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RAG Solution with LlamaIndex and Qdrant

Project Overview

This is a Retrieval Augmented Generation (RAG) solution built using LlamaIndex as the primary framework and Qdrant as the vector storage. The project is designed to load documents from a shared data directory, store them in a vector database, and enable semantic search and chat capabilities using local Ollama models.

The system has been enhanced to properly handle Russian language documents with Cyrillic characters, ensuring proper encoding during document loading, storage, and retrieval.

Key Technologies

  • RAG Framework: LlamaIndex
  • Vector Storage: Qdrant
  • Embedding Models: Ollama (configurable via environment variables)
  • Chat Models: Ollama (configurable via environment variables)
  • Data Directory: ./../../../data (relative to project root)
  • Logging: loguru with file rotation and stdout logging

Architecture Components

  • CLI entry point (cli.py)
  • Configuration module (config.py) - manages model strategies and environment variables
  • Document enrichment module (enrichment.py)
  • Vector storage configuration (vector_storage.py)
  • Retrieval module (retrieval.py)
  • Chat agent (agent.py)

Building and Running

Prerequisites

  1. Python virtual environment (already created in venv folder)
  2. Ollama running locally on default port 11434
  3. Qdrant running locally (REST API on port 6333, gRPC on port 6334)
  4. Data files in the ./../../../data directory

Setup Process

  1. Activate the virtual environment:

    source venv/bin/activate

  2. Install required packages based on the document extensions found in the data directory (see EXTENSIONS.md for details)

  3. Configure environment variables in .env file (copy from .env.dist)

  4. Run the CLI to initialize the system:

    python cli.py ping  # Should return "pong"

Available Commands

  • ping: Basic connectivity test
  • enrich: Load and process documents from the data directory into vector storage
  • chat: Start an interactive chat session with the RAG system

Development Conventions

Logging

  • Use loguru for all logging
  • Log to both file (logs/dev.log) with rotation and stdout
  • Use appropriate log levels (DEBUG, INFO, WARNING, ERROR)

Environment Variables

  • CHAT_STRATEGY: Strategy for chat models ("ollama" or "openai")
  • EMBEDDING_STRATEGY: Strategy for embedding models ("ollama" or "openai")
  • OLLAMA_EMBEDDING_MODEL: Name of the Ollama model to use for embeddings
  • OLLAMA_CHAT_MODEL: Name of the Ollama model to use for chat functionality
  • OPENAI_CHAT_URL: URL for OpenAI-compatible chat API (when using OpenAI strategy)
  • OPENAI_CHAT_KEY: API key for OpenAI-compatible chat API (when using OpenAI strategy)
  • OPENAI_EMBEDDING_MODEL: Name of the OpenAI embedding model (when using OpenAI strategy)
  • OPENAI_EMBEDDING_BASE_URL: Base URL for OpenAI-compatible embedding API (when using OpenAI strategy)
  • OPENAI_EMBEDDING_API_KEY: API key for OpenAI-compatible embedding API (when using OpenAI strategy)

Document Processing

  • Support multiple file formats based on EXTENSIONS.md
  • Use text splitters appropriate for each document type
  • Store metadata (filename, page, section, paragraph) with embeddings
  • Track processed documents to avoid re-processing (using SQLite if needed)
  • Proper encoding handling for Russian/Cyrillic text during loading and retrieval

Vector Storage

  • Collection name: "documents_llamaindex"
  • Initialize automatically if not exists
  • Support for Ollama embeddings by default
  • Optional OpenAI embedding support via OpenRouter (commented out)

Project Phases

Phase 1: CLI Entry Point

  • Virtual environment setup
  • CLI creation with click library
  • Basic "ping" command implementation

Phase 2: Framework Installation

  • LlamaIndex installation
  • Data folder analysis and EXTENSIONS.md creation
  • Required loader libraries installation

Phase 3: Vector Storage Setup

  • Qdrant library installation
  • Vector storage initialization module
  • Collection creation strategy for "documents_llamaindex"
  • Ollama embedding model configuration
  • Optional OpenAI embedding via OpenRouter (commented)

Phase 4: Document Enrichment

  • Document loading module with appropriate loaders
  • Text splitting strategies implementation
  • Document tracking mechanism
  • CLI command for enrichment
  • Russian language/Cyrillic text encoding support during document loading

Phase 5: Retrieval Feature

  • Retrieval module configuration
  • Query processing with metadata retrieval
  • Russian language/Cyrillic text encoding support

Phase 6: Model Strategy

  • Add CHAT_STRATEGY and EMBEDDING_STRATEGY environment variables
  • Add OpenAI configuration options to .env files
  • Create reusable model configuration function
  • Update all modules to use the new configuration system
  • Ensure proper .env loading across all modules

Phase 7: Enhanced Logging and Progress Tracking

  • Added progress bar using tqdm to show processing progress
  • Added logging to show total files and processed count during document enrichment
  • Enhanced user feedback during document processing with percentage and counts

Phase 8: Chat Agent

  • Agent module with Ollama integration
  • Integration with retrieval module
  • CLI command for chat functionality

File Structure

llamaindex/
├── venv/                 # Python virtual environment
├── cli.py               # CLI entry point
├── config.py            # Configuration module for model strategies
├── vector_storage.py    # Vector storage configuration
├── enrichment.py        # Document loading and processing
├── retrieval.py         # Search and retrieval functionality
├── agent.py             # Chat agent implementation (to be created)
├── EXTENSIONS.md        # Supported file extensions and loaders
├── .env.dist            # Environment variable template
├── .env                 # Local environment variables
├── logs/                # Log files directory
│   └── dev.log          # Main log file with rotation
└── PLANNING.md          # Project planning document

Data Directory

The system expects documents to be placed in ./../../../data relative to the project root. The system will analyze this directory to determine supported file types and appropriate loaders.

Testing

  • Unit tests for individual modules
  • Integration tests for end-to-end functionality
  • CLI command tests

Troubleshooting

  • Ensure Ollama is running on port 11434
  • Verify Qdrant is accessible on ports 6333 (REST) and 6334 (gRPC)
  • Check that the data directory contains supported file types
  • Review logs in logs/dev.log for detailed error information
  • For Russian/Cyrillic text issues, ensure proper encoding handling is configured in both enrichment and retrieval modules

Important Notes

  • Do not test long-running or heavy system scripts during development as they can consume significant system resources and take hours to complete
  • The enrich command processes all files in the data directory and may require substantial memory and processing time