# Flask API Documentation

## Overview

The Research AI Assistant API provides a RESTful interface for interacting with an AI-powered research assistant. The API uses local GPU models for inference and supports conversational interactions with context management.

**Base URL (HF Spaces):** `https://jatinautonomouslabs-research-ai-assistant-api.hf.space`

**Alternative Base URL:** `https://huggingface.co/spaces/JatinAutonomousLabs/Research_AI_Assistant_API`

**API Version:** 1.0

**Content-Type:** `application/json`

> **Note:** For Hugging Face Spaces Docker deployments, use the `.hf.space` domain format. The space name is converted to lowercase with hyphens.

## Features

- 🤖 **AI-Powered Responses** - Local GPU model inference (Tesla T4)
- 💬 **Conversational Context** - Maintains conversation history and user context
- 🔒 **CORS Enabled** - Ready for web integration
- ⚡ **Async Processing** - Efficient request handling
- 📊 **Transparent Reasoning** - Returns reasoning chains and performance metrics

---

## Authentication

Currently, the API does not require authentication. However, for production use, you should:

1. Set `HF_TOKEN` environment variable for Hugging Face model access
2. Implement API key authentication if needed

---

## Endpoints

### 1. Get API Information

**Endpoint:** `GET /`

**Description:** Returns API information, version, and available endpoints.

**Request:**
```http
GET / HTTP/1.1
Host: huggingface.co
```

**Response:**
```json
{
  "name": "AI Assistant Flask API",
  "version": "1.0",
  "status": "running",
  "orchestrator_ready": true,
  "features": {
    "local_gpu_models": true,
    "max_workers": 4,
    "hardware": "NVIDIA T4 Medium"
  },
  "endpoints": {
    "health": "GET /api/health",
    "chat": "POST /api/chat",
    "initialize": "POST /api/initialize"
  }
}
```

**Status Codes:**
- `200 OK` - Success

---

### 2. Health Check

**Endpoint:** `GET /api/health`

**Description:** Checks if the API and orchestrator are ready to handle requests.

**Request:**
```http
GET /api/health HTTP/1.1
Host: huggingface.co
```

**Response:**
```json
{
  "status": "healthy",
  "orchestrator_ready": true
}
```

**Status Codes:**
- `200 OK` - API is healthy
  - `orchestrator_ready: true` - Ready to process requests
  - `orchestrator_ready: false` - Still initializing

**Example Response (Initializing):**
```json
{
  "status": "initializing",
  "orchestrator_ready": false
}
```

---

### 3. Chat Endpoint

**Endpoint:** `POST /api/chat`

**Description:** Send a message to the AI assistant and receive a response with reasoning and context.

**Request Headers:**
```http
Content-Type: application/json
```

**Request Body:**
```json
{
  "message": "Explain quantum entanglement in simple terms",
  "history": [
    ["User message 1", "Assistant response 1"],
    ["User message 2", "Assistant response 2"]
  ],
  "session_id": "session-123",
  "user_id": "user-456"
}
```

**Request Fields:**

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `message` | string | ✅ Yes | User's message/question (max 10,000 characters) |
| `history` | array | ❌ No | Conversation history as array of `[user, assistant]` pairs |
| `session_id` | string | ❌ No | Unique session identifier for context continuity |
| `user_id` | string | ❌ No | User identifier (defaults to "anonymous") |

**Response (Success):**
```json
{
  "success": true,
  "message": "Quantum entanglement is when two particles become linked...",
  "history": [
    ["Explain quantum entanglement", "Quantum entanglement is when two particles become linked..."]
  ],
  "reasoning": {
    "intent": "educational_query",
    "steps": ["Understanding request", "Gathering information", "Synthesizing response"],
    "confidence": 0.95
  },
  "performance": {
    "response_time_ms": 2345,
    "tokens_generated": 156,
    "model_used": "mistralai/Mistral-7B-Instruct-v0.2"
  }
}
```

**Response Fields:**

| Field | Type | Description |
|-------|------|-------------|
| `success` | boolean | Whether the request was successful |
| `message` | string | AI assistant's response |
| `history` | array | Updated conversation history including the new exchange |
| `reasoning` | object | AI reasoning process and confidence metrics |
| `performance` | object | Performance metrics (response time, tokens, model used) |

**Status Codes:**
- `200 OK` - Request processed successfully
- `400 Bad Request` - Invalid request (missing message, empty message, too long, wrong type)
- `500 Internal Server Error` - Server error processing request
- `503 Service Unavailable` - Orchestrator not ready (still initializing)

**Error Response:**
```json
{
  "success": false,
  "error": "Message is required",
  "message": "Error processing your request. Please try again."
}
```

