Internal Knowledge Assistant

A lightweight Retrieval-Augmented Generation project for querying internal documents.

What it does

• Loads company documents from local folders
• Splits text into chunks
• Generates embeddings using Sentence Transformers
• Stores vectors in Faiss & ChromaDB
• Retrieves relevant context for LLM responses

Tech stack

• Python 3.12+
• LangChain
• Sentence Transformers
• ChromaDB, Faiss
• Groq API for LLM inference

Project structure

• app/main.py: Streamlit UI entry point
• src/data_loader.py: Multi-format document loading
• src/embedding.py: Chunking and embedding generation
• src/search.py: Retrieval and answer generation
• config/: Basic runtime configuration
• tests/: Basic project checks
• docker/: Docker build files
• data/: Source documents and vector store data

Quick start

1. Install dependencies:

uv sync

2. Add your API key to a .env file:

GROQ_API_KEY=your_key_here

3. Run locally:

streamlit run app/main.py

Run with Docker

Build and run the app in a container:

docker build -t internal-knowledge-assistant .
docker run -p 8501:8501 internal-knowledge-assistant

Or with Docker Compose:

docker compose up --build

The app will be available at http://localhost:8501 and MLflow UI at http://localhost:5000.

Notes

• Keep input files inside folders like data/pdf_files and data/txt_files.
• Vector store files are persisted under faiss_store by default.