https://academy.langchain.com/courses/intro-to-langgraph

https://github.com/shangxiang0907/langchain-academy

Streaming 流式传输

Review 复习

In module 2, covered a few ways to customize graph state and memory.

在模块 2 中，我们介绍了几种自定义图状态和内存的方法。

We built up to a Chatbot with external memory that can sustain long-running conversations.

我们构建了一个具备外部内存的聊天机器人，可支持长时间运行的对话。

Goals 学习目标

This module will dive into human-in-the-loop, which builds on memory and allows users to interact directly with graphs in various ways.

本模块将深入探讨 human-in-the-loop（人在环路中），它基于内存机制，允许用户以多种方式直接与图进行交互。

To set the stage for human-in-the-loop, we’ll first dive into streaming, which provides several ways to visualize graph output (e.g., node state or chat model tokens) over the course of execution.

为 human-in-the-loop 做好铺垫，我们将首先深入流式传输，它提供了多种方式来可视化图执行过程中的输出（例如节点状态或聊天模型生成的 token）。

%%capture --no-stderr
%pip install --quiet -U langgraph langchain_openai langgraph_sdk

Streaming 流式传输

LangGraph is built with first class support for streaming.

LangGraph 内置了对流式传输的一等支持。

Let’s set up our Chatbot from Module 2, and show various way to stream outputs from the graph during execution.

让我们复用模块 2 中构建的聊天机器人，并演示在图执行过程中流式传输输出的多种方式。

import os, getpass

def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")

from dotenv import find_dotenv, load_dotenv

load_dotenv(find_dotenv(usecwd=True))
_set_env("OPENAI_API_KEY")

Note that we use RunnableConfig with call_model to enable token-wise streaming.

注意：我们使用带 call_model 的 RunnableConfig 来启用逐 token 流式传输。

This is only needed with python < 3.11.

这仅在 Python < 3.11 时需要。

We include in case you are running this notebook in CoLab, which will use python 3.x.

我们包含此配置，以防你在 Colab 中运行该笔记本（Colab 使用的是 Python 3.x）。

from IPython.display import Image, display
from typing import Literal

import os
from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, RemoveMessage
from langchain_core.runnables import RunnableConfig

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import StateGraph, START, END
from langgraph.graph import MessagesState

# LLM
model = ChatOpenAI(model=os.getenv("OPENAI_MODEL", "qwen-plus"), base_url=os.getenv("OPENAI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"), temperature=0) 

# State 
class State(MessagesState):
    summary: str

# Define the logic to call the model
def call_model(state: State, config: RunnableConfig):
    
    # Get summary if it exists
    summary = state.get("summary", "")

    # If there is summary, then we add it
    if summary:
        
        # Add summary to system message
        system_message = f"Summary of conversation earlier: {summary}"

        # Append summary to any newer messages
        messages = [SystemMessage(content=system_message)] + state["messages"]
    
    else:
        messages = state["messages"]
    
    response = model.invoke(messages, config)
    return {"messages": response}

def summarize_conversation(state: State):
    
    # First, we get any existing summary
    summary = state.get("summary", "")

    # Create our summarization prompt 
    if summary:
        
        # A summary already exists
        summary_message = (
            f"This is summary of the conversation to date: {summary}\n\n"
            "Extend the summary by taking into account the new messages above:"
        )
        
    else:
        summary_message = "Create a summary of the conversation above:"

    # Add prompt to our history
    messages = state["messages"] + [HumanMessage(content=summary_message)]
    response = model.invoke(messages)
    
    # Delete all but the 2 most recent messages
    delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    return {"summary": response.content, "messages": delete_messages}

# Determine whether to end or summarize the conversation
def should_continue(state: State)-> Literal ["summarize_conversation",END]:
    
    """Return the next node to execute."""
    
    messages = state["messages"]
    
    # If there are more than six messages, then we summarize the conversation
    if len(messages) > 6:
        return "summarize_conversation"
    
    # Otherwise we can just end
    return END

# Define a new graph
workflow = StateGraph(State)
workflow.add_node("conversation", call_model)
workflow.add_node(summarize_conversation)

# Set the entrypoint as conversation
workflow.add_edge(START, "conversation")
workflow.add_conditional_edges("conversation", should_continue)
workflow.add_edge("summarize_conversation", END)

# Compile
memory = MemorySaver()
graph = workflow.compile(checkpointer=memory)
display(Image(graph.get_graph().draw_mermaid_png()))

Streaming full state 流式传输完整状态

Now, let’s talk about ways to stream our graph state.

