GenAI RAG Document Assistant

Challenge / Objective

Objective: build a document-questioning application that retrieves relevant information from uploaded documents and generates contextual answers, instead of relying on what a general-purpose model happens to recall.

Approach

Treat retrieval as the core of the system rather than an add-on: ingest and chunk documents, embed the chunks, store them in a vector database, and retrieve per question so the model only ever answers from supplied context.

Solution

An ingestion pipeline, a vector store, a retrieval step and a generation step, exposed through a Streamlit interface where a user can upload documents and ask questions in sequence.

• Document ingestion and chunking
• Embeddings and vector retrieval
• Context-grounded generation
• Inspectable retrieval step

Architecture

Document upload → processing → chunking → embeddings → vector storage → retrieval → LLM generation → contextual answer. A user question follows the same path in reverse: question → embedding → vector search → relevant context → LLM → answer.

Implementation

Python for ingestion, chunking and embedding; a vector database for storage and similarity search; LangChain and LangGraph for orchestrating the retrieval and generation steps; Streamlit for the interface.

Technology

• AI

  • LLM
  • Embeddings
  • LangChain
  • LangGraph

• Development

  • Python
  • Streamlit

• Data

  • Vector Database

Current state

Current state: the pipeline works end to end as a demonstrable application — upload, retrieve, answer. It is a capability showcase and has not been run at production scale, so no throughput, latency or answer-quality figures are published.

Key takeaways

Chunking strategy affects answer quality more than model choice at this scale, and keeping retrieval and generation as separate, inspectable steps makes it far easier to see why a given answer came out the way it did.

Related services

• AI Agents & Chatbots
• Python, Data & AI

Project