|
||||
|
||||
#### Goal
|
||||
Create a Steward agent that analyzes requests with full conversation context and recommends relevant household capabilities.
|
||||
|
||||
#### Tasks
|
||||
|
||||
**2.1 Create Steward Agent**
|
||||
|
||||
Location: `src/agents/steward/agent.py`
|
||||
|
||||
Structured output schema:
|
||||
```python
|
||||
class ConversationContext(BaseModel):
|
||||
"""Contextual information from conversation history."""
|
||||
has_previous_context: bool
|
||||
relevant_turns: list[int] # 0-indexed turn numbers
|
||||
context_summary: str # Summary for Butler
|
||||
|
||||
class StewardRecommendation(BaseModel):
|
||||
"""Structured recommendation from Steward analysis."""
|
||||
recommended_capabilities: list[str]
|
||||
reasoning: str
|
||||
estimated_complexity: Literal["simple", "moderate", "complex"]
|
||||
conversation_context: ConversationContext
|
||||
missing_capabilities: Optional[str] = None
|
||||
```
|
||||
|
||||
Key features:
|
||||
- Uses same model as Tatlock (`ollama:mistral-nemo`) for VRAM efficiency
|
||||
- Receives FULL conversation history
|
||||
- Queries household registry via tool
|
||||
- Conservative recommendations (avoid over-inclusion)
|
||||
- Explicit handling of missing capabilities
|
||||
|
||||
**2.2 Steward System Prompt**
|
||||
|
||||
Responsibilities:
|
||||
1. **Capability Recommendation**: Query registry, recommend only necessary tools
|
||||
2. **Conversation Analysis**: Identify references to previous topics
|
||||
3. **Complexity Assessment**: Simple/moderate/complex classification
|
||||
4. **Missing Capability Detection**: Suggest what's needed if no tools available
|
||||
|
||||
**2.3 Steward Service Layer with Logging**
|
||||
|
||||
Location: `src/agents/steward/service.py`
|
||||
|
||||
```python
|
||||
async def analyze_request(
|
||||
user_request: str,
|
||||
conversation_history: list[dict] # FULL conversation
|
||||
) -> StewardRecommendation:
|
||||
"""Analyze request with full conversation context."""
|
||||
|
||||
async with log_operation("steward_analysis", {...}) as log_ctx:
|
||||
result = await steward_agent.run(
|
||||
user_request,
|
||||
message_history=convert_to_pydantic_history(conversation_history),
|
||||
usage_limits=UsageLimits(request_limit=3)
|
||||
)
|
||||
|
||||
# Log and benchmark
|
||||
log_ctx["recommendation_count"] = len(result.data.recommended_capabilities)
|
||||
await benchmark_store.record(...)
|
||||
|
||||
return result.data
|
||||
```
|
||||
|
||||
**2.4 Testing**
|
||||
|
||||
Test scenarios:
|
||||
- Calculator request → recommends tatlock_core
|
||||
- Simple greeting → recommends []
|
||||
- Web search request → recommends tatlock_core
|
||||
- Request referencing previous turn → identifies context
|
||||
- Impossible request → returns missing_capabilities
|
||||
|
||||
#### Success Criteria
|
||||
- ✅ Steward queries household registry successfully
|
||||
- ✅ Produces structured recommendations
|
||||
- ✅ Analyzes full conversation context
|
||||
- ✅ Handles missing capabilities gracefully
|
||||
- ✅ Conservative recommendations (> 90% accuracy)
|
||||
- ✅ Benchmarks recorded to Redis
|
||||
|
||||
---
|
||||
|
||||
|
||||
### Milestone 3: Request Preprocessing & Tool Tracking (Week 5-6)
|
||||
|
||||
#### Goal
|
||||
Wire Steward into request flow, implement tool scoping, and track tool usage.
|
||||
|
||||
#### Tasks
|
||||
|
||||
**3.1 Create Preprocessing Pipeline**
|
||||
|
||||
Location: `src/core/preprocessing.py`
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class EnrichedRequest:
|
||||
"""Request enriched with Steward's analysis."""
|
||||
original_request: str
|
||||
steward_note: str # Formatted note for Tatlock
|
||||
scoped_toolset: CombinedToolset # Only recommended tools
|
||||
recommendation: StewardRecommendation
|
||||
steward_reasoning_output: str # For streaming to user
|
||||
|
||||
async def preprocess_request(
|
||||
user_request: str,
|
||||
conversation_history: list[dict] # FULL conversation
|
||||
) -> EnrichedRequest:
|
||||
"""Analyze via Steward and prepare scoped context."""
|
||||
# Call Steward with full conversation
|
||||
recommendation = await analyze_request(user_request, conversation_history)
|
||||
|
||||
# Format note to Tatlock (includes conversation context)
|
||||
steward_note = format_steward_note(recommendation)
|
||||
|
||||
# Create scoped toolset
|
||||
scoped_toolset = household_registry.get_scoped_toolset(
|
||||
recommendation.recommended_capabilities
|
||||
)
|
||||
|
||||
return EnrichedRequest(...)
|
||||
```
|
||||
|
||||
Note formatting:
|
||||
- Includes conversation context summary
|
||||
- Highlights missing capabilities if applicable
|
||||
- Provides complexity estimate
|
||||
|
||||
**3.2 Tool Usage Tracking**
|
||||
|
||||
Location: `src/core/tool_tracking.py`
|
||||
|
||||
```python
|
||||
class ToolCallTracker:
|
||||
"""Tracks tool calls for benchmarking."""
|
||||
|
||||
def __init__(self, recommended_tools: list[str]):
|
||||
self.recommended_tools = set(recommended_tools)
|
||||
self.actual_calls: dict[str, list[float]] = {}
|
||||
|
||||
async def track_call(self, tool_name: str, duration: float):
|
||||
"""Record a tool call with timing."""
|
||||
# Log if tool wasn't recommended
|
||||
if tool_name not in self.recommended_tools:
|
||||
logger.warning("tool_call_not_recommended", ...)
|
||||
|
||||
# Record benchmark to Redis
|
||||
await benchmark_store.record(...)
|
||||
|
||||
async def finalize(self):
|
||||
"""Log unused recommended tools."""
|
||||
unused = self.recommended_tools - set(self.actual_calls.keys())
|
||||
# Record benchmarks for unused tools
|
||||
```
|
||||
|
||||
**3.3 Integrate with Responses API**
|
||||
|
||||
Modify `src/responses/service.py`:
|
||||
```python
|
||||
async def generate_response(request: ResponseRequest) -> ResponseOutput:
|
||||
# Preprocess via Steward (with full conversation)
|
||||
enriched = await preprocess_request(
|
||||
user_message,
|
||||
conversation_history=request.input[:-1]
|
||||
)
|
||||
|
||||
# Run Tatlock with scoped tools and tracker
|
||||
result = await run_tatlock_with_scoped_tools(
|
||||
enriched.original_request,
|
||||
enriched.steward_note,
|
||||
enriched.scoped_toolset,
|
||||
enriched.recommendation.recommended_capabilities, # For tracking
|
||||
message_history,
|
||||
usage_tracker
|
||||
)
|
||||
|
||||
# Build response with Steward reasoning
|
||||
return build_response_with_steward_reasoning(...)
|
||||
```
|
||||
|
||||
**3.4 Update Tatlock Agent**
|
||||
|
||||
Location: `src/agents/tatlock.py`
|
||||
|
||||
```python
|
||||
async def run_tatlock_with_scoped_tools(
|
||||
user_request: str,
|
||||
steward_note: str,
|
||||
scoped_toolset: CombinedToolset,
|
||||
recommended_tools: list[str],
|
||||
message_history: list[dict],
|
||||
usage: UsageeLimits
|
||||
):
|
||||
# Initialize tracker
|
||||
tracker = ToolCallTracker(recommended_tools)
|
||||
|
||||
# Prepend Steward's note (invisible to user, visible to Tatlock)
|
||||
enriched_prompt = f"{steward_note}\n\n{user_request}"
|
||||
|
||||
# Run with ONLY scoped tools
|
||||
result = await tatlock_agent.run(
|
||||
enriched_prompt,
|
||||
message_history=convert_to_pydantic_history(message_history),
|
||||
toolsets=[scoped_toolset], # Tool scoping enforced
|
||||
deps=tracker, # For tracking
|
||||
usage=usage
|
||||
)
|
||||
|
||||
# Finalize tracking
|
||||
await tracker.finalize()
|
||||
|
||||
return result
|
||||
```
|
||||
|
||||
**3.5 Add Streaming Transparency**
|
||||
|
||||
Modify `src/responses/streaming.py`:
|
||||
- Stream Steward's reasoning first
|
||||
- Then stream Tatlock's response
|
||||
- Include conversation context notes
|
||||
- Format missing capabilities warnings
|
||||
|
||||
**3.6 Testing**
|
||||
|
||||
Integration tests:
|
||||
- Full Steward → Tatlock flow
|
||||
- Tool scoping enforcement (can't use non-recommended tools)
|
||||
- Tool usage tracking (recommended vs. actual)
|
||||
- Conversation context propagation
|
||||
- Missing capabilities handling
|
||||
|
||||
#### Success Criteria
|
||||
- ✅ Full request flow working (User → Steward → Tatlock)
|
||||
- ✅ Steward reasoning visible in output stream
|
||||
- ✅ Tool scoping enforced (only recommended tools available)
|
||||
- ✅ Tool usage tracked and logged to Redis
|
||||
- ✅ Conversation context passed through pipeline
|
||||
- ✅ Integration tests pass end-to-end
|
||||
|
||||
---
|
||||
|
||||
|
||||
### Milestone 4: Testing, Benchmarking & Refinement (Week 7)
|
||||
|
||||
#### Goal
|
||||
Validate the system, optimize performance, refine prompts, and establish monitoring.
|
||||
|
||||
#### Tasks
|
||||
|
||||
**4.1 Comprehensive Testing**
|
||||
|
||||
Test categories:
|
||||
- End-to-end integration tests (full request flow)
|
||||
- Performance benchmarks (latency targets)
|
||||
- Prompt refinement (recommendation accuracy)
|
||||
- Edge cases (errors, timeouts, missing capabilities)
|
||||
- Conversation context accuracy
|
||||
|
||||
**4.2 Performance Validation**
|
||||
|
||||
Targets:
|
||||
- Steward analysis: < 2 seconds
|
||||
- Total added latency: < 3 seconds
|
||||
- Model stays hot in VRAM (no reload delays)
|
||||
- Tool recommendation accuracy: > 90%
|
||||
|
||||
**4.3 Benchmark Analysis Tools**
|
||||
|
||||
Create `scripts/benchmark_analysis.py`:
|
||||
|
||||
```bash
|
||||
# View Steward performance over last 24 hours
|
||||
python scripts/benchmark_analysis.py --operation steward_analysis --hours 24
|
||||
|
||||
# Analyze tool recommendation accuracy
|
||||
python scripts/benchmark_analysis.py --tool-accuracy --days 7
|
||||
```
|
||||
|
||||
Metrics to track:
|
||||
- Average Steward analysis time
|
||||
- Recommendation count distribution
|
||||
- Tool accuracy (recommended & used, recommended but unused, not recommended but used)
|
||||
- Recommendation precision percentage
|
||||
|
||||
**4.4 Prompt Engineering**
|
||||
|
||||
Iterate on Steward system prompt:
|
||||
- Test with diverse request types
|
||||
- Tune conservativeness (balance false positives/negatives)
|
||||
- Validate conversation context analysis
|
||||
- Test missing capability detection
|
||||
|
||||
**4.5 Documentation**
|
||||
|
||||
Update documentation:
|
||||
- README.md: Steward explanation and examples
|
||||
- AGENTS.md: Household registration pattern
|
||||
- IMPLEMENTATION_ROADMAP.md: Mark Phase 2 complete
|
||||
- Add benchmark analysis guide
|
||||
|
||||
#### Success Criteria
|
||||
- ✅ < 3 seconds added latency for Steward analysis
|
||||
- ✅ > 90% recommendation accuracy (manual evaluation)
|
||||
- ✅ All integration tests pass
|
||||
- ✅ Benchmark tools functional
|
||||
- ✅ Documentation complete and accurate
|
||||
- ✅ Ready for Phase 3/4 (expert agents)
|
||||
|
||||
---
|
||||
|
||||
## Architecture Diagram
|
||||
|
||||
```
|
||||
User Request
|
||||
↓
|
||||
Orchestrator (FastAPI)
|
||||
↓
|
||||
Preprocessing Pipeline
|
||||
├─→ Steward Agent
|
||||
│ ├─ Receives: FULL conversation history
|
||||
│ ├─ Analyzes: Context, references, requirements
|
||||
│ ├─ Queries: Household registry (capabilities)
|
||||
│ ├─ Outputs: StewardRecommendation
|
||||
│ │ ├─ recommended_capabilities: list[str]
|
||||
│ │ ├─ conversation_context: ConversationContext
|
||||
│ │ ├─ missing_capabilities: str | None
|
||||
│ │ └─ reasoning: str
|
||||
│ └─ Logs: Performance benchmarks → Redis
|
||||
│
|
||||
├─→ Create Scoped Toolset
|
||||
│ └─ CombinedToolset from recommended capabilities
|
||||
│
|
||||
└─→ Format Steward Note
|
||||
└─ Includes conversation context for Tatlock
|
||||
↓
|
||||
Tatlock Agent (with scoped tools)
|
||||
├─ Receives: Enriched request + Steward note
|
||||
├─ Has access to: ONLY recommended tools
|
||||
├─ Tool calls tracked: ToolCallTracker
|
||||
└─ Logs: Tool usage benchmarks → Redis
|
||||
↓
|
||||
Response to User
|
||||
├─ Steward's reasoning (streamed first)
|
||||
└─ Tatlock's response (streamed second)
|
||||
|
||||
Background:
|
||||
└─ Redis: Performance benchmarks, tool usage analysis
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Design Decisions Summary
|
||||
|
||||
### 1. Logging & Performance Benchmarks
|
||||
**Decision**: Full observability with Redis-backed benchmark storage
|
||||
|
||||
**Rationale**:
|
||||
- Track Steward recommendations vs. Tatlock's actual tool usage
|
||||
- Measure performance metrics (latency, token usage)
|
||||
- Cross-session analysis for optimization
|
||||
- Identify recommendation accuracy over time
|
||||
|
||||
### 2. Steward Fallback Behavior
|
||||
**Decision**: Explicit missing capability communication
|
||||
|
||||
**Rationale**:
|
||||
- No suitable tools → Steward states "missing capabilities" with description
|
||||
- Can suggest what type of tool would be helpful
|
||||
- Code errors → standard exception handlers (don't suppress real errors)
|
||||
- Better UX than silent failures or defaulting to all tools
|
||||
|
||||
### 3. Conversation History for Steward
|
||||
**Decision**: Steward sees FULL conversation, not just current turn
|
||||
|
||||
**Rationale**:
|
||||
- Can identify references to previous topics
|
||||
- Provides contextual notes to Butler
|
||||
- "Two sets of eyes" on conversation
|
||||
- Example: "User mentioned Python debugging in turn 3, relevant details: async code"
|
||||
|
||||
### 4. Registry Pattern
|
||||
**Decision**: Separate Household Registry from Model Registry
|
||||
|
||||
**Rationale**:
|
||||
- Tools belong to household members, not models
|
||||
- Clean separation of concerns
|
||||
- Executive summaries for coordination, details for execution
|
||||
|
||||
### 5. Tool Composition
|
||||
**Decision**: PydanticAI FunctionToolset + CombinedToolset
|
||||
|
||||
**Rationale**:
|
||||
- Native PydanticAI pattern
|
||||
- Clean composition and filtering
|
||||
- Dynamic scoping per request
|
||||
|
||||
### 6. Tool Scoping
|
||||
**Decision**: Compile-time scoping via toolset creation
|
||||
|
||||
**Rationale**:
|
||||