现在，我们来讨论流式传输图状态的几种方式。

.stream and .astream are sync and async methods for streaming back results.

.stream 和 .astream 是用于同步和异步流式返回结果的方法。

LangGraph supports a few different streaming modes for graph state.

LangGraph 支持若干种不同的流式传输模式，用于图状态。

• values: This streams the full state of the graph after each node is called.

  • values：每次调用节点后，流式传输图的完整状态。

• updates: This streams updates to the state of the graph after each node is called.

  • updates：每次调用节点后，流式传输图状态的更新。

Let’s look at stream_mode="updates".

我们来看 stream_mode="updates"。

Because we stream with updates, we only see updates to the state after node in the graph is run.

由于我们使用 updates 模式流式传输，因此仅能看到图中每个节点运行后状态的更新。

Each chunk is a dict with node_name as the key and the updated state as the value.

每个 chunk 是一个字典，其键为 node_name，值为更新后的状态。

# Create a thread
config = {"configurable": {"thread_id": "1"}}

# Start conversation
for chunk in graph.stream({"messages": [HumanMessage(content="hi! I'm Lance")]}, config, stream_mode="updates"):
    print(chunk)

{'conversation': {'messages': AIMessage(content='Hello Lance! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 11, 'total_tokens': 21, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_f64f290af2', 'id': 'chatcmpl-CSqvbTb4Lar7zEfryASuhaZVBnKOw', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--b612e04b-3ff6-4b47-8b88-628f35e44aa7-0', usage_metadata={'input_tokens': 11, 'output_tokens': 10, 'total_tokens': 21, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})}}

Let’s now just print the state update.

现在，我们仅打印状态更新。

# Start conversation
for chunk in graph.stream({"messages": [HumanMessage(content="hi! I'm Lance")]}, config, stream_mode="updates"):
    chunk['conversation']["messages"].pretty_print()

==================================[1m Ai Message [0m==================================

Hi Lance! How's it going? What can I do for you today?

Now, we can see stream_mode="values".

接下来，我们来看 stream_mode="values"。

This is the full state of the graph after the conversation node is called.

这是 conversation 节点被调用后图的完整状态。

# Start conversation, again
config = {"configurable": {"thread_id": "2"}}

# Start conversation
input_message = HumanMessage(content="hi! I'm Lance")
for event in graph.stream({"messages": [input_message]}, config, stream_mode="values"):
    for m in event['messages']:
        m.pretty_print()
    print("---"*25)

================================[1m Human Message [0m=================================

hi! I'm Lance
---------------------------------------------------------------------------
================================[1m Human Message [0m=================================

hi! I'm Lance
==================================[1m Ai Message [0m==================================

Hello, Lance! How can I assist you today?
---------------------------------------------------------------------------

Streaming tokens 流式传输 token

We often want to stream more than graph state.

我们通常希望流式传输的内容不仅限于图状态。

In particular, with chat model calls it is common to stream the tokens as they are generated.

特别是，在调用聊天模型时，常需流式传输其逐个生成的 token。

We can do this using the .astream_events method, which streams back events as they happen inside nodes!

我们可以使用 .astream_events 方法实现这一点，该方法会在节点内部事件发生时实时流式返回事件！

Each event is a dict with a few keys:

每个事件是一个包含若干键的字典：

• event: This is the type of event that is being emitted.

  • event：正在发出的事件类型。

• name: This is the name of event.

  • name：事件的名称。

• data: This is the data associated with the event.

  • data：与该事件关联的数据。

• metadata: Containslanggraph_node, the node emitting the event.

  • metadata：包含 langgraph_node，即发出该事件的节点。

Let’s have a look.

