The Callback system in GraphAI provides a powerful mechanism for streaming data between nodes, particularly useful for handling streaming LLM outputs or other incremental data processing.

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Callback Basics

At its core, the Callback is an asyncio-based system that:
  1. Provides a queue for passing streaming data between components
  2. Handles special tokens to mark node start/end events
  3. Structures streaming content for easy consumption by downstream processes
  4. Can be integrated with any async compatible streaming system

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Creating a Callback

The Graph automatically creates a callback when needed, but you can also create and customize one: 
from graphai import Callback

# Create a callback with default settings
callback = Callback()

# Create a callback with custom settings
callback = Callback(
    identifier="custom_id",  # Used for special tokens
    special_token_format="<{identifier}:{token}:{params}>",  # Format for special tokens
    token_format="{token}"  # Format for regular tokens
)

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Callback In Nodes

To use callbacks in a node, mark it with stream=True: 
from graphai import node

@node(stream=True)
async def streaming_node(input: dict, callback):
    """This node receives a callback parameter because stream=True."""
    # Process input
    for chunk in process_chunks(input["data"]):
        # Stream output chunks
        await callback.acall(chunk)
    
    # Return final result
    return {"result": "streaming complete"}
Important points:
  • The stream=True parameter tells GraphAI to inject a callback
  • The node must have a callback parameter
  • The callback can be used to stream output chunks

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Streaming from LLMs

A common use case is streaming output from an LLM: 
@node(stream=True)
async def llm_node(input: dict, callback):
    """Stream output from an LLM."""
    from openai import AsyncOpenAI
    
    client = AsyncOpenAI()
    
    # Start streaming response
    stream = await client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": input["query"]}
        ],
        stream=True
    )
    
    response_text = ""
    
    # Stream chunks through the callback
    async for chunk in stream:
        if chunk.choices[0].delta.content:
            content = chunk.choices[0].delta.content
            response_text += content
            await callback.acall(content)
    
    # Return the complete response
    return {"response": response_text}

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Callback Methods

The Callback provides several key methods:

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Streaming Content

# Synchronous callback (use in sync contexts)
callback(token="chunk of text")

# Async callback (preferred)
await callback.acall(token="chunk of text")

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Node Management

# Mark the start of a node
await callback.start_node(node_name="my_node")

# Mark the end of a node
await callback.end_node(node_name="my_node")

# Close the callback stream
await callback.close()

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Consuming a Callback Stream

You can consume a callback’s stream using its async iterator: 
async def consume_stream(callback):
    async for token in callback.aiter():
        # Process each token
        print(token, end="", flush=True)
This is especially useful for web applications that need to provide real-time updates.

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Special Tokens

GraphAI uses special tokens to mark events in the stream: 
<graphai:node_name:start>  # Marks the start of a node
<graphai:node_name>        # Identifies the node
<graphai:node_name:end>    # Marks the end of a node
<graphai:END>              # Marks the end of the stream
These tokens can be customized using the special_token_format parameter.

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Example: Web Server with Streaming

Here’s how to use callbacks with a FastAPI server: 
from fastapi import FastAPI, Request
from fastapi.responses import StreamingResponse
from graphai import Graph, node
import asyncio

app = FastAPI()

@node(start=True, stream=True)
async def llm_stream(input: dict, callback):
    # Streaming LLM implementation...
    for chunk in get_llm_response(input["query"]):
        await callback.acall(chunk)
    return {"status": "complete"}

@node(end=True)
async def final_node(input: dict):
    return {"status": "success"}

# Create graph
graph = Graph()
graph.add_node(llm_stream())
graph.add_node(final_node())
graph.add_edge(llm_stream, final_node)

@app.post("/stream")
async def stream_endpoint(request: Request):
    data = await request.json()
    
    # Get a callback from the graph
    callback = graph.get_callback()
    
    # Start graph execution in background
    asyncio.create_task(graph.execute({"query": data["query"]}))
    
    # Return streaming response
    return StreamingResponse(
        callback.aiter(),
        media_type="text/event-stream"
    )

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Callback Configuration

You can customize the callback’s behavior: 
callback = Callback(
    # Custom identifier (default is "graphai")
    identifier="myapp",
    
    # Custom format for special tokens
    special_token_format="<[{identifier}|{token}|{params}]>",
    
    # Custom format for regular tokens
    token_format="TOKEN: {token}"
)

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Advanced: Custom Processing of Special Tokens

You can implement custom processing of special tokens: 
async def process_stream(callback):
    current_node = None
    buffer = ""
    
    async for token in callback.aiter():
        # Check if it's a special token
        if token.startswith("<graphai:") and token.endswith(">"):
            # Handle node start
            if ":start" in token:
                node_name = token.split(":")[1].split(":start")[0]
                current_node = node_name
                # Handle node start event
                
            # Handle node end
            elif ":end" in token:
                # Handle node end event
                current_node = None
                
            # Handle stream end
            elif token == "<graphai:END>":
                # Handle end of stream
                break
                
        # Regular token
        else:
            # Process regular token
            buffer += token
            # Maybe do something with buffer
            
    return buffer

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Best Practices

  1. Use async whenever possible: The callback system is built on asyncio
  2. Close the callback when done: Always call await callback.close() when finished
  3. Keep streaming chunks small: Don’t stream large objects; break them into manageable chunks
  4. Handle special tokens correctly: When consuming streams, handle special tokens appropriately

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Next Steps

  • Learn about Graphs for orchestrating node execution
  • Explore Nodes for processing logic
  • Check out State for maintaining context across nodes