Architecting GitQuery AI: A Deep Dive into Building a Production-Ready RAG System for GitHub Repositories

Introduction: In today's fast-paced development landscape, quickly understanding new codebases is paramount. From accelerating new team member onboarding to rapidly grasping external project architectures, the ability to query a repository's documentation semantically can be a game-changer. This post details the technical journey of building GitQuery AI, a Retrieval-Augmented Generation (RAG) application that allows developers to interactively ask questions about any public GitHub repository.

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The Problem: Information Overload & Onboarding Tax Traditional methods of codebase understanding—cloning, greping, and exhaustive manual documentation reading—are time-consuming and inefficient. The goal for GitQuery AI was to create a natural language interface that could provide instant, accurate answers by understanding the context of the codebase.

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Architectural Overview: The RAG Pipeline Our solution is centered around a robust RAG pipeline:

1. Data Ingestion: Fetching and preparing repository content.

2. Vectorization: Converting text into high-dimensional numerical representations (embeddings).

3. Vector Storage & Search: Storing these embeddings and performing efficient similarity lookups.

4. Generative AI: Leveraging an LLM to synthesize answers from retrieved context.

Here's a simplified architecture diagram: `` (Imagine a diagram here: User UI -> Edge Function (Ingestion) -> Supabase (pgvector) <-> Edge Function (Retrieval) -> Gemini API -> User UI)

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Key Technical Decisions & Implementations:

1. Google Gemini 1.5 Flash: The LLM Backbone

   • Decision: We chose Gemini 1.5 Flash for its exceptional performance, cost-effectiveness, and, critically, its 1 million token context window. This massive context size is invaluable for processing large READMEs, design documents, or even multiple concatenated code files, allowing the LLM to understand broader architectural patterns without losing detail.

   • Implementation: We utilized text-embedding-004 for generating high-quality 768-dimensional embeddings and the main Gemini 1.5 Flash model for chat completion, grounding its responses with retrieved context.

2. Supabase & pgvector: The Semantic Memory

   • Decision: Supabase offered a compelling solution with its managed PostgreSQL database and seamless integration of the pgvector extension. This allowed us to store our vector embeddings directly within the same database as our metadata, simplifying our stack and reducing latency.

   • Implementation: Our documents table stores content (text chunks), metadata (source, file path), and the embedding (vector(768)). We implemented a match_documents SQL function leveraging cosine similarity (<=>) for efficient nearest-neighbor search. This function is exposed via a Supabase Edge Function for secure access.

3. Intelligent Data Ingestion with Edge Functions

   • Mechanism: A Supabase Edge Function serves as our ingestion pipeline. When a user provides a GitHub repository URL, this function:

     1. Fetches the README.md (and potentially other key documentation files) via the GitHub REST API.

     2. Performs intelligent text chunking to break down large documents into manageable segments suitable for embedding.

     3. Sends these chunks to Gemini's text-embedding-004 model.

     4. Stores the resulting embeddings and associated metadata into our pgvector table.

   • Challenge & Solution: Ensuring effective chunking was critical. Overly large chunks could lead to irrelevant data being retrieved, while too small chunks could break context. We adopted a strategy that prioritizes paragraph and section boundaries.

4. Frontend: A Focused Developer Experience

   • Stack: Built with React 18, TypeScript, and Tailwind CSS on a Vite build system.

   • UI/UX: The interface is designed for clarity: a repository URL input, a processing status indicator, and a streamlined chat interface. Shadcn UI components provided a strong foundation for a modern, accessible look.

Learnings & Future Enhancements:

• Prompt Engineering is Key: The quality of answers heavily depends on the system prompt used to instruct Gemini. Crafting prompts that effectively leverage retrieved context while maintaining a helpful persona was an iterative process.

• Cost Optimization: The switch to Gemini 1.5 Flash demonstrated significant cost savings compared to other LLM providers for the same (or better) performance on this specific task.

• Scalability: Supabase's managed services and Edge Functions provide a scalable backbone for handling concurrent requests, an important consideration for a public-facing tool.

Future work includes integrating more advanced chunking strategies (e.g., recursive character text splitter), enabling multi-file analysis, and building an authentication layer for private repositories.

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Conclusion: GitQuery AI is more than just a chat application; it's a testament to how modern AI and database technologies can be combined to create powerful, practical tools that genuinely enhance developer productivity. Building this project from the ground up provided invaluable experience in architecting and deploying complex RAG systems.

Call to Action: Check out the repo and contribute: Search GitQueryAI in Github#2026 Jan #RAG #GenerativeAI #LLM #Supabase #GeminiAPI #React #TypeScript #VectorDatabase #DeveloperTools