我们来看一下。

config = {"configurable": {"thread_id": "3"}}
input_message = HumanMessage(content="Tell me about the 49ers NFL team")
async for event in graph.astream_events({"messages": [input_message]}, config, version="v2"):
    print(f"Node: {event['metadata'].get('langgraph_node','')}. Type: {event['event']}. Name: {event['name']}")

Node: . Type: on_chain_start. Name: LangGraph
Node: conversation. Type: on_chain_start. Name: conversation
Node: conversation. Type: on_chat_model_start. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
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Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
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Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_stream. Name: ChatOpenAI
Node: conversation. Type: on_chat_model_end. Name: ChatOpenAI
Node: conversation. Type: on_chain_start. Name: should_continue
Node: conversation. Type: on_chain_end. Name: should_continue
Node: conversation. Type: on_chain_stream. Name: conversation
Node: conversation. Type: on_chain_end. Name: conversation
Node: . Type: on_chain_stream. Name: LangGraph
Node: . Type: on_chain_end. Name: LangGraph

The central point is that tokens from chat models within your graph have the on_chat_model_stream type.

关键在于，图内聊天模型生成的 token 具有 on_chat_model_stream 类型。

We can use event['metadata']['langgraph_node'] to select the node to stream from.

我们可以使用 event['metadata']['langgraph_node'] 来选择要从中流式传输的节点。

And we can use event['data'] to get the actual data for each event, which in this case is an AIMessageChunk.

我们可以使用 event['data'] 获取每个事件的实际数据，此处为 AIMessageChunk。

node_to_stream = 'conversation'
config = {"configurable": {"thread_id": "4"}}
input_message = HumanMessage(content="Tell me about the 49ers NFL team")
async for event in graph.astream_events({"messages": [input_message]}, config, version="v2"):
    # Get chat model tokens from a particular node 
    if event["event"] == "on_chat_model_stream" and event['metadata'].get('langgraph_node','') == node_to_stream:
        print(event["data"])

{'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='The', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' San', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Francisco', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' ', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='49', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='ers', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' are', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' a', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' professional', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' American', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' football', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' team', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' based', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' in', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' the', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' San', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Francisco', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Bay', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Area', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='.', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' They', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' compete', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' in', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' the', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' National', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Football', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' League', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' (', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='NFL', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=')', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' as', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' a', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' member', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' club', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' of', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' the', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' league', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content="'s", additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' National', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Football', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Conference', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' (', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='N', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='FC', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=')', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' West', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' division', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='.', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' The', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' team', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' was', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' founded', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' in', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' ', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='194', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
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{'chunk': AIMessageChunk(content=' Los', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Angeles', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Rams', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=',', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' and', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' historically', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' with', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' the', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Dallas', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Cowboys', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' and', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Green', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Bay', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Packers', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='.\n\n', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='-', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' **', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='Recent', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Performance', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='**', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=':', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' In', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' recent', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' years', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=',', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' the', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' ', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='49', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='ers', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' have', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' experienced', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' a', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' resurgence', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=',', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' including', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' a', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Super', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Bowl', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' appearance', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' in', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' the', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' ', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='201', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='9', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' season', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' (', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='Super', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Bowl', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' LIV', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='),', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' where', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' they', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' were', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' defeated', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' by', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' the', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Kansas', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' City', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' Chiefs', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='.\n\n', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='The', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' ', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='49', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='ers', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' are', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' known', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' for', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' their', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' rich', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' history', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=',', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' passionate', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' fan', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' base', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=',', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' and', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' significant', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' contributions', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' to', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' the', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' NFL', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content="'s", additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' development', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' and', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content=' popularity', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='.', additional_kwargs={}, response_metadata={'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8')}
{'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb3c3cb84d', 'service_tier': 'default', 'model_provider': 'openai'}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8', chunk_position='last')}
{'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8', usage_metadata={'input_tokens': 16, 'output_tokens': 382, 'total_tokens': 398, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})}
{'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='lc_run--2dc940a7-76b1-4cb1-8e3c-4c18bd4598a8', chunk_position='last')}

As you see above, just use the chunk key to get the AIMessageChunk.

