krishna0506/Document_Intelligence

Document Intelligence RAG System

A Retrieval-Augmented Generation (RAG) based document question-answering system built using FastAPI, LangChain, ChromaDB, and Groq LLM.

This project allows users to ingest documents, create embeddings, retrieve relevant context, and generate AI-powered answers.

Features

• —Document ingestion and chunking
• —Vector embeddings using ChromaDB
• —Semantic search and retrieval
• —LLM-based answer generation
• —FastAPI REST API
• —Modular project structure

Project Structure

documentintelligence/ │ ├── app/ │ ├── api.py │ ├── chunking.py │ ├── embeddings.py │ ├── generator.py │ ├── guardrails.py │ ├── ingest.py │ ├── main.py │ ├── retriever.py │ ├── schemas.py │ └── vectorstore.py │ ├── data/ │ ├── clientxrequirements.txt │ ├── screeningchecklistpython.txt │ ├── compliancepolicy.txt │ ├── ratecard2026.txt │ └── placementchecklist.txt │ ├── requirements.txt └── README.md

Setup Instructions

1. Create Virtual Environment

Windows: python -m venv .venv .venv\Scripts\activate

Mac/Linux: python -m venv .venv source .venv/bin/activate

2. Install Dependencies

pip install -r requirements.txt

3. Environment Variables

Create a file named .env in the project root:

GROQAPIKEY=yourapikey_here

4. Run the Application

uvicorn document_intelligence.app.main:app --reload

Open in browser: http://127.0.0.1:8000/docs

How It Works

1. 1.Add documents to the data/ folder.
2. 2.Run ingestion to create embeddings.
3. 3.Ask questions through API endpoints.
4. 4.The system retrieves relevant content and generates answers.

Technologies

• —Python
• —FastAPI
• —LangChain
• —ChromaDB
• —Groq LLM

Notes

• —Keep documents organized inside the data folder.
• —Update paths in ingest.py if you change folder structure.
• —Store API keys securely using .env file.

License

Educational and internal project use.