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What is DevLens AI?

DevLens AI is a codebase intelligence platform. Paste any GitHub repository URL and instantly get:

  • 🗂️ A navigable file structure with noise (node_modules, lockfiles, build artifacts) filtered out
  • 🔥 Git-history hotspot detection — surfaces the files changed most often, statistically the riskiest to touch
  • 🤖 AI-generated plain-English explanations for any file, cached so they're instant on repeat views
  • 🔍 Hybrid semantic search — ask "where is authentication handled?" in plain English and get a grounded, cited answer synthesized from the actual code, not a hallucinated guess
  • 🗺️ An interactive architecture map showing how files depend on each other
  • 📚 Auto-generated module documentation, exportable as Markdown
  • 🧭 An AI-synthesized "where should I start?" onboarding guide for new contributors
  • 📊 A code quality score combining churn, size, and complexity signals


Why This Exists

Every developer who joins an unfamiliar codebase hits the same wall: hundreds of files, no map, and no fast way to answer "where does X happen?" or "what will break if I change this?"

Most tools that attempt this stop at a thin wrapper around an embedding API. DevLens AI is built differently — it combines real static analysis (AST-aware parsing, git history mining, dependency graphs) with an LLM that acts as an explanation layer on top, not the whole product.


🏗️ Architecture

                    ┌─────────────────────────┐
                    │      GitHub URL          │
                    └────────────┬─────────────┘
                                 ▼
                    ┌─────────────────────────┐
                    │   Shallow Clone (temp)   │
                    └────────────┬─────────────┘
                                 ▼
        ┌────────────────────────┼────────────────────────┐
        ▼                        ▼                        ▼
┌───────────────┐      ┌──────────────────┐      ┌──────────────────┐
│  AST Parsing   │      │  Git History      │      │  Content Capture │
│  (tree-sitter) │      │  Mining (churn)   │      │  (pre-cleanup)   │
└───────┬────────┘      └────────┬──────────┘      └────────┬─────────┘
        ▼                        ▼                           ▼
┌───────────────┐      ┌──────────────────┐      ┌──────────────────┐
│  Chunking +    │      │  Hotspot Ranking  │      │  AI Summarization │
│  Embeddings    │      │                    │      │  (Groq)           │
└───────┬────────┘      └────────┬──────────┘      └────────┬─────────┘
        ▼                        ▼                           ▼
┌─────────────────────────────────────────────────────────────────┐
│           Supabase (Postgres + pgvector + full-text)             │
└──────────────────────────────┬────────────────────────────────────┘
                                 ▼
                    ┌─────────────────────────┐
                    │  Hybrid Search (RRF) +   │
                    │  RAG Answer Generation   │
                    └────────────┬─────────────┘
                                 ▼
                    ┌─────────────────────────┐
                    │   React Frontend (UI)    │
                    └─────────────────────────┘

⚙️ Tech Stack

Layer Technology
Frontend React · Vite · TypeScript · Tailwind CSS · React Flow
Backend Python · FastAPI
Database Supabase (Postgres) · pgvector · full-text search (GIN)
AST Parsing tree-sitter (Python, JS, TS/TSX)
Embeddings sentence-transformers (all-MiniLM-L6-v2, local, free)
LLM Groq (llama-3.3-70b-versatile)
Git Analysis GitPython

🧠 What Makes This Different

Most student RAG projects:

  • Fixed-size text chunking
  • Pure vector similarity search
  • No evaluation of retrieval quality
  • LLM allowed to answer from general knowledge
  • Single confident answer, even when wrong

DevLens AI:

  • AST-aware chunking (function/class boundaries)
  • Hybrid search — keyword (full-text) + vector, combined via Reciprocal Rank Fusion
  • Explicit grounding — answers only from retrieved chunks, cites file + line
  • Clearly states when no relevant code was found, instead of hallucinating
  • Real git-history mining for risk signals, not guessed

✨ Features

🔍 Hybrid Semantic Search + RAG

Ask a natural-language question about the codebase. DevLens embeds the query, retrieves candidates via both keyword and vector search, fuses the rankings, and asks the LLM to synthesize a grounded answer — citing exact files and line ranges. If nothing relevant is found, it says so explicitly.

🔥 Git Hotspot Detection

Mines full commit history via git log --numstat to rank files by churn frequency — a well-established proxy for bug-proneness and complexity.

🗺️ Interactive Architecture Map

Renders the extracted import/dependency graph as a zoomable, clickable node-link diagram via React Flow, with hotspot files visually flagged.

📚 Auto-Generated Documentation

Synthesizes per-file AI summaries into cohesive, module-level documentation — exportable as a single Markdown file.

🧭 Onboarding Guide

Combines detected entry points, structurally central files (high import in-degree), and hotspots into an AI-suggested reading order for new contributors.

📊 Code Quality Score

A composite, repo-relative score per file combining churn, size, and complexity — surfaced directly in the file tree.


🚀 Getting Started

# Clone the repo
git clone https://github.com/<your-username>/DevLens-AI.git
cd DevLens-AI

# Backend setup
cd backend
python -m venv .venv
.venv\Scripts\Activate.ps1   # Windows
pip install -r requirements.txt
cp .env.example .env          # add your Supabase + Groq keys
python app/main.py

# Frontend setup (new terminal)
cd frontend
npm install
npm run dev

Built by Rishabh · B.Tech AI & Data Science, VESIT · Team AlgoMinds