- Tools not even visible to LLM
|
||||
- Cleaner than runtime permission checks
|
||||
- Enforced at PydanticAI level
|
||||
|
||||
### 7. Organization
|
||||
**Decision**: Domain-based household directories
|
||||
|
||||
**Rationale**:
|
||||
- Each household member owns their tools
|
||||
- Clear bounded contexts
|
||||
- Example: `src/agents/tatlock_core/`, `src/agents/librarian/` (future)
|
||||
|
||||
---
|
||||
|
||||
## Infrastructure Requirements
|
||||
|
||||
### Redis Setup
|
||||
|
||||
Development (quick start):
|
||||
```bash
|
||||
# Docker (recommended)
|
||||
docker run -d -p 6379:6379 --name tatlock-redis redis:7-alpine
|
||||
|
||||
# Or local installation
|
||||
# macOS: brew install redis && brew services start redis
|
||||
# Linux: sudo apt install redis-server && sudo systemctl start redis
|
||||
```
|
||||
|
||||
Production (docker-compose.yml):
|
||||
```yaml
|
||||
services:
|
||||
redis:
|
||||
image: redis:7-alpine
|
||||
ports:
|
||||
- "6379:6379"
|
||||
volumes:
|
||||
- redis_data:/data
|
||||
command: redis-server --appendonly yes
|
||||
|
||||
volumes:
|
||||
redis_data:
|
||||
```
|
||||
|
||||
### Dependencies Update
|
||||
|
||||
Add to `requirements.txt`:
|
||||
```txt
|
||||
redis[hiredis]>=5.0.0,<6.0.0
|
||||
structlog>=24.1.0,<25.0.0
|
||||
```
|
||||
|
||||
### Configuration
|
||||
|
||||
Add to `.env`:
|
||||
```env
|
||||
# Redis Configuration
|
||||
REDIS_URL=redis://localhost:6379/1
|
||||
|
||||
# Logging
|
||||
LOG_LEVEL=INFO
|
||||
LOG_FORMAT=json
|
||||
ENABLE_BENCHMARKS=true
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Timeline
|
||||
|
||||
**Week 1-2**: Household Registry + Logging Infrastructure
|
||||
- Household registry with Toolsets
|
||||
- Structured logging with structlog
|
||||
- Redis benchmark storage
|
||||
- Tatlock core reorganization
|
||||
- Tests: Registry + benchmarking
|
||||
|
||||
**Week 3-4**: Steward Agent with Context Analysis
|
||||
- Steward agent with conversation context
|
||||
- ConversationContext in recommendations
|
||||
- Missing capabilities handling
|
||||
- Tests: Context analysis, missing capabilities
|
||||
|
||||
**Week 5-6**: Integration + Tool Tracking
|
||||
- Request preprocessing with full conversation
|
||||
- Tool usage tracking middleware
|
||||
- Scoped toolset creation
|
||||
- Streaming transparency
|
||||
- Tests: Full flow + tool tracking
|
||||
|
||||
**Week 7**: Testing, Benchmarking & Refinement
|
||||
- End-to-end integration tests
|
||||
- Benchmark analysis tools
|
||||
- Prompt refinement
|
||||
- Performance validation
|
||||
- Documentation updates
|
||||
|
||||
**Total: 4-5 weeks** (core implementation complete in 6 weeks, polish in week 7)
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Technical
|
||||
- ✅ Household registry operational with executive summaries
|
||||
- ✅ Steward produces accurate recommendations (> 90%)
|
||||
- ✅ Steward analyzes full conversation context
|
||||
- ✅ Tool scoping enforced (Tatlock can't use non-recommended tools)
|
||||
- ✅ Model efficiency preserved (no reload delays)
|
||||
- ✅ Added latency < 3 seconds
|
||||
- ✅ Performance benchmarks recorded to Redis
|
||||
- ✅ Tool usage tracking (recommended vs. actual)
|
||||
|
||||
### Observability
|
||||
- ✅ Structured logging (JSON format)
|
||||
- ✅ Benchmark analysis tools available
|
||||
- ✅ Tool recommendation accuracy measurable
|
||||
- ✅ Cross-session performance trends visible
|
||||
|
||||
### Error Handling
|
||||
- ✅ Missing capabilities explicitly communicated
|
||||
- ✅ Steward can guide user toward needed resources
|
||||
- ✅ Code errors properly surfaced (not suppressed)
|
||||
|
||||
### Architectural
|
||||
- ✅ PydanticAI patterns followed (Toolsets, decorators, structured outputs)
|
||||
- ✅ Clean separation: registry vs. agents vs. tools
|
||||
- ✅ Two-tier abstraction working (summaries vs. details)
|
||||
- ✅ Future-proof for expert agents (Phase 4)
|
||||
|
||||
### Testing
|
||||
- ✅ Maintain 80%+ test coverage
|
||||
- ✅ Integration tests for full flow
|
||||
- ✅ Performance benchmarks established
|
||||
|
||||
---
|
||||
|
||||
## Future-Proofing for Phase 4
|
||||
|
||||
### Expert Agent Pattern (Template)
|
||||
|
||||
When adding The Librarian, The Developer, etc., follow this structure:
|
||||
|
||||
```
|
||||
src/agents/librarian/
|
||||
├── __init__.py
|
||||
├── agent.py # Librarian PydanticAI agent
|
||||
├── tools.py # Librarian-specific tools (wiki, research, etc.)
|
||||
├── toolset.py # PydanticAI toolset creation
|
||||
└── capability.py # Executive summary for registry
|
||||
```
|
||||
|
||||
Example capability registration:
|
||||
```python
|
||||
# capability.py
|
||||
LIBRARIAN_CAPABILITY = HouseholdCapability(
|
||||
name="librarian",
|
||||
role="The Librarian",
|
||||
category="research",
|
||||
description="Research assistance, knowledge management, and information synthesis",
|
||||
domains=["research", "knowledge_base", "documentation"],
|
||||
cost="medium",
|
||||
requires_network=True
|
||||
)
|
||||
|
||||
def register_librarian():
|
||||
household_registry.register(
|
||||
name="librarian",
|
||||
capability=LIBRARIAN_CAPABILITY,
|
||||
toolset=librarian_toolset,
|
||||
agent=librarian_agent # Expert agent for delegation
|
||||
)
|
||||
```
|
||||
|
||||
Tatlock delegation pattern (Phase 4):
|
||||
```python
|
||||
@tatlock_agent.tool
|
||||
async def consult_librarian(
|
||||
ctx: RunContext[None],
|
||||
research_query: str
|
||||
) -> str:
|
||||
"""Consult the Librarian for research assistance."""
|
||||
from src.agents.librarian.agent import librarian_agent
|
||||
|
||||
result = await librarian_agent.run(
|
||||
research_query,
|
||||
usage=ctx.usage # Aggregate usage
|
||||
)
|
||||
return result.data
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Risk Mitigation
|
||||
|
||||
### Identified Risks
|
||||
|
||||
1. **Steward recommendations too broad**
|
||||
- Mitigation: Conservative prompt engineering, benchmark tracking, iterate based on false positives
|
||||
|
||||
2. **Added latency unacceptable**
|
||||
- Mitigation: Stream Steward reasoning for transparency, optimize prompt, use same base model
|
||||
|
||||
3. **Tool registry becomes unwieldy**
|
||||
- Mitigation: Good categorization, semantic search (future), regular pruning
|
||||
|
||||
4. **Model VRAM competition**
|
||||
- Mitigation: Use same base model for Steward and Tatlock, sequential calls
|
||||
|
||||
5. **Redis dependency**
|
||||
- Mitigation: Make benchmarking optional, graceful degradation if Redis unavailable
|
||||
|
||||
---
|
||||
|
||||
## Open Questions - RESOLVED
|
||||
|
||||
All major design questions have been resolved. See "Design Decisions Summary" section above.
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
### Immediate (Today/This Week)
|
||||
1. Set up Redis (Docker or local)
|
||||
2. Create `src/core/logging_config.py` with structured logging
|
||||
3. Create `src/core/benchmarks.py` with Redis storage
|
||||
4. Add `redis` and `structlog` to requirements.txt
|
||||
5. Create household registry skeleton
|
||||
|
||||
### Week 1-2
|
||||
1. Complete household registry with Toolset integration
|
||||
2. Reorganize Tatlock core tools into domain directory
|
||||
3. Implement logging infrastructure
|
||||
4. Write tests for registry + benchmarking
|
||||
|
||||
### Week 3-4
|
||||
1. Create Steward agent with conversation context
|
||||
2. Implement missing capabilities handling
|
||||
3. Test context analysis accuracy
|
||||
4. Iterate on system prompt
|
||||
|
||||
### Week 5-6
|
||||
1. Build preprocessing pipeline
|
||||
2. Integrate with Responses API
|
||||
3. Implement tool tracking
|
||||
4. Add streaming transparency
|
||||
|
||||
### Week 7
|
||||
1. End-to-end testing
|
||||
2. Benchmark analysis
|
||||
3. Performance optimization
|
||||
4. Documentation updates
|
||||
|
||||
---
|
||||
|
||||
## Document Status
|
||||
|
||||
**Status**: Active Planning Document
|
||||
**Created**: 2025-12-07
|
||||
**Last Updated**: 2025-12-07
|
||||
**Version**: 1.0
|
||||
**Next Review**: After Milestone 1 completion
|
||||
|
||||
---
|
||||
|
||||
**Reference Documents**:
|
||||
- [PHILOSOPHY.md](PHILOSOPHY.md) - System vision and architecture
|
||||
- [IMPLEMENTATION_ROADMAP.md](IMPLEMENTATION_ROADMAP.md) - Full project roadmap
|
||||
- [AGENTS.md](AGENTS.md) - Agent development guidelines
|
||||
- [README.md](README.md) - User documentation
|
||||
|
||||
@@ -6,12 +6,25 @@ A privacy-first, offline-capable personal assistant system that coordinates spec
|
||||
|
||||
## Current Status
|
||||
|
||||
- ✅ **Production-ready testing API** with OpenAI Responses API format
|
||||
- ✅ **Production-ready API** with OpenAI Responses API format
|
||||
- ✅ **Open WebUI integration** with reasoning bubbles (`<think>` tags)
|
||||
- ✅ **Conversation history** with auto-generated IDs and context management
|
||||
- ✅ **Tatlock PydanticAI Agent** - Real LLM integration with Ollama + permanent tools
|
||||
- ✅ **Permanent Tools** - Calculator, date/time toolkit, web search (SearXNG)
|
||||
- ✅ **Comprehensive testing** - 131 tests, 81.78% coverage
|
||||
- ✅ **Two-tier architecture** - The Steward analyzes requests, Tatlock coordinates execution
|
||||
- ✅ **Multi-agent coordination** - Expert household staff for specialized tasks
|
||||
- ✅ **Memory system** - User profile, preferences, and semantic recall
|
||||
- ✅ **Comprehensive testing** - 399 tests with good coverage
|
||||
|
||||
### The Household Staff
|
||||
|
||||
| Agent | Role | Status |
|
||||
|-------|------|--------|
|
||||
| **Tatlock** | The Butler - Primary interface with witty personality | ✅ Active |
|
||||
| **The Steward** | Request analysis and capability recommendation | ✅ Active |
|
||||
| **The Librarian** | Research, wiki management, knowledge synthesis | ✅ Active |
|
||||
| **The Biographer** | User memory - profiles, preferences, facts | ✅ Active |
|
||||
| **The Developer** | Code assistance, debugging, architecture | 🔜 Planned |
|
||||
| **The Secretary** | Scheduling, calendars, reminders | 🔜 Planned |
|
||||
| **The Handyman** | System administration, monitoring | 🔜 Planned |
|
||||
| **The Housekeeper** | Home automation (Home Assistant) | 🔜 Planned |
|
||||
|
||||
## Features
|
||||
|
||||
@@ -45,24 +58,27 @@ 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)
|
||||
- **LLM Backend**: Ollama (mistral-nemo:latest by default)
|
||||
- **Personality**: Witty British butler, research-oriented
|
||||
- **Permanent Tools**:
|
||||
- **Calculator**: Safe mathematical expression evaluation (arithmetic, algebra, trigonometry, logarithms)
|
||||
- **Date/Time Toolkit**: Current time, relative dates ("1 week ago"), time differences
|
||||
- **Core Tools**:
|
||||
- **Calculator**: Safe mathematical expression evaluation
|
||||
- **Date/Time Toolkit**: Current time, relative dates, time differences
|
||||
- **Web Search**: Privacy-preserving search via SearXNG
|
||||
- **Capabilities**: Streaming, reasoning, tool calling
|
||||
- **Phase**: Phase 1 - Basic Integration (full household coordination coming in future phases)
|
||||
- **Household Coordination**:
|
||||
- **The Steward**: Analyzes requests and recommends capabilities
|
||||
- **The Librarian**: Research via library-desk HybridRAG + wiki
|
||||
- **The Biographer**: User memory and preference management
|
||||
- **Capabilities**: Streaming, reasoning, tool calling, multi-agent delegation
|
||||
|
||||
## Requirements
|
||||
|
||||
- Python 3.12+ (Python 3.12.11 recommended)
|
||||
- **Ollama** (for Tatlock agent): Running locally or network-accessible
|
||||
- Download: https://ollama.ai/
|
||||
- Model: `ollama pull mistral-nemo:latest`
|
||||
- **SearXNG** (for web search tool): Optional but recommended
|
||||
- Docker: `docker run -d -p 8087:8080 searxng/searxng`
|
||||
- Or use public instance (less private)
|
||||
- **External Services** (must be running separately):
|
||||
- **Ollama**: LLM inference (mistral-nemo:latest, nomic-embed-text)
|
||||
- **Redis**: Caching and session memory
|
||||
- **Qdrant**: Vector storage for The Biographer's memory
|
||||
- **SearXNG**: Web search (optional)
|
||||
- **library-desk**: Research API for The Librarian (optional)
|
||||
|
||||
## Quick Start
|
||||
|
||||
@@ -251,15 +267,18 @@ Interactive documentation available at:
|
||||
# Run all tests
|
||||
pytest
|
||||
|
||||
# Run unit tests only (no external services needed)
|
||||
pytest --ignore=tests/e2e --ignore=tests/integration
|
||||
|
||||
# Run with coverage
|
||||
pytest --cov=src --cov-report=term-missing
|
||||
|
||||
# Current: 131 tests, 81.78% coverage
|
||||
# Current: ~400 tests
|
||||
```
|
||||
|
||||
**Test Categories:**
|
||||
- Unit tests: Agent tools, streaming, schemas
|
||||
- Integration tests: Full API stack with real Ollama calls
|
||||
- Unit tests: Agent tools, capabilities, schemas, memory service
|
||||
- Integration tests: Full API stack with real Ollama
|
||||
- End-to-end tests: Chat completions, responses API
|
||||
|
||||
## Deployment
|
||||
@@ -291,9 +310,25 @@ API_PORT=8000
|
||||
# Ollama Configuration
|
||||
OLLAMA_HOST=http://localhost:11434
|
||||
OLLAMA_DEFAULT_MODEL=mistral-nemo:latest
|
||||
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
|
||||
OLLAMA_TIMEOUT=120
|
||||
|
||||
# SearXNG Configuration (for web search tool)
|
||||
# Redis Configuration
|
||||
REDIS_HOST=localhost
|
||||
REDIS_PORT=6379
|
||||
REDIS_MEMORY_DB=2
|
||||
REDIS_MEMORY_TTL_HOURS=24
|
||||
|
||||
# Qdrant Configuration (for memory)
|
||||
QDRANT_HOST=localhost
|
||||
QDRANT_PORT=6333
|
||||
QDRANT_EMBEDDING_DIM=768
|
||||
|
||||
# Library-desk Configuration (for The Librarian)
|
||||
LIBRARY_DESK_HOST=http://localhost:8089
|
||||
LIBRARY_DESK_TIMEOUT=60
|
||||
|
||||
# SearXNG Configuration (for web search)
|
||||
SEARXNG_HOST=http://localhost:8087
|
||||
SEARXNG_TIMEOUT=30
|
||||
|
||||
@@ -339,23 +374,32 @@ See `.env.example` for full configuration options.
