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>
This commit is contained in:
Claude
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@@ -37,7 +37,7 @@ Instead of configuring functions in Open WebUI (or any other UI), the Core API b
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│
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ Agent Orchestrator (LangGraph) │
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│ Agent Orchestrator (PydanticAI) │
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│ ┌──────────────────────────────────────────────┐ │
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│ │ Reasoning Loop: │ │
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│ │ 1. Analyze user intent │ │
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@@ -63,51 +63,59 @@ Instead of configuring functions in Open WebUI (or any other UI), the Core API b
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## Implementation Options
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### Option 1: LangGraph (Recommended)
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### Option 1: PydanticAI (Current Implementation)
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**Pros:**
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- Built-in agent loops and tool calling
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- State management for multi-step reasoning
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- Streaming support for intermediate steps
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- Well-documented patterns
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- Active development
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- Type-safe tool definitions with Pydantic models
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- Built-in streaming support with structured output
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- Native Ollama integration via HTTP API
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- Lightweight and minimal dependencies
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- Clear separation of concerns with dependency injection
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- Excellent debugging with structured validation
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**Cons:**
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- Additional dependency (~50MB)
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- Learning curve for LangGraph concepts
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- Some overhead vs custom implementation
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- Relatively new framework (less established patterns)
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- Manual agent loop implementation required
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- Less built-in state management compared to stateful frameworks
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**Example flow:**
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```python
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from langgraph.prebuilt import create_react_agent
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from langchain_core.tools import tool
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from pydantic_ai import Agent, RunContext
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from pydantic import BaseModel
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@tool
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def deploy_service(service_name: str, compose_yaml: str) -> str:
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"""Deploy a containerized service via Portainer"""
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# Use existing infrastructure controller
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return portainer_client.deploy_stack(...)
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class DeployServiceParams(BaseModel):
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service_name: str
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compose_yaml: str
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@tool
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def web_search(query: str) -> str:
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"""Search the web and extract content"""
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# Use existing web scraper
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return scraper.scrape(...)
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class WebSearchParams(BaseModel):
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query: str
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agent = create_react_agent(
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model=ChatOllama(model="gemma:7b"),
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tools=[deploy_service, web_search, ...],
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state_modifier="You are a homelab infrastructure assistant..."
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agent = Agent(
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model="ollama:mistral-tools:7b",
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system_prompt="You are a homelab infrastructure assistant...",
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result_type=str
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)
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@agent.tool
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async def deploy_service(ctx: RunContext[None], params: DeployServiceParams) -> str:
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"""Deploy a containerized service via Portainer"""
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return await portainer_client.deploy_stack(
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params.service_name,
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params.compose_yaml
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)
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@agent.tool
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async def web_search(ctx: RunContext[None], params: WebSearchParams) -> str:
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"""Search the web and extract content"""
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return await scraper.scrape(params.query)
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# Streaming with reasoning
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for chunk in agent.stream({"messages": [user_message]}):
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if "thinking" in chunk:
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yield f"data: {json.dumps({'reasoning': chunk['thinking']})}\n\n"
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if "tool_calls" in chunk:
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yield f"data: {json.dumps({'tool': chunk['tool_calls'][0]['name']})}\n\n"
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if "response" in chunk:
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yield f"data: {json.dumps({'content': chunk['response']})}\n\n"
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async with agent.run_stream(user_message) as stream:
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async for chunk in stream.stream_text():
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if chunk.type == "tool_call":
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yield f"data: {json.dumps({'tool': chunk.tool_name})}\n\n"
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elif chunk.type == "text":
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yield f"data: {json.dumps({'content': chunk.content})}\n\n"
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```
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### Option 2: Custom Agent Loop
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@@ -122,6 +130,7 @@ for chunk in agent.stream({"messages": [user_message]}):
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- More code to maintain
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- Need to implement tool calling protocol
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- Reinventing some wheels
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- Manual type validation
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**Example flow:**
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```python
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@@ -147,26 +156,27 @@ class UnifiedAgent:
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yield {"type": "content", "content": response}
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```
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### Option 3: Hybrid (LangChain Tools + Custom Orchestration)
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### Option 3: Hybrid (PydanticAI + Custom Extensions)
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Use LangChain's tool framework but custom agent loop:
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- Leverage `@tool` decorator for easy tool definitions
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- Custom routing logic for model selection
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- Manual streaming control
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Use PydanticAI's agent framework with custom enhancements:
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- Leverage type-safe tool definitions
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- Add custom routing logic for multi-model selection
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- Enhanced streaming control for reasoning output
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- Custom dependency injection for context management
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## Recommended Approach: LangGraph with Custom Extensions
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## Recommended Approach: PydanticAI with Custom Extensions
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**Phase 1: Core Agent (Week 1)**
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- Set up LangGraph agent with basic tools
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- Set up PydanticAI agent with basic tools
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- Implement streaming with reasoning output
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- Wire up existing infrastructure tools
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- Test with simple queries
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**Phase 2: Advanced Routing (Week 2)**
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- Multi-model routing (small for simple, large for complex)
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- Parallel tool execution
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- Error handling and retries
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- Context management
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- Parallel tool execution via async tools
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- Error handling and retries with custom logic
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- Context management using RunContext
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**Phase 3: Multi-Modal (Week 3)**
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- Image analysis (if needed)
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@@ -179,23 +189,34 @@ Use LangChain's tool framework but custom agent loop:
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### Tier 1: Infrastructure Tools (Existing)
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```python
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@tool
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async def list_services() -> List[Dict]:
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from pydantic_ai import Agent, RunContext
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from pydantic import BaseModel
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class ListServicesResult(BaseModel):
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services: List[Dict]
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class DeployServiceParams(BaseModel):
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name: str
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compose: str
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@agent.tool
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async def list_services(ctx: RunContext[None]) -> ListServicesResult:
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"""List all running Docker services"""
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return await portainer_client.list_containers()
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services = await portainer_client.list_containers()
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return ListServicesResult(services=services)
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@tool
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async def deploy_service(name: str, compose: str) -> str:
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@agent.tool
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async def deploy_service(ctx: RunContext[None], params: DeployServiceParams) -> str:
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"""Deploy a new service from Docker Compose YAML"""
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return await portainer_client.deploy_stack(name, compose)
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return await portainer_client.deploy_stack(params.name, params.compose)
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@tool
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async def create_proxy(domain: str, target: str) -> str:
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@agent.tool
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async def create_proxy(ctx: RunContext[None], domain: str, target: str) -> str:
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"""Create Nginx reverse proxy for a service"""
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return await npm_client.create_proxy_host(domain, target)
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@tool
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async def check_service_health(service: str) -> Dict:
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@agent.tool
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async def check_service_health(ctx: RunContext[None], service: str) -> Dict:
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"""Check if a service is healthy"""
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return await kuma_client.get_monitor_status(service)
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```
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@@ -203,18 +224,18 @@ async def check_service_health(service: str) -> Dict:
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### Tier 2: Knowledge Tools
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```python
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@tool
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async def web_search(query: str) -> str:
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@agent.tool
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async def web_search(ctx: RunContext[None], query: str) -> str:
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"""Search the web and extract main content"""
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return await scraper.scrape_url(query)
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@tool
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async def query_memory(question: str) -> List[str]:
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@agent.tool
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async def query_memory(ctx: RunContext[None], question: str) -> List[str]:
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"""Search conversation history for relevant context"""
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return await memory.semantic_search(question)
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@tool
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async def read_documentation(topic: str) -> str:
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@agent.tool
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async def read_documentation(ctx: RunContext[None], topic: str) -> str:
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"""Read project documentation"""
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docs_path = f"/docs/{topic}.md"
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return read_file(docs_path)
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@@ -223,14 +244,14 @@ async def read_documentation(topic: str) -> str:
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### Tier 3: Execution Tools (Future)
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```python
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@tool
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async def execute_python(code: str) -> str:
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@agent.tool
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async def execute_python(ctx: RunContext[None], code: str) -> str:
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"""Execute Python code in sandbox"""
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# Future: Integrate code interpreter
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pass
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@tool
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async def query_database(sql: str) -> List[Dict]:
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@agent.tool
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async def query_database(ctx: RunContext[None], sql: str) -> List[Dict]:
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"""Query PostgreSQL database"""
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# Future: Safe SQL execution
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pass
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@@ -246,7 +267,7 @@ async def query_database(sql: str) -> List[Dict]:
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"type": "thinking", # or "tool_call", "content", "error"
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"content": "Searching the web for nginx configuration...",
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"tool": "web_search", # optional, if type is tool_call
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"model": "gemma:7b" # optional, which model is being used
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"model": "mistral-tools:7b" # optional, which model is being used
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}
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# Example stream
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@@ -281,53 +302,56 @@ Open WebUI already supports streaming, we just need to format it correctly:
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### Intent-Based Routing
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```python
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from pydantic_ai import Agent
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class ModelRouter:
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MODELS = {
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"simple": "gemma:2b", # Fast, <100 tokens
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"general": "gemma:7b", # Balanced
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"expert": "mistral:7b", # Complex reasoning
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"code": "codestral:latest" # Code tasks
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"simple": "ollama:gemma:2b", # Fast, <100 tokens
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"general": "ollama:gemma:7b", # Balanced
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"expert": "ollama:mistral:7b", # Complex reasoning
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"code": "ollama:codestral:latest" # Code tasks
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}
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async def select_model(self, message: str, context: str) -> str:
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# Use lightweight model for routing decision
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prompt = f"""Analyze this request and categorize:
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routing_agent = Agent(
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model="ollama:gemma:2b",
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result_type=str,
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system_prompt="""Analyze this request and categorize:
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User: {message}
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Context: {context}
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Categories:
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- simple: Greetings, basic facts, short answers
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- general: Normal conversation, explanations
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- expert: Complex reasoning, multi-step problems
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- code: Programming tasks, debugging
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Categories:
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- simple: Greetings, basic facts, short answers
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- general: Normal conversation, explanations
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- expert: Complex reasoning, multi-step problems
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- code: Programming tasks, debugging
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Return ONLY the category.
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"""
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)
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Return ONLY the category.
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"""
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category = await ollama.generate(model="gemma:2b", prompt=prompt)
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return self.MODELS[category.strip()]
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result = await routing_agent.run(f"User: {message}\nContext: {context}")
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return self.MODELS[result.data.strip()]
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```
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## Next Steps
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1. **Prototype LangGraph agent** (2-3 hours)
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- Basic agent with 2-3 tools
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- Streaming with thinking output
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- Test with Open WebUI
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1. **Enhance PydanticAI agent** (2-3 hours)
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- Add more infrastructure tools
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- Improve streaming with reasoning output
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- Test with complex queries
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2. **Integrate existing tools** (3-4 hours)
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- Wrap infrastructure controller as tools
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- Wrap web scraper as tool
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- Test tool calling
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2. **Integrate remaining tools** (3-4 hours)
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- Migrate all infrastructure controller tools
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- Add web scraper tool improvements
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- Test multi-tool workflows
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3. **Model routing** (2 hours)
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- Implement intent analysis
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- Add model selection logic
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- Test performance
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3. **Model routing enhancements** (2 hours)
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- Refine intent analysis
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- Add model selection metrics
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- Test performance improvements
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4. **Production deployment** (2 hours)
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- Error handling
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4. **Production hardening** (2 hours)
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- Enhanced error handling
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- Rate limiting
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- Logging and monitoring
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- Update API documentation
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@@ -391,20 +415,20 @@ User: "How do I configure Headscale?"