---

### 4. Initialize Orchestrator

**Endpoint:** `POST /api/initialize`

**Description:** Manually trigger orchestrator initialization (useful if initialization failed on startup).

**Request:**
```http
POST /api/initialize HTTP/1.1
Host: huggingface.co
Content-Type: application/json
```

**Request Body:**
```json
{}
```

**Response (Success):**
```json
{
  "success": true,
  "message": "Orchestrator initialized successfully"
}
```

**Response (Failure):**
```json
{
  "success": false,
  "message": "Initialization failed. Check logs for details."
}
```

**Status Codes:**
- `200 OK` - Initialization successful
- `500 Internal Server Error` - Initialization failed

---

## Code Examples

### Python

```python
import requests
import json

BASE_URL = "https://jatinautonomouslabs-research-ai-assistant-api.hf.space"

# Check health
def check_health():
    response = requests.get(f"{BASE_URL}/api/health")
    return response.json()

# Send chat message
def send_message(message, session_id=None, user_id=None, history=None):
    payload = {
        "message": message,
        "session_id": session_id,
        "user_id": user_id or "anonymous",
        "history": history or []
    }
    
    response = requests.post(
        f"{BASE_URL}/api/chat",
        json=payload,
        headers={"Content-Type": "application/json"}
    )
    
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"API Error: {response.status_code} - {response.text}")

# Example usage
if __name__ == "__main__":
    # Check if API is ready
    health = check_health()
    print(f"API Status: {health}")
    
    if health.get("orchestrator_ready"):
        # Send a message
        result = send_message(
            message="What is machine learning?",
            session_id="my-session-123",
            user_id="user-456"
        )
        
        print(f"Response: {result['message']}")
        print(f"Reasoning: {result.get('reasoning', {})}")
        
        # Continue conversation
        history = result['history']
        result2 = send_message(
            message="Can you explain neural networks?",
            session_id="my-session-123",
            user_id="user-456",
            history=history
        )
        print(f"Follow-up Response: {result2['message']}")
```

### JavaScript (Fetch API)

```javascript
const BASE_URL = 'https://jatinautonomouslabs-research-ai-assistant-api.hf.space';

// Check health
async function checkHealth() {
    const response = await fetch(`${BASE_URL}/api/health`);
    return await response.json();
}

// Send chat message
async function sendMessage(message, sessionId = null, userId = null, history = []) {
    const payload = {
        message: message,
        session_id: sessionId,
        user_id: userId || 'anonymous',
        history: history
    };
    
    const response = await fetch(`${BASE_URL}/api/chat`, {
        method: 'POST',
        headers: {
            'Content-Type': 'application/json'
        },
        body: JSON.stringify(payload)
    });
    
    if (!response.ok) {
        const error = await response.json();
        throw new Error(`API Error: ${response.status} - ${error.error || error.message}`);
    }
    
    return await response.json();
}

// Example usage
async function main() {
    try {
        // Check if API is ready
        const health = await checkHealth();
        console.log('API Status:', health);
        
        if (health.orchestrator_ready) {
            // Send a message
            const result = await sendMessage(
                'What is machine learning?',
                'my-session-123',
                'user-456'
            );
            
            console.log('Response:', result.message);
            console.log('Reasoning:', result.reasoning);
            
            // Continue conversation
            const result2 = await sendMessage(
                'Can you explain neural networks?',
                'my-session-123',
                'user-456',
                result.history
            );
            console.log('Follow-up Response:', result2.message);
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();
```

### cURL

```bash
# Check health
curl -X GET "https://jatinautonomouslabs-research-ai-assistant-api.hf.space/api/health"

# Send chat message
curl -X POST "https://jatinautonomouslabs-research-ai-assistant-api.hf.space/api/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "message": "What is machine learning?",
    "session_id": "my-session-123",
    "user_id": "user-456",
    "history": []
  }'

# Continue conversation
curl -X POST "https://jatinautonomouslabs-research-ai-assistant-api.hf.space/api/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "message": "Can you explain neural networks?",
    "session_id": "my-session-123",
    "user_id": "user-456",
    "history": [
      ["What is machine learning?", "Machine learning is a subset of artificial intelligence..."]
    ]
  }'
```

