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CodeRAG

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1.0.0

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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 `venvScriptsactivate`

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

Start the Backend:

To start the backend (indexing, embeddings, and monitoring):
```
python main.py
```
Start the Frontend:

To launch the Streamlit UI:
```
streamlit run app.py
```

Usage

Ask a Question: Type your question or code request into the interface. The model will search the indexed codebase and provide suggestions or improvements.
Review Suggestions: You'll receive a merged or fixed version of the code based on the model's analysis.
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.