AI-Code-Navigator

An AI-powered Q&A assistant for developers that answers natural language queries about a codebase by combining large language models, vector search, and GitHub integration.

Overview

This Q&A Bot is an AI-powered assistant designed to help developers query large codebases efficiently. By combining large language models (LLMs) with vector search technologies, the system provides precise answers to natural language questions about repository contents. It integrates directly with GitHub Issues and Pull Requests, enabling context-aware discussions and automated responses.

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Features

• Natural Language Querying
  Ask questions such as "Where is the login function implemented?" or "Which files handle database connections?"

• Semantic Code Search
  Uses vector embeddings (via FAISS or Pinecone) to retrieve relevant code snippets.

• LLM-Powered Responses
  Generates context-aware explanations and summaries of relevant code sections.

• GitHub Integration
  Responds directly to questions in Issues and Pull Requests through the GitHub API.

• Scalable Architecture
  Designed to work with both small projects and enterprise-scale repositories.

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Architecture

1. Code Embedding

   • Repository files are parsed and converted into embeddings using a pre-trained model.
   • Embeddings are stored in a vector database (FAISS or Pinecone).

2. Query Processing

   • Developer submits a natural language query.
   • The query is embedded and matched against the code embeddings.

3. Answer Generation

   • Relevant code snippets are retrieved.
   • An LLM processes the context and generates a natural language answer.

4. GitHub Integration

   • Bot listens to Issues and PR comments.
   • Answers are posted automatically to the relevant discussion thread.

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Tech Stack

• Backend: Python (FastAPI or Flask)
• Vector Database: FAISS (local) or Pinecone (cloud)
• LLM: OpenAI API or Hugging Face Transformers
• GitHub Integration: GitHub REST/GraphQL API
• Task Queue (Optional): Celery / Redis for background jobs

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Installation

Prerequisites

• Python 3.9+
• Access to an LLM API key (OpenAI / Hugging Face)
• GitHub personal access token
• FAISS or Pinecone account

Setup

# Clone repository
git clone https://github.com/your-username/codebase-qa-bot.git
cd codebase-qa-bot

# Create virtual environment
python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

# Install dependencies
pip install -r requirements.txt