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## Technology Stack
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- **Agent Framework:** LangGraph 0.2.x
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- **LLM Integration:** LangChain-Ollama
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- **Tool Framework:** LangChain Tools
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- **Agent Framework:** PydanticAI
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- **LLM Integration:** Native Ollama SDK (HTTP API)
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- **Tool Framework:** PydanticAI Tools with Pydantic validation
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- **Streaming:** SSE (Server-Sent Events)
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- **State Management:** LangGraph StateGraph
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- **State Management:** RunContext dependency injection
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- **Memory:** Existing Qdrant integration
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## Risk Mitigation
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**Risk:** LangGraph adds complexity
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- **Mitigation:** Start simple, add features incrementally
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**Risk:** PydanticAI is relatively new
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- **Mitigation:** Strong typing provides safety, active development community
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**Risk:** Tool calling may be slow
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- **Mitigation:** Parallel execution, caching, optimized tools
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- **Mitigation:** Async tools enable parallel execution, caching, optimized tools
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**Risk:** Reasoning output may be verbose
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- **Mitigation:** Configurable verbosity, collapsible UI elements
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# AI Orchestrator Phase 1 - Test Results
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**Date:** 2025-11-13
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**Service:** Core API v1.0.0-phase1
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**Endpoint:** http://localhost:8083
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**Status:** ✅ ALL TESTS PASSING - ZERO ISSUES
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## Test Summary
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| Test | Status | Result |
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|------|--------|--------|
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| Health Check | ✅ PASS | Service healthy, Ollama connected |
|
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| Models List | ✅ PASS | Returns 11 models (4 aliases + 7 local) |
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| Non-Streaming Chat | ✅ PASS | Correct response format, token usage |
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| Streaming Chat | ✅ PASS | SSE format, proper chunking |
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| Model Aliasing | ✅ PASS | All aliases working correctly |
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| Error Handling | ✅ PASS | Proper validation errors |
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| Multi-turn Conversation | ✅ PASS | Handles conversation history |
|
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| Token Usage | ✅ PASS | Accurate token counting |
|
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| Performance | ✅ PASS | 227-284ms average response time |
|
||||
| Model ID Formatting | ✅ PASS | Clean IDs (issue fixed) |
|
||||
|
||||
**Overall Score: 10/10 Tests Passed (100%)**
|
||||
|
||||
---
|
||||
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## Detailed Test Results
|
||||
|
||||
### Test 1: Health Check ✅
|
||||
**Endpoint:** `GET /health`
|
||||
|
||||
```json
|
||||
{
|
||||
"status": "healthy",
|
||||
"ollama_connected": true
|
||||
}
|
||||
```
|
||||
|
||||
**Result:** ✅ Service operational, Ollama connectivity confirmed
|
||||
|
||||
---
|
||||
|
||||
### Test 2: Models List ✅
|
||||
**Endpoint:** `GET /v1/models`
|
||||
|
||||
**Models Returned (all with clean IDs):**
|
||||
```json
|
||||
{
|
||||
"object": "list",
|
||||
"data": [
|
||||
{"id": "gpt-3.5-turbo", "object": "model", "owned_by": "local"},
|
||||
{"id": "gpt-4", "object": "model", "owned_by": "local"},
|
||||
{"id": "gpt-4-turbo", "object": "model", "owned_by": "local"},
|
||||
{"id": "gpt-4-code", "object": "model", "owned_by": "local"},
|
||||
{"id": "gemma:2b", "object": "model", "owned_by": "local"},
|
||||
{"id": "gemma:7b", "object": "model", "owned_by": "local"},
|
||||
{"id": "mistral:7b", "object": "model", "owned_by": "local"},
|
||||
{"id": "gemma2:9b", "object": "model", "owned_by": "local"},
|
||||
{"id": "mixtral:8x7b", "object": "model", "owned_by": "local"},
|
||||
{"id": "codestral:latest", "object": "model", "owned_by": "local"},
|
||||
{"id": "codegemma:latest", "object": "model", "owned_by": "local"}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
**Result:** ✅ All 11 models present with properly formatted IDs
|
||||
- ✅ 4 OpenAI aliases (gpt-3.5-turbo, gpt-4, gpt-4-turbo, gpt-4-code)
|
||||
- ✅ 2 lightweight models (gemma:2b, gemma:7b)
|
||||
- ✅ 3 heavy models (mistral:7b, gemma2:9b, mixtral:8x7b)
|
||||
- ✅ 2 code models (codestral:latest, codegemma:latest)
|
||||
- ✅ No extra quotes or formatting issues
|
||||
|
||||
---
|
||||
|
||||
### Test 3: Non-Streaming Chat Completion ✅
|
||||
**Endpoint:** `POST /v1/chat/completions`
|
||||
**Request:**
|
||||
```json
|
||||
{
|
||||
"model": "gpt-3.5-turbo",
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a helpful assistant. Respond in exactly 10 words."},
|
||||
{"role": "user", "content": "What is the capital of France?"}
|
||||
],
|
||||
"stream": false,
|
||||
"temperature": 0.5,
|
||||
"max_tokens": 30
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"id": "chatcmpl-1763064184644",
|
||||
"object": "chat.completion",
|
||||
"created": 1763064199,
|
||||
"model": "gpt-3.5-turbo",
|
||||
"choices": [{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "The capital of France is Paris."
|
||||
},
|
||||
"finish_reason": "stop"
|
||||
}],
|
||||
"usage": {
|
||||
"prompt_tokens": 51,
|
||||
"completion_tokens": 8,
|
||||
"total_tokens": 59
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Result:** ✅ Perfect OpenAI-compatible response format
|
||||
- ✅ All required fields present
|
||||
- ✅ Token usage tracking working
|
||||
- ✅ Correct finish_reason
|
||||
- ✅ Model name preserved in response
|
||||
|
||||
---
|
||||
|
||||
### Test 4: Streaming Chat Completion ✅
|
||||
**Endpoint:** `POST /v1/chat/completions` (stream=true)
|
||||
**Request:** "Count from 1 to 5"
|
||||
|
||||
**Response Format (SSE):**
|
||||
```
|
||||
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"role":"assistant","content":null},"finish_reason":null}]}
|
||||
|
||||
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"content":"1"},"finish_reason":null}]}
|
||||
|
||||
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{"content":"\n"},"finish_reason":null}]}
|
||||
|
||||
... [continues with 2, 3, 4, 5]
|
||||
|
||||
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":...,"model":"gpt-3.5-turbo","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
|
||||
|
||||
data: [DONE]
|
||||
```
|
||||
|
||||
**Result:** ✅ Proper SSE format
|
||||
- ✅ First chunk includes role
|
||||
- ✅ Content chunks stream correctly
|
||||
- ✅ Final chunk with finish_reason
|
||||
- ✅ [DONE] marker sent
|
||||
- ✅ Compatible with OpenAI clients
|
||||
|
||||
---
|
||||
|
||||
### Test 5: Model Aliasing ✅
|
||||
**Test Cases:**
|
||||
|
||||
**5a: gpt-3.5-turbo → gemma:7b**
|
||||
- Request model: `gpt-3.5-turbo`
|
||||
- Log: `Model resolution: gpt-3.5-turbo → gemma:7b`
|
||||
- Response model field: `gpt-3.5-turbo` (preserves alias)
|
||||
- ✅ Working correctly
|
||||
|
||||
**5b: gpt-4 → mistral:7b**
|
||||
- Request model: `gpt-4`
|
||||
- Log: `Model resolution: gpt-4 → mistral:7b`
|
||||
- Response model field: `gpt-4`
|
||||
- ✅ Working correctly
|
||||
|
||||
**5c: Direct model (gemma:7b)**
|
||||
- Request model: `gemma:7b`
|
||||
- No resolution needed
|
||||
- Response model field: `gemma:7b`
|
||||
- ✅ Working correctly
|
||||
|
||||
**Result:** ✅ All alias mappings functional
|
||||
- Model resolution logged correctly
|
||||
- Response preserves requested model name
|
||||
- Direct model names work without aliasing
|
||||
|
||||
---
|
||||
|
||||
### Test 6: Error Handling ✅
|
||||
**Test Cases:**
|
||||
|
||||
**6a: Missing required field**
|
||||
```json
|
||||
{"model": "gpt-3.5-turbo", "stream": false}
|
||||
```
|
||||
Response: HTTP 422, `"msg": "Field required", "loc": ["body", "messages"]`
|
||||
✅ Proper validation error
|
||||
|
||||
**6b: Empty messages array**
|
||||
```json
|
||||
{"model": "gpt-3.5-turbo", "messages": [], "stream": false}
|
||||
```
|
||||
Response: HTTP 422, `"msg": "List should have at least 1 item after validation"`
|
||||
✅ Array length validation working
|
||||
|
||||
**6c: Invalid temperature (5.0, max is 2.0)**
|
||||
Response: HTTP 422, `"msg": "Input should be less than or equal to 2"`
|
||||
✅ Range validation working
|
||||
|
||||
**6d: Invalid JSON**
|
||||
Response: HTTP 422, `"type": "json_invalid"`
|
||||
✅ JSON parsing errors handled
|
||||
|
||||
**Result:** ✅ All edge cases handled with proper Pydantic validation
|
||||
|
||||
---
|
||||
|
||||
### Test 7: Multi-turn Conversation ✅
|
||||
**Request:**
|
||||
```json
|
||||
{
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a math tutor."},
|
||||
{"role": "user", "content": "What is 2+2?"},
|
||||
{"role": "assistant", "content": "2+2 equals 4."},
|
||||
{"role": "user", "content": "What about 3+3?"}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
**Response:** "3+3 equals 6. Would you like to ask anything else today?"
|
||||
|
||||
**Result:** ✅ Correctly processes conversation history
|
||||
- System message understood
|
||||
- Previous assistant response incorporated
|
||||
- Context maintained across turns
|
||||
|
||||
---
|
||||
|
||||
### Test 8: Token Usage Reporting ✅
|
||||
**Request:** Simple "Hello" message
|
||||
|
||||
**Token Usage:**
|
||||
- Prompt tokens: 28
|
||||
- Completion tokens: 19
|
||||
- Total tokens: 47
|
||||
|
||||
**Result:** ✅ Accurate token counting from Ollama
|
||||
|
||||
---
|
||||
|
||||
### Test 9: Performance Benchmark ✅
|
||||
**5 consecutive requests (simple "Hi" prompts, max_tokens=5)**
|
||||
|
||||
| Request | Response Time |
|
||||
|---------|--------------|
|
||||
| 1 | 257ms |
|
||||
| 2 | 221ms |
|
||||
| 3 | 239ms |
|
||||
| 4 | 284ms |
|
||||
| 5 | 227ms |
|
||||
|
||||
**Average: 245.6ms**
|
||||
**Min: 221ms**
|
||||
**Max: 284ms**
|
||||
|
||||
**Result:** ✅ Excellent performance
|
||||
- All requests under 300ms
|
||||
- Consistent response times
|
||||
- No degradation with concurrent requests
|
||||
|
||||
---
|
||||
|
||||
### Test 10: Model ID Formatting Fix ✅
|
||||
**Issue:** Model IDs initially had extra quotes (`"gemma:2b"`, `gemma:7b"`)
|
||||
|
||||
**Root Cause:** Parsing methods in `config.py` weren't stripping quote characters
|
||||
|
||||
**Fix Applied:**
|
||||
```python
|
||||
# Before:
|
||||
return [m.strip() for m in self.lightweight_models.split(",") if m.strip()]
|
||||
|
||||
# After:
|
||||
return [m.strip().strip('"').strip("'") for m in self.lightweight_models.split(",") if m.strip()]
|
||||
```
|
||||
|
||||
**Verification:**
|
||||
```bash
|
||||
✓ Total models: 11
|
||||
✓ gpt-3.5-turbo
|
||||
✓ gpt-4
|
||||
✓ gpt-4-turbo
|
||||
✓ gpt-4-code
|
||||
✓ gemma:2b # No quotes!
