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FastAPI PDF Legal Q&A API Documentation

Overview

The FastAPI PDF Legal Q&A API provides REST endpoints for uploading PDF documents and asking questions about their content using RAG (Retrieval-Augmented Generation) with local Ollama models.

Features

  • PDF Upload & Processing: Upload PDF files and extract text content
  • Question Answering: Ask questions about the loaded PDF using enhanced legal reasoning
  • Health Monitoring: Check API status, Ollama connectivity, and PDF loading status
  • Interactive Documentation: Auto-generated Swagger UI at /docs
  • Cross-Origin Support: CORS enabled for web applications

Quick Start

1. Start the Server

# Option 1: Direct Python
python fastapi_app.py

# Option 2: Using batch file (Windows)
run_fastapi.bat

# Option 3: Using uvicorn directly
uvicorn fastapi_app:app --host 0.0.0.0 --port 8000 --reload

2. Access the API

3. Test the API

# Option 1: Use test script
python test_api.py

# Option 2: Use batch file (Windows)
test_api.bat

API Endpoints

Health & Status

GET /health

Check API health and dependencies.

Response:

{
  "status": "healthy",
  "message": "API is running properly",
  "ollama_available": true,
  "pdf_loaded": false
}

GET /pdf-info

Get information about the currently loaded PDF.

Response:

{
  "filename": "document.pdf",
  "upload_time": "2024-01-15T10:30:00",
  "chunks_count": 45,
  "total_characters": 15420
}

PDF Management

POST /upload-pdf

Upload and process a PDF file.

Request:

  • Content-Type: multipart/form-data
  • Body: PDF file

Response:

{
  "status": "success",
  "message": "PDF 'document.pdf' processed successfully",
  "pdf_info": {
    "filename": "document.pdf",
    "upload_time": "2024-01-15T10:30:00",
    "chunks_count": 45,
    "total_characters": 15420
  }
}

DELETE /clear-pdf

Clear the currently loaded PDF from memory.

Response:

{
  "status": "success",
  "message": "PDF cleared successfully"
}

Question Answering

POST /ask

Ask a question about the loaded PDF.

Request:

{
  "question": "What are the main legal requirements?",
  "use_enhanced_reasoning": true
}

Response:

{
  "question": "What are the main legal requirements?",
  "answer": "Based on the document, the main legal requirements are...",
  "confidence": null,
  "sources": [],
  "timestamp": "2024-01-15T10:35:00",
  "processing_time": 2.34
}

Utility Endpoints

GET /models

List available Ollama models.

Response:

{
  "models": {
    "models": [
      {
        "name": "llama3.2:1b",
        "model": "llama3.2:1b",
        "size": 1234567890
      }
    ]
  }
}

POST /test-ollama

Test Ollama connection and functionality.

Response:

{
  "status": "success",
  "message": "Ollama is working",
  "response": "OK"
}

Usage Examples

Python Client Example

import requests

BASE_URL = "http://localhost:8000"

# Check health
response = requests.get(f"{BASE_URL}/health")
print(response.json())

# Upload PDF
with open("document.pdf", "rb") as f:
    files = {"file": ("document.pdf", f, "application/pdf")}
    response = requests.post(f"{BASE_URL}/upload-pdf", files=files)
    print(response.json())

# Ask question
question_data = {
    "question": "What is this document about?",
    "use_enhanced_reasoning": True
}
response = requests.post(f"{BASE_URL}/ask", json=question_data)
print(response.json())

cURL Examples

# Health check
curl -X GET "http://localhost:8000/health"

# Upload PDF
curl -X POST "http://localhost:8000/upload-pdf" \
  -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@document.pdf"

# Ask question
curl -X POST "http://localhost:8000/ask" \
  -H "accept: application/json" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "What is this document about?",
    "use_enhanced_reasoning": true
  }'

JavaScript/Fetch Example

// Upload PDF
const formData = new FormData();
formData.append('file', pdfFile);

fetch('http://localhost:8000/upload-pdf', {
  method: 'POST',
  body: formData
})
.then(response => response.json())
.then(data => console.log(data));

// Ask question
fetch('http://localhost:8000/ask', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    question: 'What is this document about?',
    use_enhanced_reasoning: true
  })
})
.then(response => response.json())
.then(data => console.log(data));

Error Handling

The API returns appropriate HTTP status codes:

  • 200: Success
  • 400: Bad Request (invalid file, empty question, etc.)
  • 404: Not Found (no PDF loaded)
  • 500: Internal Server Error

Error responses include details:

{
  "detail": "No PDF loaded. Please upload a PDF first."
}

Configuration

Environment Variables

You can configure the API using environment variables:

  • HOST: Server host (default: 0.0.0.0)
  • PORT: Server port (default: 8000)
  • OLLAMA_MODEL: Default Ollama model (default: llama3.2:1b)

Model Configuration

The API uses the Enhanced Legal RAG system which can be configured in enhanced_legal_rag.py:

  • Embedding model: all-MiniLM-L6-v2
  • Chunking strategy: Legal structure-based
  • Ollama model: llama3.2:1b

Dependencies

  • FastAPI: Web framework
  • Uvicorn: ASGI server
  • Enhanced Legal RAG: Custom PDF analysis system
  • Ollama: Local LLM inference
  • FAISS: Vector similarity search
  • SentenceTransformers: Text embeddings

Troubleshooting

Common Issues

  1. Ollama not available

    • Ensure Ollama is installed and running
    • Check if the model is downloaded: ollama pull llama3.2:1b

  2. PDF upload fails

    • Check file format (must be PDF)
    • Verify file is not corrupted
    • Check file size limits

  3. Questions not working

    • Ensure PDF is uploaded first
    • Check if question is not empty
    • Verify Ollama model is available

Logs

The API provides detailed logging. Check the console output for error messages and processing information.

Development

Running in Development Mode

uvicorn fastapi_app:app --host 0.0.0.0 --port 8000 --reload

Testing

Use the provided test script to validate all endpoints:

python test_api.py

Production Deployment

For production deployment, consider:

  1. Security: Configure CORS properly, add authentication
  2. Performance: Use production ASGI server, load balancing
  3. Monitoring: Add logging, metrics, health checks
  4. Storage: Implement persistent storage for PDFs
  5. Scaling: Consider containerization with Docker