📄 Enterprise Document Intelligence (RAG Pipeline)

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📌 Overview

Enterprise Document Intelligence is a Retrieval-Augmented Generation (RAG) application that enables users to ask natural language questions about PDF documents.

The application extracts text from uploaded PDFs, generates semantic embeddings using a local Hugging Face model, stores them in a FAISS vector index, retrieves relevant document chunks, and sends only the retrieved context to a large language model through the Groq API to generate responses.

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Demo

Upload PDF

Ask Questions

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✨ Features

• PDF document upload
• Automatic text extraction
• Local embedding generation
• FAISS vector search
• Context-aware question answering
• Interactive Streamlit interface
• Modular RAG pipeline

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🏗 Architecture

PDF Upload
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      ▼
Text Extraction
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Text Chunking
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      ▼
Hugging Face Embeddings
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      ▼
FAISS Vector Store
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Similarity Search
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Retrieved Context
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      ▼
Groq LLM
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      ▼
Generated Answer

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🛠 Tech Stack

Component	Technology
Language	Python
Frontend	Streamlit
Framework	LangChain
Embeddings	all-MiniLM-L6-v2
Vector Database	FAISS
LLM	Mixtral-8x7B via Groq
PDF Processing	PyPDF2

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📂 Project Structure

enterprise-doc-intelligence/

├── app.py
├── config.py
├── prompt_templates.py
├── requirements.txt
├── screenshots/
├── utils/
└── README.md

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⚙️ Installation

git clone https://github.com/yourusername/enterprise-doc-intelligence.git

cd enterprise-doc-intelligence

python -m venv .venv

source .venv/bin/activate

pip install -r requirements.txt

# Add your GROQ_API_KEY to a .env file

streamlit run app.py

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🚀 How It Works

1. Upload one or more PDF documents.
2. Extract document text.
3. Split text into manageable chunks.
4. Generate semantic embeddings locally.
5. Store embeddings in a FAISS vector index.
6. Retrieve the most relevant chunks for a query.
7. Send the retrieved context to the Groq LLM.
8. Display the generated answer.

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📈 Resume Highlights

• Built an end-to-end Retrieval-Augmented Generation (RAG) application using LangChain, FAISS, and Groq.
• Implemented local semantic embedding generation using Hugging Face models to reduce reliance on external embedding services.
• Designed a modular document-processing pipeline for PDF ingestion, retrieval, and question answering.
• Developed an interactive Streamlit interface for document analysis using natural language queries.

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🔮 Future Improvements

• Pinecone or Qdrant integration
• Docker deployment
• User authentication
• Role-based access control
• AWS S3 document storage
• REST API
• Citation highlighting
• Multi-document chat history

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📄 License

MIT License