This project demonstrates the development of an AI-powered research assistant using OpenAI, LlamaIndex, Pinecone, and Streamlit. The goal is to build an intelligent agent that can understand user questions, retrieve relevant information from a knowledge base, and generate useful responses using a Large Language Model.

The system uses LlamaIndex to organize and retrieve information, Pinecone as a vector database for efficient semantic search, and OpenAI models to understand queries and generate natural-language responses. Streamlit provides a simple and interactive web interface through which users can communicate with the AI agent.

Key Features

• AI-powered question answering
• Document and knowledge-base retrieval
• Semantic search using vector embeddings
• Context-aware responses using an LLM
• Pinecone-based vector storage
• Interactive Streamlit interface
• Integration of multiple AI tools into a single workflow

Project Objective

The main objective is to understand how modern AI agents combine LLMs, vector databases, retrieval systems, and user interfaces to create practical AI applications. This project provides a foundation for building more advanced systems such as research assistants, customer-support agents, document analysis tools, and intelligent business applications.

Technologies Used

Python • OpenAI • LlamaIndex • Pinecone • Streamlit • Vector Embeddings • Large Language Models

This project showcases how these technologies can work together to transform unstructured information into an interactive and intelligent AI application.