CodebaseRag - Multi-Repository Code Search Assistant

A Streamlit-powered RAG (Retrieval-Augmented Generation) application that enables users to search and query across multiple codebases. The app features code screenshot analysis capabilities and provides context-aware responses using Pinecone for vector search.

Features

• Search across multiple GitHub repositories simultaneously
• Upload and analyze code screenshots using OCR
• RAG-powered responses using Pinecone vector database
• Interactive chat interface
• Support for switching between different codebases
• OCR capability for code screenshots

Prerequisites

• Python 3.8+
• Tesseract OCR
• API keys for:
  • Pinecone
  • Groq
• GitHub repositories indexed in Pinecone

Installation

1. Install Tesseract OCR:

# Ubuntu
sudo apt-get install tesseract-ocr

# macOS
brew install tesseract

# Windows
# Download and install from https://github.com/UB-Mannheim/tesseract/wiki

2. Install Python dependencies:

pip install -r requirements.txt

Configuration

1. Create a .env file in the project root with your API keys:

OPENAI_API_KEY=your-key
GROQ_API_KEY=your-key
PINECONE_API_KEY=your-key
PINECONE_ENV=us-east-1
PINECONE_INDEX_SECURE=codebase1-rag
PINECONE_INDEX_CHATBOT=codebase2-rag

2. Create a .streamlit/secrets.toml file:

OPENAI_API_KEY = "your-key"
GROQ_API_KEY = "your-key"
PINECONE_API_KEY = "your-key"
PINECONE_ENV = "us-east-1"
PINECONE_INDEX_SECURE = "codebase1-rag"
PINECONE_INDEX_CHATBOT = "codebase2-rag"

Running Locally

1. Activate your virtual environment (if using one)

python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

2. Run the Streamlit app:

streamlit run chat.py

3. Open your browser and navigate to http://localhost:8501

Usage

1. Select one or both codebases to search from the available options
2. (Optional) Upload a code screenshot for specific code-related questions
3. Enter your query in the chat input
4. View the AI-powered response based on the selected codebases and uploaded screenshot

Deploying to Streamlit Cloud

1. Fork this repository
2. Create a new app on Streamlit Cloud
3. Connect your GitHub repository
4. Add your secrets in the Streamlit Cloud dashboard:
   • Go to App Settings > Secrets
   • Add all the required API keys and configuration values
5. Deploy the app

Project Structure

.
├── chat.py              # Main Streamlit application
├── embeddings.py        # Embedding generation utilities
├── rag_utils.py         # RAG search and processing functions
├── requirements.txt     # Python dependencies
└── packages.txt         # System dependencies (Tesseract)
------.env# not included, add this yourself.
------.streamlit/secrets.toml # not inlcuded, add this yourself.

Limitations

• Requires pre-indexed repositories in Pinecone
• OCR quality depends on screenshot clarity
• API keys required for full functionality