Enterprise RAG Platform

Turn documents into answers. Instantly.

Upload contracts, research papers, or financial reports. Ask questions in plain English. Get precise, cited answers in seconds.

---

The Problem

Knowledge workers spend 2.5 hours daily searching for information buried in documents. Legal teams review contracts manually. Researchers dig through papers. Finance teams hunt for clauses in agreements.

The Solution

Enterprise RAG eliminates that friction:

Upload documents → Ask questions → Get cited answers in <5 seconds

No more Ctrl+F. No more reading 50 pages to find one clause. Just ask.

---

Features

Feature	What You Get
Multi-document upload	Process multiple files at once with batch progress
Streaming answers	Watch answers generate in real-time with thinking indicator
Inline citations	Every claim linked to source document + page number
3 AI models	GPT-OSS 120B, Llama 3.3 70B, Gemma 3 27B
Session isolation	Your documents are private to your session
Auto-cleanup	Documents auto-deleted after 7 days

---

Architecture

flowchart LR
    subgraph Input
        A[📄 PDF / DOCX / TXT]
    end
    
    subgraph Processing
        B[✂️ Chunk<br/>1000 chars]
        C[🧠 Embed<br/>bge-small-en-v1.5]
        D[(💾 ChromaDB)]
    end
    
    subgraph Query
        E[💬 Question]
        F[🎯 Top-4 Retrieval]
        G[🤖 LLM Stream]
        H[📝 Cited Answer]
    end
    
    A --> B --> C --> D
    E --> F --> G --> H
    D --> F

Loading

Stack: LangChain · ChromaDB · sentence-transformers · Groq + OpenRouter

---

Quick Start

Docker (Recommended)

git clone https://github.com/pkgprateek/rag-document-qa-workflow.git
cd rag-document-qa-workflow

# Add your API keys
echo "GROQ_API_KEY=your_key" > .env
echo "OPENROUTER_API_KEY=your_key" >> .env

docker compose up

Open http://localhost:7860

Local Development

uv venv && source .venv/bin/activate
uv pip install -r requirements.txt
python app/main.py

Get Free API Keys:

• Groq — Required (GPT-OSS, Llama)
• OpenRouter — Optional (Gemma)

---

Performance

Metric	Value
Query latency	50-200ms (p95)
Document processing	3-4s for 100 pages
Citation accuracy	93-96% relevance
Streaming	First token in <500ms

---

Enterprise Pilots

2-week paid pilots for teams ready to deploy RAG on their infrastructure:

Week	Deliverables
Week 1	Document ingestion, chunking tuned for your domain
Week 2	Deployment, team training, ROI analysis

Includes: Custom RAG system · Performance benchmarks · 30-day support

---

Contact

Prateek Kumar Goel

---

_{MIT License · Built with ❤️ for enterprise document intelligence}