Working chat with AI agent with retrieving data

services/rag/langchain/QWEN.md

@@ -75,9 +75,9 @@ The project is organized into 6 development phases as outlined in `PLANNING.md`:
 - [x] Implement metadata retrieval (filename, page, section, etc.)
 
 ### Phase 6: Chat Agent
-- [ ] Create `agent.py` with Ollama-powered chat agent
-- [ ] Integrate with retrieval functionality
-- [ ] Add CLI command for chat interaction
+- [x] Create `agent.py` with Ollama-powered chat agent
+- [x] Integrate with retrieval functionality
+- [x] Add CLI command for chat interaction
 
 ## Environment Configuration
 
@@ -169,6 +169,18 @@ The project is in early development phase. The virtual environment is set up and
 - Retrieval returns documents with metadata including source, filename, page number, file extension, etc.
 - Used QdrantVectorStore from langchain-qdrant package for compatibility with newer LangChain versions
 
+### Phase 6 Implementation Notes
+- Created `agent.py` module with Ollama-powered chat agent using LangGraph
+- Integrated the agent with retrieval functionality to provide context-aware responses
+- Added CLI command `chat` for interactive conversation with the RAG agent
+- Agent uses document retrieval tool to fetch relevant information based on user queries
+- Implemented proper error handling and conversation history management
+
+### Issue Fix Notes
+- Fixed DocumentRetrievalTool class to properly declare and initialize the retriever field
+- Resolved Pydantic field declaration issue that caused "object has no field" error
+- Ensured proper initialization sequence for the retriever within the tool class
+
 ### Troubleshooting Notes
 - If encountering "No module named 'unstructured_inference'" error, install unstructured-inference
 - If seeing OCR-related errors, ensure tesseract is installed at the system level and unstructured-pytesseract is available