Commit Graph
4 Commits
Author SHA1 Message Date
jpmschweitzerandClaude 66f6e54fc3 refactor(core-ai): comprehensive cleanup - PydanticAI only architecture
Remove all obsolete agent implementations and framework references.
Keep only PydanticAI (primary) and SimpleLiteLLM (fallback).

This cleanup eliminates confusion between multiple frameworks that were
tried during development (LangChain, LangGraph, ADK, OllamaNative) and
establishes PydanticAI as the single agent framework going forward.

BREAKING CHANGES:
- Removed OllamaNativeAgent - use PydanticAgent instead
- Removed /test/ollama-tools diagnostic endpoint
- Default /v1/chat/completions now uses PydanticAgent

Files Deleted (32 total):
- Obsolete agents: ollama_native_agent.py
- Diagnostic files: ARCHITECTURE.md, DIAGNOSTIC_RESULTS.md, PHASE*.md
- Legacy tools: src/tools.py
- Test files: test_ai_flow_quality.py, test_02/03 (diagnostic layers)
- Documentation: ADK_Ollama_Research.md, agent-flow-diagrams.md
- Session docs: 3 files with LangChain/LangGraph implementations
- Plans: 5 completed plans about obsolete frameworks
- Migration docs: MIGRATION_PLAN_LANGCHAIN_TO_ADK.md

Files Modified (8 total):
- main.py: Refactored to PydanticAI only (305 lines vs 457 before)
- agents/__init__.py: Removed OllamaNativeAgent exports
- README.md: Complete rewrite for PydanticAI architecture
- prompts.py: Updated for PydanticAI (infrastructure tool guidance)
- STATUS.md: Updated to v0.11.0-pydantic-ai
- CHANGELOG.md: Added v0.11.0 entry documenting cleanup
- plans/active/*.md: Updated to reference PydanticAI

Current Architecture:
- Framework: PydanticAI with native Ollama SDK
- Agents: PydanticAgent (primary) + SimpleLiteLLMAgent (fallback)
- Model: mistral-nemo:latest
- Tools: 6 core + 28+ OpenAPI-discovered
- Memory: 3-tier system with Qdrant
- VRAM: ~4-6GB

Lines Removed: ~3000+ lines of obsolete code

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-03 14:24:52 +01:00
jpmschweitzerandClaude a6249e6cd0 feat(core-ai): add OllamaNativeAgent with native Ollama tool calling
Implement native Ollama agent that bypasses OpenAI-compatible API and uses
Ollama's native /api/chat endpoint for improved tool calling reliability.

Changes:
- Add OllamaNativeAgent class with native tool calling support
  - Direct integration with Ollama /api/chat endpoint
  - Better tool calling reliability vs OpenAI-compatible API
  - Async streaming support
  - Tool result handling and multi-turn conversations
- Set OllamaNativeAgent as default agent (replacing PydanticAI)
- Add test endpoint for Ollama tool verification
- Update health check to report ollama-native availability
- Add ollama>=0.4.0 to requirements for native library support

Technical Details:
- Uses Ollama's native tool format (not OpenAI functions)
- Handles tool execution and response synthesis
- Maintains conversation context across tool calls
- Model: mistral-nemo:latest (primary reasoning model)

Motivation:
PydanticAI uses Ollama's OpenAI-compatible endpoint which has less
reliable tool calling. The native API provides better tool support
and more consistent behavior.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-02 18:35:16 +01:00
jpmschweitzer 8487b2a366 feat(core-ai): Implement multi-tiered conversation memory
Introduces a comprehensive, multi-tiered memory system to provide conversation history and context for the AI agent. This lays the foundation for more stateful and intelligent interactions.

Key components of this implementation:

- **Multi-Tiered Memory Architecture:**
  - **Tier 1 (Working Memory):** A fast, in-memory buffer (`ConversationBufferMemory`) that holds the most recent turns of a conversation for immediate access.
  - **Tier 3 (Long-Term Memory):** A persistent, semantic search-based memory store using Qdrant (`QdrantConversationMemory`). It stores all conversation turns as vector embeddings, enabling long-term recall and similarity search.

- **Qdrant Integration:**
  - The `qdrant-client` is added to manage collections and perform vector search operations.
  - Each user is assigned a dedicated Qdrant collection for multi-tenancy.

- **Ollama Embedding Client:**
  - A new `OllamaEmbeddingClient` generates text embeddings via the Ollama API, replacing the need for local sentence-transformer models. This significantly reduces the service's dependency footprint.

- **Configuration and Stack Updates:**
  - The `config.py` and `core-ai.yml` stack file are updated with new settings for enabling memory, configuring Qdrant, and specifying the embedding model.

- **Utility and Schema Additions:**
  - New Pydantic schemas (`memory/schemas.py`) define the data structures for conversation turns and memory management.
  - Utility functions (`utils.py`) are added for user ID sanitization and collection naming.

This feature enhances the agent's capabilities by allowing it to maintain context across multiple turns and sessions, leading to more coherent and relevant responses.
2025-11-30 11:35:45 +01:00
jpmschweitzerandClaude 53267e1665 feat(ai): migrate from Google ADK to PydanticAI with working tool calling
Major Changes:
- Replace Google ADK with PydanticAI framework for agent orchestration
- Implement OpenAI-compatible API endpoint for Ollama integration
- Fix streaming response to send deltas instead of cumulative text
- Add /chat/completions route alias for Open-WebUI compatibility
- Enable tool calling with 5 local tools (calculate, date/time utilities)

Architecture:
- Core-AI service: Standalone Python service with PydanticAI agent
- PydanticAI: Uses OpenAI-compatible Ollama API at /v1 endpoint
- Tool Registry: Shared tool system between core-ai and core-api
- Streaming: Fixed async context issues and delta calculation

Verified Working:
✅ Chat completion (streaming & non-streaming)
✅ Tool calling with mistral-nemo and mistral-tools models
✅ Open-WebUI integration via core-ai:8086
✅ 5 tools: calculate, get_current_time, get_current_date, calculate_date_difference, add_days_to_date
✅ Proper streaming deltas (no repetition)

Technical Details:
- PydanticAI 1.25.0+ with full Ollama support
- Async context manager issue resolved via chunk collection
- Delta calculation: chunk[len(previous):] to extract new content only
- Routes: /v1/chat/completions and /chat/completions (Open-WebUI compat)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-30 10:31:14 +01:00