rag-solution/services/rag/langchain/QWEN.md

RAG Solution with Langchain

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 or OpenAI-compatible APIs.

The project follows a phased development approach with CLI entry points for different functionalities like document loading, retrieval, and chat.

Key Technologies:

• Framework: Langchain
• Vector Storage: Qdrant
• Embeddings: Ollama (with fallback option for OpenAI via OpenRouter)
• Chat Models: Ollama and OpenAI-compatible APIs
• Data Directory: ./../../../data (relative to project root)
• Virtual Environment: Python venv in venv/ directory

Project Structure

rag-solution/services/rag/langchain/
├── .env               # Environment variables
├── .env.dist          # Environment variable template
├── .gitignore         # Git ignore rules
├── app.py             # Main application file (currently empty)
├── cli.py             # CLI entrypoint with click library
├── EXTENSIONS.md      # Supported file extensions and LangChain loaders
├── enrichment.py      # Document enrichment module for loading documents to vector storage
├── PLANNING.md        # Development roadmap and phases
├── QWEN.md            # Current file - project context
├── requirements.txt   # Python dependencies
├── vector_storage.py  # Vector storage module with Qdrant and Ollama embeddings
└── venv/              # Virtual environment

Dependencies

The project relies on several key libraries:

• 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
• click for CLI interface
• loguru for logging (to be installed per requirements)
• python-dotenv for environment management

Development Phases

The project is organized into 8 development phases as outlined in PLANNING.md:

Phase 1: CLI Entrypoint

• Virtual environment setup
• Create CLI with click library
• Implement "ping" command that outputs "pong"

Phase 2: Framework Installation & Data Analysis

• Install Langchain as base RAG framework
• Analyze data folder extensions and create EXTENSIONS.md
• Install required loader libraries
• Configure environment variables

Phase 3: Vector Storage Setup

• Install Qdrant client library
• Create vector_storage.py for initialization
• Configure Ollama embeddings using OLLAMA_EMBEDDING_MODEL
• Prepare OpenAI fallback (commented)

Phase 4: Document Loading Module

• Create enrichment.py for loading documents to vector storage
• Implement text splitting strategies
• Add document tracking to prevent re-processing
• Integrate with CLI

Phase 5: Retrieval Feature

• Create retrieval.py for querying vector storage
• 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

Phase 7: OpenAI Integration for Chat Model

• Create OpenAI-compatible integration using .env variables OPENAI_CHAT_URL and OPENAI_CHAT_KEY
• Make this integration optional using .env variable CHAT_MODEL_STRATEGY with "ollama" as default
• 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:

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

Since the project is in early development stages, the following steps are planned:

1. Setup Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   

2. Install Missing Dependencies (as development progresses):

   pip install loguru qdrant-client  # Examples of needed libraries
   

3. Configure Environment:

   cp .env.dist .env
   # Edit .env with appropriate values
   

4. Run CLI Commands:

   python cli.py ping
   

Development Conventions

• Use loguru for logging with rotation to logs/dev.log and stdout
• Follow Langchain best practices for RAG implementations
• Prioritize open-source solutions that don't require external API keys
• Implement proper error handling and document processing tracking
• Use modular code organization with separate files for different components

Current Status

The project is in early development phase. The virtual environment is set up and dependencies are defined, but the core functionality (CLI, document loading, vector storage, etc.) is yet to be implemented according to the planned phases.

Important Implementation Notes

OCR and Computer Vision Setup

• Added Tesseract OCR support for image text extraction
• Installed pytesseract and unstructured-pytesseract packages
• Configured image loaders to use OCR strategy ("ocr_only") for extracting text from images
• This resolves the "OCRAgent instance" error when processing image files

Russian Language Processing Configuration

• Installed spaCy library and Russian language model (ru_core_news_sm)
• Configured unstructured loaders to use Russian as the primary language ("languages": ["rus"])
• This improves processing accuracy for Russian documents in the dataset

Qdrant Collection Auto-Creation Fix

• Fixed issue where Qdrant collections were not being created automatically
• Implemented logic to check if collection exists and create it if needed
• Uses Qdrant client's create_collection method with proper vector size detection
• Resolves the "Collection doesn't exist" 404 error during document insertion

Document Tracking Improvement

• Modified document tracking to only mark documents as processed after successful vector storage insertion
• This prevents documents from being marked as processed if vector storage insertion fails

Dependency Management

• Added several new dependencies for enhanced functionality:
  • pdf2image for PDF-to-image conversion
  • unstructured-inference for advanced document analysis
  • python-pptx for PowerPoint processing
  • pi-heif for HEIF image format support
  • spacy and ru-core-news-sm for Russian NLP

Error Handling Improvements

• Enhanced error handling for optional dependencies in document loaders
• Added graceful degradation when optional modules are not available

Phase 5 Implementation Notes

• Created retrieval.py module with LangChain Retriever functionality
• Implemented search functions that retrieve documents with metadata from Qdrant vector storage
• Added CLI command retrieve to search the vector database based on a query
• 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

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
• 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
• For language detection issues, verify that appropriate spaCy models are downloaded