|
||||
```
|
||||
tatlock/
|
||||
├── src/
|
||||
│ ├── agents/ # Agent interface and implementations
|
||||
│ │ ├── base.py # AgentInterface abstract class
|
||||
│ │ ├── lorem_tester.py # Mock agent for testing
|
||||
│ │ ├── tatlock.py # Real PydanticAI butler agent
|
||||
│ │ ├── tools.py # Permanent tools (calculator, date/time, search)
|
||||
│ │ └── registry.py # Model registry
|
||||
│ ├── responses/ # Responses API (primary endpoint)
|
||||
│ ├── chat/ # Chat Completions wrapper
|
||||
│ ├── models/ # Models listing
|
||||
│ ├── core/ # Shared utilities and config
|
||||
│ └── main.py # Application entry point
|
||||
├── tests/ # Comprehensive test suite (131 tests)
|
||||
├── AGENTS.md # LLM agent development guidelines
|
||||
├── PHILOSOPHY.md # System vision and architecture
|
||||
├── IMPLEMENTATION_ROADMAP.md # Development phases
|
||||
├── CHANGELOG.md # Version history
|
||||
└── README.md # This file
|
||||
│ ├── agents/ # Agent implementations
|
||||
│ │ ├── biographer/ # The Biographer - memory management
|
||||
│ │ ├── librarian/ # The Librarian - research & wiki
|
||||
│ │ ├── 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
|
||||
│ ├── responses/ # Responses API (primary endpoint)
|
||||
│ ├── chat/ # Chat Completions wrapper
|
||||
│ ├── models/ # Models listing
|
||||
│ ├── core/ # Shared infrastructure
|
||||
│ │ ├── config.py # Configuration management
|
||||
│ │ ├── context.py # Request context (ContextVar)
|
||||
│ │ ├── memory_service.py # Direct memory access
|
||||
│ │ ├── memory_cache.py # Redis session cache
|
||||
│ │ ├── embeddings.py # Ollama embedding client
|
||||
│ │ ├── qdrant.py # Vector database client
|
||||
│ │ └── 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
|
||||
├── CHANGELOG.md # Version history
|
||||
└── README.md # This file
|
||||
```
|
||||
|
||||
## Development
|
||||
@@ -388,8 +432,8 @@ For LLM agent development guidelines and architectural decisions, see [AGENTS.md
|
||||
|
||||
## Version
|
||||
|
||||
Current version: **0.2.5** - Phase 2: The Steward (Two-Tier Architecture)
|
||||
Current version: **1.2.2** - CI fix
|
||||
|
||||
---
|
||||
|
||||
**Note**: This is a production-ready testing API with mock responses. The architecture is designed for easy integration with real LLM backends (PydanticAI, Ollama, OpenAI, etc.).
|
||||
**Note**: Tatlock is a production-ready homelab butler. All household staff use PydanticAI with Ollama for local LLM inference.
|
||||
|
||||
@@ -1,424 +0,0 @@
|
||||
# Library-Desk API Requirements for Tatlock Integration
|
||||
|
||||
## Overview
|
||||
|
||||
The Librarian agent in Tatlock needs additional endpoints in library-desk to support wiki page editing and content management. Currently, the API provides read operations but The Librarian needs write capabilities for:
|
||||
|
||||
- Creating new wiki pages
|
||||
- Updating existing wiki pages (content, title, tags, description)
|
||||
|
||||
## Required Endpoints
|
||||
|
||||
### 1. Create Wiki Page (Already Exists)
|
||||
|
||||
**Endpoint:** `POST /wiki/pages`
|
||||
|
||||
This endpoint already exists and works correctly.
|
||||
|
||||
### 2. Update Wiki Page (Needs Enhancement)
|
||||
|
||||
**Endpoint:** `PUT /wiki/pages/{page_id}`
|
||||
|
||||
**Current Status:** May exist but needs verification that it supports partial updates.
|
||||
|
||||
**Required Behavior:**
|
||||
- Accept partial updates (only provided fields should be updated)
|
||||
- Support updating: `content`, `title`, `tags`, `description`
|
||||
- Auto-update vector embeddings after content changes
|
||||
- Auto-update knowledge graph after content changes
|
||||
|
||||
**Request Body:**
|
||||
```json
|
||||
{
|
||||
"content": "# New Content\n\nOptional - only if changing content",
|
||||
"title": "Optional - only if renaming",
|
||||
"tags": ["optional", "list", "of", "new", "tags"],
|
||||
"description": "Optional new description"
|
||||
}
|
||||
```
|
||||
|
||||
**Query Parameters:**
|
||||
- `user`: User identifier for multi-tenancy (required)
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"id": 42,
|
||||
"path": "/projects/example",
|
||||
"title": "Updated Title",
|
||||
"description": "Updated description",
|
||||
"content": "# New Content...",
|
||||
"tags": ["updated", "tags"],
|
||||
"updated_at": "2024-01-15T10:30:00Z"
|
||||
}
|
||||
```
|
||||
|
||||
**Notes:**
|
||||
- Should trigger background tasks to re-index vectors and refresh graph entities
|
||||
- Should validate that user has access to the page (namespace check)
|
||||
- Should preserve fields that are not provided in the request
|
||||
|
||||
## Use Cases for The Librarian
|
||||
|
||||
### Adding New Knowledge
|
||||
When a user says "Add this to the wiki" or "Create a page about X":
|
||||
- Librarian uses `POST /wiki/pages` to create the page
|
||||
- Tags are assigned based on context (dossiers)
|
||||
|
||||
### Correcting Information
|
||||
When a user says "Update the page about X" or "Fix this fact":
|
||||
1. Librarian searches for the page with `GET /wiki/search`
|
||||
2. Fetches full content with `GET /wiki/pages/{id}`
|
||||
3. Updates with corrected content via `PUT /wiki/pages/{id}`
|
||||
|
||||
### Organizing Knowledge
|
||||
When a user says "Add this page to the projects dossier":
|
||||
- Librarian updates just the tags field via `PUT /wiki/pages/{id}`
|
||||
|
||||
## Integration Notes
|
||||
|
||||
- The Librarian will call these endpoints via HTTP from Tatlock
|
||||
- Authentication uses Bearer token (LIBRARY_DESK_API_KEY)
|
||||
- All operations are scoped to the user's namespace
|
||||
- Background processing (vectors, graph) should not block the response
|
||||
|
||||
## Testing Checklist
|
||||
|
||||
- [ ] `PUT /wiki/pages/{page_id}` accepts partial updates
|
||||
- [ ] Updating content triggers vector re-indexing
|
||||
- [ ] Updating content triggers graph entity extraction
|
||||
- [ ] Tags can be updated independently of content
|
||||
- [ ] Description can be updated independently
|
||||
- [ ] Title can be updated (with path remaining the same)
|
||||
- [ ] User namespace validation works correctly
|
||||
|
||||
|
||||
===== IMPLEMENTATION INSTRUCTIONS =========
|
||||
# Librarian Wiki Integration Guide
|
||||
|
||||
This document provides implementation instructions for integrating the library-desk wiki endpoints into the Librarian agent (Tatlock).
|
||||
|
||||
## Available Endpoints
|
||||
|
||||
### 1. Create Wiki Page
|
||||
|
||||
**Endpoint:** `POST /wiki/pages`
|
||||
|
||||
Use this for simple page creation when the Librarian already has the content.
|
||||
|
||||
```python
|
||||
async def create_wiki_page(
|
||||
title: str,
|
||||
path: str,
|
||||
content: str,
|
||||
tags: list[str],
|
||||
description: str = "",
|
||||
user: str = "default"
|
||||
) -> dict:
|
||||
"""Create a new wiki page."""
|
||||
response = await http_client.post(
|
||||
f"{LIBRARY_DESK_URL}/wiki/pages",
|
||||
headers={"Authorization": f"Bearer {LIBRARY_DESK_API_KEY}"},
|
||||
json={
|
||||
"title": title,
|
||||
"path": path,
|
||||
"content": content,
|
||||
"tags": tags,
|
||||
"description": description,
|
||||
"user": user
|
||||
}
|
||||
)
|
||||
return response.json()
|
||||
```
|
||||
|
||||
**When to use:**
|
||||
- User provides specific content to add
|
||||
- Librarian has already composed the content
|
||||
- Simple note-taking or quick additions
|
||||
|
||||
---
|
||||
|
||||
### 2. Smart Create Wiki Page (Recommended for Research)
|
||||
|
||||
**Endpoint:** `POST /wiki/pages/smart-create`
|
||||
|
||||
Use this when the Librarian should research a topic before creating the page. This endpoint:
|
||||
1. Searches existing wiki, knowledge graph, and web for context
|
||||
2. Uses LLM to synthesize findings into structured content
|
||||
3. Creates the page with proper attribution
|
||||
4. Automatically links entities bidirectionally
|
||||
|
||||
```python
|
||||
async def smart_create_wiki_page(
|
||||
topic: str,
|
||||
tags: list[str],
|
||||
user: str = "default",
|
||||
path: str | None = None,
|
||||
include_web_research: bool = True,
|
||||
include_wiki_search: bool = True
|
||||
) -> dict:
|
||||
"""Create a wiki page with HybridRAG research."""
|
||||
response = await http_client.post(
|
||||
f"{LIBRARY_DESK_URL}/wiki/pages/smart-create",
|
||||
headers={"Authorization": f"Bearer {LIBRARY_DESK_API_KEY}"},
|
||||
json={
|
||||
"topic": topic,
|
||||
"path": path, # Optional - auto-generated from topic if not provided
|
||||
"tags": tags,
|
||||
"user": user,
|
||||
"include_web_research": include_web_research,
|
||||
"include_wiki_search": include_wiki_search
|
||||
}
|
||||
)
|
||||
return response.json()
|
||||
```
|
||||
|
||||
**Response includes:**
|
||||
```json
|
||||
{
|
||||
"page": {
|
||||
"id": 123,
|
||||
"path": "/users/jpmschweitzer/technology/docker-orchestration",
|
||||
"title": "Docker orchestration",
|
||||
"content": "# Docker Orchestration\n\n...",
|
||||
"tags": ["technology", "devops"],
|
||||
"created_at": "2024-01-15T10:30:00Z",
|
||||
"updated_at": "2024-01-15T10:30:00Z"
|
||||
},
|
||||
"research_summary": {
|
||||
"wiki_results": 3,
|
||||
"web_results": 8,
|
||||
"graph_entities": 5,
|
||||
"keywords_extracted": 12,
|
||||
"timing_ms": 4500
|
||||
},
|
||||
"sources_used": 11,
|
||||
"search_id": "uuid-for-reference",
|
||||
"entity_linking": {
|
||||
"forward_links": 5,
|
||||
"backward_links": 3,
|
||||
"pages_updated": 2
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**When to use:**
|
||||
- User says "Create a page about X"
|
||||
- User says "Add information about X to the wiki"
|
||||
- Librarian needs to research before writing
|
||||
- Topic benefits from context from existing knowledge
|
||||
|
||||
---
|
||||
|
||||
### 3. Update Wiki Page
|
||||
|
||||
**Endpoint:** `PUT /wiki/pages/{page_id}`
|
||||
|
||||
Use this for modifying existing pages. Supports partial updates.
|
||||
|
||||
```python
|
||||
async def update_wiki_page(
|
||||
page_id: int,
|
||||
user: str = "default",
|
||||
content: str | None = None,
|
||||
title: str | None = None,
|
||||
tags: list[str] | None = None,
|
||||
description: str | None = None
|
||||
) -> dict:
|
||||
"""Update an existing wiki page (partial updates supported)."""
|
||||
# Only include fields that are being updated
|
||||
update_data = {}
|
||||
if content is not None:
|
||||
update_data["content"] = content
|
||||
if title is not None:
|
||||
update_data["title"] = title
|
||||
if tags is not None:
|
||||
update_data["tags"] = tags
|
||||
if description is not None:
|
||||
update_data["description"] = description
|
||||
|
||||
response = await http_client.put(
|
||||
f"{LIBRARY_DESK_URL}/wiki/pages/{page_id}?user={user}",
|
||||
headers={"Authorization": f"Bearer {LIBRARY_DESK_API_KEY}"},
|
||||
json=update_data
|
||||
)
|
||||
return response.json()
|
||||
```
|
||||
|
||||
**When to use:**
|
||||
- User says "Update the page about X"
|
||||
- User says "Fix this information"
|
||||
- User says "Add this page to the projects dossier" (update tags only)
|
||||
- Correcting or enhancing existing content
|
||||
|
||||
---
|
||||
|
||||
### 4. Search Wiki Pages
|
||||
|
||||
**Endpoint:** `GET /wiki/search`
|
||||
|
||||
Use this to find existing pages before updating.
|
||||
|
||||
```python
|
||||
async def search_wiki(
|
||||
query: str,
|
||||
user: str = "default"
|
||||
) -> dict:
|
||||
"""Search wiki pages."""