|
||||
✓ gemma:7b # No quotes!
|
||||
✓ mistral:7b # No quotes!
|
||||
✓ gemma2:9b
|
||||
✓ mixtral:8x7b # No quotes!
|
||||
✓ codestral:latest # No quotes!
|
||||
✓ codegemma:latest # No quotes!
|
||||
```
|
||||
|
||||
**Result:** ✅ Issue completely resolved
|
||||
- All model IDs properly formatted
|
||||
- No quotes or extra characters
|
||||
- Functionality unaffected
|
||||
|
||||
---
|
||||
|
||||
## Container Health
|
||||
|
||||
**Container:** core-api
|
||||
**Status:** Up and healthy
|
||||
**Ports:** 0.0.0.0:8083->8083/tcp
|
||||
**Health Check:** Passing (30s interval)
|
||||
**Uptime:** Stable (restarted once for fix)
|
||||
|
||||
**Recent Activity:**
|
||||
- Successfully processed 30+ chat requests during testing
|
||||
- Zero errors or crashes
|
||||
- Ollama connectivity stable
|
||||
- Hot-reload functioning correctly
|
||||
|
||||
---
|
||||
|
||||
## OpenAI API Compatibility
|
||||
|
||||
**Compatibility Score: 100%**
|
||||
|
||||
✅ **Request Format:**
|
||||
- All OpenAI fields supported (model, messages, temperature, max_tokens, etc.)
|
||||
- Proper Pydantic validation
|
||||
- Streaming boolean works correctly
|
||||
|
||||
✅ **Response Format:**
|
||||
- All required fields present (id, object, created, model, choices, usage)
|
||||
- Choice structure matches OpenAI exactly
|
||||
- Finish reasons correct ("stop")
|
||||
|
||||
✅ **Streaming Format:**
|
||||
- Server-Sent Events (SSE) format
|
||||
- Proper chunk structure
|
||||
- [DONE] marker
|
||||
- Compatible with OpenAI client libraries
|
||||
|
||||
✅ **Model Endpoints:**
|
||||
- /v1/models returns proper format
|
||||
- Model objects match OpenAI structure
|
||||
- Model IDs properly formatted
|
||||
|
||||
---
|
||||
|
||||
## Known Issues
|
||||
|
||||
**None - All issues resolved!** ✅
|
||||
|
||||
### Previously Fixed
|
||||
|
||||
1. **Model ID Formatting** ✅ FIXED
|
||||
- ~~Some model IDs had extra quotes~~
|
||||
- Fixed by updating config.py parsing methods
|
||||
- All model IDs now clean
|
||||
|
||||
---
|
||||
|
||||
## Future Enhancements (Planned Phases)
|
||||
|
||||
**Phase 2 - Memory Systems:**
|
||||
- [ ] Tier 1: ConversationBufferMemory (in-memory)
|
||||
- [ ] Tier 2: ConversationSummaryMemory (SQLite)
|
||||
- [ ] Tier 3: VectorStoreRetrieverMemory (Qdrant)
|
||||
|
||||
**Phase 3 - Multi-Agent Workflows:**
|
||||
- [ ] Router agent
|
||||
- [ ] Chat agent
|
||||
- [ ] Research agent
|
||||
- [ ] Code agent
|
||||
|
||||
**Phase 4 - Tool Integration:**
|
||||
- [ ] Web search (DuckDuckGo)
|
||||
- [ ] Web scraping (Core API)
|
||||
- [ ] Document search (Qdrant)
|
||||
|
||||
**Phase 5 - RAG & Advanced Memory:**
|
||||
- [ ] Hybrid retrieval
|
||||
- [ ] Document upload
|
||||
- [ ] Re-ranking
|
||||
|
||||
**Phase 6 - Production Hardening:**
|
||||
- [ ] Metrics and monitoring
|
||||
- [ ] Performance optimization
|
||||
- [ ] Load testing
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
**Phase 1 Status: ✅ 100% COMPLETE - PRODUCTION READY**
|
||||
|
||||
All core functionality is working perfectly:
|
||||
- ✅ OpenAI-compatible API endpoints
|
||||
- ✅ Model aliasing system (4 aliases)
|
||||
- ✅ Streaming and non-streaming responses
|
||||
- ✅ Error handling and validation
|
||||
- ✅ Performance within targets (<300ms)
|
||||
- ✅ All formatting issues resolved
|
||||
- ✅ Zero known bugs
|
||||
|
||||
**Ready for:**
|
||||
- ✅ Open WebUI integration (endpoint: http://core-api:8083/v1)
|
||||
- ✅ OpenAI client library usage
|
||||
- ✅ Production deployment
|
||||
- ✅ Phase 2 development (Memory Systems)
|
||||
|
||||
**Phase 1 Achievements:**
|
||||
- 10/10 tests passing
|
||||
- 100% OpenAI compatibility
|
||||
- Sub-300ms response times
|
||||
- Zero regressions
|
||||
- Clean, maintainable code
|
||||
|
||||
---
|
||||
|
||||
**Test Suite Completed: 2025-11-13**
|
||||
**Final Status: All issues resolved, ready for Phase 2**
|
||||
**Next Step: Begin Phase 2 (Memory Systems) implementation**
|
||||
|
||||
---
|
||||
|
||||
## Files Modified During Phase 1
|
||||
|
||||
### New Files Created
|
||||
- `services/core-api/src/api/v1/chat.py` (207 lines)
|
||||
- `services/core-api/src/api/v1/models.py` (35 lines)
|
||||
- `services/core-api/src/api/v1/schemas.py` (133 lines)
|
||||
- `services/core-api/src/models/ollama_client.py` (202 lines)
|
||||
|
||||
### Files Modified
|
||||
- `services/core-api/src/main.py` - Added v1 routes
|
||||
- `services/core-api/src/config.py` - Added model configuration and aliases
|
||||
- `services/core-api/requirements.txt` - Dependencies up to date
|
||||
- `stacks/core-api.yml` - Environment variables for models
|
||||
|
||||
### Documentation Updated
|
||||
- `CONTAINERS.md` - Core API section updated
|
||||
- `STATUS.md` - Phase 1 completion documented
|
||||
- `docs/ai-orchestrator-plan.md` - Phase 1 marked complete
|
||||
- `docs/phase1-test-results.md` - This document
|
||||
|
||||
**Total Lines Added: ~600+ lines of production code**
|
||||
**Total Time: 1 day (2025-11-13)**
|
||||
@@ -1,595 +0,0 @@
|
||||
# Phase 2: Memory Systems - Implementation Status & Research Findings
|
||||
|
||||
**Last Updated:** 2025-11-23
|
||||
**Research Completed:** 2025-11-23
|
||||
**Status:** 85% Complete - Critical Fixes Needed
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
Phase 2 memory infrastructure is **architecturally sound** and follows **2024/2025 industry best practices**, but has **critical implementation gaps** preventing it from working in production.
|
||||
|
||||
**Architecture Grade:** 8.5/10 ⭐⭐⭐⭐
|
||||
**Implementation Status:** 🔴 Non-functional (memory storage bypassed)
|
||||
|
||||
---
|
||||
|
||||
## Current Implementation Review
|
||||
|
||||
### ✅ What's Working (Excellent Foundation)
|
||||
|
||||
#### 1. Multi-Tier Memory Architecture
|
||||
**Implementation:**
|
||||
- Tier 1: In-memory buffer (ConversationBufferMemory) - last 10 turns
|
||||
- Tier 2/3: Unified Qdrant storage (QdrantConversationMemory) - persistent + semantic
|
||||
|
||||
**Industry Validation:**
|
||||
- ✅ Aligns with [hybrid memory architecture recommendations](https://www.analyticsvidhya.com/blog/2024/11/langchain-memory/)
|
||||
- ✅ Follows [dual-retrieval patterns](https://principia-agentica.io/blog/2025/09/19/memory-in-agents-episodic-vs-semantic-and-the-hybrid-that-works/) (episodic + semantic)
|
||||
- ✅ Buffer size (10 turns) validated by [ConvoMem research](https://arxiv.org/html/2511.10523) - shows long context viable up to 150 conversations
|
||||
|
||||
**Files:**
|
||||
- `src/memory/tier1_buffer.py` - ✅ Fully functional
|
||||
- `src/memory/qdrant_memory.py` - ✅ Fully functional
|
||||
- `src/memory/manager.py` - ✅ Orchestration ready
|
||||
|
||||
#### 2. Qdrant Vector Database Selection
|
||||
**Status:** ✅ Excellent choice
|
||||
|
||||
**Industry Support:**
|
||||
- Recommended for [agentic vector search](https://qdrant.tech/articles/agentic-builders-guide/)
|
||||
- Used in [production long-term memory systems](https://dev.to/einarcesar/long-term-memory-for-llms-using-vector-store-a-practical-approach-with-n8n-and-qdrant-2ha7)
|
||||
- [n8n workflow templates](https://n8n.io/workflows/6829-build-persistent-chat-memory-with-gpt-4o-mini-and-qdrant-vector-database/) demonstrate production patterns
|
||||
|
||||
**Performance Benefits:**
|
||||
- Token consumption reduction: 60-80% vs full conversation histories
|
||||
- Fast semantic search: < 50ms
|
||||
- Collection exists: `core_api_conversations`
|
||||
|
||||
#### 3. Dual-Mode Retrieval
|
||||
**Implementation:**
|
||||
- Tier 2 mode: Chronological retrieval (filter by conversation_id)
|
||||
- Tier 3 mode: Semantic search (vector similarity)
|
||||
|
||||
**Industry Alignment:**
|
||||
Research shows this is [current best practice](https://principia-agentica.io/blog/2025/09/19/memory-in-agents-episodic-vs-semantic-and-the-hybrid-that-works/):
|
||||
> "A customer support copilot pulls the last conversation turns (episodic) while also recalling policy knowledge (semantic), then merges and de-dupes"
|
||||
|
||||
#### 4. Auto-Consolidation Logic
|
||||
**Implementation:**
|
||||
- Triggers every 10 messages
|
||||
- Moves buffer → Qdrant
|
||||
- Automatic pruning
|
||||
|
||||
**Industry Alignment:** ✅ Solid approach
|
||||
|
||||
---
|
||||
|
||||
## 🔴 Critical Issues (Blocking Production Use)
|
||||
|
||||
### Issue #1: Memory Storage Bypassed in Agent Path
|
||||
|
||||
**Problem:**
|
||||
Memory storage code is **unreachable** when unified agent is active (which is 100% of requests).
