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Flowise Service API

The MOOD MNKY Flowise service provides advanced chatflow management and AI workflow orchestration capabilities. This documentation covers all available endpoints, integration patterns, and best practices.

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Base URL

https://mnky-mind-flowise.moodmnky.com

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Available Endpoints

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Chatflow Management

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Prediction & Execution

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Analytics & Telemetry

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Service Health

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Authentication

All requests to the Flowise service require authentication: 
Authorization: Bearer your-api-key
To obtain API credentials:
  1. Access the developer portal
  2. Create a new API key with required permissions
  3. Follow our security guidelines for key management

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Request Format

All requests should use JSON: 
Content-Type: application/json

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Response Format

Successful responses follow this format: 
{
  "success": true,
  "data": {
    // Response data
  }
}
Error responses: 
{
  "success": false,
  "error": {
    "code": "error_code",
    "message": "Error description"
  }
}

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Streaming Responses

For streaming endpoints, use socket.io-client: 
import socketIOClient from 'socket.io-client'

const socket = socketIOClient("https://mnky-mind-flowise.moodmnky.com")

socket.on('connect', () => {
  console.log('Connected:', socket.id)
})

socket.on('start', () => {
  console.log('Stream started')
})

socket.on('token', (token) => {
  console.log('Token:', token)
})

socket.on('sourceDocuments', (docs) => {
  console.log('Source documents:', docs)
})

socket.on('end', () => {
  console.log('Stream ended')
})

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Rate Limiting

The service implements tiered rate limits:
TierChat RequestsManagement Requests
Basic60/min300/min
Pro300/min1000/min
EnterpriseCustomCustom
Rate limit headers:
  • X-RateLimit-Limit
  • X-RateLimit-Remaining
  • X-RateLimit-Reset

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Analytics Integration

The service supports multiple analytics providers:
  • Langsmith
  • Langfuse
  • LLMonitor
Configure analytics in your requests: 
{
  "question": "Hello!",
  "overrideConfig": {
    "analytics": {
      "langFuse": {
        "userId": "user123"
      }
    }
  }
}

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Monitoring

Access Prometheus metrics at: 
https://mnky-mind-flowise.moodmnky.com/metrics
Available metrics:
  • Request latencies
  • Chatflow execution stats
  • Memory usage
  • Error rates
  • Custom business metrics

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Best Practices

  1. Chatflow Management
    • Version control your chatflows
    • Test in development environment
    • Use descriptive chatflow names
    • Document node configurations
  2. Production Usage
    • Implement proper error handling
    • Use streaming for long responses
    • Monitor response times
    • Set appropriate timeouts
  3. Analytics
    • Track user interactions
    • Monitor completion rates
    • Analyze error patterns
    • Measure response quality

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Examples

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Curl

# Create a chat prediction
curl -X POST https://mnky-mind-flowise.moodmnky.com/api/v1/prediction/{chatflowid} \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "question": "Hello!",
    "overrideConfig": {
      "analytics": {
        "langFuse": {
          "userId": "user123"
        }
      }
    }
  }'

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Python

import requests

API_KEY = "your-api-key"
BASE_URL = "https://mnky-mind-flowise.moodmnky.com"

def chat_prediction(chatflow_id, question):
    response = requests.post(
        f"{BASE_URL}/api/v1/prediction/{chatflow_id}",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "question": question
        }
    )
    return response.json()

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JavaScript

const API_KEY = 'your-api-key';
const BASE_URL = 'https://mnky-mind-flowise.moodmnky.com';

async function chatPrediction(chatflowId, question) {
  const response = await fetch(
    `${BASE_URL}/api/v1/prediction/${chatflowId}`,
    {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        question
      })
    }
  );
  return await response.json();
}

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