|
||||
response = await http_client.get(
|
||||
f"{LIBRARY_DESK_URL}/wiki/search",
|
||||
headers={"Authorization": f"Bearer {LIBRARY_DESK_API_KEY}"},
|
||||
params={"q": query, "user": user}
|
||||
)
|
||||
return response.json()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 5. Get Wiki Page
|
||||
|
||||
**Endpoint:** `GET /wiki/pages/{page_id}`
|
||||
|
||||
Use this to fetch full page content before editing.
|
||||
|
||||
```python
|
||||
async def get_wiki_page(
|
||||
page_id: int,
|
||||
user: str = "default"
|
||||
) -> dict:
|
||||
"""Get a wiki page by ID."""
|
||||
response = await http_client.get(
|
||||
f"{LIBRARY_DESK_URL}/wiki/pages/{page_id}",
|
||||
headers={"Authorization": f"Bearer {LIBRARY_DESK_API_KEY}"},
|
||||
params={"user": user}
|
||||
)
|
||||
return response.json()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Decision Flow for Librarian
|
||||
|
||||
```
|
||||
User Request
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────┐
|
||||
│ Does user want to CREATE or UPDATE a page? │
|
||||
└─────────────────────────────────────────────┘
|
||||
│ │
|
||||
▼ ▼
|
||||
CREATE UPDATE
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────┐ ┌──────────────────────┐
|
||||
│ Does Librarian │ │ Search for the page │
|
||||
│ need to research│ │ GET /wiki/search │
|
||||
│ the topic? │ └──────────────────────┘
|
||||
└─────────────────┘ │
|
||||
│ │ ▼
|
||||
▼ ▼ ┌──────────────────────┐
|
||||
YES NO │ Get full page content│
|
||||
│ │ │ GET /wiki/pages/{id} │
|
||||
▼ ▼ └──────────────────────┘
|
||||
┌─────────┐ ┌─────────┐ │
|
||||
│ smart- │ │ POST │ ▼
|
||||
│ create │ │ /wiki/ │ ┌──────────────────────┐
|
||||
│ │ │ pages │ │ Update the page │
|
||||
└─────────┘ └─────────┘ │ PUT /wiki/pages/{id} │
|
||||
└──────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Common Use Cases
|
||||
|
||||
### 1. "Create a page about Docker Compose"
|
||||
|
||||
```python
|
||||
# Use smart-create for research-backed content
|
||||
result = await smart_create_wiki_page(
|
||||
topic="Docker Compose",
|
||||
tags=["technology", "devops", "containers"],
|
||||
user="jpmschweitzer"
|
||||
)
|
||||
# Returns page with synthesized content from wiki + web research
|
||||
```
|
||||
|
||||
### 2. "Add this note to the wiki: Remember to renew SSL cert on Jan 15"
|
||||
|
||||
```python
|
||||
# Use simple create for user-provided content
|
||||
result = await create_wiki_page(
|
||||
title="SSL Certificate Renewal Reminder",
|
||||
path="/reminders/ssl-renewal",
|
||||
content="# SSL Certificate Renewal\n\nRemember to renew SSL cert on Jan 15",
|
||||
tags=["reminders", "infrastructure"],
|
||||
user="jpmschweitzer"
|
||||
)
|
||||
```
|
||||
|
||||
### 3. "Update the page about my home server to add the new IP"
|
||||
|
||||
```python
|
||||
# 1. Search for the page
|
||||
search_results = await search_wiki("home server", user="jpmschweitzer")
|
||||
page_id = search_results["results"][0]["id"]
|
||||
|
||||
# 2. Get current content
|
||||
page = await get_wiki_page(page_id, user="jpmschweitzer")
|
||||
|
||||
# 3. Modify content (Librarian edits the markdown)
|
||||
new_content = page["content"] + "\n\n## Updated IP\n\nNew IP: 192.168.1.100"
|
||||
|
||||
# 4. Update the page
|
||||
result = await update_wiki_page(
|
||||
page_id=page_id,
|
||||
content=new_content,
|
||||
user="jpmschweitzer"
|
||||
)
|
||||
```
|
||||
|
||||
### 4. "Add this page to the projects dossier"
|
||||
|
||||
```python
|
||||
# Update only tags (partial update)
|
||||
result = await update_wiki_page(
|
||||
page_id=page_id,
|
||||
tags=["projects", "existing-tag"], # Add "projects" tag
|
||||
user="jpmschweitzer"
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Background Processing
|
||||
|
||||
All write operations trigger background tasks that:
|
||||
|
||||
1. **Vector Indexing:** Chunks content and generates embeddings in Qdrant
|
||||
2. **Graph Extraction:** Extracts entities and creates Neo4j relationships
|
||||
3. **Entity Linking:** (smart-create only) Links entities bidirectionally
|
||||
|
||||
These run asynchronously and don't block the API response.
|
||||
|
||||
---
|
||||
|
||||
## Authentication
|
||||
|
||||
All endpoints require Bearer token authentication:
|
||||
|
||||
```
|
||||
Authorization: Bearer {LIBRARY_DESK_API_KEY}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Multi-Tenancy
|
||||
|
||||
All operations are scoped to the user's namespace:
|
||||
- Pages are stored under `/users/{user}/...`
|
||||
- Vector collections are per-user: `library_desk_{user}`
|
||||
- Graph nodes are labeled per-user: `User_{User}_Document`
|
||||
|
||||
Always pass the `user` parameter to ensure proper isolation.
|
||||
+1
-1
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "tatlock"
|
||||
version = "1.1.0"
|
||||
version = "1.2.2"
|
||||
description = "OpenAI-compatible API with Ollama backend"
|
||||
requires-python = ">=3.12"
|
||||
dependencies = []
|
||||
|
||||
+4
-3
@@ -16,9 +16,10 @@ pydantic>=2.11,<2.13
|
||||
|
||||
# AI/LLM integration
|
||||
# PydanticAI: Agent framework for using Pydantic with LLMs
|
||||
# Latest: 1.27.0 (Dec 5, 2025) - No known CVEs
|
||||
# Supports Ollama backend out of the box
|
||||
pydantic-ai>=1.27,<1.28
|
||||
# Using slim version with only openai extra (Ollama uses OpenAI-compatible API)
|
||||
# This avoids installing SDKs for anthropic, cohere, google, groq, huggingface, etc.
|
||||
# See DEPENDENCY_SLIM.md for rollback instructions if this breaks
|
||||
pydantic-ai-slim[openai]>=1.27,<1.28
|
||||
|
||||
# HTTP client for Ollama communication
|
||||
# Latest: 0.28.1 - No known CVEs
|
||||
|
||||
@@ -0,0 +1,34 @@
|
||||
"""
|
||||
The Biographer - Expert for recording and recalling the user's story.
|
||||
|
||||
The Biographer serves as the household's memory keeper, responsible for:
|
||||
- Recording and recalling facts about the user's life
|
||||
- Storing personal information, preferences, and insights
|
||||
- Answering questions like "What car do I drive?", "Where do I work?"
|
||||
- Managing what the household knows and remembers
|
||||
|
||||
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,
|
||||
run_biographer_stream,
|
||||
)
|
||||
from src.agents.biographer.capability import (
|
||||
BIOGRAPHER_CAPABILITY,
|
||||
get_biographer_capability,
|
||||
register_biographer,
|
||||
unregister_biographer,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BIOGRAPHER_CAPABILITY",
|
||||
"get_biographer_capability",
|
||||
"get_biographer_agent",
|
||||
"register_biographer",
|
||||
"unregister_biographer",
|
||||
"run_biographer",
|
||||
"run_biographer_stream",
|
||||
]
|
||||
@@ -0,0 +1,273 @@
|
||||
"""
|
||||
The Biographer - Expert for recording and recalling the user's story.
|
||||
|
||||
A PydanticAI agent that serves as the household's memory keeper:
|
||||
- Records facts about the user's life, work, and preferences
|
||||
- Recalls information semantically ("What car do I drive?")
|
||||
- Manages user profile and preferences
|
||||
- Forgets information when requested
|
||||
"""
|
||||
from typing import Any, Optional
|
||||
|
||||
from pydantic_ai import Agent
|
||||
|
||||
from src.agents.biographer.tools import (
|
||||
forget_memory,
|
||||
list_memories,
|
||||
recall_semantic,
|
||||
store_insight,
|
||||
update_preference,
|
||||
update_profile,
|
||||
)
|
||||
from src.core.config import config
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
# The Biographer's system prompt
|
||||
BIOGRAPHER_SYSTEM_PROMPT = """You are The Biographer, the household's memory keeper in the Tatlock estate.
|
||||
|
||||
Your role is to record, recall, and manage the story of the user's life:
|
||||
- Personal facts (vehicle, pets, family members, hobbies, interests)
|
||||
- Life details (employer, occupation, significant events)
|
||||
- Profile information (name, location, timezone)
|
||||
- Preferences (units, theme, communication style)
|
||||
|
||||
## Your Character
|
||||
|
||||
You are a discreet and attentive chronicler. Like a personal biographer who has been
|
||||
with the household for years, you:
|
||||
- Listen carefully and remember important details
|
||||
- Recall information accurately when asked
|
||||
- Never gossip or volunteer unnecessary information
|
||||
- Respect privacy absolutely
|
||||
- Acknowledge when you don't know something rather than guessing
|
||||
|
||||
## Your Tools
|
||||
|
||||
### Recalling the Story
|
||||
- **recall_semantic**: Your primary tool for answering questions about the user
|
||||
- "What car do I drive?" → searches for car-related memories
|
||||
- "Where do I work?" → finds employment information
|
||||
- Finds relevant memories even without exact keywords
|
||||
- **list_memories**: Browse all recorded memories of a type
|
||||
- Use when user asks "What do you know about me?"
|
||||
- Shows everything you've recorded
|
||||
|
||||
### Recording New Details
|
||||
- **store_insight**: Record new facts from conversation
|
||||
- User says "My car is a Tesla" → store_insight("car", "Tesla Model 3")
|
||||
- User says "I work at Acme" → store_insight("employer", "Acme Corp")
|
||||
- Use for facts that don't fit standard profile fields
|
||||
- **update_profile**: Update core biographical fields
|
||||
- name, location, timezone only
|
||||
- "I live in Amsterdam" → update_profile("location", "Amsterdam")
|
||||
- **update_preference**: Record user preferences
|
||||
- temperature_unit, distance_unit, theme, etc.
|
||||
- "Use Celsius please" → update_preference("temperature_unit", "celsius")
|
||||
|
||||
### Managing Records
|
||||
- **forget_memory**: Remove specific records
|
||||
- User asks to forget something → honor immediately
|
||||
- Information becomes outdated → remove it
|
||||
|
||||
## Guidelines
|
||||
|
||||
### What to Record
|
||||
- Explicit statements: "I drive a Tesla", "My wife is Sarah"
|
||||
- Corrections: "Actually, I moved to Berlin"
|
||||
- Preferences: "I prefer metric units"
|
||||
|
||||
### What NOT to Record
|
||||
- Sensitive data: passwords, financial details, health information
|
||||
- Temporary information: "I'm tired today"
|
||||
- Speculation or assumptions
|
||||
|
||||
### Responding to Tatlock
|
||||
Your responses go to Tatlock (the butler) who synthesizes the final answer. Be:
|
||||
- Direct and factual
|
||||
- Clear about what you found or didn't find
|
||||
- Structured for easy integration with other responses
|
||||
|
||||
When you don't have information:
|
||||
"I have no record of the user's [topic]. Would you like me to record this information?"
|
||||
|
||||
When recalling:
|
||||
"According to my records, [information]. This was recorded [source/when if available]."
|
||||
"""
|
||||
|
||||
# Lazy initialization to avoid connection issues during imports
|
||||
_biographer_agent: Optional[Agent[None, str]] = None
|
||||
|
||||
|
||||
def _create_biographer_agent() -> Agent[None, str]:
|
||||
"""Create The Biographer PydanticAI agent."""
|
||||
# Import required classes for Ollama configuration
|
||||
from pydantic_ai.models.openai import OpenAIChatModel
|
||||
from pydantic_ai.providers.ollama import OllamaProvider
|
||||
|
||||
# PydanticAI expects Ollama base URL to end with /v1
|
||||
clean_host = str(config.OLLAMA_HOST).rstrip('/')
|
||||
base_url = f"{clean_host}/v1"
|
||||
|
||||
# Create Ollama model with provider
|
||||
model = OpenAIChatModel(
|
||||
model_name=config.OLLAMA_DEFAULT_MODEL,
|
||||
provider=OllamaProvider(base_url=base_url)
|
||||
)
|
||||
|
||||
agent: Agent[None, str] = Agent(
|
||||
model=model,
|
||||
system_prompt=BIOGRAPHER_SYSTEM_PROMPT,
|
||||
retries=2,
|
||||
)
|
||||
|
||||
# Register recall tools
|
||||
agent.tool_plain(recall_semantic)
|
||||
agent.tool_plain(list_memories)
|
||||
|
||||
# Register recording tools
|
||||
agent.tool_plain(store_insight)
|
||||
agent.tool_plain(update_profile)
|
||||
agent.tool_plain(update_preference)
|
||||
|
||||
# Register management tools
|
||||
agent.tool_plain(forget_memory)
|
||||
|
||||
logger.info(
|
||||
"biographer_agent_created",
|
||||
model=config.OLLAMA_DEFAULT_MODEL,
|
||||
tool_count=6,
|
||||
)
|
||||
|
||||
return agent
|
||||
|
||||
|
||||
def get_biographer_agent() -> Agent[None, str]:
|
||||
"""
|
||||
Get The Biographer agent instance (lazy initialization).
|
||||
|
||||
Returns:
|
||||
PydanticAI Agent configured for memory tasks
|
||||
"""
|
||||
global _biographer_agent
|
||||
if _biographer_agent is None:
|
||||
_biographer_agent = _create_biographer_agent()
|
||||
return _biographer_agent
|
||||
|
||||
|
||||
async def run_biographer(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Execute a memory task with The Biographer.
|
||||
|
||||
This is the main entry point for delegating memory tasks
|
||||
from Tatlock or other agents.
|
||||
|
||||
Args:
|
||||
task: The memory task or question
|
||||
context: Additional context from conversation
|
||||
message_history: Optional conversation history
|
||||
|
||||
Returns:
|
||||
Memory results or confirmation
|
||||
|
||||
Example:
|
||||
result = await run_biographer(
|
||||
task="What car do I drive?",
|
||||
context="User is asking about their vehicle",
|
||||
)
|
||||
"""
|
||||
agent = get_biographer_agent()
|
||||
|
||||
# Build prompt with context if provided
|
||||
prompt = task
|
||||
if context:
|
||||
prompt = f"Context: {context}\n\nTask: {task}"
|
||||
|
||||
logger.info(
|
||||
"biographer_task_started",
|
||||
task=task[:100],
|
||||
has_context=bool(context),
|
||||
has_history=bool(message_history),
|
||||
)
|
||||
|
||||
try:
|
||||
result = await agent.run(
|
||||
prompt,
|
||||
message_history=message_history,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"biographer_task_completed",
|
||||
task=task[:50],
|
||||
output_length=len(result.output),
|
||||
)
|
||||
|
||||
return result.output
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"biographer_task_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
return f"The Biographer encountered an error: {str(e)}"
|
||||
|
||||
|
||||
async def run_biographer_stream(
|
||||
task: str,
|
||||
context: str = "",
|
||||
message_history: Optional[list[Any]] = None,
|
||||
):
|
||||
"""
|
||||
Execute a memory task with streaming output.