|
||||
|
||||
**Location:** `src/controllers/ai_controller.py:307-386`
|
||||
|
||||
**Root Cause:**
|
||||
```python
|
||||
# Line 308-372: Agent executes and RETURNS immediately
|
||||
if AGENT_AVAILABLE:
|
||||
# ... agent.chat() ...
|
||||
return response # ← Returns here, never reaches line 377
|
||||
|
||||
# Line 377-386: Memory storage (NEVER EXECUTED)
|
||||
if request.store_in_memory:
|
||||
await store_conversation_turn(...)
|
||||
```
|
||||
|
||||
**Evidence:**
|
||||
```bash
|
||||
# Qdrant collection stats:
|
||||
curl http://qdrant:6333/collections/core_api_conversations
|
||||
{
|
||||
"points_count": 0, # ← No conversations stored!
|
||||
"indexed_vectors_count": 0
|
||||
}
|
||||
|
||||
# Logs show memory enabled but nothing stored:
|
||||
store_in_memory=True # ← Flag is set
|
||||
# But 0 points in Qdrant
|
||||
```
|
||||
|
||||
**Industry Pattern:**
|
||||
[Long-term agentic memory](https://medium.com/@anil.jain.baba/long-term-agentic-memory-with-langgraph-824050b09852) shows memory must be:
|
||||
1. **Stored BEFORE agent returns** (user message)
|
||||
2. **Stored AFTER agent completes** (assistant response)
|
||||
3. **Integrated with agent lifecycle** (not in fallback path)
|
||||
|
||||
**Fix Required:** Add memory storage calls inside agent code path (lines 307-372)
|
||||
|
||||
---
|
||||
|
||||
### Issue #2: Embedding Dimension Mismatch
|
||||
|
||||
**Problem:**
|
||||
Configuration specifies one dimension, Qdrant collection uses another.
|
||||
|
||||
**Current State:**
|
||||
- Config: `embedding_model = "nomic-embed-text"` → 768 dimensions
|
||||
- Qdrant Collection: 384 dimensions (wrong!)
|
||||
|
||||
**Evidence:**
|
||||
```json
|
||||
// From curl http://qdrant:6333/collections/core_api_conversations
|
||||
{
|
||||
"config": {
|
||||
"params": {
|
||||
"vectors": {
|
||||
"size": 384, // ← Wrong!
|
||||
"distance": "Cosine"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Industry Guidance:**
|
||||
From [Ollama embedding models best practices](https://docs.ollama.com/capabilities/embeddings/):
|
||||
- **all-minilm**: 384d - fastest (14.7ms/1K tokens), CPU-friendly
|
||||
- **nomic-embed-text**: 768d - better accuracy (81.2% vs 80.04%), 2048 token context
|
||||
- **mxbai-embed-large**: 1024d - highest quality
|
||||
|
||||
**Performance Research:**
|
||||
[Nomic vs MiniLM comparison](https://medium.com/@guptak650/nomic-embeddings-a-cheaper-and-better-way-to-create-embeddings-6590868b438f):
|
||||
- **nomic-embed**: 81.2% accuracy, 2048 token context, 768d
|
||||
- **all-MiniLM-L6-v2**: 80.04% accuracy, blazing fast, 384d
|
||||
|
||||
**Fix Options:**
|
||||
|
||||
**Option A:** Recreate collection for 768d (nomic-embed-text)
|
||||
```bash
|
||||
# Drop existing collection
|
||||
curl -X DELETE http://qdrant:6333/collections/core_api_conversations
|
||||
|
||||
# Will auto-recreate with 768d on next memory operation
|
||||
```
|
||||
|
||||
**Option B:** Switch to 384d model (all-minilm)
|
||||
```python
|
||||
# config.py
|
||||
embedding_model: str = "all-minilm"
|
||||
embedding_dimension: int = 384
|
||||
```
|
||||
|
||||
**Recommendation:**
|
||||
- **For homelab with GPU:** Use nomic-embed-text (768d) - better accuracy, longer context
|
||||
- **For speed priority:** Use all-minilm (384d) - 6x faster
|
||||
|
||||
**Advanced Option:** [Matryoshka embeddings](https://www.nomic.ai/blog/posts/nomic-embed-matryoshka) - nomic-embed v1.5 supports variable dimensions (64-768), can truncate 768→384 with minimal accuracy loss
|
||||
|
||||
---
|
||||
|
||||
### Issue #3: Memory Retrieval Not Implemented
|
||||
|
||||
**Problem:**
|
||||
Agent doesn't load previous conversation context from memory.
|
||||
|
||||
**Current Behavior:**
|
||||
```python
|
||||
# ai_controller.py:312-318
|
||||
history = []
|
||||
for msg in request.messages[:-1]: # Uses request messages only
|
||||
history.append({"role": msg.role.value, "content": msg.content})
|
||||
|
||||
# ← Should load from memory manager here!
|
||||
agent = get_unified_agent()
|
||||
response = agent.chat(message=user_message, conversation_history=history)
|
||||
```
|
||||
|
||||
**Industry Pattern:**
|
||||
[Redis + LangGraph memory integration](https://redis.io/blog/langgraph-redis-build-smarter-ai-agents-with-memory-persistence/):
|
||||
1. Check if conversation_id exists in memory
|
||||
2. Retrieve recent turns from memory manager
|
||||
3. Include in conversation_history passed to LLM
|
||||
4. Fall back to request.messages if no memory
|
||||
|
||||
**Fix Required:**
|
||||
```python
|
||||
# Load from memory if conversation exists
|
||||
memory_manager = get_memory_manager()
|
||||
if await memory_manager.buffer_memory.conversation_exists(conversation_id):
|
||||
# Get recent turns from memory
|
||||
memory_turns = await memory_manager.get_recent_turns(conversation_id, limit=10)
|
||||
# Convert to history format
|
||||
history = [{"role": t.role.value, "content": t.content} for t in memory_turns]
|
||||
else:
|
||||
# Fall back to request messages
|
||||
history = [{"role": m.role.value, "content": m.content} for m in request.messages[:-1]]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🟡 Architecture Gaps (Recommended Improvements)
|
||||
|
||||
### Gap #1: LangGraph Checkpointing Not Used
|
||||
|
||||
**Current Approach:**
|
||||
Custom memory management with manual storage/retrieval.
|
||||
|
||||
**Industry Standard (2024):**
|
||||
[LangGraph native persistence](https://docs.langchain.com/oss/python/langgraph/persistence/) via checkpointers:
|
||||
- `langgraph-checkpoint-sqlite` - For local/dev
|
||||
- `langgraph-checkpoint-postgres` - For production (recommended)
|
||||
- `langgraph-checkpoint-redis` - For high-performance
|
||||
|
||||
**Benefits You're Missing:**
|
||||
- Thread-scoped state management
|
||||
- Automatic error recovery at any step
|
||||
- Human-in-the-loop intervention points
|
||||
- Time travel debugging
|
||||
- Cross-thread memory stores
|
||||
|
||||
**Example from Research:**
|
||||
[Mastering Persistence in LangGraph](https://medium.com/@vinodkrane/mastering-persistence-in-langgraph-checkpoints-threads-and-beyond-21e412aaed60):
|
||||
- Checkpoints save graph state at every super-step
|
||||
- Enables powerful capabilities: session memory, error recovery, fault tolerance
|
||||
- Thread-based conversation management
|
||||
|
||||
**Long-term Recommendation:**
|
||||
Consider migrating to LangGraph checkpointers for production. Your current system works but doesn't leverage the framework's full capabilities.
|
||||
|
||||
**Time Investment:** 4-6 hours (bigger refactor)
|
||||
|
||||
---
|
||||
|
||||
### Gap #2: Cross-Thread Memory Not Implemented
|
||||
|
||||
**Current Limitation:**
|
||||
Memory is conversation-scoped only. No learning across conversations.
|
||||
|
||||
**Industry Trend (2024/2025):**
|
||||
[Cross-thread memory stores](https://www.mongodb.com/company/blog/product-release-announcements/powering-long-term-memory-for-agents-langgraph):
|
||||
- Remember user preferences across all conversations
|
||||
- Learn from historical interactions
|
||||
- Extract and store user facts (name, preferences, context)
|
||||
|
||||
**Examples:**
|
||||
- MongoDB Store for LangGraph (cross-thread memory)
|
||||
- mem0 / Cognee (agentic memory systems)
|
||||
- Redis cross-thread capabilities
|
||||
|
||||
**Priority:** Low (advanced feature for future)
|
||||
|
||||
---
|
||||
|
||||
## 📊 Comparison: Implementation vs Industry Standards
|
||||
|
||||
| Feature | Your Status | Industry Standard | Alignment | Priority |
|
||||
|---------|-------------|------------------|-----------|----------|
|
||||
| Multi-tier memory (buffer + vector) | ✅ Implemented | ✅ Recommended | ⭐⭐⭐⭐⭐ Perfect | - |
|
||||
| Qdrant vector database | ✅ Configured | ✅ Recommended | ⭐⭐⭐⭐⭐ Perfect | - |
|
||||
| Semantic + chronological search | ✅ Implemented | ✅ Recommended | ⭐⭐⭐⭐⭐ Perfect | - |
|
||||
| Auto-consolidation (10 turns) | ✅ Implemented | ✅ Recommended | ⭐⭐⭐⭐⭐ Perfect | - |
|
||||
| **Memory storage in agent** | ❌ Bypassed | ✅ Required | 🔴 Critical Gap | **P1** |
|
||||
| **Embedding dimension match** | ❌ Mismatch | ✅ Required | 🔴 Critical Bug | **P1** |
|
||||
| **Memory retrieval in context** | ❌ Not implemented | ✅ Required | 🔴 Critical Gap | **P2** |
|
||||
| LangGraph checkpointing | ❌ Not used | 🟡 Recommended | 🟡 Optional | P3 |
|
||||
| Cross-thread memory | ❌ Not implemented | 🟡 Advanced | ⚪ Future | P4 |
|
||||
| Human-in-the-loop | ❌ Not implemented | 🟡 Advanced | ⚪ Future | P4 |
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Prioritized Action Plan
|
||||
|
||||
### **Priority 1: Critical Fixes** 🔴 (MUST DO - 2-3 hours)
|
||||
|
||||
#### Task 1.1: Integrate Memory Storage with Agent Path
|
||||
**Problem:** Memory storage unreachable
|
||||
**File:** `src/controllers/ai_controller.py:307-386`
|
||||
|
||||
**Changes Required:**
|
||||
1. Store user message BEFORE agent.chat() call
|
||||
2. Store assistant response AFTER agent returns
|
||||
3. Handle both streaming and non-streaming modes
|
||||
4. Move storage inside try block (lines 309-375)
|
||||
|
||||
**Code Pattern:**
|
||||
```python
|
||||
# Before agent call
|
||||
if request.store_in_memory:
|
||||
await store_conversation_turn(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=user_message
|
||||
)
|
||||
|
||||
# Agent executes
|
||||
response_text = await agent.chat_completion(...)
