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AI Research Assistant – Multi-Agent Research & Automation Platform
Developed an AI-powered Research Assistant designed to streamline research workflows, analyze documents, generate summaries, and organize information using a multi-agent architecture.
The platform combines AI agents with Retrieval-Augmented Generation (RAG) capabilities to help users retrieve relevant information from uploaded documents and produce structured, context-aware responses.
Key Features
AI-Powered Research: Assist with research tasks and information analysis using AI agents.
Document Processing: Upload documents and extract useful information for research.
Retrieval-Augmented Generation (RAG): Retrieve relevant document context to support more grounded responses.
AI Summarization: Generate concise summaries from research materials.
Multi-Agent Workflow: Use specialized agents for research, summarization, critique, and editing.
Vector Search: Retrieve relevant information using a vector-based search system.
Full-Stack Architecture: Integrate a modern frontend with a Python backend for AI-powered research workflows.
Tech Stack
Next.js, FastAPI, Python, LangGraph, FAISS, SQLite, Groq API.
Project Goal
The goal of this project is to make research more efficient by reducing manual document review, simplifying information retrieval, and organizing complex information into clear, useful outputs.
Skills: AI Development, Multi-Agent Systems, RAG, LLM Integration, Document Processing, Vector Search, Full-Stack Development, Workflow Automation.
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