AI-Powered Document Retrieval System (RAG Pipeline)

An AI-powered retrieval and reasoning system for unstructured documents, built using semantic embeddings and Retrieval-Augmented Generation (RAG). The system transforms raw document collections into a queryable knowledge space where users can retrieve and reason over information using natural language.

Demo: https://doc-search-app-gray.vercel.app/

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System Overview

This project implements a full end-to-end RAG pipeline:

• Documents are ingested and processed into semantic chunks
• Each chunk is embedded into a high-dimensional vector space
• Vectors are stored and indexed for similarity search
• Queries are embedded and matched using semantic similarity
• Retrieved context is used by an LLM to generate grounded responses

The result is a system that enables context-aware information retrieval and AI-assisted reasoning over private document collections.

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Architecture

• Frontend: Next.js (document upload, search interface, results visualization)
• Backend: Next.js API routes (ingestion, retrieval, query orchestration)
• Database: Supabase PostgreSQL with pgvector (vector similarity search)
• Storage: Supabase Storage (raw document persistence)
• AI Layer: OpenAI embeddings + LLM for response generation

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Core Capabilities

• Document ingestion and structured chunking pipeline
• Semantic search using vector embeddings
• Approximate nearest-neighbor retrieval via cosine similarity
• Context-grounded answer generation (RAG)
• Unified interface for upload, retrieval, and interaction

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Key Idea

Instead of keyword-based search, the system operates in a semantic vector space, enabling retrieval based on meaning rather than exact text matching.

This allows:

• retrieval across paraphrases and implicit meaning
• improved robustness over traditional search systems
• integration of retrieval with generative AI models

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Technologies

• Next.js
• TypeScript
• OpenAI API (Embeddings + LLM)
• Supabase (PostgreSQL + Storage)
• pgvector extension
• Tailwind CSS

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Credits

This project was inspired by and partially built using:
https://www.freecodecamp.org/news/how-to-build-an-ai-powered-rag-search-application-with-nextjs-supabase-and-openai/