### Node.js (Axios)

```javascript
const axios = require('axios');

const BASE_URL = 'https://jatinautonomouslabs-research-ai-assistant-api.hf.space';

// Check health
async function checkHealth() {
    const response = await axios.get(`${BASE_URL}/api/health`);
    return response.data;
}

// Send chat message
async function sendMessage(message, sessionId = null, userId = null, history = []) {
    try {
        const response = await axios.post(`${BASE_URL}/api/chat`, {
            message: message,
            session_id: sessionId,
            user_id: userId || 'anonymous',
            history: history
        }, {
            headers: {
                'Content-Type': 'application/json'
            }
        });
        
        return response.data;
    } catch (error) {
        if (error.response) {
            throw new Error(`API Error: ${error.response.status} - ${error.response.data.error || error.response.data.message}`);
        }
        throw error;
    }
}

// Example usage
(async () => {
    try {
        const health = await checkHealth();
        console.log('API Status:', health);
        
        if (health.orchestrator_ready) {
            const result = await sendMessage(
                'What is machine learning?',
                'my-session-123',
                'user-456'
            );
            
            console.log('Response:', result.message);
        }
    } catch (error) {
        console.error('Error:', error.message);
    }
})();
```

---

## Error Handling

### Common Error Responses

#### 400 Bad Request

**Missing Message:**
```json
{
  "success": false,
  "error": "Message is required"
}
```

**Empty Message:**
```json
{
  "success": false,
  "error": "Message cannot be empty"
}
```

**Message Too Long:**
```json
{
  "success": false,
  "error": "Message too long. Maximum length is 10000 characters"
}
```

**Invalid Type:**
```json
{
  "success": false,
  "error": "Message must be a string"
}
```

#### 503 Service Unavailable

**Orchestrator Not Ready:**
```json
{
  "success": false,
  "error": "Orchestrator not ready",
  "message": "AI system is initializing. Please try again in a moment."
}
```

**Solution:** Wait a few seconds and retry, or check the `/api/health` endpoint.

#### 500 Internal Server Error

**Generic Error:**
```json
{
  "success": false,
  "error": "Error message here",
  "message": "Error processing your request. Please try again."
}
```

---

## Best Practices

### 1. Session Management

- **Use consistent session IDs** for maintaining conversation context
- **Generate unique session IDs** per user conversation thread
- **Include conversation history** in subsequent requests for better context

```python
# Good: Maintains context
session_id = "user-123-session-1"
history = []

# First message
result1 = send_message("What is AI?", session_id=session_id, history=history)
history = result1['history']

# Follow-up message (includes context)
result2 = send_message("Can you explain more?", session_id=session_id, history=history)
```

### 2. Error Handling

Always implement retry logic for 503 errors:

```python
import time

def send_message_with_retry(message, max_retries=3, retry_delay=2):
    for attempt in range(max_retries):
        try:
            result = send_message(message)
            return result
        except Exception as e:
            if "503" in str(e) and attempt < max_retries - 1:
                time.sleep(retry_delay)
                continue
            raise
```

### 3. Health Checks

Check API health before sending requests:

```python
def is_api_ready():
    try:
        health = check_health()
        return health.get("orchestrator_ready", False)
    except:
        return False

if is_api_ready():
    # Send request
    result = send_message("Hello")
else:
    print("API is not ready yet")
```

### 4. Rate Limiting

- **No explicit rate limits** are currently enforced
- **Recommended:** Implement client-side rate limiting (e.g., 1 request per second)
- **Consider:** Implementing request queuing for high-volume applications

### 5. Message Length

- **Maximum:** 10,000 characters per message
- **Recommended:** Keep messages concise for faster processing
- **For long content:** Split into multiple messages or summarize

### 6. Context Management

- **Include history** in requests to maintain conversation context
- **Session IDs** help track conversations across multiple requests
- **User IDs** enable personalization and user-specific context

---

## Integration Examples

### React Component

```jsx
import React, { useState, useEffect } from 'react';

const AIAssistant = () => {
    const [message, setMessage] = useState('');
    const [history, setHistory] = useState([]);
    const [loading, setLoading] = useState(false);
    const [sessionId] = useState(`session-${Date.now()}`);
    
    const sendMessage = async () => {
        if (!message.trim()) return;
        
        setLoading(true);
        try {
            const response = await fetch('https://jatinautonomouslabs-research-ai-assistant-api.hf.space/api/chat', {
                method: 'POST',
                headers: { 'Content-Type': 'application/json' },
                body: JSON.stringify({
                    message: message,
                    session_id: sessionId,
                    user_id: 'user-123',
                    history: history
                })
            });
            
            const data = await response.json();
            if (data.success) {
                setHistory(data.history);
                setMessage('');
            }
        } catch (error) {
            console.error('Error:', error);
        } finally {
            setLoading(false);
        }
    };
    
    return (
        <div>
            <div className="chat-history">
                {history.map(([user, assistant], idx) => (
                    <div key={idx}>
                        <div><strong>You:</strong> {user}</div>
                        <div><strong>Assistant:</strong> {assistant}</div>
                    </div>
                ))}
            </div>
            <input
                value={message}
                onChange={(e) => setMessage(e.target.value)}
                onKeyPress={(e) => e.key === 'Enter' && sendMessage()}
                disabled={loading}
            />
            <button onClick={sendMessage} disabled={loading}>
                {loading ? 'Sending...' : 'Send'}
            </button>
        </div>
    );
};
```

