RAG-based VectorDB-LLM Query Engine

This project implements an AI-powered document query system using LangChain, ChromaDB, and OpenAI's language models. It enables users to create a searchable database from markdown documents and query it using natural language.

Features

• Vector database creation from markdown documents
• Embedding and query cost estimation
• Similarity searches on the database
• AI-powered response generation for user queries

Architecture Diagram

Requirements

• Python 3.7+
• Dependencies listed in requirements.txt

Installation

1. Clone this repository
2. Create a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`
   

3. Install required packages:

   pip install -r requirements.txt
   

4. Set up your OpenAI API key in a .env file:

   OPENAI_API_KEY=your_api_key_here
   

Getting Started

Follow these steps to quickly set up and use the RAG-based VectorDB-LLM Query Engine:

1. Create a database from your markdown documents:

   python create_database.py --data_folder data/go-docs --chroma_db_path chroma_go_docs/
   

   This command will process the markdown files in the data/go-docs directory and create a vector database in the chroma_go_docs/ folder.

2. Query the database with a natural language question:

   python query_data.py --query_text "Explain goroutines in go in a sentence" --chroma_db_path chroma_go_docs/ --prompt_model gpt-3.5-turbo
   

3. View the AI-generated response:

   Goroutines are lightweight, concurrent functions or methods in Go that run independently, managed by the Go runtime, allowing for efficient parallel execution and easy implementation of concurrent programming patterns.
   

Usage

For more detailed usage instructions, refer to the following sections:

1. Create the Database

   python create_database.py --data_folder path/to/your/markdown/files --chroma_db_path path/to/save/database
   

2. Query the Database

   python query_data.py --query_text "Your question here" --chroma_db_path path/to/database --prompt_model gpt-3.5-turbo
   

File Structure

• create_database.py: Database creation script
• query_data.py: Database querying script
• estimate_cost.py: Cost estimation module
• get_token_count.py: Token counting utility
• data/: Markdown documents directory
• chroma/: ChromaDB database storage (gitignored)

Notes

• Uses OpenAI's text-embedding-3-small for embeddings and gpt-3.5-turbo for responses by default
• Place markdown files in data/ or specify a custom path
• ChromaDB database stored in chroma/ (gitignored)

Troubleshooting

• Ensure compatible Python version and correct package installation
• Verify OpenAI API key and account credits for API errors

License

This project is licensed under the terms of the MIT License. For more information, please refer to the LICENSE file.

Built with

• LangChain
• ChromaDB
• OpenAI
• tiktoken

For questions or issues, please open an issue on the GitHub repository.