Case Study · AI / Machine Learning
Functional Full-Stack RAG Document Intelligence System
Upload PDF documents and query them through semantic retrieval and context-grounded AI responses.
A full-stack Retrieval-Augmented Generation system that enables users to upload PDF documents and ask natural-language questions grounded in their content. The application combines document parsing, semantic chunking, OpenAI embeddings, Supabase vector search and an AI chat interface to deliver context-aware answers while reducing unsupported responses.
- ROLE
- Lead Developer · AI & Full-Stack Engineer
- YEAR
- 2025
TECH_STACK
Technologies
CONTEXT
Technical context
Organizations increasingly rely on large collections of PDF documents, but locating specific information through manual search is slow and inefficient. Conventional chatbots can improve accessibility, yet without access to trusted source material they may generate unsupported or inaccurate responses. This project explores how Retrieval-Augmented Generation can transform static documents into an interactive knowledge source. It combines automated PDF ingestion, vector-based semantic retrieval and context-grounded language generation within a full-stack application designed for practical document question answering.
PROBLEM
Problem
Traditional document search depends on exact keywords and requires users to manually inspect multiple files to find relevant information. This becomes inefficient when the knowledge base contains long or numerous PDF documents, while general-purpose AI assistants may produce answers that are not grounded in the available source material. The challenge was to build a complete pipeline that could reliably extract and index document content, retrieve the most relevant passages for each query and generate clear answers based only on that retrieved context. The system also needed to support document management, semantic search and a practical conversational interface within one full-stack application.
OVERVIEW
Project overview
The application is structured as a full-stack Retrieval-Augmented Generation platform that connects a Next.js frontend with a NestJS backend, LangChain orchestration, OpenAI models and a Supabase vector database.
Users upload PDF documents through the web interface, after which the backend extracts their text and divides the content into overlapping chunks to retain contextual continuity between adjacent sections.
Each chunk is converted into an OpenAI embedding and stored in Supabase with pgvector support, creating a searchable semantic index of the uploaded document collection.
When a question is submitted, the backend generates an embedding for the query and performs similarity search against the vector store to retrieve the six most relevant document chunks.
The retrieved passages are assembled into a structured prompt together with the user’s question and sent to an OpenAI chat model configured for deterministic, context-grounded response generation.
The prompt instructs the model to answer only from the available document context and to acknowledge when the requested information cannot be found, reducing unsupported responses.
The frontend combines document upload, indexed-file management and conversational question answering in a single interface, while the backend exposes dedicated endpoints for ingestion, retrieval, listing and deletion.
The resulting system demonstrates the complete RAG lifecycle from document processing and vector indexing to semantic retrieval and grounded natural-language answer generation.
APPROACH
Technical approach
- Built a full-stack RAG application for uploading, indexing and querying PDF documents through natural-language conversations.
- Implemented PDF text extraction and document ingestion using LangChain loaders.
- Split extracted content into overlapping chunks to preserve context across document boundaries.
- Generated vector embeddings with OpenAI and stored them in Supabase using pgvector.
- Configured semantic similarity search to retrieve the six most relevant document chunks for each user query.
- Constructed grounded prompts that combine the retrieved passages with the user’s question before calling the language model.
- Configured deterministic answer generation with a low-temperature OpenAI chat model.
- Added safeguards instructing the model not to invent information when the answer is absent from the retrieved context.
- Developed a Next.js and TypeScript frontend for document upload, file management and conversational question answering.
- Built NestJS API endpoints for document ingestion, querying, listing and deletion.
- Integrated the complete ingestion, retrieval and generation pipeline into a functional document-intelligence system.
FOCUS
Technical focus
- Retrieval-Augmented Generation
- Document Ingestion Pipeline
- PDF Text Extraction
- Semantic Chunking
- Vector Embedding Generation
- Supabase Vector Storage
- PostgreSQL pgvector Search
- Semantic Similarity Retrieval
- Context-Grounded Question Answering
- Prompt Engineering
- Hallucination Reduction
- LangChain Orchestration
- OpenAI Model Integration
- Full-Stack AI Architecture
- Document Management
- REST API Design
ARCHITECTURE
Architecture notes
- The system uses a Next.js and React frontend connected to a NestJS backend through REST API endpoints.
- The backend separates document ingestion, retrieval and response generation into distinct processing stages.
- Uploaded PDF files are parsed with PDFLoader and divided into overlapping text chunks using RecursiveCharacterTextSplitter.
- The chunking configuration uses 1,000-character segments with a 200-character overlap to preserve context between adjacent sections.
- Each document chunk is transformed into an OpenAI vector embedding and stored in a Supabase PostgreSQL database with pgvector support.
- The vector store uses a dedicated documents table and the match_documents function for semantic similarity retrieval.
- User questions are embedded and compared against the indexed document vectors to retrieve the six most relevant chunks.
- Retrieved chunks are combined into a structured context before being passed to the OpenAI chat model.
- The generation prompt restricts answers to the retrieved document context and instructs the model to acknowledge missing information.
- The OpenAI chat model is configured with a temperature of 0 to produce more deterministic responses.
- The backend exposes dedicated endpoints for PDF upload, question answering, document listing and document deletion.
- The architecture is modular, allowing the embedding model, vector database, retrieval settings or language model to be replaced independently.
HIGHLIGHTS
Engineering highlights
- Functional Full-Stack RAG Document Intelligence System
- Natural-Language Question Answering Over PDF Documents
- Automated PDF Ingestion and Text Extraction
- Context-Preserving Document Chunking
- OpenAI Vector Embedding Generation
- Supabase PostgreSQL Vector Storage with pgvector
- Semantic Similarity Search Across Uploaded Documents
- Retrieval of the Six Most Relevant Context Chunks
- Context-Grounded AI Response Generation
- Prompt-Based Hallucination Reduction
- Deterministic OpenAI Chat Responses
- Integrated Document Upload and Management
- Modular Next.js and NestJS Architecture
- Reusable Foundation for Private Knowledge Assistants
OUTCOME
Outcome
The project resulted in a functional full-stack RAG system that transforms uploaded PDF documents into an interactive, searchable knowledge base. Users can upload and manage documents, ask natural-language questions and receive answers generated from semantically retrieved source content. The system validates the complete Retrieval-Augmented Generation workflow, from document ingestion and chunking to embedding creation, vector search and grounded response generation. It provides a reusable foundation for internal knowledge assistants, research tools, document support systems and other applications that require reliable question answering over private document collections.