Codebase RAG Sage 🌲✨

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What Is Codebase RAG Sage?

Codebase RAG Sage is your AI-powered assistant for exploring and understanding codebases. It merges Retrieval-Augmented Generation (RAG) with Streamlit, Pinecone, and Groq’s LLaMA models to deliver contextual, code-aware answers to your queries.

Key Features

• Conversational Code Exploration – Ask anything about your codebase in natural language and get informed responses grounded in actual source files.
• Semantic Search – Powered by embeddings (via HuggingFaceEmbeddings), for smarter, context-aware retrieval.
• Namespace-Based Code Indexing – Handle multiple repositories, each with a distinct namespace in Pinecone.
• Streamlit Web Interface – Elegant and intuitive UI for all your codebase queries.

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Demo Snapshot

(Here you can show how the app looks—like the input field, cloned repo display, and Q&A section.)

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Quick Getting Started

1. Clone the Repo

git clone https://github.com/rahatmoktadir03/codebase-rag-sage.git
cd codebase-rag-sage

2. Create & Activate Virtual Environment

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

3. Install Dependencies

pip install -r requirements.txt

4. Setup Environment Variables

• Create a .env file or export these:

  export PINECONE_API_KEY=your_pinecone_api_key
  export GROQ_API_KEY=your_groq_api_key

5. Create Pinecone Index (if needed)

import pinecone
pinecone.init(api_key="YOUR_KEY")
pinecone.create_index("codebase-rag", dimension=768)