AI Software Engineer Assistant

An intelligent AI-powered coding workspace that understands repositories, explains code, answers architectural questions, generates tests, detects bugs, and assists developers with deep codebase intelligence.

Think: GitHub Copilot + Codebase Chat + AI Engineering Workspace.

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🚀 Features

Core Features

• Repository ingestion and indexing
• AI-powered codebase chat
• Semantic code search
• AST-aware code chunking
• Streaming AI responses
• Repository-aware question answering
• Authentication and user workspaces
• Persistent chat history

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Advanced Features

• Unit test generation
• Bug detection and fix suggestions
• Architecture diagram generation
• Dependency graph visualization
• Multi-language repository support
• Intelligent code explanations
• PR review assistance
• Symbol-aware retrieval

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🧠 Why This Project Matters

This project demonstrates:

• AI Engineering
• Retrieval-Augmented Generation (RAG)
• Vector Databases
• Backend Architecture
• Developer Tooling
• Distributed Systems Thinking
• Modern Full-Stack Engineering
• Production AI Systems

It is designed as a flagship portfolio project for software engineering and AI engineering roles.

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🏗️ System Architecture

                        ┌─────────────────┐
                        │   Next.js UI    │
                        │ React Frontend  │
                        └────────┬────────┘
                                 │
                           HTTPS/API
                                 │
                    ┌────────────▼────────────┐
                    │       FastAPI API       │
                    │ AI Orchestration Layer  │
                    └───────┬───────┬─────────┘
                            │       │
               ┌────────────┘       └────────────┐
               │                                 │
       ┌───────▼────────┐              ┌────────▼────────┐
       │ PostgreSQL     │              │ Redis Cache     │
       │ chats/users    │              │ sessions/cache  │
       └────────────────┘              └─────────────────┘

               ┌────────────────────────────────┐
               │ Vector Database (Qdrant)       │
               │ embeddings + semantic search   │
               └────────────────────────────────┘

                            │
                   ┌────────▼────────┐
                   │ Repo Ingestion  │
                   │ Git + Parsers   │
                   └────────┬────────┘
                            │
                 ┌──────────▼──────────┐
                 │ AST + Embedding     │
                 │ Processing Pipeline │
                 └─────────────────────┘

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⚙️ Tech Stack

Backend

• FastAPI
• Python
• PostgreSQL
• Redis
• Celery / Background Workers
• Docker

AI & Retrieval

• OpenAI / Local LLMs
• Qdrant / Pinecone
• Embeddings
• RAG Pipelines
• AST Parsing
• Tree-sitter

Frontend

• Next.js
• React
• TailwindCSS
• TypeScript

Deployment

• Docker
• Vercel
• Railway / Render / Fly.io

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📁 Project Structure

ai-software-engineer-assistant/
│
├── backend/
│   ├── api/
│   ├── ingestion/
│   ├── parsers/
│   ├── embeddings/
│   ├── rag/
│   ├── services/
│   ├── workers/
│   ├── models/
│   └── main.py
│
├── frontend/
│   ├── app/
│   ├── components/
│   ├── hooks/
│   ├── lib/
│   └── styles/
│
├── docker/
├── infra/
├── scripts/
├── docs/
└── README.md

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🔥 MVP Goals

Version 1

• Upload/index repositories
• Ask questions about code
• Semantic search
• Streaming chat responses
• File-aware citations
• Authentication
• Persistent workspaces

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🧩 Future Roadmap

Version 2

• Test generation
• AI code fixes
• PR review assistant
• Architecture graph generation

Version 3

• Multi-repository understanding
• Autonomous coding agents
• Team collaboration
• IDE integration

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🧠 How It Works

1. Repository Ingestion

The system clones repositories and parses files while ignoring unnecessary directories such as:

• node_modules
• build/
• dist/
• binaries
• .git/

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2. Intelligent Chunking

Instead of splitting files arbitrarily, the assistant uses:

• AST parsing
• Function-level chunking
• Class-level chunking
• Symbol-aware extraction

This dramatically improves retrieval quality.

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3. Embedding Pipeline

Each chunk is embedded using modern embedding models and stored inside a vector database.

Metadata stored:

• file path
• language
• symbols
• repository
• function names

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4. Retrieval-Augmented Generation (RAG)

When a user asks a question:

1. Query embedding is generated
2. Relevant chunks are retrieved
3. Context is reranked
4. LLM generates repository-aware answers
5. Streaming response is returned to frontend

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🔒 Authentication

Supports:

• JWT Authentication
• OAuth (GitHub planned)
• Workspace isolation
• Session management

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⚡ Streaming Responses

The platform uses:

• FastAPI StreamingResponse
• Server-Sent Events (SSE)

to provide real-time AI responses similar to ChatGPT and Cursor.

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📊 Planned AI Capabilities

• Explain code
• Detect bugs
• Generate unit tests
• Refactor suggestions
• Security analysis
• Architecture visualization
• Dependency analysis
• AI pair programming

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🐳 Running Locally

Clone Repository

git clone https://github.com/your-username/ai-software-engineer-assistant.git
cd ai-software-engineer-assistant

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Backend Setup

cd backend

python -m venv venv
source venv/bin/activate

pip install -r requirements.txt

uvicorn main:app --reload

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Frontend Setup

cd frontend

npm install
npm run dev

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Docker

docker-compose up --build

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🧪 Example Questions

• "How does authentication work?"
• "Explain the repository architecture."
• "Generate tests for the payment service."
• "Find potential bugs in the auth module."
• "Which services depend on Redis?"
• "Show me API flow for user login."

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📈 Engineering Highlights

This project showcases:

• Advanced RAG systems
• AI infrastructure engineering
• Semantic retrieval systems
• Backend API design
• Full-stack development
• Streaming systems
• Distributed architecture
• Developer tooling engineering

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🎯 Ideal Use Cases

• AI pair programming
• Large repository understanding
• Developer onboarding
• Technical documentation
• Code review assistance
• Engineering productivity

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🛠️ Planned Integrations

• GitHub
• GitLab
• Bitbucket
• VSCode Extension
• Slack
• CI/CD pipelines

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📜 License

MIT License

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👨‍💻 Author

Built as a flagship AI engineering project focused on modern developer tooling and intelligent code understanding systems.