|
||||
|
||||
# After agent returns
|
||||
if request.store_in_memory:
|
||||
await store_conversation_turn(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=response_text,
|
||||
tokens={"prompt": ..., "completion": ..., "total": ...}
|
||||
)
|
||||
```
|
||||
|
||||
**Success Criteria:**
|
||||
- Qdrant collection `points_count` > 0 after API calls
|
||||
- Both user and assistant messages stored
|
||||
- No errors in logs
|
||||
|
||||
---
|
||||
|
||||
#### Task 1.2: Fix Embedding Dimension Mismatch
|
||||
**Problem:** Collection (384d) ≠ Config (768d)
|
||||
|
||||
**Decision Required:** Choose embedding model strategy
|
||||
|
||||
**Option A: Use nomic-embed-text (768d)** - Recommended for GPU homelab
|
||||
```bash
|
||||
# 1. Drop existing collection
|
||||
docker exec core-api curl -X DELETE http://qdrant:6333/collections/core_api_conversations
|
||||
|
||||
# 2. Collection will auto-recreate with 768d on next memory operation
|
||||
# 3. Verify in config.py:
|
||||
# embedding_model = "nomic-embed-text"
|
||||
# embedding_dimension = 768
|
||||
```
|
||||
|
||||
**Option B: Use all-minilm (384d)** - Faster, keep existing collection
|
||||
```python
|
||||
# config.py changes:
|
||||
embedding_model: str = "all-minilm" # Was: nomic-embed-text
|
||||
embedding_dimension: int = 384 # Was: 768
|
||||
```
|
||||
|
||||
**Success Criteria:**
|
||||
- Collection dimension matches config dimension
|
||||
- Embeddings generate successfully
|
||||
- No errors during consolidation
|
||||
|
||||
---
|
||||
|
||||
### **Priority 2: Memory Retrieval** 🟡 (SHOULD DO - 1-2 hours)
|
||||
|
||||
#### Task 2.1: Load Previous Conversation Context
|
||||
**Problem:** Agent doesn't retrieve past conversations from memory
|
||||
**File:** `src/controllers/ai_controller.py:312-318`
|
||||
|
||||
**Changes Required:**
|
||||
```python
|
||||
# Check if conversation exists in memory
|
||||
memory_manager = get_memory_manager()
|
||||
conversation_exists = await memory_manager.buffer_memory.conversation_exists(conversation_id)
|
||||
|
||||
if conversation_exists:
|
||||
# Load from memory
|
||||
memory_turns = await memory_manager.get_recent_turns(conversation_id, limit=10)
|
||||
history = [{"role": t.role.value, "content": t.content} for t in memory_turns]
|
||||
else:
|
||||
# Fall back to request messages
|
||||
history = []
|
||||
for msg in request.messages[:-1]:
|
||||
history.append({"role": msg.role.value, "content": msg.content})
|
||||
```
|
||||
|
||||
**Success Criteria:**
|
||||
- Multi-turn conversations maintain context
|
||||
- Agent recalls previous messages
|
||||
- New conversations start fresh (no memory loaded)
|
||||
|
||||
---
|
||||
|
||||
### **Priority 3: Architecture Enhancement** 🟡 (NICE TO HAVE - 4-6 hours)
|
||||
|
||||
#### Task 3.1: Migrate to LangGraph Checkpointers
|
||||
**Current:** Custom memory management
|
||||
**Industry Standard:** LangGraph native persistence
|
||||
|
||||
**Research Sources:**
|
||||
- [LangGraph persistence docs](https://docs.langchain.com/oss/python/langgraph/persistence/)
|
||||
- [Mastering persistence in LangGraph](https://medium.com/@vinodkrane/mastering-persistence-in-langgraph-checkpoints-threads-and-beyond-21e412aaed60)
|
||||
- [LangGraph v0.2 checkpointer libraries](https://blog.langchain.com/langgraph-v0-2/)
|
||||
|
||||
**Implementation:**
|
||||
1. Add `langgraph-checkpoint-postgres` to requirements
|
||||
2. Configure checkpointer in agent initialization
|
||||
3. Replace custom memory calls with checkpoint API
|
||||
4. Leverage thread-based conversation management
|
||||
|
||||
**Benefits:**
|
||||
- Native framework support
|
||||
- Error recovery at any step
|
||||
- Human-in-the-loop capabilities
|
||||
- Time travel debugging
|
||||
- Easier maintenance
|
||||
|
||||
**Decision:** Defer until current implementation is proven and stable
|
||||
|
||||
---
|
||||
|
||||
### **Priority 4: Advanced Features** ⚪ (FUTURE)
|
||||
|
||||
#### Task 4.1: Cross-Thread Memory
|
||||
**Purpose:** Remember user preferences across all conversations
|
||||
|
||||
**Research:**
|
||||
- [MongoDB cross-thread memory](https://www.mongodb.com/company/blog/product-release-announcements/powering-long-term-memory-for-agents-langgraph)
|
||||
- [Redis multi-conversation persistence](https://redis.io/blog/langgraph-redis-build-smarter-ai-agents-with-memory-persistence/)
|
||||
|
||||
**Defer:** Until core memory system proven in production
|
||||
|
||||
#### Task 4.2: Memory Summarization
|
||||
**Purpose:** Compress old conversations to reduce token usage
|
||||
|
||||
**Pattern:** Conversation Summary Buffer Memory ([LangChain docs](https://www.analyticsvidhya.com/blog/2024/11/langchain-memory/))
|
||||
|
||||
**Defer:** Until memory usage becomes a concern
|
||||
|
||||
#### Task 4.3: User Fact Extraction
|
||||
**Purpose:** Automatically extract and store user preferences, context, facts
|
||||
|
||||
**Tools:** mem0, Cognee (agentic memory systems)
|
||||
|
||||
**Defer:** Advanced feature for future iterations
|
||||
|
||||
---
|
||||
|
||||
## 🧪 Testing Strategy
|
||||
|
||||
### Phase 1: Unit Tests (After Fixes)
|
||||
```bash
|
||||
# Run existing test suite
|
||||
docker exec core-api python /app/tests/test_memory_simple.py
|
||||
|
||||
# Expected: All 3 tests pass
|
||||
# - Embedding Client: ✅
|
||||
# - Qdrant Memory: ✅
|
||||
# - Full Integration: ✅
|
||||
```
|
||||
|
||||
### Phase 2: Integration Tests (After P1)
|
||||
```bash
|
||||
# 1. Make API request
|
||||
curl -X POST http://localhost:8083/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "Tatlock",
|
||||
"messages": [{"role": "user", "content": "Hello, remember my name is John"}],
|
||||
"conversation_id": "test_123",
|
||||
"store_in_memory": true
|
||||
}'
|
||||
|
||||
# 2. Verify storage in Qdrant
|
||||
docker exec core-api curl -s http://qdrant:6333/collections/core_api_conversations
|
||||
|
||||
# Expected: points_count > 0
|
||||
|
||||
# 3. Test recall (send follow-up)
|
||||
curl -X POST http://localhost:8083/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "Tatlock",
|
||||
"messages": [{"role": "user", "content": "What is my name?"}],
|
||||
"conversation_id": "test_123",
|
||||
"store_in_memory": true
|
||||
}'
|
||||
|
||||
# Expected: Agent recalls "John"
|
||||
```
|
||||
|
||||
### Phase 3: Persistence Tests (After P2)
|
||||
```bash
|
||||
# 1. Create conversation
|
||||
# 2. Restart core-api container
|
||||
docker restart core-api
|
||||
|
||||
# 3. Send follow-up message with same conversation_id
|
||||
# Expected: Memory persists, agent recalls previous context
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Research Sources
|
||||
|
||||
### Memory Architecture
|
||||
- [Long Term Memory for LLMs using Vector Store](https://dev.to/einarcesar/long-term-memory-for-llms-using-vector-store-a-practical-approach-with-n8n-and-qdrant-2ha7)
|
||||
- [Build Persistent Chat Memory with Qdrant](https://n8n.io/workflows/6829-build-persistent-chat-memory-with-gpt-4o-mini-and-qdrant-vector-database/)
|
||||
- [Beyond Vector Databases: True Long-Term AI Memory](https://vardhmanandroid2015.medium.com/beyond-vector-databases-architectures-for-true-long-term-ai-memory-0d4629d1a006)
|
||||
- [Memory in Agents: Episodic vs Semantic](https://principia-agentica.io/blog/2025/09/19/memory-in-agents-episodic-vs-semantic-and-the-hybrid-that-works/)
|
||||
|
||||
### LangGraph Persistence
|
||||
- [Mastering Persistence in LangGraph](https://medium.com/@vinodkrane/mastering-persistence-in-langgraph-checkpoints-threads-and-beyond-21e412aaed60)
|
||||
- [LangGraph Persistence Docs](https://docs.langchain.com/oss/python/langgraph/persistence/)
|
||||
- [LangGraph v0.2 Checkpointer Libraries](https://blog.langchain.com/langgraph-v0-2/)
|
||||
- [Long-Term Agentic Memory With LangGraph](https://medium.com/@anil.jain.baba/long-term-agentic-memory-with-langgraph-824050b09852)
|
||||
- [Redis + LangGraph Memory Integration](https://redis.io/blog/langgraph-redis-build-smarter-ai-agents-with-memory-persistence/)
|
||||
- [MongoDB Cross-Thread Memory](https://www.mongodb.com/company/blog/product-release-announcements/powering-long-term-memory-for-agents-langgraph)
|
||||
|
||||
### RAG vs Memory
|
||||
- [RAG vs Memory for AI Agents](https://dev.to/bobur/rag-vs-memory-for-ai-agents-whats-the-difference-2ad0)
|
||||
- [The Evolution from RAG to Agent Memory](https://www.leoniemonigatti.com/blog/from-rag-to-agent-memory.html)
|
||||
- [Enhancing AI Conversations with LangChain Memory](https://www.analyticsvidhya.com/blog/2024/11/langchain-memory/)
|
||||
- [Memory and Hybrid Search in RAG](https://www.analyticsvidhya.com/blog/2024/09/memory-and-hybrid-search-in-rag-using-llamaindex/)
|
||||
- [ConvoMem Benchmark: First 150 Conversations](https://arxiv.org/html/2511.10523)
|
||||
|
||||
### Embedding Models
|
||||
- [Nomic Embeddings Guide](https://medium.com/@guptak650/nomic-embeddings-a-cheaper-and-better-way-to-create-embeddings-6590868b438f)
|
||||
- [Best Open-Source Embedding Models Benchmarked](https://supermemory.ai/blog/best-open-source-embedding-models-benchmarked-and-ranked/)
|
||||
- [Ollama Embedding Models Guide](https://docs.ollama.com/capabilities/embeddings/)
|
||||
- [Best Ollama Embedding Models for RAG](https://www.arsturn.com/blog/picking-the-perfect-partner-a-guide-to-choosing-the-best-embedding-models-in-ollama)
|
||||
- [Nomic Embed Matryoshka (Variable Dimensions)](https://www.nomic.ai/blog/posts/nomic-embed-matryoshka)
|
||||
|
||||
### Qdrant Best Practices
|
||||
- [Building Agentic Vector Search with Qdrant](https://qdrant.tech/articles/agentic-builders-guide/)
|
||||
- [Qdrant Official Documentation](https://qdrant.tech/documentation/)
|
||||
- [Qdrant Storage Concepts](https://qdrant.tech/documentation/concepts/storage/)
|
||||
|
||||
---
|
||||
|
||||
## Timeline Estimate
|
||||
|
||||
**Priority 1 (Critical Fixes):** 2-3 hours
|
||||
- Task 1.1: Memory storage integration (1.5 hours)
|
||||
- Task 1.2: Dimension fix (30 minutes)
|
||||
- Testing (30 minutes)
|
||||
|
||||
**Priority 2 (Memory Retrieval):** 1-2 hours
|
||||
- Task 2.1: Context loading (1 hour)
|
||||
- Testing (30 minutes)
|
||||
|
||||
**Priority 3 (LangGraph Migration):** 4-6 hours
|
||||
- Research and planning (1 hour)
|
||||
- Implementation (3-4 hours)
|
||||
- Testing (1 hour)
|
||||
|
||||
**Total to production-ready:** 3-5 hours (P1 + P2)
|
||||
**Total with architecture upgrade:** 7-11 hours (P1 + P2 + P3)
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Phase 1 Complete (P1 Fixed):
|
||||
- ✅ Qdrant `points_count` > 0 after conversations
|
||||
- ✅ Both user and assistant messages stored
|
||||
- ✅ No memory-related errors in logs
|
||||
- ✅ Embeddings match collection dimension
|
||||
|
||||
### Phase 2 Complete (P2 Fixed):
|
||||
- ✅ Agent recalls previous conversation context
|
||||
- ✅ Multi-turn conversations work correctly
|
||||
- ✅ Memory persists across container restarts
|
||||
- ✅ New conversations start with empty context
|
||||
|
||||
### Production Ready:
|
||||
- ✅ All integration tests pass
|
||||
- ✅ Memory consolidation triggers correctly
|
||||
- ✅ Semantic search returns relevant results
|
||||
- ✅ Performance meets targets (< 50ms retrieval)
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
**Architecture: Excellent (8.5/10)** ⭐⭐⭐⭐
|
||||
**Implementation: Incomplete (requires fixes)** 🔴
|
||||
|
||||
Your design follows **current industry best practices** for 2024/2025:
|
||||
- ✅ Multi-tier memory (buffer + vector)
|
||||
- ✅ Hybrid search (episodic + semantic)
|
||||
- ✅ Qdrant for production-grade vector storage
|
||||
- ✅ Auto-consolidation and pruning
|
||||
|
||||
The issues are **implementation bugs** (storage bypassed, dimension mismatch) and **missing integration** (memory retrieval), NOT architectural flaws.
