Skip to content

README.md

Latest commit

 

History

History
48 lines (32 loc) · 1.27 KB

README.md

File metadata and controls

48 lines (32 loc) · 1.27 KB
Raw

Enterprise Knowledge RAG Assistant

A retrieval-augmented generation assistant for answering questions from enterprise engineering knowledge documents.

Architecture Diagram

Enterprise Knowledge RAG Architecture

Problem

Enterprise engineering teams often depend on scattered documents, release notes, design notes, regression reports, and debugging summaries. Searching manually is slow and context is easily lost.

Solution

This project builds a RAG-based assistant that retrieves relevant document chunks and generates grounded answers with source references.

Architecture

Documents → Chunking → Embeddings → Vector Database → Retriever → LLM → Answer with citations

Key Features

  • Document ingestion
  • Text chunking
  • Embedding generation
  • Vector search
  • Context-aware answer generation
  • Source citation support
  • Streamlit user interface

Tech Stack

  • Python
  • Streamlit
  • ChromaDB
  • Sentence Transformers
  • LangChain / LlamaIndex
  • OpenAI-compatible LLM API

Portfolio Relevance

This project demonstrates practical AI infrastructure skills:

  • RAG system design
  • Vector database usage
  • Retrieval quality evaluation
  • Enterprise knowledge assistant architecture
  • LLM application engineering