Final Project

Imagine building a cutting-edge AI application that can understand, store, and retrieve human language with context and relevance—just like a smart assistant. In this project, we’ll explore the practical side of building real-world generative AI systems by combining powerful tools like the OpenAI API, PostgreSQL with vector search capabilities, and LangChain.

This capstone experience will help us set up infrastructure, connect language models to data, and implement retrieval-augmented generation (RAG) to build applications that respond to user queries with contextual information from our data sources.

Why does this project matter?

This project combines modern AI development with production-ready tools to give us a realistic, hands-on experience building a complete GenAI stack. It prepares us to tackle real-world scenarios where data retrieval, vector search, and LLMs are used together to build intelligent applications.

By completing this project, we will:

• Learn how to build LLM apps that combine static knowledge with real-time, dynamic information retrieval.

• Get hands-on experience working with embeddings, vector stores, and similarity search using pgvector and LangChain.

• Understand how to structure, connect, and execute an end-to-end RAG pipeline using OpenAI’s APIs.

What will we accomplish?

By the end of this project, we’ll have:

• Set up a managed PostgreSQL database and enabled the pgvector extension for vector similarity search.

• Connected OpenAI’s API and created embedding and LLM instances.

• Built a pipeline to process and store content from a data source (like a web page) into a vector database.

• Implemented similarity search and integrated RAG-based responses for answering natural language questions using our data.

• Executed the full program flow from data ingestion to intelligent query response.