openai compatible integration done

services/rag/langchain/QWEN.md

@@ -2,7 +2,7 @@
 
 ## Project Overview
 
-This is a Retrieval-Augmented Generation (RAG) solution built using the Langchain framework. The project is designed to load documents from a data directory, store them in a vector database (Qdrant), and enable semantic search and chat capabilities using local LLMs via Ollama.
+This is a Retrieval-Augmented Generation (RAG) solution built using the Langchain framework. The project is designed to load documents from a data directory, store them in a vector database (Qdrant), and enable semantic search and chat capabilities using local LLMs via Ollama or OpenAI-compatible APIs.
 
 The project follows a phased development approach with CLI entry points for different functionalities like document loading, retrieval, and chat.
 
@@ -10,7 +10,7 @@ The project follows a phased development approach with CLI entry points for diff
 - **Framework**: Langchain
 - **Vector Storage**: Qdrant
 - **Embeddings**: Ollama (with fallback option for OpenAI via OpenRouter)
-- **Chat Models**: Ollama
+- **Chat Models**: Ollama and OpenAI-compatible APIs
 - **Data Directory**: `./../../../data` (relative to project root)
 - **Virtual Environment**: Python venv in `venv/` directory
 
@@ -35,7 +35,7 @@ rag-solution/services/rag/langchain/
 ## Dependencies
 
 The project relies on several key libraries:
-- `langchain` and related ecosystem (`langchain-community`, `langchain-core`, `langchain-ollama`)
+- `langchain` and related ecosystem (`langchain-community`, `langchain-core`, `langchain-ollama`, `langchain-openai`)
 - `langgraph` for workflow management
 - `qdrant-client` for vector storage (to be installed)
 - `ollama` for local LLM interaction
@@ -45,7 +45,7 @@ The project relies on several key libraries:
 
 ## Development Phases
 
-The project is organized into 6 development phases as outlined in `PLANNING.md`:
+The project is organized into 8 development phases as outlined in `PLANNING.md`:
 
 ### Phase 1: CLI Entrypoint
 - [x] Virtual environment setup
@@ -79,13 +79,25 @@ The project is organized into 6 development phases as outlined in `PLANNING.md`:
 - [x] Integrate with retrieval functionality
 - [x] Add CLI command for chat interaction
 
+### Phase 7: OpenAI Integration for Chat Model
+- [x] Create OpenAI-compatible integration using `.env` variables `OPENAI_CHAT_URL` and `OPENAI_CHAT_KEY`
+- [x] Make this integration optional using `.env` variable `CHAT_MODEL_STRATEGY` with "ollama" as default
+- [x] Allow switching between "ollama" and "openai" strategies
+
+### Phase 8: HTTP Endpoint
+- [ ] Create web framework with POST endpoint `/api/test-query` for agent queries
+
 ## Environment Configuration
 
 The project uses environment variables for configuration:
 
 ```env
-OLLAMA_EMBEDDING_MODEL=MODEL  # Name of the Ollama model for embeddings
-OLLAMA_CHAT_MODEL=MODEL       # Name of the Ollama model for chat
+OLLAMA_EMBEDDING_MODEL=MODEL      # Name of the Ollama model for embeddings
+OLLAMA_CHAT_MODEL=MODEL           # Name of the Ollama model for chat
+OPENAI_CHAT_URL=URL               # OpenAI-compatible API URL
+OPENAI_CHAT_KEY=KEY               # Authorization token for OpenAI-compatible API
+OPENAI_CHAT_MODEL=MODEL           # Name of the OpenAI-compatible model to use
+CHAT_MODEL_STRATEGY=ollama        # Strategy to use: "ollama" (default) or "openai"
 ```
 
 ## Building and Running
@@ -176,6 +188,13 @@ The project is in early development phase. The virtual environment is set up and
 - Agent uses document retrieval tool to fetch relevant information based on user queries
 - Implemented proper error handling and conversation history management
 
+### Phase 7 Implementation Notes
+- Enhanced `agent.py` to support both Ollama and OpenAI-compatible chat models
+- Added conditional logic to select chat model based on `CHAT_MODEL_STRATEGY` environment variable
+- When strategy is "openai", uses `ChatOpenAI` with `OPENAI_CHAT_URL` and `OPENAI_CHAT_KEY` from environment
+- When strategy is "ollama" (default), uses existing `ChatOllama` implementation
+- Updated CLI chat command to show which model strategy is being used
+
 ### 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