README.md

metadata

title: Intelligent Document QnA API
emoji: 📚
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860

Intelligent Document Q&A API

A high-performance API that allows you to find answers within your documents using powerful language models. Ingest PDFs, DOCX files, or emails, and get back precise answers to your questions.

This project is designed to be simple to set up and use, acting as a robust backend for any application that needs document-based question-answering capabilities.

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🚀 What Makes This Project Different?

Unlike many existing RAG APIs, this project is:

• Model-Agnostic and Multi-Provider Friendly
  Supports both Google Gemini and Gemini AI out of the box — no hard dependency on OpenAI.

• Cloud-Ready and Free-Tier Optimized
  Specifically engineered to run smoothly on platforms like Render, with memory-efficient caching and lazy model loading.

• Format-Intelligent
  Automatically detects and uses the correct loader for .pdf, .docx, and .eml files — no manual preprocessing required.

• Minimal Memory Footprint
  Designed for low-resource environments — ideal for free-tier deployments, research prototypes, or student projects.

• Clean JSON Output
  Filters out verbose LLM reasoning ("Thought: Let's find the answer...") and returns only the clean, relevant answers.

This makes it ideal for developers, students, and startups looking to build document Q&A apps without the complexity or cost of large RAG systems.

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✨ Features

• Multi-Format Support
  Natively handles .pdf, .docx, and .eml files.

• Persistent Storage
  Uses Supabase with pgvector to store document embeddings. Process a document once and query it instantly anytime after.

• High-Quality Answers
  Leverages state-of-the-art language models from Gemini AI and Google for accurate embeddings and intelligent Q&A.

• Asynchronous & Fast
  Built with FastAPI for high-performance, non-blocking I/O.

• Easy to Deploy
  Ready to be containerized with Docker or deployed to any modern cloud platform.

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🚀 Getting Started

Follow these steps to get the API server running on your local machine.

✅ Prerequisites

• Python 3.8+
• A Supabase account with a project created
• API keys from Google AI Studio and Gemini AI

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📁 1. Clone the Repository

git clone https://github.com/hacketthadwin/intelligent-document-qna-api.git
cd intelligent-document-qna-api

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🗃️ 2. Set Up Your Supabase Database

Enable the vector extension in your Supabase project:

1. Go to your Supabase project dashboard
2. Navigate to the SQL Editor
3. Run the following SQL command:

create extension if not exists vector;

A documents table will be automatically created the first time a document is processed via LangChain.

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🔐 3. Configure Environment Variables

Create a .env file in the root of your project directory. Use this template:

# --- Service Keys ---
GOOGLE_API_KEY="your_google_api_key_here"
# --- Supabase Credentials for Vector Store ---
SUPABASE_URL="https://your_supabase_project_id.supabase.co"
SUPABASE_SERVICE_KEY="your_supabase_service_role_key_here"

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📦 4. Install Dependencies

Install required packages using pip:

pip install -r requirements.txt

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▶️ 5. Run the API Server

Start the FastAPI server with:

python -m uvicorn main:app --reload

Visit http://127.0.0.1:8000 to access the API.
Interactive docs available at http://127.0.0.1:8000/docs

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⚙️ API Usage

POST /query

This endpoint ingests a document (if it's new) and answers questions about it.

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📤 Request Body

• document_url (string, required): Public URL to the document you want to query
• questions (array of strings, required): One or more questions to ask

✅ Example using curl

curl -X POST "http://127.0.0.1:8000/query" \
-H "Content-Type: application/json" \
-d '{
  "document_url": "https://arxiv.org/pdf/1706.03762.pdf",
  "questions": [
    "What is the title of this paper?",
    "Summarize the abstract in one sentence."
  ]
}'

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📥 Example Success Response

{
  "answers": [
    "The title of the paper is 'Attention Is All You Need'.",
    "The abstract introduces the Transformer, a new network architecture based solely on attention mechanisms that is more parallelizable and requires significantly less time to train than existing models."
  ],
  "document_url": "https://arxiv.org/pdf/1706.03762.pdf",
  "message": "New document processed and vectors stored in database."
}

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🤝 Contributing

Contributions are welcome! If you have ideas for features or improvements:

• Open an issue to discuss
• Fork the repository
• Create a new branch
• Make your changes
• Submit a pull request

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📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.