AI Research Assistant
A production-grade AI research assistant built with modern LLM application architecture using FastAPI, Next.js, LangChain, Gemini, ChromaDB, PostgreSQL, and Tavily Search.
Overview
The platform combines conversational AI, Retrieval-Augmented Generation (RAG), streaming responses, persistent memory, and AI tool-calling workflows to deliver context-aware research and document intelligence experiences.
Features
Conversational AI System
- Real-time streaming AI responses
- Persistent multi-session conversations
- Context-aware memory handling
- Session restoration and history tracking
- Dynamic AI tool-calling workflows
Retrieval-Augmented Generation (RAG)
- PDF upload and ingestion pipeline
- Semantic chunking and embeddings generation
- Vector search using ChromaDB
- Contextual document retrieval
- Source citation rendering
AI Tool Calling
- Integrated Tavily web search tool
- Real-time information retrieval
- AI-controlled tool selection
- Context-aware response augmentation
System Architecture
Next.js Frontend
↓
FastAPI API Layer
↓
AI Service Layer
├── Conversational AI
├── RAG Pipeline
├── Tool Calling
└── Web Search Integration
↓
Gemini API + ChromaDB
↓
PostgreSQL PersistenceTech Stack
Frontend
- Next.js & React
- TypeScript
- Tailwind CSS
AI Stack
- LangChain
- Gemini 3.1 Flash Lite
- Tavily Search API
- ChromaDB
Infrastructure
- Neon PostgreSQL
- Vercel
- Railway
Future Improvements
- LangGraph workflow orchestration
- Multi-agent research pipelines
- Persistent vector storage
- Redis caching & background task processing
- Dockerized deployment
- OAuth login & production observability