|
||||
|
||||
Yields text deltas as The Biographer generates the response.
|
||||
|
||||
Args:
|
||||
task: The memory 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_biographer_stream("What do you know about me?"):
|
||||
print(delta, end="", flush=True)
|
||||
"""
|
||||
agent = get_biographer_agent()
|
||||
|
||||
# Build prompt with context if provided
|
||||
prompt = task
|
||||
if context:
|
||||
prompt = f"Context: {context}\n\nTask: {task}"
|
||||
|
||||
logger.info(
|
||||
"biographer_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("biographer_stream_completed", task=task[:50])
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"biographer_stream_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
yield f"\n\nThe Biographer encountered an error: {str(e)}"
|
||||
@@ -0,0 +1,88 @@
|
||||
"""
|
||||
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 (
|
||||
HouseholdCapability,
|
||||
get_household_registry,
|
||||
)
|
||||
from src.core.logging_config import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
# The Biographer's capability summary for Steward coordination
|
||||
BIOGRAPHER_CAPABILITY = HouseholdCapability(
|
||||
name="biographer",
|
||||
role="The Biographer",
|
||||
category="context",
|
||||
description=(
|
||||
"Memory keeper for the user's story: can RECALL personal facts "
|
||||
"(car, job, family, pets), RECORD new information learned from "
|
||||
"conversation, UPDATE profile (name, location, timezone) and "
|
||||
"preferences (units, theme), and FORGET information when requested. "
|
||||
"Use for: 'what car do I drive?', 'remember that I...', "
|
||||
"'forget my...', 'what do you know about me?'"
|
||||
),
|
||||
domains=[
|
||||
"remember",
|
||||
"recall",
|
||||
"forget",
|
||||
"memory",
|
||||
"preferences",
|
||||
"profile",
|
||||
"personal",
|
||||
"know",
|
||||
"about me",
|
||||
"my",
|
||||
],
|
||||
cost="low", # Mostly vector search, minimal LLM
|
||||
requires_network=False, # All local (Qdrant, Redis)
|
||||
)
|
||||
|
||||
|
||||
def get_biographer_capability() -> HouseholdCapability:
|
||||
"""Get The Biographer's capability definition."""
|
||||
return BIOGRAPHER_CAPABILITY
|
||||
|
||||
|
||||
def register_biographer() -> None:
|
||||
"""
|
||||
Register The Biographer with the Household Registry.
|
||||
|
||||
This makes The Biographer available for:
|
||||
- Steward recommendations (via capability summary)
|
||||
- Tatlock delegation (via agent reference)
|
||||
- Tool scoping (via tool list)
|
||||
"""
|
||||
registry = get_household_registry()
|
||||
|
||||
# Check if already registered
|
||||
if "biographer" in registry:
|
||||
logger.debug("biographer_already_registered")
|
||||
return
|
||||
|
||||
registry.register(
|
||||
name="biographer",
|
||||
capability=BIOGRAPHER_CAPABILITY,
|
||||
tools=BIOGRAPHER_TOOLS,
|
||||
agent=get_biographer_agent(),
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"biographer_registered",
|
||||
role=BIOGRAPHER_CAPABILITY.role,
|
||||
domains=BIOGRAPHER_CAPABILITY.domains,
|
||||
tool_count=len(BIOGRAPHER_TOOLS),
|
||||
)
|
||||
|
||||
|
||||
def unregister_biographer() -> None:
|
||||
"""Unregister The Biographer from the Household Registry."""
|
||||
registry = get_household_registry()
|
||||
registry.unregister("biographer")
|
||||
logger.info("biographer_unregistered")
|
||||
@@ -0,0 +1,462 @@
|
||||
"""
|
||||
Biographer tools for PydanticAI agent.
|
||||
|
||||
These tools enable The Biographer to record and recall the user's story:
|
||||
- recall_semantic: Find memories by meaning/concept
|
||||
- store_insight: Record new facts about the user
|
||||
- list_memories: Browse recorded memories by type
|
||||
- forget_memory: Remove specific memories
|
||||
|
||||
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
|
||||
from src.core.memory_service import MemoryType, memory_service
|
||||
from src.core.qdrant import get_qdrant_client
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Semantic Recall
|
||||
# ============================================================================
|
||||
|
||||
async def recall_semantic(
|
||||
query: str,
|
||||
memory_type: str | None = None,
|
||||
limit: int = 5,
|
||||
) -> str:
|
||||
"""
|
||||
Search memories by semantic similarity.
|
||||
|
||||
Use this to find memories that are conceptually related to
|
||||
the query, even if exact words don't match. This is the main
|
||||
tool for answering questions like "What car do I drive?" or
|
||||
"What did I mention about my job?"
|
||||
|
||||
Args:
|
||||
query: Natural language query to search for
|
||||
memory_type: Optional filter: "user_profile", "preference", "learned_fact"
|
||||
limit: Maximum memories to return (default: 5)
|
||||
|
||||
Returns:
|
||||
Matching memories with their content and relevance scores
|
||||
|
||||
Examples:
|
||||
recall_semantic("What is my car?")
|
||||
recall_semantic("work preferences", memory_type="preference")
|
||||
recall_semantic("family members")
|
||||
"""
|
||||
try:
|
||||
user = get_user()
|
||||
embedding_client = get_embedding_client()
|
||||
qdrant = get_qdrant_client()
|
||||
|
||||
# Generate embedding for query
|
||||
query_vector = await embedding_client.embed(query)
|
||||
if not query_vector:
|
||||
return "Unable to process query - embedding generation failed"
|
||||
|
||||
# Search memories
|
||||
results = await qdrant.search_memories(
|
||||
user=user,
|
||||
query_vector=query_vector,
|
||||
limit=limit,
|
||||
memory_type=memory_type,
|
||||
)
|
||||
|
||||
if not results:
|
||||
return f"No memories found related to '{query}'"
|
||||
|
||||
output_parts = [f"## Memories matching: {query}\n"]
|
||||
|
||||
for i, memory in enumerate(results, 1):
|
||||
mem_type = memory.get("type", "unknown")
|
||||
key = memory.get("key", "")
|
||||
value = memory.get("value", "")
|
||||
score = memory.get("score", 0.0)
|
||||
source = memory.get("source", "unknown")
|
||||
|
||||
type_icon = {
|
||||
"user_profile": "👤",
|
||||
"preference": "⚙️",
|
||||
"learned_fact": "💡",
|
||||
}.get(mem_type, "📝")
|
||||
|
||||
output_parts.append(f"{i}. {type_icon} **{key}** (relevance: {score:.2f})")
|
||||
output_parts.append(f" {value}")
|
||||
output_parts.append(f" _Type: {mem_type}, Source: {source}_")
|
||||
output_parts.append("")
|
||||
|
||||
logger.info(
|
||||
"memory_recall_semantic",
|
||||
query=query[:50],
|
||||
result_count=len(results),
|
||||
user=user,
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("memory_recall_semantic_error", error=str(e), query=query[:50])
|
||||
return f"Error searching memories: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Store Memory
|
||||
# ============================================================================
|
||||
|
||||
async def store_insight(
|
||||
key: str,
|
||||
value: str,
|
||||
keywords: list[str] | None = None,
|
||||
importance: float = 0.5,
|
||||
) -> str:
|
||||
"""
|
||||
Store a new insight or learned fact about the user.
|
||||
|
||||
Use this when:
|
||||
- User explicitly asks to remember something
|
||||
- User shares personal information worth remembering
|
||||
- You learn something from conversation that should persist
|
||||
|
||||
The memory will be stored with vector embedding for semantic search
|
||||
and can be recalled later using recall_semantic.
|
||||
|
||||
Args:
|
||||
key: Short identifier for the memory (e.g., "car", "employer", "pet")
|
||||
value: The actual information to remember
|
||||
keywords: Optional keywords for better search (auto-extracted if not provided)
|
||||
importance: How important is this? 0.0 (trivial) to 1.0 (critical)
|
||||
|
||||
Returns:
|
||||
Confirmation of stored memory
|
||||
|
||||
Examples:
|
||||
store_insight("car", "User drives a Tesla Model 3")
|
||||
store_insight("employer", "Works at Acme Corp as software engineer", importance=0.8)
|
||||
store_insight("coffee", "Prefers oat milk lattes", keywords=["coffee", "drink", "preference"])
|
||||
"""
|
||||
try:
|
||||
# Auto-generate keywords if not provided
|
||||
if not keywords:
|
||||
keywords = [key]
|
||||
# Extract simple keywords from value
|
||||
words = value.lower().split()
|
||||
keywords.extend([w for w in words if len(w) > 4][:5])
|
||||
|
||||
success = await memory_service.store_fact(
|
||||
key=key,
|
||||
value=value,
|
||||
keywords=keywords,
|
||||
importance=importance,
|
||||
source="conversation",
|
||||
)
|
||||
|
||||
if success:
|
||||
output_parts = [
|
||||
"## Memory Stored",
|
||||
f"**Key:** {key}",
|
||||
f"**Value:** {value}",
|
||||
f"**Keywords:** {', '.join(keywords)}",
|
||||
f"**Importance:** {importance:.1f}",
|
||||
"",
|
||||
"_Memory is now searchable via semantic recall._"
|
||||
]
|
||||
|
||||
logger.info(
|
||||
"memory_store_insight",
|
||||
key=key,
|
||||
importance=importance,
|
||||
user=get_user(),
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
else:
|
||||
return f"Failed to store memory for key '{key}'"
|
||||
|
||||
except Exception as e:
|
||||
logger.error("memory_store_insight_error", error=str(e), key=key)
|
||||
return f"Error storing memory: {str(e)}"
|
||||
|
||||
|
||||
async def update_profile(
|
||||
key: str,
|
||||
value: str,
|
||||
) -> str:
|
||||
"""
|
||||
Update user profile information.
|
||||
|
||||
Use this for core identity information:
|
||||
- name, location, timezone
|
||||
- language preferences
|
||||
- occupation
|
||||
|
||||
Profile data has high importance and is used for context
|
||||
by the Steward during request analysis.
|
||||
|
||||
Args:
|
||||
key: Profile field (e.g., "name", "location", "timezone")
|
||||
value: The value to set
|
||||
|
||||
Returns:
|
||||
Confirmation of profile update
|
||||
|
||||
Examples:
|
||||
update_profile("location", "Amsterdam, Netherlands")
|
||||
update_profile("timezone", "Europe/Amsterdam")
|
||||
update_profile("name", "John")
|
||||
"""
|
||||
try:
|
||||
success = await memory_service.set_profile(
|
||||
key=key,
|
||||
value=value,
|
||||
keywords=[key, "profile"],
|
||||
)
|
||||
|
||||
if success:
|
||||
output_parts = [
|
||||
"## Profile Updated",
|
||||
f"**{key}:** {value}",
|
||||
"",
|
||||
"_Profile data is automatically included in context._"
|
||||
]
|
||||
|
||||
logger.info(
|
||||
"memory_update_profile",
|
||||
key=key,
|
||||
user=get_user(),
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
else:
|
||||
return f"Failed to update profile field '{key}'"
|
||||
|
||||
except Exception as e:
|
||||
logger.error("memory_update_profile_error", error=str(e), key=key)
|
||||
return f"Error updating profile: {str(e)}"
|
||||
|
||||
|
||||
async def update_preference(
|
||||
key: str,
|
||||
value: str,
|
||||
) -> str:
|
||||
"""
|
||||
Update user preferences.
|
||||
|
||||
Use this for settings and preferences:
|
||||
- temperature_unit (celsius/fahrenheit)
|
||||
- distance_unit (metric/imperial)
|
||||
- theme, language, etc.
|
||||
|
||||
Preferences are used by agents to customize responses.
|
||||
|
||||
Args:
|
||||
key: Preference name (e.g., "temperature_unit", "theme")
|
||||
value: Preference value
|
||||
|
||||
Returns:
|
||||
Confirmation of preference update
|
||||
|
||||
Examples:
|
||||
update_preference("temperature_unit", "celsius")
|
||||
update_preference("distance_unit", "metric")
|
||||
update_preference("theme", "dark")
|
||||
"""
|
||||
try:
|
||||
success = await memory_service.set_preference(
|
||||
key=key,
|
||||
value=value,
|
||||
)
|
||||
|
||||
if success:
|
||||
output_parts = [
|
||||
"## Preference Updated",
|
||||
f"**{key}:** {value}",
|
||||
"",
|
||||
"_Preference will be applied to future responses._"
|
||||
]
|
||||
|
||||
logger.info(
|
||||
"memory_update_preference",
|
||||
key=key,
|
||||
user=get_user(),
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
else:
|
||||
return f"Failed to update preference '{key}'"
|
||||
|
||||
except Exception as e:
|
||||
logger.error("memory_update_preference_error", error=str(e), key=key)
|
||||
return f"Error updating preference: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# List Memories
|
||||
# ============================================================================
|
||||
|
||||
async def list_memories(
|
||||
memory_type: str = "learned_fact",
|
||||
limit: int = 20,
|
||||
) -> str:
|
||||
"""
|
||||
List stored memories of a specific type.
|
||||
|
||||
Use this to browse what's stored in memory without
|
||||
a specific search query.
|
||||
|
||||
Args:
|
||||
memory_type: Type to list: "user_profile", "preference", "learned_fact"
|
||||
limit: Maximum memories to return (default: 20)
|
||||
|
||||
Returns:
|
||||
List of memories with their keys and values
|
||||
|
||||
Examples:
|
||||
list_memories("user_profile")
|
||||
list_memories("preference")
|
||||
list_memories("learned_fact", limit=10)
|
||||
"""
|
||||
try:
|
||||
user = get_user()
|
||||
qdrant = get_qdrant_client()
|
||||
|
||||
# Convert string to MemoryType
|
||||
try:
|
||||
mem_type = MemoryType(memory_type)
|
||||
except ValueError:
|
||||
return f"Invalid memory type '{memory_type}'. Use: user_profile, preference, or learned_fact"
|
||||
|
||||
# Get all memories of type
|
||||
results = qdrant._client.scroll(
|
||||
collection_name=f"memories_{user}",
|
||||
scroll_filter={
|
||||
"must": [
|
||||
{"key": "type", "match": {"value": memory_type}},
|
||||
]
|
||||
},
|
||||
limit=limit,
|
||||
with_payload=True,
|
||||
with_vectors=False,
|
||||
)
|
||||
|
||||
points, _ = results
|
||||
if not points:
|
||||
return f"No {memory_type} memories found"
|
||||
|
||||
type_icon = {
|
||||
"user_profile": "👤",
|
||||
"preference": "⚙️",
|
||||
"learned_fact": "💡",
|
||||
}.get(memory_type, "📝")
|
||||
|
||||
output_parts = [f"## {type_icon} {memory_type.replace('_', ' ').title()} Memories\n"]
|
||||
|
||||
for point in points:
|
||||
payload = point.payload
|
||||
key = payload.get("key", "unknown")
|
||||
value = payload.get("value", "")
|
||||
importance = payload.get("importance", 0.5)
|
||||
|
||||
output_parts.append(f"- **{key}**: {value}")
|
||||
if importance > 0.7:
|
||||
output_parts.append(f" _(importance: {importance:.1f})_")
|
||||
|
||||
logger.info(
|
||||
"memory_list",
|
||||
memory_type=memory_type,
|
||||
count=len(points),
|
||||
user=user,
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("memory_list_error", error=str(e), memory_type=memory_type)
|
||||
return f"Error listing memories: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Forget Memory
|
||||
# ============================================================================
|
||||
|
||||
async def forget_memory(
|
||||
key: str,
|
||||
memory_type: str = "learned_fact",
|
||||
) -> str:
|
||||
"""
|
||||
Remove a specific memory.