|
||||
|
||||
**Recommendation:** Complete Priority 1 and 2 fixes (3-5 hours total) to have a production-ready memory system that matches industry standards.
|
||||
|
||||
---
|
||||
|
||||
**Next Steps:** Review this document with stakeholders, then proceed with Priority 1 fixes.
|
||||
@@ -1,322 +0,0 @@
|
||||
# Phase 3: Multi-Agent Workflows - COMPLETE ✅
|
||||
|
||||
**Completion Date**: 2025-11-24
|
||||
**Status**: ✅ All Success Criteria Met
|
||||
**Duration**: 1 day (as planned)
|
||||
|
||||
## Summary
|
||||
|
||||
Successfully implemented Phase 3 research capabilities using the "extend current unified agent" approach (Option A). The agent can now detect research queries, search the web using DuckDuckGo, scrape content from results, and synthesize information with source citations.
|
||||
|
||||
## Implementation Approach
|
||||
|
||||
**Chosen Strategy**: Option A - Extend Current Unified Agent
|
||||
**Rationale**: Builds on working foundation, minimal disruption, reuses existing infrastructure
|
||||
|
||||
## What Was Implemented
|
||||
|
||||
### 1. Web Search Tool with DuckDuckGo ✅
|
||||
**File**: [`services/core-api/src/agent/tools.py`](../../services/core-api/src/agent/tools.py#L154-L221)
|
||||
|
||||
```python
|
||||
@tool
|
||||
async def web_search(query: str, num_results: int = 3) -> str:
|
||||
"""Search the web using DuckDuckGo and extract content from top results"""
|
||||
# - Searches DuckDuckGo for query
|
||||
# - Scrapes content from each result (first 500 chars)
|
||||
# - Falls back to snippet if scraping fails
|
||||
# - Returns formatted results with titles, URLs, and content
|
||||
```
|
||||
|
||||
**Key Features**:
|
||||
- DuckDuckGo integration (`duckduckgo-search~=4.1.0`)
|
||||
- Automatic content extraction using existing `WebScraperService`
|
||||
- Fallback to search snippets if scraping fails
|
||||
- Formatted output with source URLs for LLM synthesis
|
||||
|
||||
### 2. Separate Web Scrape Tool ✅
|
||||
**File**: [`services/core-api/src/agent/tools.py`](../../services/core-api/src/agent/tools.py#L224-L256)
|
||||
|
||||
```python
|
||||
@tool
|
||||
async def web_scrape(url: str) -> str:
|
||||
"""Fetch and extract content from a specific web page"""
|
||||
# - For follow-up deep reads of specific URLs
|
||||
# - Returns up to 4000 chars of content
|
||||
```
|
||||
|
||||
### 3. Research Detection in System Prompt ✅
|
||||
**File**: [`services/core-api/src/agent/orchestrator.py`](../../services/core-api/src/agent/orchestrator.py#L75-L94)
|
||||
|
||||
Added comprehensive research mode instructions:
|
||||
```
|
||||
Research Mode - Web Search:
|
||||
When the user asks for current information, recent news, or topics requiring web research:
|
||||
1. Use the web_search tool to find relevant sources
|
||||
2. The tool will automatically search DuckDuckGo and extract content from top results
|
||||
3. Synthesize information from multiple sources in your response
|
||||
4. Always cite the URLs of your sources
|
||||
|
||||
Examples of research queries:
|
||||
- "What's the latest news about [topic]?"
|
||||
- "Research [topic] for me"
|
||||
- "Find information about [topic]"
|
||||
- "What are people saying about [topic]?"
|
||||
- "Look up [topic]"
|
||||
```
|
||||
|
||||
### 4. Enhanced Progress Indicators ✅
|
||||
**File**: [`services/core-api/src/agent/streaming.py`](../../services/core-api/src/agent/streaming.py#L57-L89)
|
||||
|
||||
Added specialized icons for different tool types:
|
||||
```python
|
||||
tool_icons = {
|
||||
"web_search": "🔍 Searching web",
|
||||
"web_scrape": "📄 Reading page",
|
||||
"list_services": "🔧 Listing services",
|
||||
# ... more tools
|
||||
}
|
||||
```
|
||||
|
||||
**User Experience**:
|
||||
- Clear visual feedback during research
|
||||
- Different icons for different operations
|
||||
- No flooding with too many updates
|
||||
|
||||
### 5. Dependency Management Improvements ✅
|
||||
|
||||
**Changed to Major Version Pinning**:
|
||||
```python
|
||||
# Before: fastapi==0.115.0
|
||||
# After: fastapi~=0.115.0
|
||||
```
|
||||
|
||||
**Automated Installation on Boot**:
|
||||
- Container now runs `pip install -r requirements.txt` on every restart
|
||||
- No need to rebuild images for dependency changes
|
||||
- Documented in [README.md](../../services/core-api/README.md#L47-L67)
|
||||
|
||||
## Test Results
|
||||
|
||||
**Test Script**: [`/tmp/test_phase3_research.py`](/tmp/test_phase3_research.py)
|
||||
|
||||
### Automated Test Results ✅
|
||||
|
||||
```
|
||||
Total Tests: 6
|
||||
Passed: 6 ✅
|
||||
Failed: 0 ❌
|
||||
Success Rate: 100.0%
|
||||
|
||||
Test Cases:
|
||||
✅ Latest AI News 4.3s (used web_search)
|
||||
✅ Framework Comparison 4.7s (used web_search)
|
||||
✅ Model Information 7.2s (used web_search)
|
||||
✅ Product Research 4.7s (used web_search)
|
||||
✅ Technical Lookup 5.8s (used web_search)
|
||||
✅ Simple Chat (Control) 0.3s (no tool)
|
||||
```
|
||||
|
||||
### Success Criteria Validation ✅
|
||||
|
||||
| Criterion | Target | Actual | Status |
|
||||
|-----------|--------|--------|--------|
|
||||
| Research Detection Accuracy | >80% | 100% | ✅ |
|
||||
| Average Response Time | <10s | 5.3s | ✅ |
|
||||
| Source Citation Rate | >90% | 100% | ✅ |
|
||||
|
||||
**All Phase 3 criteria met!**
|
||||
|
||||
## Example Research Workflow
|
||||
|
||||
**User Query**: "What's the latest news about AI?"
|
||||
|
||||
**Agent Behavior**:
|
||||
1. 💭 Detects research query from system prompt instructions
|
||||
2. 🔍 Calls `web_search("latest news AI")`
|
||||
3. 📄 Tool scrapes 3 search results from DuckDuckGo
|
||||
4. 🧠 Agent synthesizes information from results
|
||||
5. ✅ Returns response with source URLs cited
|
||||
|
||||
**Response Sample**:
|
||||
```
|
||||
As your humble servant, I have taken the liberty of conducting a brief
|
||||
search on the latest developments in Artificial Intelligence. Here are
|
||||
some of the headlines that caught my eye:
|
||||
|
||||
1. "Google Brain Unveils New AI Model Capable of Understanding Context"
|
||||
Link: https://www.extremetech.com/artificial-intelligence/...
|
||||
|
||||
2. "Microsoft Announces Breakthrough in AI Ethics with New Guidelines"
|
||||
Link: https://www.forbes.com/sites/bernardmarr/...
|
||||
|
||||
[Full synthesis of information from sources]
|
||||
```
|
||||
|
||||
## Architecture Changes
|
||||
|
||||
### Before Phase 3:
|
||||
```
|
||||
User → Core API → Unified Agent (mistral:7b)
|
||||
↓
|
||||
Infrastructure Tools
|
||||
(list_services, get_service_details, etc.)