### Python CLI Tool

```python
#!/usr/bin/env python3
import requests
import sys

BASE_URL = "https://jatinautonomouslabs-research-ai-assistant-api.hf.space"

class ChatCLI:
    def __init__(self):
        self.session_id = f"cli-session-{hash(__file__)}"
        self.history = []
    
    def chat(self, message):
        response = requests.post(
            f"{BASE_URL}/api/chat",
            json={
                "message": message,
                "session_id": self.session_id,
                "user_id": "cli-user",
                "history": self.history
            }
        )
        
        if response.status_code == 200:
            data = response.json()
            self.history = data['history']
            return data['message']
        else:
            return f"Error: {response.status_code} - {response.text}"
    
    def run(self):
        print("AI Assistant CLI (Type 'exit' to quit)")
        print("=" * 50)
        
        while True:
            user_input = input("\nYou: ").strip()
            if user_input.lower() in ['exit', 'quit']:
                break
            
            print("Assistant: ", end="", flush=True)
            response = self.chat(user_input)
            print(response)

if __name__ == "__main__":
    cli = ChatCLI()
    cli.run()
```

---

## Response Times

- **Typical Response:** 2-10 seconds
- **First Request:** May take longer due to model loading (10-30 seconds)
- **Subsequent Requests:** Faster due to cached models (2-5 seconds)

**Factors Affecting Response Time:**
- Message length
- Model loading (first request)
- GPU availability
- Concurrent requests

---

## Troubleshooting

### Common Issues

#### 404 Not Found

**Problem:** Getting 404 when accessing the API

**Solutions:**
1. **Verify the Space is running:**
   - Check the Hugging Face Space page to ensure it's built and running
   - Wait for the initial build to complete (5-10 minutes)

2. **Check URL format:**
   - ✅ Correct: `https://jatinautonomouslabs-research-ai-assistant-api.hf.space`
   - ❌ Wrong: `https://jatinautonomouslabs-research_ai_assistant_api.hf.space` (underscores)
   - ✅ Alternative: `https://huggingface.co/spaces/JatinAutonomousLabs/Research_AI_Assistant_API`

3. **Verify endpoint paths:**
   - Health: `GET /api/health`
   - Chat: `POST /api/chat`
   - Root: `GET /`

4. **Test with root endpoint first:**
   ```bash
   curl https://jatinautonomouslabs-research-ai-assistant-api.hf.space/
   ```

#### 503 Service Unavailable

**Problem:** Orchestrator not ready

**Solutions:**
1. Wait 30-60 seconds for initialization
2. Check `/api/health` endpoint
3. Use `/api/initialize` to manually trigger initialization

#### CORS Errors

**Problem:** CORS errors in browser

**Solutions:**
- The API has CORS enabled for all origins
- If issues persist, check browser console for specific errors
- Ensure you're using the correct base URL

### Testing API Connectivity

**Quick Health Check:**
```bash
# Test root endpoint
curl https://jatinautonomouslabs-research-ai-assistant-api.hf.space/

# Test health endpoint
curl https://jatinautonomouslabs-research-ai-assistant-api.hf.space/api/health
```

**Python Test Script:**
```python
import requests

BASE_URL = "https://jatinautonomouslabs-research-ai-assistant-api.hf.space"

# Test root
try:
    response = requests.get(f"{BASE_URL}/", timeout=10)
    print(f"Root endpoint: {response.status_code} - {response.json()}")
except Exception as e:
    print(f"Root endpoint failed: {e}")

# Test health
try:
    response = requests.get(f"{BASE_URL}/api/health", timeout=10)
    print(f"Health endpoint: {response.status_code} - {response.json()}")
except Exception as e:
    print(f"Health endpoint failed: {e}")
```

## Support

For issues, questions, or contributions:
- **Repository:** [GitHub Repository URL]
- **Hugging Face Space:** [https://huggingface.co/spaces/JatinAutonomousLabs/Research_AI_Assistant_API](https://huggingface.co/spaces/JatinAutonomousLabs/Research_AI_Assistant_API)

---

## Changelog

### Version 1.0 (Current)
- Initial API release
- Chat endpoint with context management
- Health check endpoint
- Local GPU model inference
- CORS enabled for web integration

---

## License

This API is provided as-is. Please refer to the main project README for license information.