|
||||
|
||||
Use this when:
|
||||
- User asks to forget something
|
||||
- Information is outdated or incorrect
|
||||
- Privacy concerns
|
||||
|
||||
Args:
|
||||
key: Key of the memory to forget
|
||||
memory_type: Type of memory: "user_profile", "preference", "learned_fact"
|
||||
|
||||
Returns:
|
||||
Confirmation of deletion
|
||||
|
||||
Examples:
|
||||
forget_memory("old_car")
|
||||
forget_memory("location", memory_type="user_profile")
|
||||
forget_memory("theme", memory_type="preference")
|
||||
"""
|
||||
try:
|
||||
# Convert string to MemoryType
|
||||
try:
|
||||
mem_type = MemoryType(memory_type)
|
||||
except ValueError:
|
||||
return f"Invalid memory type '{memory_type}'. Use: user_profile, preference, or learned_fact"
|
||||
|
||||
success = await memory_service.delete_memory(
|
||||
key=key,
|
||||
memory_type=mem_type,
|
||||
)
|
||||
|
||||
if success:
|
||||
output_parts = [
|
||||
"## Memory Forgotten",
|
||||
f"**Key:** {key}",
|
||||
f"**Type:** {memory_type}",
|
||||
"",
|
||||
"_Memory has been removed._"
|
||||
]
|
||||
|
||||
logger.info(
|
||||
"memory_forget",
|
||||
key=key,
|
||||
memory_type=memory_type,
|
||||
user=get_user(),
|
||||
)
|
||||
|
||||
return "\n".join(output_parts)
|
||||
else:
|
||||
return f"Memory '{key}' not found or already deleted"
|
||||
|
||||
except Exception as e:
|
||||
logger.error("memory_forget_error", error=str(e), key=key)
|
||||
return f"Error forgetting memory: {str(e)}"
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Tool Collection for Registration
|
||||
# ============================================================================
|
||||
|
||||
# All tools available to The Biographer
|
||||
BIOGRAPHER_TOOLS = [
|
||||
# Recall
|
||||
recall_semantic,
|
||||
list_memories,
|
||||
# Record
|
||||
store_insight,
|
||||
update_profile,
|
||||
update_preference,
|
||||
# Manage
|
||||
forget_memory,
|
||||
]
|
||||
@@ -146,7 +146,84 @@ async def delegate_to_librarian(
|
||||
)
|
||||
|
||||
|
||||
async def delegate_to_biographer(
|
||||
task: str,
|
||||
context: str = "",
|
||||
) -> DelegationResult:
|
||||
"""
|
||||
Delegate a memory task to The Biographer.
|
||||
|
||||
The Biographer handles:
|
||||
- Semantic recall ("What car do I drive?", "What's my job?")
|
||||
- Recording new facts from conversation
|
||||
- Profile updates (name, location, timezone)
|
||||
- Preference updates (units, theme)
|
||||
- Memory management (forget, list)
|
||||
|
||||
For direct key-based lookups (get location, get timezone), use
|
||||
memory_service directly - it's faster and doesn't require LLM.
|
||||
|
||||
Args:
|
||||
task: Clear description of what needs to be done.
|
||||
Include the action verb (recall, remember, forget, etc.)
|
||||
Example: "What car do I drive?"
|
||||
Example: "Remember that I work at Acme Corp"
|
||||
context: Additional context from the user's request or
|
||||
conversation history
|
||||
|
||||
Returns:
|
||||
DelegationResult with The Biographer's response
|
||||
|
||||
Example:
|
||||
>>> result = await delegate_to_biographer(
|
||||
... task="What do you know about my preferences?",
|
||||
... context="User is asking about stored information",
|
||||
... )
|
||||
>>> if result.success:
|
||||
... print(result.output)
|
||||
"""
|
||||
from src.agents.biographer.agent import run_biographer
|
||||
|
||||
logger.info(
|
||||
"delegation_to_biographer_started",
|
||||
task=task[:100],
|
||||
has_context=bool(context),
|
||||
)
|
||||
|
||||
try:
|
||||
# Use run() not run_stream() - avoids Ollama bug
|
||||
output = await run_biographer(task=task, context=context)
|
||||
|
||||
logger.info(
|
||||
"delegation_to_biographer_completed",
|
||||
task=task[:50],
|
||||
output_length=len(output),
|
||||
)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="biographer",
|
||||
task=task,
|
||||
success=True,
|
||||
output=output,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"delegation_to_biographer_error",
|
||||
task=task[:50],
|
||||
error=str(e),
|
||||
exc_info=True,
|
||||
)
|
||||
|
||||
return DelegationResult(
|
||||
expert_name="biographer",
|
||||
task=task,
|
||||
success=False,
|
||||
output="",
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
|
||||
# Future expert delegation wrappers will be added here:
|
||||
# - delegate_to_memory(task, context) -> DelegationResult
|
||||
# - delegate_to_home_automation(task, context) -> DelegationResult
|
||||
# - delegate_to_developer(task, context) -> DelegationResult
|
||||
|
||||
@@ -4,7 +4,7 @@ Steward agent schemas.
|
||||
Defines the structured output models for Steward's request analysis
|
||||
and capability recommendations.
|
||||
"""
|
||||
from typing import Literal, Optional
|
||||
from typing import Any, Literal, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
@@ -56,6 +56,10 @@ class StewardRecommendation(BaseModel):
|
||||
default=None,
|
||||
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)"
|
||||
)
|
||||
|
||||
def format_for_butler(self) -> str:
|
||||
"""
|
||||
@@ -88,6 +92,23 @@ class StewardRecommendation(BaseModel):
|
||||
if self.missing_capabilities:
|
||||
lines.append(f"⚠️ Missing: {self.missing_capabilities}")
|
||||
|
||||
# Memory context (user profile and preferences)
|
||||
if self.memory_context:
|
||||
profile = self.memory_context.get("profile", {})
|
||||
preferences = self.memory_context.get("preferences", {})
|
||||
|
||||
if profile or preferences:
|
||||
lines.append("-" * 40)
|
||||
lines.append("User Context:")
|
||||
|
||||
if profile:
|
||||
for key, value in profile.items():
|
||||
lines.append(f" • {key}: {value}")
|
||||
|
||||
if preferences:
|
||||
prefs_str = ", ".join(f"{k}={v}" for k, v in preferences.items())
|
||||
lines.append(f" • preferences: {prefs_str}")
|
||||
|
||||
lines.append("=" * 40)
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
@@ -5,13 +5,15 @@ Provides high-level interface for request analysis with logging,
|
||||
benchmarking, and error handling.
|
||||
|
||||
Parses plain text recommendations into structured data.
|
||||
Includes memory pre-fetch for user context injection.
|
||||
"""
|
||||
import re
|
||||
from typing import Optional
|
||||
from typing import Any, Optional
|
||||
|
||||
from src.core.benchmarks import PerformanceBenchmark, get_benchmark_store
|
||||
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
|
||||
|
||||
@@ -147,6 +149,69 @@ def _extract_missing_capabilities(text: str) -> Optional[str]:
|
||||
return None
|
||||
|
||||
|
||||
async def _prefetch_memory_context(user_request: str) -> dict[str, Any]:
|
||||
"""
|
||||
Pre-fetch user context that might be needed for this request.
|
||||
|
||||
This is the "direct access" layer - fast lookups without LLM overhead.
|
||||
Uses simple keyword matching to determine what context to fetch.
|
||||
|
||||
Args:
|
||||
user_request: The user's request text
|
||||
|
||||
Returns:
|
||||
Dict with profile and/or preferences data
|
||||
|
||||
Example:
|
||||
>>> ctx = await _prefetch_memory_context("What's the weather?")
|
||||
>>> ctx
|
||||
{"profile": {"location": "Amsterdam"}}
|
||||
"""
|
||||
request_lower = user_request.lower()
|
||||
|
||||
# Determine what context might be needed based on keywords
|
||||
profile_keys = []
|
||||
|
||||
# Location-related queries
|
||||
if any(word in request_lower for word in [
|
||||
"weather", "temperature", "forecast", "nearby", "local",
|
||||
"directions", "distance", "map", "here"
|
||||
]):
|
||||
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"
|
||||
]):
|
||||
profile_keys.append("timezone")
|
||||
|
||||
# Personal queries
|
||||
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"
|
||||
])
|
||||
|
||||
try:
|
||||
return await memory_service.prefetch_context(
|
||||
include_profile=bool(profile_keys),
|
||||
include_preferences=include_preferences,
|
||||
profile_keys=profile_keys if profile_keys else None,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
"steward_prefetch_memory_failed",
|
||||
error=str(e),
|
||||
)
|
||||
return {}
|
||||
|
||||
|
||||
async def analyze_request(
|
||||
user_request: str,
|
||||
conversation_history: list[dict],
|
||||
@@ -186,6 +251,10 @@ async def analyze_request(
|
||||
}
|
||||
) as log_ctx:
|
||||
try:
|
||||
# Pre-fetch user context from memory (fast, no LLM)
|
||||
memory_context = await _prefetch_memory_context(user_request)
|
||||
log_ctx["memory_context_keys"] = list(memory_context.keys())
|
||||
|
||||
# Get Steward agent
|
||||
steward = get_steward_agent()
|
||||
|
||||
@@ -193,6 +262,7 @@ async def analyze_request(
|
||||
"steward_analyzing_request",
|
||||
request=user_request,
|
||||
history_turns=len(conversation_history),
|
||||
memory_context=bool(memory_context),
|
||||
)
|
||||
|
||||
# Get plain text analysis from Steward
|
||||
@@ -212,7 +282,8 @@ async def analyze_request(
|
||||
reasoning=analysis_text,
|
||||
estimated_complexity=complexity,
|
||||
conversation_context=context,
|
||||
missing_capabilities=missing
|
||||
missing_capabilities=missing,
|
||||
memory_context=memory_context,
|
||||
)
|
||||
|
||||
# Update log context with results
|
||||
|
||||
@@ -0,0 +1,619 @@
|
||||
"""
|
||||
Memory service for direct key-based access.
|
||||
|
||||
Provides fast, LLM-free access to user memories for:
|
||||
- Known-key lookups (location, timezone, preferences)
|
||||
- Session context (current topic, recent entities)
|
||||
- Structured storage (explicit user instructions)
|
||||
|
||||
This is the "direct access layer" - no LLM interpretation.
|
||||
For semantic/fuzzy queries, use the Memory Agent instead.
|
||||
|
||||
Usage:
|
||||
from src.core.memory_service import memory_service
|
||||
|
||||
# Get user's location (fast, no LLM)
|
||||
location = await memory_service.get_profile("location")
|
||||
|
||||
# Set a preference
|
||||
await memory_service.set_preference("temperature_unit", "celsius")
|
||||
|
||||
# Get session context
|
||||
ctx = await memory_service.get_session_context(conversation_id)
|
||||
"""
|
||||
from datetime import datetime, timezone
|
||||
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 .embeddings import get_embedding_client
|
||||
from .logging_config import get_logger
|
||||
from .memory_cache import get_memory_cache
|
||||
from .multi_tenancy import get_memory_collection_name
|
||||
from .qdrant import get_qdrant_client
|
||||
|
||||
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"
|
||||
|
||||
|
||||
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
|
||||
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())
|
||||
|
||||
|
||||
class MemoryService:
|
||||
"""
|
||||
Direct access to user memories without LLM overhead.
|
||||
|
||||
Use this for:
|
||||
- Known-key lookups: get_profile("location"), get_preference("units")
|
||||
- Explicit storage: set_preference("theme", "dark")
|
||||
- Session context: get_session_context(), update_session_context()
|
||||
|
||||
Do NOT use for:
|
||||
- Fuzzy queries: "What car do I drive?" → Use Memory Agent
|
||||
- Semantic recall: "What did I mention about X?" → Use Memory Agent
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize memory service with lazy client loading."""
|
||||
self._qdrant = None
|
||||
self._embedding = None
|
||||
self._cache = None
|
||||
|
||||
@property
|
||||
def qdrant(self):
|
||||
"""Lazy-load Qdrant client."""
|
||||
if self._qdrant is None:
|
||||
self._qdrant = get_qdrant_client()
|
||||
return self._qdrant
|
||||
|
||||
@property
|
||||
def embedding(self):
|
||||
"""Lazy-load embedding client."""
|
||||
if self._embedding is None:
|
||||
self._embedding = get_embedding_client()
|
||||
return self._embedding
|
||||
|
||||
@property
|
||||
def cache(self):
|
||||
"""Lazy-load Redis cache."""
|
||||
if self._cache is None:
|
||||
self._cache = get_memory_cache()
|
||||
return self._cache
|
||||
|
||||
# =========================================================================
|
||||
# Profile Methods (user_profile type)
|
||||
# =========================================================================
|
||||
|
||||
async def get_profile(self, key: str, user: str | None = None) -> str | None:
|
||||
"""
|
||||
Get a user profile value by key.
|
||||
|
||||
Args:
|
||||
key: Profile key (e.g., "location", "timezone", "name")
|
||||
user: User ID (defaults to current request context)
|
||||
|
||||
Returns:
|
||||
Profile value or None if not found
|
||||
|
||||
Example:
|
||||
>>> location = await memory_service.get_profile("location")
|
||||
>>> location
|
||||
"Amsterdam, Netherlands"
|
||||
"""
|
||||
user = user or get_user()
|
||||
return await self._get_memory(user, MemoryType.USER_PROFILE, key)
|
||||
|
||||
async def set_profile(
|
||||
self,
|
||||
key: str,
|
||||
value: str,
|
||||
user: str | None = None,
|
||||
keywords: list[str] | None = None,
|
||||
) -> bool:
|
||||
"""
|
||||
Set a user profile value.
