Important Note

This POC was nice for it's time. However tools like Cursor and Windsurf are now applying this principle embedded in the IDE.

Project Motivation

This project came from a simple idea: what if you could provide an entire codebase to an LLM instead of just small pieces? Most coding assistants, like co-pilots, work on a limited scope, but I wanted something that could handle the full context of a project.

By integrating the full codebase with Retrieval-Augmented Generation (RAG), this POC aims to improve the quality and relevance of code suggestions. The goal is to see how having the complete code available for real-time querying can enhance productivity.

CodeRAG

CodeRAG is an AI-powered code retrieval and augmentation tool that leverages OpenAI's models (such as gpt-4 or gpt-3.5-turbo) for real-time codebase querying, indexing, and improvement. This project integrates a Retrieval-Augmented Generation (RAG) system to help developers seamlessly search through code, receive suggestions, and implement improvements.

Features

• Real-time Codebase Indexing: Automatically indexes code files upon changes, with real-time updates.
• Vector Database Search: Utilizes FAISS or a similar vector database for fast, efficient code search using embeddings.
• Conversational Coding Assistance: Integrates OpenAI's GPT models to provide contextual code suggestions, improvements, and fixes.
• Configurable Settings: Environment-specific settings are managed using a .env file for API keys, model selection, and directories.

Tech Stack

• OpenAI API: Leverages GPT-4o (or any other OpenAI model) for conversational and coding improvements.
• Python: Core functionality and API interactions.
• FAISS (Facebook AI Similarity Search): For vector-based searching.
• python-dotenv: For managing environment variables.
• Retrieval-Augmented Generation (RAG): Combines search and generative models.

Setup Instructions

Prerequisites

• Python 3.8+
• OpenAI API Key (You can get one here)
• FAISS

Step 1: Clone the Repository

git clone https://github.com/yourusername/CodeRAG.git
cd CodeRAG

Step 2: Install Dependencies

Create a virtual environment (recommended):

python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install required packages:

pip install -r requirements.txt

Step 3: Configure Environment Variables

Create a .env file in the root of the project and add the following variables:

OPENAI_API_KEY=your_openai_api_key
OPENAI_EMBEDDING_MODEL=text-embedding-ada-002
OPENAI_CHAT_MODEL=gpt-4o
WATCHED_DIR=path_to_your_code_directory
FAISS_INDEX_FILE=path_to_faiss_index
EMBEDDING_DIM=1536  # Modify if you're using a different embedding model

Step 4: Run the Application

1. Start the Backend:

   To start the backend (indexing, embeddings, and monitoring):

   python main.py

2. Start the Frontend:

   To launch the Streamlit UI:

   streamlit run app.py

Usage

1. Ask a Question: Type your question or code request into the interface. The model will search the indexed codebase and provide suggestions or improvements.
2. Review Suggestions: You'll receive a merged or fixed version of the code based on the model's analysis.
3. Conversational History: The system keeps track of your queries and the AI responses for better context in future interactions.

Project Structure

• main.py: The main script to run the application.
• prompt_flow.py: Handles querying OpenAI's API and manages the search and conversational history.
• coderag/config.py: Stores configuration and environment variables.
• coderag/search.py: Manages vector database (FAISS) searches for relevant code snippets.
• .env: Holds environment-specific settings (OpenAI API keys, model configuration, etc.).
• requirements.txt: Lists the Python dependencies needed to run the project.

Contributing

Feel free to fork this repository, open issues, and submit pull requests.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

License

This project is licensed under the Apache License. See the LICENSE file for details.