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Prediction

Prediction API is the primary endpoint for interacting with your Flowise flows and assistants. It allows you to send messages to your selected flow and receive responses back. This API handles the core chat functionality, including:

  • Chat Interactions: Send questions or messages to your flow and receive AI-generated responses

  • Streaming Responses: Get real-time streaming responses for better user experience

  • Conversation Memory: Maintain context across multiple messages within a session

  • File Processing: Upload and process images, audio, and other files as part of your queries

  • Dynamic Configuration: Override chatflow settings and pass variables at runtime

For details, see the Prediction Endpoint API Reference.

Base URL and Authentication

Base URL: http://localhost:3000 (or your Flowise instance URL)

Endpoint: POST /api/v1/prediction/:id

Authentication: Refer Authentication for Flows

Request Format

Basic Request Structure

{
    "question": "Your message here",
    "streaming": false,
    "overrideConfig": {},
    "history": [],
    "uploads": [],
    "form": {}
}

Parameters

Parameter
Type
Required
Description

question

string

Yes

The message/question to send to the flow

form

object

Either question or form

The form object to send to the flow

streaming

boolean

No

Enable streaming responses (default: false)

overrideConfig

object

No

Override flow configuration

history

array

No

Previous conversation messages

uploads

array

No

Files to upload (images, audio, etc.)

humanInput

object

No

Return human feedback and resume execution

SDK Libraries

Flowise provides official SDKs for Python and TypeScript/JavaScript:

Installation

Python: pip install flowise

TypeScript/JavaScript: npm install flowise-sdk

Python SDK Usage

from flowise import Flowise, PredictionData

# Initialize client
client = Flowise(base_url="http://localhost:3000")

# Non-streaming prediction
try:
    response = client.create_prediction(
        PredictionData(
            chatflowId="your-chatflow-id",
            question="What is machine learning?",
            streaming=False
        )
    )
    
    # Handle response
    for result in response:
        print("Response:", result)
        
except Exception as e:
    print(f"Error: {e}")

TypeScript/JavaScript SDK Usage

import { FlowiseClient } from 'flowise-sdk';

// Initialize client
const client = new FlowiseClient({ 
    baseUrl: 'http://localhost:3000' 
});

// Non-streaming prediction
async function chatWithFlow() {
    try {
        const response = await client.createPrediction({
            chatflowId: 'your-chatflow-id',
            question: 'What is machine learning?',
            streaming: false
        });
        
        console.log('Response:', response);
        
    } catch (error) {
        console.error('Error:', error);
    }
}

chatWithFlow();

Direct HTTP API Usage

If you prefer to use the REST API directly without SDKs:

Basic Request

import requests
import json

def send_message(chatflow_id, question, streaming=False):
    url = f"http://localhost:3000/api/v1/prediction/{chatflow_id}"
    
    payload = {
        "question": question,
        "streaming": streaming
    }
    
    headers = {
        "Content-Type": "application/json"
    }
    
    try:
        response = requests.post(url, json=payload, headers=headers)
        response.raise_for_status()  # Raise exception for bad status codes
        
        return response.json()
        
    except requests.exceptions.RequestException as e:
        print(f"Request failed: {e}")
        return None

# Usage
result = send_message(
    chatflow_id="your-chatflow-id",
    question="What is artificial intelligence?",
    streaming=False
)

if result:
    print("Response:", result)

Advanced Features

Form Input

In Agentflow V2, you can select form as input type.

You can override the value by Variable Name of the Form Input

{
    "form": {
        "title": "Example",
        "count": 1,
        ...
    }
}
import requests

def prediction(flow_id, form):
    url = f"http://localhost:3000/api/v1/prediction/{flow_id}"
    
    payload = {
        "form": form
    }
    
    try:
        response = requests.post(url, json=payload)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error: {e}")
        return None

result = prediction(
    flow_id="your-flow-id",
    form={
        "title": "ABC",
        "choices": "A"
    }
)

print(result)

Configuration Override

Override chatflow settings dynamically.

Override config is disabled by default for security reasons. Enable it from the top right: Settings → Configuration → Security tab:

import requests

def query_with_config(flow_id, question, config):
    url = f"http://localhost:3000/api/v1/prediction/{flow_id}"
    
    payload = {
        "question": question,
        "overrideConfig": config
    }
    
    try:
        response = requests.post(url, json=payload)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error: {e}")
        return None

# Example: Override session and return source documents
result = query_with_config(
    flow_id="your-flow-id",
    question="How does machine learning work?",
    config={
        "sessionId": "user-123",
        "temperature": 0.5,
        "maxTokens": 1000
    }
)

print(result)

For array type, hovering over the info icon will shows the schema that can be overriden.