|
||||
|
||||
Args:
|
||||
key: Profile key (e.g., "location", "timezone")
|
||||
value: Profile value
|
||||
user: User ID (defaults to current request context)
|
||||
keywords: Optional keywords for semantic search
|
||||
|
||||
Returns:
|
||||
True if successful
|
||||
|
||||
Example:
|
||||
>>> await memory_service.set_profile("location", "Amsterdam, Netherlands")
|
||||
True
|
||||
"""
|
||||
user = user or get_user()
|
||||
return await self._set_memory(
|
||||
user=user,
|
||||
memory_type=MemoryType.USER_PROFILE,
|
||||
key=key,
|
||||
value=value,
|
||||
keywords=keywords or [key],
|
||||
importance=0.9, # Profile data is important
|
||||
)
|
||||
|
||||
# =========================================================================
|
||||
# Preference Methods (preference type)
|
||||
# =========================================================================
|
||||
|
||||
async def get_preference(self, key: str, user: str | None = None) -> str | None:
|
||||
"""
|
||||
Get a user preference by key.
|
||||
|
||||
Args:
|
||||
key: Preference key (e.g., "temperature_unit", "language", "theme")
|
||||
user: User ID (defaults to current request context)
|
||||
|
||||
Returns:
|
||||
Preference value or None if not found
|
||||
|
||||
Example:
|
||||
>>> units = await memory_service.get_preference("temperature_unit")
|
||||
>>> units
|
||||
"celsius"
|
||||
"""
|
||||
user = user or get_user()
|
||||
return await self._get_memory(user, MemoryType.PREFERENCE, key)
|
||||
|
||||
async def set_preference(
|
||||
self,
|
||||
key: str,
|
||||
value: str,
|
||||
user: str | None = None,
|
||||
) -> bool:
|
||||
"""
|
||||
Set a user preference.
|
||||
|
||||
Args:
|
||||
key: Preference key
|
||||
value: Preference value
|
||||
user: User ID (defaults to current request context)
|
||||
|
||||
Returns:
|
||||
True if successful
|
||||
|
||||
Example:
|
||||
>>> await memory_service.set_preference("theme", "dark")
|
||||
True
|
||||
"""
|
||||
user = user or get_user()
|
||||
return await self._set_memory(
|
||||
user=user,
|
||||
memory_type=MemoryType.PREFERENCE,
|
||||
key=key,
|
||||
value=value,
|
||||
keywords=[key, "preference"],
|
||||
importance=0.7,
|
||||
)
|
||||
|
||||
async def get_all_preferences(self, user: str | None = None) -> dict[str, str]:
|
||||
"""
|
||||
Get all preferences for a user.
|
||||
|
||||
Returns:
|
||||
Dict of key -> value for all preferences
|
||||
"""
|
||||
user = user or get_user()
|
||||
memories = await self._get_all_by_type(user, MemoryType.PREFERENCE)
|
||||
return {m["key"]: m["value"] for m in memories}
|
||||
|
||||
# =========================================================================
|
||||
# Learned Facts (learned_fact type) - for direct storage only
|
||||
# =========================================================================
|
||||
|
||||
async def store_fact(
|
||||
self,
|
||||
key: str,
|
||||
value: str,
|
||||
user: str | None = None,
|
||||
keywords: list[str] | None = None,
|
||||
importance: float = 0.5,
|
||||
source: str = "explicit",
|
||||
) -> bool:
|
||||
"""
|
||||
Store a learned fact about the user.
|
||||
|
||||
Use this for explicit user statements like:
|
||||
- "Remember that my car is a Tesla"
|
||||
- "I work at Acme Corp"
|
||||
|
||||
For semantic extraction from conversation, use the Memory Agent.
|
||||
|
||||
Args:
|
||||
key: Fact identifier (e.g., "car", "employer")
|
||||
value: The fact content
|
||||
user: User ID
|
||||
keywords: Keywords for semantic search
|
||||
importance: 0.0-1.0 importance score
|
||||
source: "explicit" | "inferred" | "conversation"
|
||||
|
||||
Returns:
|
||||
True if successful
|
||||
"""
|
||||
user = user or get_user()
|
||||
return await self._set_memory(
|
||||
user=user,
|
||||
memory_type=MemoryType.LEARNED_FACT,
|
||||
key=key,
|
||||
value=value,
|
||||
keywords=keywords or [key],
|
||||
importance=importance,
|
||||
source=source,
|
||||
)
|
||||
|
||||
async def get_fact(self, key: str, user: str | None = None) -> str | None:
|
||||
"""
|
||||
Get a specific fact by key.
|
||||
|
||||
For semantic/fuzzy queries, use the Memory Agent.
|
||||
"""
|
||||
user = user or get_user()
|
||||
return await self._get_memory(user, MemoryType.LEARNED_FACT, key)
|
||||
|
||||
# =========================================================================
|
||||
# Session Context (Redis-backed, 24h TTL)
|
||||
# =========================================================================
|
||||
|
||||
async def get_session_context(
|
||||
self,
|
||||
conversation_id: str | None = None,
|
||||
user: str | None = None,
|
||||
) -> dict[str, Any] | None:
|
||||
"""
|
||||
Get session context for current conversation.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID (defaults to current context)
|
||||
user: User ID (defaults to current context)
|
||||
|
||||
Returns:
|
||||
Session context dict or None
|
||||
"""
|
||||
user = user or get_user()
|
||||
conversation_id = conversation_id or get_conversation_id()
|
||||
|
||||
if not conversation_id:
|
||||
return None
|
||||
|
||||
return await self.cache.get_session_context(user, conversation_id)
|
||||
|
||||
async def set_session_context(
|
||||
self,
|
||||
context: dict[str, Any],
|
||||
conversation_id: str | None = None,
|
||||
user: str | None = None,
|
||||
) -> bool:
|
||||
"""
|
||||
Set session context for current conversation.
|
||||
|
||||
Args:
|
||||
context: Context data to store
|
||||
conversation_id: Conversation ID
|
||||
user: User ID
|
||||
|
||||
Returns:
|
||||
True if successful
|
||||
"""
|
||||
user = user or get_user()
|
||||
conversation_id = conversation_id or get_conversation_id()
|
||||
|
||||
if not conversation_id:
|
||||
logger.warning("memory_service_no_conversation_id")
|
||||
return False
|
||||
|
||||
return await self.cache.set_session_context(user, conversation_id, context)
|
||||
|
||||
async def update_session_context(
|
||||
self,
|
||||
updates: dict[str, Any],
|
||||
conversation_id: str | None = None,
|
||||
user: str | None = None,
|
||||
) -> bool:
|
||||
"""
|
||||
Update session context (merge with existing).
|
||||
|
||||
Args:
|
||||
updates: Fields to update
|
||||
conversation_id: Conversation ID
|
||||
user: User ID
|
||||
|
||||
Returns:
|
||||
True if successful
|
||||
"""
|
||||
user = user or get_user()
|
||||
conversation_id = conversation_id or get_conversation_id()
|
||||
|
||||
if not conversation_id:
|
||||
return False
|
||||
|
||||
return await self.cache.update_session_context(user, conversation_id, updates)
|
||||
|
||||
async def get_recent_entities(
|
||||
self,
|
||||
conversation_id: str | None = None,
|
||||
user: str | None = None,
|
||||
) -> list[str]:
|
||||
"""
|
||||
Get recently mentioned entities in conversation.
|
||||
|
||||
Returns:
|
||||
List of entity names
|
||||
"""
|
||||
user = user or get_user()
|
||||
conversation_id = conversation_id or get_conversation_id()
|
||||
|
||||
if not conversation_id:
|
||||
return []
|
||||
|
||||
return await self.cache.get_recent_entities(user, conversation_id)
|
||||
|
||||
async def add_recent_entities(
|
||||
self,
|
||||
entities: list[str],
|
||||
conversation_id: str | None = None,
|
||||
user: str | None = None,
|
||||
) -> bool:
|
||||
"""
|
||||
Add entities to recent entities set.
|
||||
|
||||
Args:
|
||||
entities: Entity names to add
|
||||
conversation_id: Conversation ID
|
||||
user: User ID
|
||||
|
||||
Returns:
|
||||
True if successful
|
||||
"""
|
||||
user = user or get_user()
|
||||
conversation_id = conversation_id or get_conversation_id()
|
||||
|
||||
if not conversation_id:
|
||||
return False
|
||||
|
||||
return await self.cache.add_recent_entities(user, conversation_id, entities)
|
||||
|
||||
# =========================================================================
|
||||
# Bulk / Pre-fetch Methods (for Steward)
|
||||
# =========================================================================
|
||||
|
||||
async def prefetch_context(
|
||||
self,
|
||||
user: str | None = None,
|
||||
include_profile: bool = True,
|
||||
include_preferences: bool = True,
|
||||
profile_keys: list[str] | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Pre-fetch commonly needed context for Steward.
|
||||
|
||||
This is the main entry point for Steward to get user context
|
||||
before analyzing a request.
|
||||
|
||||
Args:
|
||||
user: User ID
|
||||
include_profile: Include profile data
|
||||
include_preferences: Include preferences
|
||||
profile_keys: Specific profile keys to fetch (None = common ones)
|
||||
|
||||
Returns:
|
||||
Dict with profile and preferences data
|
||||
|
||||
Example:
|
||||
>>> ctx = await memory_service.prefetch_context()
|
||||
>>> ctx
|
||||
{
|
||||
"profile": {"location": "Amsterdam", "timezone": "Europe/Amsterdam"},
|
||||
"preferences": {"temperature_unit": "celsius"}
|
||||
}
|
||||
"""
|
||||
user = user or get_user()
|
||||
result: dict[str, Any] = {}
|
||||
|
||||
if include_profile:
|
||||
profile_keys = profile_keys or ["location", "timezone", "name"]
|
||||
profile = {}
|
||||
for key in profile_keys:
|
||||
value = await self.get_profile(key, user)
|
||||
if value:
|
||||
profile[key] = value
|
||||
if profile:
|
||||
result["profile"] = profile
|
||||
|
||||
if include_preferences:
|
||||
preferences = await self.get_all_preferences(user)
|
||||
if preferences:
|
||||
result["preferences"] = preferences
|
||||
|
||||
logger.debug(
|
||||
"memory_service_prefetch",
|
||||
user=user,
|
||||
profile_keys=list(result.get("profile", {}).keys()),
|
||||
preference_keys=list(result.get("preferences", {}).keys()),
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
# =========================================================================
|
||||
# Internal Methods
|
||||
# =========================================================================
|
||||
|
||||
async def _get_memory(
|
||||
self,
|
||||
user: str,
|
||||
memory_type: MemoryType,
|
||||
key: str,
|
||||
) -> str | None:
|
||||
"""Get a memory by type and key (exact match)."""
|
||||
collection = get_memory_collection_name(user)
|
||||
|
||||
try:
|
||||
# Search with filter for exact type + key match
|
||||
# We use a dummy vector since we're filtering by payload
|
||||
results = self.qdrant._client.scroll(
|
||||
collection_name=collection,
|
||||
scroll_filter={
|
||||
"must": [
|
||||
{"key": "type", "match": {"value": memory_type.value}},
|
||||
{"key": "key", "match": {"value": key}},
|
||||
]
|
||||
},
|
||||
limit=1,
|
||||
with_payload=True,
|
||||
with_vectors=False,
|
||||
)
|
||||
|
||||
points, _ = results
|
||||
if points:
|
||||
return points[0].payload.get("value")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
"memory_service_get_failed",
|
||||
user=user,
|
||||
type=memory_type.value,
|
||||
key=key,
|
||||
error=str(e),
|
||||
)
|
||||
return None
|
||||
|
||||
async def _set_memory(
|
||||
self,
|
||||
user: str,
|
||||
memory_type: MemoryType,
|
||||
key: str,
|
||||
value: str,
|
||||
keywords: list[str],
|
||||
importance: float = 0.5,
|
||||
source: str = "explicit",
|
||||
) -> bool:
|
||||
"""Set a memory (upsert by type + key)."""
|
||||
try:
|
||||
# Generate embedding for semantic search
|
||||
embedding = await self.embedding.embed(f"{key}: {value}")
|
||||
if not embedding:
|
||||
logger.error("memory_service_embedding_failed", key=key)
|
||||
return False
|
||||
|
||||
# Create memory ID from type + key for idempotent upserts
|
||||
memory_id = f"{memory_type.value}:{key}"
|
||||
|
||||
payload = {
|
||||
"type": memory_type.value,
|
||||
"key": key,
|
||||
"value": value,
|
||||
"keywords": keywords,
|
||||
"importance": importance,
|
||||
"source": source,
|
||||
"updated_at": datetime.now(timezone.utc).isoformat(),
|
||||
}
|
||||
|
||||
result = await self.qdrant.upsert_memory(
|
||||
user=user,
|
||||
memory_id=memory_id,
|
||||
vector=embedding,
|
||||
payload=payload,
|
||||
)
|
||||
|
||||
if result:
|
||||
logger.debug(
|
||||
"memory_service_set",
|
||||
user=user,
|
||||
type=memory_type.value,
|
||||
key=key,
|
||||
)
|
||||
return True
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"memory_service_set_failed",
|
||||
user=user,
|
||||
type=memory_type.value,
|
||||
key=key,
|
||||
error=str(e),
|
||||
)
|
||||
return False
|
||||
|
||||
async def _get_all_by_type(
|
||||
self,
|
||||
user: str,
|
||||
memory_type: MemoryType,
|
||||
limit: int = 100,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Get all memories of a specific type."""
|
||||
collection = get_memory_collection_name(user)
|
||||
|
||||
try:
|
||||
results = self.qdrant._client.scroll(
|
||||
collection_name=collection,
|
||||
scroll_filter={
|
||||
"must": [
|
||||
{"key": "type", "match": {"value": memory_type.value}},
|
||||
]
|
||||
},
|
||||
limit=limit,
|
||||
with_payload=True,
|
||||
with_vectors=False,
|
||||
)
|
||||
|
||||
points, _ = results
|
||||
return [p.payload for p in points]
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
"memory_service_get_all_failed",
|
||||
user=user,
|
||||
type=memory_type.value,
|
||||
error=str(e),
|
||||
)
|
||||
return []
|
||||
|
||||
async def delete_memory(
|
||||
self,
|
||||
key: str,
|
||||
memory_type: MemoryType,
|
||||
user: str | None = None,
|
||||
) -> bool:
|
||||
"""
|
||||
Delete a specific memory.
