GenAI RAG Document Assistant

Overview

A generative AI application where users upload documents and ask questions about them. Answers are generated from passages retrieved out of the uploaded material rather than from model recall, so responses stay tied to the source content.

Problem / Objective

Objective: build a document-questioning application that retrieves relevant information from uploaded documents and generates contextual answers.

General-purpose models answer confidently about documents they have never seen, which makes them unusable for internal or client documentation.

Solution

A retrieval-augmented generation pipeline: documents are ingested and chunked, chunks are embedded and stored in a vector database, relevant chunks are retrieved per question, and the model answers from that retrieved context.

Key capabilities

• Document ingestion
• Document chunking
• Embedding generation
• Vector retrieval
• Context-aware answer generation

Technology

• AI

  • LLM
  • Embeddings
  • LangChain
  • LangGraph

• Development

  • Python
  • Streamlit

• Data

  • Vector Database

Implementation

Python handles ingestion, chunking and embedding. Chunks are stored in a vector database and retrieved per question, then passed as context to the language model. Orchestration is built with LangChain and LangGraph, and the interface is a Streamlit application.

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