|
||||
```
|
||||
|
||||
### After Phase 3:
|
||||
```
|
||||
User → Core API → Unified Agent (mistral:7b)
|
||||
↓
|
||||
┌─────────┴──────────┐
|
||||
▼ ▼
|
||||
Infrastructure Tools Research Tools
|
||||
(7 tools) (web_search, web_scrape)
|
||||
│ │
|
||||
▼ ▼
|
||||
Portainer/NPM/Kuma DuckDuckGo + Scraper
|
||||
```
|
||||
|
||||
## Files Modified
|
||||
|
||||
### Core Implementation:
|
||||
1. [`services/core-api/requirements.txt`](../../services/core-api/requirements.txt) - Added duckduckgo-search, changed to `~=` pinning
|
||||
2. [`services/core-api/src/agent/tools.py`](../../services/core-api/src/agent/tools.py) - Added web_search and web_scrape tools
|
||||
3. [`services/core-api/src/agent/orchestrator.py`](../../services/core-api/src/agent/orchestrator.py) - Enhanced system prompt with research detection
|
||||
4. [`services/core-api/src/agent/streaming.py`](../../services/core-api/src/agent/streaming.py) - Added enhanced progress indicators
|
||||
|
||||
### Infrastructure:
|
||||
5. [`stacks/core-api.yml`](../../stacks/core-api.yml) - Updated startup command to always run pip install
|
||||
|
||||
### Documentation:
|
||||
6. [`services/core-api/README.md`](../../services/core-api/README.md) - Added dependency management documentation
|
||||
7. [`plans/active/phase3-multi-agent-workflows.md`](../active/phase3-multi-agent-workflows.md) - Implementation plan
|
||||
8. This completion document
|
||||
|
||||
## Dependencies Added
|
||||
|
||||
```txt
|
||||
duckduckgo-search~=4.1.0 # Web search integration
|
||||
└─ curl-cffi~=0.13.0 # Auto-installed dependency
|
||||
```
|
||||
|
||||
## Technical Details
|
||||
|
||||
### Why DuckDuckGo?
|
||||
- No API key required
|
||||
- No rate limiting for reasonable use
|
||||
- Good quality results
|
||||
- Privacy-focused (no tracking)
|
||||
|
||||
### Content Extraction Strategy
|
||||
1. **Primary**: Use existing `WebScraperService` with Trafilatura
|
||||
2. **Fallback**: Use DuckDuckGo snippet if scraping fails
|
||||
3. **Limit**: First 500 chars per result to manage context window
|
||||
|
||||
### Tool Count
|
||||
- Total tools available: **8 tools** (was 7, added 1)
|
||||
- Infrastructure: 5 tools
|
||||
- Knowledge: 3 tools (web_search, web_scrape, read_documentation)
|
||||
- System: 1 tool (get_system_status)
|
||||
|
||||
## What Was NOT Implemented (Deferred)
|
||||
|
||||
As per Phase 3 plan, these were explicitly deferred to Phase 4+:
|
||||
|
||||
❌ Code specialist agent (codestral)
|
||||
❌ Tool executor agent (separate from router)
|
||||
❌ Model switching based on complexity
|
||||
❌ Supervisor pattern for agent coordination
|
||||
❌ Research history metadata in memory (decided to defer)
|
||||
|
||||
**Rationale**: Keep Phase 3 focused on core research capability. Multi-agent patterns and memory enhancements can be added incrementally in later phases.
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Response Times:
|
||||
- Simple chat: 0.3s (no tool usage)
|
||||
- Research queries: 4-7s average
|
||||
- Peak: 7.2s (still well under 10s target)
|
||||
|
||||
### VRAM Usage:
|
||||
- Unchanged from Phase 2
|
||||
- mistral:7b orchestrator: 5.1GB
|
||||
- No additional models loaded
|
||||
|
||||
### Reliability:
|
||||
- 100% tool calling success rate in tests
|
||||
- Graceful fallback to snippets if scraping fails
|
||||
- No breaking of existing functionality
|
||||
|
||||
## Phase 3 Completion Checklist
|
||||
|
||||
✅ Web search tool integrated (DuckDuckGo)
|
||||
✅ Agent detects research queries automatically
|
||||
✅ Multi-step research workflows work (search → scrape → synthesize)
|
||||
✅ Progress indicators show during research
|
||||
✅ Research results cite sources
|
||||
✅ All automated tests pass
|
||||
✅ Documentation updated
|
||||
✅ Dependency management improved
|
||||
|
||||
**Phase 3 Status**: ✅ **COMPLETE**
|
||||
|
||||
## User Validation
|
||||
|
||||
**Manual Testing Required**:
|
||||
1. Open Open WebUI
|
||||
2. Start chat with Tatlock model
|
||||
3. Try research queries:
|
||||
- "What's the latest news about AI?"
|
||||
- "Research LangGraph framework for me"
|
||||
- "Find information about Qdrant"
|
||||
4. Verify:
|
||||
- 🔍 Progress indicator appears
|
||||
- URLs are cited in response
|
||||
- Information is synthesized (not just pasted)
|
||||
- Response formatting is clean
|
||||
|
||||
## Next Phase Preview
|
||||
|
||||
**Phase 4 Candidates**:
|
||||
|
||||
### Option A: Enhanced Tool Integration
|
||||
- Infrastructure tools (restart services, check logs)
|
||||
- File operations (Nextcloud integration)
|
||||
- Calendar management (CalDAV)
|
||||
|
||||
### Option B: Code Agent Specialist
|
||||
- Add codestral:22b as code expert
|
||||
- Route programming questions to codestral
|
||||
- Keep mistral:7b for orchestration
|
||||
|
||||
### Option C: Memory System Enhancements
|
||||
- Add research metadata tagging
|
||||
- Implement conversation summarization
|
||||
- Improve context retrieval
|
||||
|
||||
### Option D: Multi-Agent Patterns
|
||||
- Implement proper agent routing
|
||||
- Add specialist agents for different domains
|
||||
- Supervisor pattern for coordination
|
||||
|
||||
**Recommendation**: Discuss with user which Phase 4 direction is most valuable.
|
||||
|
||||
---
|
||||
|
||||
## Lessons Learned
|
||||
|
||||
1. **Extend vs Rewrite**: Option A (extend) was the right choice - minimal risk, fast implementation
|
||||
2. **Dependency Management**: Major version pinning (`~=`) + auto-install on boot is much better than manual rebuilds
|
||||
3. **Test First**: Having clear success criteria and automated tests made validation straightforward
|
||||
4. **Progressive Enhancement**: Adding capabilities to working system is lower risk than big rewrites
|
||||
|
||||
## Statistics
|
||||
|
||||
- **Planning**: 1 hour (Phase 3 plan document)
|
||||
- **Implementation**: 2 hours (code + testing)
|
||||
- **Documentation**: 30 minutes
|
||||
- **Total**: ~3.5 hours (well under 1 week estimate)
|
||||
|
||||
---
|
||||
|
||||
**Phase 3 Complete**: Multi-agent research workflows successfully implemented and validated ✅
|
||||
@@ -1,434 +0,0 @@
|
||||
# Phase 3: Multi-Agent Workflows Implementation Plan
|
||||
|
||||
**Date**: 2025-11-24
|
||||
**Status**: 🎯 Ready to Start
|
||||
**Duration**: 1 week
|
||||
**Prerequisites**: ✅ Phase 1 Complete, ✅ Phase 2 Complete
|
||||
|
||||
## Overview
|
||||
|
||||
Implement LangGraph-based multi-agent system with intelligent routing. The current unified agent (mistral:7b) will become the orchestrator/router, delegating to specialist agents for complex tasks.
|
||||
|
||||
## Current State
|
||||
|
||||
**What We Have** ✅:
|
||||
- Unified agent with tool calling (mistral:7b)
|
||||
- Basic orchestration (LangGraph ReAct agent)
|
||||
- 7 working tools (list_services, get_service_details, list_domains, etc.)
|
||||
- Streaming responses with proper formatting
|
||||
- Memory system (Tier 1-3 with Qdrant)
|
||||
- OpenAI-compatible API
|
||||
|
||||
**Current Architecture**:
|
||||
```
|
||||
User → Core API → Unified Agent (mistral:7b) → Tools
|
||||
↓
|
||||
Memory (Buffer + Qdrant)
|
||||
```
|
||||
|
||||
## Target Architecture
|
||||
|
||||
```
|
||||
User → Core API → Router Agent (mistral:7b)
|
||||
↓
|
||||
┌─────────┴──────────┐
|
||||
▼ ▼
|
||||
Chat Agent Research Agent
|
||||
(mistral:7b) (mistral:7b + tools)
|
||||
│ │
|
||||
▼ ▼
|
||||
Memory System Web Search/Scraping
|
||||
```
|
||||
|
||||
**Future Expansion** (Phase 4+):
|
||||
```
|
||||
Router Agent
|
||||
├── Chat Agent (general conversation)
|
||||
├── Research Agent (web search + synthesis)
|
||||
├── Code Agent (codestral for programming)
|
||||
└── Tool Agent (infrastructure actions)
|
||||
```
|
||||
|
||||
## Implementation Strategy
|
||||
|
||||
### Option A: Extend Current Unified Agent (Recommended)
|
||||
|
||||
**Pros**:
|
||||
- ✅ Builds on working foundation
|
||||
- ✅ Minimal disruption
|
||||
- ✅ Can migrate gradually
|
||||
- ✅ Reuses existing streaming, memory, tools
|
||||
|
||||
**Cons**:
|
||||
- ⚠️ Slightly less separation than multi-agent
|
||||
- ⚠️ All in one orchestrator file
|
||||
|
||||
**Approach**: Add routing logic to existing unified agent to detect complex tasks and create sub-workflows.
|
||||
|
||||
### Option B: Full LangGraph Multi-Agent Rewrite
|
||||
|
||||
**Pros**:
|
||||
- ✅ Clean separation of agents
|
||||
- ✅ True multi-agent pattern
|
||||
- ✅ Easier to add new agents later
|
||||
|
||||
**Cons**:
|
||||
- ❌ Major rewrite
|
||||
- ❌ Risk breaking existing functionality
|
||||
- ❌ Complex state management
|
||||
- ❌ Harder to debug
|
||||
|
||||
**Approach**: Create separate agent modules, supervisor pattern, state graph.
|
||||
|
||||
**Decision**: **Use Option A** - Extend current unified agent with routing intelligence.
|
||||
|
||||
## Phase 3 Goals
|
||||
|
||||
### Core Goals
|
||||
1. **Intelligent Task Detection**: Automatically identify when a task needs research vs simple chat
|
||||
2. **Research Workflow**: Multi-step web search → scraping → synthesis for complex queries
|
||||
3. **Proper Context Passing**: Pass memory context to sub-workflows
|
||||
4. **Streaming Updates**: Show progress during multi-step research
|
||||
|
||||
### Non-Goals (Deferred to Phase 4)
|
||||
- ❌ Code specialist agent (codestral)
|
||||
- ❌ Tool executor agent (separate from router)
|
||||
- ❌ Model switching based on complexity
|
||||
- ❌ Supervisor pattern for agent coordination
|
||||
|
||||
## Implementation Tasks
|
||||
|
||||
### Task 1: Add Research Detection
|
||||
|
||||
**File**: `services/core-api/src/agent/orchestrator.py`
|
||||
|
||||
**Changes**:
|
||||
- Add system prompt instructions for research detection
|
||||
- Detect keywords: "research", "find information about", "look up", "what's the latest"
|
||||
- Detect follow-up tool usage patterns (web_search → web_scrape)
|
||||
|
||||
**Pseudo-code**:
|
||||
```python
|
||||
SYSTEM_PROMPT = """
|
||||
...existing prompt...
|
||||
|
||||
## Research Mode
|
||||
When user asks for current information, recent news, or complex topics requiring web search:
|
||||
1. Use web_search tool to find relevant sources
|
||||
2. Use web_scrape tool (via web_search) to extract content
|
||||
3. Synthesize information from multiple sources
|
||||
4. Cite sources in your response
|
||||
|
||||
Examples of research queries:
|
||||
- "What's the latest on [topic]?"
|
||||
- "Research [topic] for me"
|
||||
- "Find information about [topic]"
|
||||
- "What are people saying about [topic]?"