|
||||
|
||||
Args:
|
||||
key: Memory key
|
||||
memory_type: Type of memory
|
||||
user: User ID
|
||||
|
||||
Returns:
|
||||
True if deleted
|
||||
"""
|
||||
user = user or get_user()
|
||||
memory_id = f"{memory_type.value}:{key}"
|
||||
|
||||
return await self.qdrant.delete_memory(user, memory_id)
|
||||
|
||||
|
||||
# Global service instance
|
||||
memory_service = MemoryService()
|
||||
@@ -5,6 +5,7 @@ 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.librarian import register_librarian
|
||||
from src.agents.tatlock_core import TATLOCK_CORE_CAPABILITY, tatlock_core_tools
|
||||
from src.core.household_registry import get_household_registry
|
||||
@@ -23,6 +24,7 @@ def register_household_members():
|
||||
Currently registers:
|
||||
- tatlock_core: Butler's core tools (calculator, datetime, web search)
|
||||
- librarian: Research and knowledge management (Phase 3)
|
||||
- biographer: User memory and context management (Phase F)
|
||||
"""
|
||||
registry = get_household_registry()
|
||||
|
||||
@@ -52,6 +54,16 @@ def register_household_members():
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
# Register The Biographer (Phase F)
|
||||
try:
|
||||
register_biographer()
|
||||
except Exception as e:
|
||||
# Don't fail startup if Biographer registration fails
|
||||
logger.warning(
|
||||
"biographer_registration_failed",
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"household_registration_complete",
|
||||
total_members=len(registry),
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
"""Tests for The Biographer agent."""
|
||||
@@ -0,0 +1,145 @@
|
||||
"""
|
||||
Tests for Biographer capability registration.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
from src.agents.biographer.capability import (
|
||||
BIOGRAPHER_CAPABILITY,
|
||||
get_biographer_capability,
|
||||
register_biographer,
|
||||
unregister_biographer,
|
||||
)
|
||||
from src.core.household_registry import HouseholdCapability
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestBiographerCapability:
|
||||
"""Tests for the Biographer capability definition."""
|
||||
|
||||
def test_capability_is_household_capability(self):
|
||||
"""Test capability is correct type."""
|
||||
assert isinstance(BIOGRAPHER_CAPABILITY, HouseholdCapability)
|
||||
|
||||
def test_capability_name(self):
|
||||
"""Test capability has correct name."""
|
||||
assert BIOGRAPHER_CAPABILITY.name == "biographer"
|
||||
|
||||
def test_capability_role(self):
|
||||
"""Test capability has correct role."""
|
||||
assert BIOGRAPHER_CAPABILITY.role == "The Biographer"
|
||||
|
||||
def test_capability_category(self):
|
||||
"""Test capability is in context category."""
|
||||
assert BIOGRAPHER_CAPABILITY.category == "context"
|
||||
|
||||
def test_capability_domains(self):
|
||||
"""Test capability covers expected domains."""
|
||||
domains = BIOGRAPHER_CAPABILITY.domains
|
||||
|
||||
assert "remember" in domains
|
||||
assert "recall" in domains
|
||||
assert "forget" in domains
|
||||
assert "memory" in domains
|
||||
assert "preferences" in domains
|
||||
assert "profile" in domains
|
||||
|
||||
def test_capability_does_not_require_network(self):
|
||||
"""Test capability does not require network access."""
|
||||
assert BIOGRAPHER_CAPABILITY.requires_network is False
|
||||
|
||||
def test_capability_low_cost(self):
|
||||
"""Test capability has low cost (vector search, minimal LLM)."""
|
||||
assert BIOGRAPHER_CAPABILITY.cost == "low"
|
||||
|
||||
def test_get_biographer_capability(self):
|
||||
"""Test getter returns same capability."""
|
||||
cap = get_biographer_capability()
|
||||
|
||||
assert cap is BIOGRAPHER_CAPABILITY
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestBiographerRegistration:
|
||||
"""Tests for Biographer registration functions."""
|
||||
|
||||
def test_register_biographer(self):
|
||||
"""Test registering biographer with registry."""
|
||||
mock_registry = MagicMock()
|
||||
mock_registry.__contains__ = MagicMock(return_value=False)
|
||||
|
||||
with patch(
|
||||
"src.agents.biographer.capability.get_household_registry",
|
||||
return_value=mock_registry,
|
||||
):
|
||||
with patch(
|
||||
"src.agents.biographer.capability.get_biographer_agent"
|
||||
) as mock_get_agent:
|
||||
mock_agent = MagicMock()
|
||||
mock_get_agent.return_value = mock_agent
|
||||
|
||||
register_biographer()
|
||||
|
||||
mock_registry.register.assert_called_once()
|
||||
call_kwargs = mock_registry.register.call_args[1]
|
||||
|
||||
assert call_kwargs["name"] == "biographer"
|
||||
assert call_kwargs["capability"] is BIOGRAPHER_CAPABILITY
|
||||
assert call_kwargs["agent"] is mock_agent
|
||||
|
||||
def test_register_biographer_already_registered(self):
|
||||
"""Test registering when already registered does nothing."""
|
||||
mock_registry = MagicMock()
|
||||
mock_registry.__contains__ = MagicMock(return_value=True)
|
||||
|
||||
with patch(
|
||||
"src.agents.biographer.capability.get_household_registry",
|
||||
return_value=mock_registry,
|
||||
):
|
||||
register_biographer()
|
||||
|
||||
# Should not call register since already registered
|
||||
mock_registry.register.assert_not_called()
|
||||
|
||||
def test_unregister_biographer(self):
|
||||
"""Test unregistering biographer from registry."""
|
||||
mock_registry = MagicMock()
|
||||
|
||||
with patch(
|
||||
"src.agents.biographer.capability.get_household_registry",
|
||||
return_value=mock_registry,
|
||||
):
|
||||
unregister_biographer()
|
||||
|
||||
mock_registry.unregister.assert_called_once_with("biographer")
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestCapabilityDescription:
|
||||
"""Tests for capability description."""
|
||||
|
||||
def test_description_mentions_recall(self):
|
||||
"""Test description mentions recall capabilities."""
|
||||
desc = BIOGRAPHER_CAPABILITY.description.lower()
|
||||
assert "recall" in desc
|
||||
|
||||
def test_description_mentions_record(self):
|
||||
"""Test description mentions recording capability."""
|
||||
desc = BIOGRAPHER_CAPABILITY.description.lower()
|
||||
assert "record" in desc
|
||||
|
||||
def test_description_mentions_forget(self):
|
||||
"""Test description mentions forget capability."""
|
||||
desc = BIOGRAPHER_CAPABILITY.description.lower()
|
||||
assert "forget" in desc
|
||||
|
||||
def test_description_mentions_profile(self):
|
||||
"""Test description mentions profile updates."""
|
||||
desc = BIOGRAPHER_CAPABILITY.description.lower()
|
||||
assert "profile" in desc
|
||||
|
||||
def test_description_mentions_preferences(self):
|
||||
"""Test description mentions preferences."""
|
||||
desc = BIOGRAPHER_CAPABILITY.description.lower()
|
||||
assert "preferences" in desc
|
||||
@@ -0,0 +1,279 @@
|
||||
"""
|
||||
Tests for the memory service (direct access layer).
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import MagicMock, patch, AsyncMock
|
||||
|
||||
from src.core.memory_service import (
|
||||
MemoryService,
|
||||
MemoryType,
|
||||
MemoryRecord,
|
||||
memory_service,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestMemoryType:
|
||||
"""Tests for MemoryType enum."""
|
||||
|
||||
def test_user_profile_type(self):
|
||||
"""Test user_profile type exists."""
|
||||
assert MemoryType.USER_PROFILE.value == "user_profile"
|
||||
|
||||
def test_preference_type(self):
|
||||
"""Test preference type exists."""
|
||||
assert MemoryType.PREFERENCE.value == "preference"
|
||||
|
||||
def test_learned_fact_type(self):
|
||||
"""Test learned_fact type exists."""
|
||||
assert MemoryType.LEARNED_FACT.value == "learned_fact"
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestMemoryRecord:
|
||||
"""Tests for MemoryRecord model."""
|
||||
|
||||
def test_create_minimal_record(self):
|
||||
"""Test creating record with minimal fields."""
|
||||
record = MemoryRecord(
|
||||
id="test_1",
|
||||
type=MemoryType.USER_PROFILE,
|
||||
key="location",
|
||||
value="Amsterdam",
|
||||
)
|
||||
|
||||
assert record.id == "test_1"
|
||||
assert record.type == MemoryType.USER_PROFILE
|
||||
assert record.key == "location"
|
||||
assert record.value == "Amsterdam"
|
||||
assert record.importance == 0.5 # Default
|
||||
assert record.source == "explicit" # Default
|
||||
|
||||
def test_create_full_record(self):
|
||||
"""Test creating record with all fields."""
|
||||
record = MemoryRecord(
|
||||
id="test_2",
|
||||
type=MemoryType.LEARNED_FACT,
|
||||
key="car",
|
||||
value="Tesla Model 3",
|
||||
keywords=["car", "vehicle", "tesla"],
|
||||
importance=0.8,
|
||||
source="conversation",
|
||||
)
|
||||
|
||||
assert record.keywords == ["car", "vehicle", "tesla"]
|
||||
assert record.importance == 0.8
|
||||
assert record.source == "conversation"
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestMemoryServiceInit:
|
||||
"""Tests for MemoryService initialization."""
|
||||
|
||||
def test_service_has_lazy_clients(self):
|
||||
"""Test service initializes with lazy client loading."""
|
||||
service = MemoryService()
|
||||
|
||||
assert service._qdrant is None
|
||||
assert service._embedding is None
|
||||
assert service._cache is None
|
||||
|
||||
def test_global_instance_exists(self):
|
||||
"""Test global memory_service instance exists."""
|
||||
assert memory_service is not None
|
||||
assert isinstance(memory_service, MemoryService)
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestMemoryServiceProfileMethods:
|
||||
"""Tests for profile-related methods."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_profile_uses_context(self):
|
||||
"""Test get_profile uses request context for user."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "_get_memory", new_callable=AsyncMock) as mock_get:
|
||||
mock_get.return_value = "Amsterdam"
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.get_profile("location")
|
||||
|
||||
mock_get.assert_called_once_with("testuser", MemoryType.USER_PROFILE, "location")
|
||||
assert result == "Amsterdam"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_profile_explicit_user(self):
|
||||
"""Test get_profile with explicit user parameter."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "_get_memory", new_callable=AsyncMock) as mock_get:
|
||||
mock_get.return_value = "Berlin"
|
||||
|
||||
result = await service.get_profile("location", user="otheruser")
|
||||
|
||||
mock_get.assert_called_once_with("otheruser", MemoryType.USER_PROFILE, "location")
|
||||
assert result == "Berlin"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_set_profile_high_importance(self):
|
||||
"""Test set_profile uses high importance (0.9)."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "_set_memory", new_callable=AsyncMock) as mock_set:
|
||||
mock_set.return_value = True
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.set_profile("timezone", "Europe/Amsterdam")
|
||||
|
||||
call_kwargs = mock_set.call_args[1]
|
||||
assert call_kwargs["importance"] == 0.9
|
||||
assert result is True
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestMemoryServicePreferenceMethods:
|
||||
"""Tests for preference-related methods."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_preference(self):
|
||||
"""Test get_preference retrieves correctly."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "_get_memory", new_callable=AsyncMock) as mock_get:
|
||||
mock_get.return_value = "celsius"
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.get_preference("temperature_unit")
|
||||
|
||||
mock_get.assert_called_once_with("testuser", MemoryType.PREFERENCE, "temperature_unit")
|
||||
assert result == "celsius"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_set_preference_medium_importance(self):
|
||||
"""Test set_preference uses medium importance (0.7)."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "_set_memory", new_callable=AsyncMock) as mock_set:
|
||||
mock_set.return_value = True
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.set_preference("theme", "dark")
|
||||
|
||||
call_kwargs = mock_set.call_args[1]
|
||||
assert call_kwargs["importance"] == 0.7
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestMemoryServiceFactMethods:
|
||||
"""Tests for fact-related methods."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_store_fact_default_importance(self):
|
||||
"""Test store_fact uses default importance (0.5)."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "_set_memory", new_callable=AsyncMock) as mock_set:
|
||||
mock_set.return_value = True
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.store_fact("car", "Tesla Model 3")
|
||||
|
||||
call_kwargs = mock_set.call_args[1]
|
||||
assert call_kwargs["importance"] == 0.5
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_store_fact_custom_importance(self):
|
||||
"""Test store_fact with custom importance."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "_set_memory", new_callable=AsyncMock) as mock_set:
|
||||
mock_set.return_value = True
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.store_fact(
|
||||
"employer",
|
||||
"Acme Corp",
|
||||
importance=0.8,
|
||||
)
|
||||
|
||||
call_kwargs = mock_set.call_args[1]
|
||||
assert call_kwargs["importance"] == 0.8
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_fact(self):
|
||||
"""Test get_fact retrieves correctly."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "_get_memory", new_callable=AsyncMock) as mock_get:
|
||||
mock_get.return_value = "Tesla Model 3"
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.get_fact("car")
|
||||
|
||||
mock_get.assert_called_once_with("testuser", MemoryType.LEARNED_FACT, "car")
|
||||
assert result == "Tesla Model 3"
|
||||
|
||||
|
||||
@pytest.mark.unit
|
||||
class TestMemoryServicePrefetch:
|
||||
"""Tests for prefetch_context method."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_prefetch_default_keys(self):
|
||||
"""Test prefetch with default profile keys."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "get_profile", new_callable=AsyncMock) as mock_profile:
|
||||
with patch.object(service, "get_all_preferences", new_callable=AsyncMock) as mock_prefs:
|
||||
mock_profile.side_effect = [
|
||||
"Amsterdam", # location
|
||||
"Europe/Amsterdam", # timezone
|
||||
"John", # name
|
||||
]
|
||||
mock_prefs.return_value = {"temperature_unit": "celsius"}
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.prefetch_context()
|
||||
|
||||
assert result["profile"]["location"] == "Amsterdam"
|
||||
assert result["profile"]["timezone"] == "Europe/Amsterdam"
|
||||
assert result["profile"]["name"] == "John"
|
||||
assert result["preferences"]["temperature_unit"] == "celsius"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_prefetch_specific_keys(self):
|
||||
"""Test prefetch with specific profile keys."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "get_profile", new_callable=AsyncMock) as mock_profile:
|
||||
with patch.object(service, "get_all_preferences", new_callable=AsyncMock) as mock_prefs:
|
||||
mock_profile.return_value = "Amsterdam"
|
||||
mock_prefs.return_value = {}
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.prefetch_context(
|
||||
profile_keys=["location"],
|
||||
include_preferences=False,
|
||||
)
|
||||
|
||||
# Should only fetch location
|
||||
mock_profile.assert_called_once()
|
||||
mock_prefs.assert_not_called()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_prefetch_no_profile(self):
|
||||
"""Test prefetch without profile data."""
|
||||
service = MemoryService()
|
||||
|
||||
with patch.object(service, "get_profile", new_callable=AsyncMock) as mock_profile:
|
||||
with patch.object(service, "get_all_preferences", new_callable=AsyncMock) as mock_prefs:
|
||||
mock_prefs.return_value = {"theme": "dark"}
|
||||
|
||||
with patch("src.core.memory_service.get_user", return_value="testuser"):
|
||||
result = await service.prefetch_context(include_profile=False)
|
||||
|
||||
mock_profile.assert_not_called()
|
||||
assert "profile" not in result
|
||||
assert result["preferences"]["theme"] == "dark"
|
||||
Reference in New Issue
Block a user