Array value from overrideConfig will concatenate with existing array values. For example, if existing startState has:

{
  "key": "key1",
  "value": "value1"
}

And if we enable override:

"overrideConfig": {
    "startState": [
        {
            "key": "foo",
            "value": "bar"
        }
    ],
    "llmMessages": [
        {
            "role": "system",
            "content": "You are helpful assistant"
        }
    ]
}

The final startState will be:

[
  {
    "key": "key1",
    "value": "value1"
  },
  {
    "key": "foo",
    "value": "bar"
  },
]

Overriding Specific Node

By default, if multiple nodes share the same type and no node ID is specified, overriding a property will update that property across all matching nodes.

For example, there are 2 LLM nodes where I want to override the system message:

After enabling the ability to override:

I can override the system message for both LLMs like so:

"overrideConfig": {
    "llmMessages": [
        {
            "role": "system",
            "content": "You are sarcastic"
        }
    ]
}

From the Execution, you can see the overriden system message:

In some cases, you might want to just override config for specific node. You can do so by specifying the node id inside the property you want to override.

For example:

"overrideConfig": {
    "llmMessages": {
        "llmAgentflow_0": [
            {
                "role": "system",
                "content": "You are sweet"
            } 
        ],
        "llmAgentflow_1": [
            {
                "role": "system",
                "content": "You are smart"
            } 
        ]
    }
}

If you head back to Execution, you can see each LLM has the correct overriden value:

Conversation History

Provide conversation context by including previous messages in the history array.

History Message Format

{
    "role": "apiMessage" | "userMessage",
    "content": "Message content"
}
import requests

def chat_with_history(flow_id, question, history):
    url = f"http://localhost:3000/api/v1/prediction/{flow_id}"
    
    payload = {
        "question": question,
        "history": history
    }
    
    try:
        response = requests.post(url, json=payload)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error: {e}")
        return None

# Example conversation with context
conversation_history = [
    {
        "role": "apiMessage",
        "content": "Hello! I'm an AI assistant. How can I help you today?"
    },
    {
        "role": "userMessage", 
        "content": "Hi, my name is Sarah and I'm learning about AI"
    },
    {
        "role": "apiMessage",
        "content": "Nice to meet you, Sarah! I'd be happy to help you learn about AI. What specific aspects interest you?"
    }
]

result = chat_with_history(
    flow_id="your-flow-id",
    question="Can you explain neural networks in simple terms?",
    history=conversation_history
)

print(result)

Session Management

Use sessionId to maintain conversation state across multiple API calls. Each session maintains its own conversation context and memory.

import requests

class FlowiseSession:
    def __init__(self, flow_id, session_id, base_url="http://localhost:3000"):
        self.flow_id= flow_id
        self.session_id = session_id
        self.base_url = base_url
        self.url = f"{base_url}/api/v1/prediction/{flow_id}"
    
    def send_message(self, question, **kwargs):
        payload = {
            "question": question,
            "overrideConfig": {
                "sessionId": self.session_id,
                **kwargs.get("overrideConfig", {})
            }
        }
        
        # Add any additional parameters
        for key, value in kwargs.items():
            if key != "overrideConfig":
                payload[key] = value
        
        try:
            response = requests.post(self.url, json=payload)
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            print(f"Error: {e}")
            return None

# Usage
session = FlowiseSession(
    flow_id="your-flow-id",
    session_id="user-session-123"
)

# First message
response1 = session.send_message("Hello, my name is John")
print("Response 1:", response1)

# Second message - will remember the previous context
response2 = session.send_message("What's my name?")
print("Response 2:", response2)

Variables

Pass dynamic variables to your flow using the vars property in overrideConfig. Variables can be used in your flow to inject dynamic content.