|
||||
"""
|
||||
```
|
||||
|
||||
**Success Criteria**:
|
||||
- Agent detects research queries correctly (>80% accuracy)
|
||||
- Automatically triggers web_search when needed
|
||||
- Follows up with synthesis
|
||||
|
||||
### Task 2: Improve Web Search Tool
|
||||
|
||||
**File**: `services/core-api/src/agent/tools.py`
|
||||
|
||||
**Current State**: We have `web_search` tool that fetches and extracts content from a URL.
|
||||
|
||||
**Enhancements Needed**:
|
||||
1. Add actual search capability (DuckDuckGo API)
|
||||
2. Return multiple results (not just one URL)
|
||||
3. Add trafilatura for better content extraction
|
||||
|
||||
**New Implementation**:
|
||||
```python
|
||||
@tool
|
||||
async def web_search(query: str, num_results: int = 3) -> str:
|
||||
"""
|
||||
Search the web using DuckDuckGo and extract content from top results.
|
||||
|
||||
Args:
|
||||
query: Search query
|
||||
num_results: Number of results to return (default 3)
|
||||
|
||||
Returns:
|
||||
Formatted results with titles, URLs, and content summaries
|
||||
"""
|
||||
from duckduckgo_search import DDGS
|
||||
|
||||
results = []
|
||||
with DDGS() as ddgs:
|
||||
search_results = list(ddgs.text(query, max_results=num_results))
|
||||
|
||||
for result in search_results:
|
||||
# Scrape each result
|
||||
content = await scrape_url(result['href'])
|
||||
results.append({
|
||||
'title': result['title'],
|
||||
'url': result['href'],
|
||||
'snippet': result['body'],
|
||||
'content': content[:500] # First 500 chars
|
||||
})
|
||||
|
||||
return format_search_results(results)
|
||||
```
|
||||
|
||||
**Dependencies**: Add to `requirements.txt`:
|
||||
```
|
||||
duckduckgo-search==4.1.1
|
||||
```
|
||||
|
||||
**Success Criteria**:
|
||||
- Returns 3+ search results
|
||||
- Each result has title, URL, snippet
|
||||
- Content extraction works for most sites
|
||||
|
||||
### Task 3: Add Research Workflow Pattern
|
||||
|
||||
**File**: `services/core-api/src/agent/orchestrator.py`
|
||||
|
||||
**Pattern**: Multi-step tool usage
|
||||
```
|
||||
1. User: "Research AI agent frameworks"
|
||||
2. Agent: [Thinking] This needs research...
|
||||
3. Agent: [Tool Call] web_search("AI agent frameworks 2025")
|
||||
4. Tool: Returns 3 results with content
|
||||
5. Agent: [Synthesizing] Based on search results...
|
||||
6. Agent: [Response] Here's what I found: ...
|
||||
```
|
||||
|
||||
**Implementation**: Already handled by LangGraph ReAct agent! Just need better tools.
|
||||
|
||||
**Success Criteria**:
|
||||
- Agent chains tool calls naturally
|
||||
- Synthesizes information from multiple sources
|
||||
- Cites sources in response
|
||||
|
||||
### Task 4: Add Progress Indicators for Research
|
||||
|
||||
**File**: `services/core-api/src/agent/streaming.py`
|
||||
|
||||
**Enhancement**: Add more granular status updates
|
||||
|
||||
**Current**:
|
||||
```python
|
||||
"[🔧 Using web_search...]"
|
||||
```
|
||||
|
||||
**Enhanced**:
|
||||
```python
|
||||
"[🔍 Searching web for: {query}...]"
|
||||
"[📄 Reading result 1/3...]"
|
||||
"[📄 Reading result 2/3...]"
|
||||
"[🧠 Synthesizing information...]"
|
||||
"[✓ Research complete]"
|
||||
```
|
||||
|
||||
**Implementation**: Enhance tool_call streaming messages
|
||||
|
||||
**Success Criteria**:
|
||||
- User sees progress during research
|
||||
- Clear indication of what's happening
|
||||
- Doesn't spam with too many updates
|
||||
|
||||
### Task 5: Test Research Workflows
|
||||
|
||||
**Test Queries**:
|
||||
1. "What's the latest news about AI?"
|
||||
2. "Research LangGraph vs CrewAI"
|
||||
3. "Find information about Mistral AI models"
|
||||
4. "What are people saying about Open WebUI?"
|
||||
5. "Look up Qdrant vector database features"
|
||||
|
||||
**Success Criteria**:
|
||||
- Agent uses web_search automatically
|
||||
- Returns multi-source synthesis
|
||||
- Cites URLs in response
|
||||
- Completes in <10 seconds
|
||||
|
||||
### Task 6: Add Research History to Memory
|
||||
|
||||
**File**: `services/core-api/src/memory/manager.py`
|
||||
|
||||
**Enhancement**: Tag research results in memory
|
||||
|
||||
**Schema Addition**:
|
||||
```python
|
||||
metadata = {
|
||||
"type": "research",
|
||||
"sources": ["url1", "url2", "url3"],
|
||||
"query": "original search query"
|
||||
}
|
||||
```
|
||||
|
||||
**Success Criteria**:
|
||||
- Research results stored in memory
|
||||
- Can recall previous research
|
||||
- Sources preserved for future reference
|
||||
|
||||
## Testing Plan
|
||||
|
||||
### Unit Tests
|
||||
```python
|
||||
# Test research detection
|
||||
def test_research_detection():
|
||||
queries = [
|
||||
("What's the weather?", False), # Not research
|
||||
("Research AI frameworks", True), # Is research
|
||||
("Find info about Kubernetes", True), # Is research
|
||||
]
|
||||
for query, expected in queries:
|
||||
assert is_research_query(query) == expected
|
||||
|
||||
# Test web search tool
|
||||
@pytest.mark.asyncio
|
||||
async def test_web_search():
|
||||
results = await web_search("LangGraph")
|
||||
assert len(results) >= 1
|
||||
assert "url" in results[0]
|
||||
assert "content" in results[0]
|
||||
```
|
||||
|
||||
### Integration Tests
|
||||
```python
|
||||
# Test research workflow
|
||||
@pytest.mark.asyncio
|
||||
async def test_research_workflow():
|
||||
agent = get_unified_agent()
|
||||
response = await agent.chat(
|
||||
"Research LangGraph for me",
|
||||
stream=False
|
||||
)
|
||||
|
||||
# Should have used web_search
|
||||
# Should have synthesized results
|
||||
# Should cite sources
|
||||
assert "http" in response # Has URLs
|
||||
assert len(response) > 200 # Detailed response
|
||||
```
|
||||
|
||||
### Manual Tests
|
||||
1. Ask research query in Open WebUI
|
||||
2. Verify agent searches web
|
||||
3. Verify progress indicators appear
|
||||
4. Verify synthesized response with sources
|
||||
5. Verify research saved to memory
|
||||
|
||||
## Dependencies
|
||||
|
||||
**New packages** needed:
|
||||
```
|
||||
# requirements.txt additions
|
||||
duckduckgo-search==4.1.1 # Web search
|
||||
```
|
||||
|
||||
**Existing packages** (already installed):
|
||||
```
|
||||
httpx==0.28.1 # HTTP client
|
||||
beautifulsoup4==4.12.3 # HTML parsing
|
||||
trafilatura==1.12.2 # Content extraction
|
||||
```
|
||||
|
||||
## Migration Plan
|
||||
|
||||
### Step 1: Add Dependencies
|
||||
```bash
|
||||
# Add to requirements.txt
|
||||
echo "duckduckgo-search==4.1.1" >> services/core-api/requirements.txt
|
||||
|
||||
# Rebuild container
|
||||
docker-compose -f stacks/core-api.yml build
|
||||
docker-compose -f stacks/core-api.yml up -d
|
||||
```
|
||||
|
||||
### Step 2: Implement Web Search Tool
|
||||
- Update `tools.py` with DuckDuckGo integration
|
||||
- Test independently
|
||||
- Add to agent's tool list (already automatic)
|
||||
|
||||
### Step 3: Update System Prompt
|
||||
- Add research detection instructions
|
||||
- Test with various queries
|
||||
- Tune detection accuracy
|
||||
|
||||
### Step 4: Enhance Streaming
|
||||
- Add research progress indicators
|
||||
- Test in Open WebUI
|
||||
- Ensure doesn't break existing functionality
|
||||
|
||||
### Step 5: Integration Testing
|
||||
- Test research workflows end-to-end
|
||||
- Verify memory storage
|
||||
- Verify source citations
|
||||
|
||||
### Step 6: User Acceptance
|
||||
- Ask user to test in Open WebUI
|
||||
- Gather feedback
|
||||
- Iterate on improvements
|
||||
|
||||
## Success Metrics
|
||||
|
||||
### Quantitative
|
||||
- **Research Detection Accuracy**: >80% (detects research queries correctly)
|
||||
- **Tool Chain Success**: >90% (completes multi-step research)
|
||||
- **Response Time**: <10s (average research query)
|
||||
- **Source Citations**: >90% (includes URLs in response)
|
||||
|
||||
### Qualitative
|
||||
- User feels agent is more capable
|
||||
- Research responses are comprehensive
|
||||
- Sources are relevant and recent
|
||||
- Progress indicators are helpful
|
||||
|
||||
## Risks & Mitigation
|
||||
|
||||
### Risk 1: Web Search Too Slow
|
||||
**Impact**: User experience degraded
|
||||
**Mitigation**:
|
||||
- Limit to 3 results max
|
||||
- Run scraping in parallel
|
||||
- Add timeout (10s)
|
||||
- Show progress to user
|
||||
|
||||
### Risk 2: Search Results Low Quality
|
||||
**Impact**: Agent gives poor answers
|
||||
**Mitigation**:
|
||||
- Use multiple search engines if needed
|
||||
- Implement result filtering
|
||||
- Let agent decide relevance
|
||||
- Allow user to refine query
|
||||
|
||||
### Risk 3: Breaking Existing Functionality
|
||||
**Impact**: Simple chat stops working
|
||||
**Mitigation**:
|
||||
- Test simple queries extensively
|
||||
- Keep research optional (agent decides)
|
||||
- Easy rollback (git revert)
|
||||
- Gradual deployment
|
||||
|
||||
## Phase 3 Completion Criteria
|
||||
|
||||
✅ **Phase 3 Complete** when:
|
||||
1. Web search tool integrated (DuckDuckGo)
|
||||
2. Agent detects research queries automatically
|
||||
3. Multi-step research workflows work
|
||||
4. Progress indicators show during research
|
||||
5. Research results cite sources
|
||||
6. Research stored in memory with metadata
|
||||
7. All tests pass
|
||||
8. User validates in Open WebUI
|
||||
|
||||
## Next Phase Preview
|
||||
|
||||
**Phase 4: Enhanced Tool Integration**
|
||||
- Infrastructure tools (restart services, check logs)
|
||||
- File operations (Nextcloud integration)
|
||||
- Calendar management (CalDAV)
|
||||
- Code agent (codestral specialist)
|
||||
|
||||
---
|
||||
|
||||
**Ready to start?** This phase should take ~1 week and builds directly on the working Phase 1+2 foundation.
|
||||
Reference in New Issue
Block a user