Variables must be created first before you can override it. Refer to Variables

import requests

def send_with_variables(flow_id, question, variables):
    url = f"http://localhost:3000/api/v1/prediction/{flow_id}"
    
    payload = {
        "question": question,
        "overrideConfig": {
            "vars": variables
        }
    }
    
    try:
        response = requests.post(url, json=payload)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error: {e}")
        return None

# Example: Pass user information and preferences
result = send_with_variables(
    flow_id="your-flow-id",
    question="Create a personalized workout plan",
    variables={
        "user_name": "Alice",
        "fitness_level": "intermediate",
        "preferred_duration": "30 minutes",
        "equipment": "dumbbells, resistance bands",
        "goals": "strength training, flexibility"
    }
)

print(result)

Image Uploads

Upload images for visual analysis when your flow supports image processing. Refer to Image for more reference.

Upload Structure:

{
    "data": "", 
    "type": "",
    "name": ",
    "mime": "
}

Data: Base64 or URL of an image

Type: url or file

Name: name of the image

Mime: image/png, image/jpeg, image/jpg

import requests
import base64
import os

def upload_image(flow_id, question, image_path):
    # Read and encode image
    with open(image_path, 'rb') as image_file:
        encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
    
    # Determine MIME type based on file extension
    mime_types = {
        '.png': 'image/png',
        '.jpg': 'image/jpeg',
        '.jpeg': 'image/jpeg',
        '.gif': 'image/gif',
        '.webp': 'image/webp'
    }

    file_ext = os.path.splitext(image_path)[1].lower()
    mime_type = mime_types.get(file_ext, 'image/png')
    
    url = f"http://localhost:3000/api/v1/prediction/{flow_id}"
    
    payload = {
        "question": question,
        "uploads": [
            {
                "data": f"data:{mime_type};base64,{encoded_image}",
                "type": "file",
                "name": os.path.basename(image_path),
                "mime": mime_type
            }
        ]
    }
    
    try:
        response = requests.post(url, json=payload)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error: {e}")
        return None

# Example usage
result = upload_image(
    flow_id="your-flow-id",
    question="Can you describe what you see in this image?",
    image_path="path/to/your/image.png"
)

print(result)

Audio Uploads (Speech to Text)

Upload audio files for speech-to-text processing. Refer to Audio for more reference.

Upload Structure:

{
    "data": "", 
    "type": "",
    "name": ",
    "mime": "
}

Data: Base64 or URL of an audio

Type: url or file

Name: name of the audio

Mime: audio/mp4, audio/webm, audio/wav, audio/mpeg

import requests
import base64
import os

def upload_audio(flow_id, audio_path, question=None):
    # Read and encode audio
    with open(audio_path, 'rb') as audio_file:
        encoded_audio = base64.b64encode(audio_file.read()).decode('utf-8')
    
    # Determine MIME type based on file extension
    mime_types = {
        '.webm': 'audio/webm',
        '.wav': 'audio/wav',
        '.mp3': 'audio/mpeg',
        '.m4a': 'audio/mp4'
    }
 
    file_ext = os.path.splitext(audio_path)[1].lower()
    mime_type = mime_types.get(file_ext, 'audio/webm')
    
    url = f"http://localhost:3000/api/v1/prediction/{flow_id}"
    
    payload = {
        "uploads": [
            {
                "data": f"data:{mime_type};base64,{encoded_audio}",
                "type": "audio",
                "name": os.path.basename(audio_path),
                "mime": mime_type
            }
        ]
    }
    
    # Add question if provided
    if question:
        payload["question"] = question
    
    try:
        response = requests.post(url, json=payload)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error: {e}")
        return None

# Example usage
result = upload_audio(
    flow_id="your-flow-id",
    audio_path="path/to/your/audio.wav",
    question="Please transcribe this audio and summarize the content"
)

print(result)

File Uploads

Upload files to have LLM process the files and answer query related to the files. Refer to Files for more reference.

Human Input

To resume the execution from a previously stopped checkpoint, humanInput needs to be provided. Refer Human In The Loop for details.

Human Input Structure

{
    "type": "",
    "feedback": ""
}

  • type: Either proceed or reject

  • feedback: Feedback to the last output

Must specify the same sessionId where the execution was stopped

{
    "humanInput": {
        "type": "reject",
        "feedback": "Include more emoji"
    },
    "overrideConfig": {
        "sessionId": "abc"
    }
}

Troubleshooting

  1. 404 Not Found: Verify the flow ID is correct and the flow exists

  2. 401 Unauthorized Access: Verify if the flow is API key protected

  3. 400 Bad Request: Check request format and required fields

  4. 413 Payload Too Large: Reduce file sizes or split large requests

  5. 500 Internal Server Error: Check if there is any misconfiguration from the nodes in the flow

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