如上所示，只需使用 chunk 键即可获取 AIMessageChunk。

config = {"configurable": {"thread_id": "5"}}
input_message = HumanMessage(content="Tell me about the 49ers NFL team")
async for event in graph.astream_events({"messages": [input_message]}, config, version="v2"):
    # Get chat model tokens from a particular node 
    if event["event"] == "on_chat_model_stream" and event['metadata'].get('langgraph_node','') == node_to_stream:
        data = event["data"]
        print(data["chunk"].content, end="|")

|The| San| Francisco| |49|ers| are| a| professional| American| football| team| based| in| the| San| Francisco| Bay| Area|.| They| compete| in| the| National| Football| League| (|NFL|)| as| a| member| club| of| the| league|'s| National| Football| Conference| (|N|FC|)| West| division|.| The| team| was| founded| in| |194|6| as| a| charter| member| of| the| All|-Amer|ica| Football| Conference| (|AA|FC|)| and| joined| the| NFL| in| |194|9| when| the| leagues| merged|.

|The| |49|ers| have| a| rich| history| and| are| one| of| the| most| successful| teams| in| NFL| history|.| They| have| won| five| Super| Bowl| championships|,| with| victories| in| Super| Bow|ls| XVI|,| XIX|,| XX|III|,| XX|IV|,| and| XX|IX|.| The| team| has| also| won| numerous| division| titles| and| made| several| playoff| appearances|.

|The| |49|ers| are| known| for| their| iconic| players| and| coaches|,| including| Hall| of| Fam|ers| like| Joe| Montana|,| Jerry| Rice|,| Steve| Young|,| Ronnie| L|ott|,| and| coach| Bill| Walsh|,| who| is| credited| with| popular|izing| the| West| Coast| offense|.| The| team's| colors| are| red| and| gold|,| and| they| play| their| home| games| at| Levi|'s| Stadium| in| Santa| Clara|,| California|,| which| they| moved| to| in| |201|4| after| previously| playing| at| Cand|lestick| Park| in| San| Francisco|.

|The| |49|ers| have| a| passionate| fan| base| and| a| stor|ied| rivalry| with| teams| like| the| Dallas| Cowboys|,| Green| Bay| Packers|,| and| Seattle| Seahawks|.| The| team's| success| in| the| |198|0|s| and| |199|0|s|,| particularly| under| the| leadership| of| Bill| Walsh| and| George| Se|if|ert|,| helped| establish| them| as| one| of| the| premier| franchises| in| the| NFL|.||||

Streaming with LangGraph API 使用 LangGraph API 进行流式传输

⚠️ Notice

⚠️ 注意

Since filming these videos, we’ve updated Studio so that it can now be run locally and accessed through your browser.

自录制这些视频以来，我们已更新 Studio，使其现在可本地运行并通过浏览器访问。

This is the preferred way to run Studio instead of using the Desktop App shown in the video.

这是运行 Studio 的首选方式，而非视频中展示的桌面应用。

It is now called LangSmith Studio instead of LangGraph Studio.

它现在被称为 LangSmith Studio，而非 LangGraph Studio。

Detailed setup instructions are available in the “Getting Setup” guide at the start of the course.

详细设置说明请参阅本课程开头的“开始设置”指南。

You can find a description of Studio here, and specific details for local deployment here.

您可在此处查看 Studio 的介绍，并在此处查阅本地部署的具体细节。

To start the local development server, run the following command in your terminal in the /studio directory in this module:

要在本地启动开发服务器，请在本模块的 /studio 目录下于终端中运行以下命令：

langgraph dev

You should see the following output:

您应看到如下输出：

- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docs

Open your browser and navigate to the Studio UI URL shown above.

打开浏览器并导航至上方显示的 Studio UI URL。

The LangGraph API supports editing graph state.

LangGraph API 支持编辑图状态。

if 'google.colab' in str(get_ipython()):
    raise Exception("Unfortunately LangGraph Studio is currently not supported on Google Colab")

from langgraph_sdk import get_client

# This is the URL of the local development server
URL = "http://127.0.0.1:2024"
client = get_client(url=URL)

# Search all hosted graphs
assistants = await client.assistants.search()

Let’s stream values, like before.

让我们像之前一样流式传输 values。

# Create a new thread
thread = await client.threads.create()
# Input message
input_message = HumanMessage(content="Multiply 2 and 3")
async for event in client.runs.stream(thread["thread_id"], 
                                      assistant_id="agent", 
                                      input={"messages": [input_message]}, 
                                      stream_mode="values"):
    print(event)

StreamPart(event='metadata', data={'run_id': '019a0358-31b4-7143-af47-2feeac0b27ce', 'attempt': 1})
StreamPart(event='values', data={'messages': [{'content': 'Multiply 2 and 3', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '9aaa247f-1e6e-4451-af25-ac678fe46d82'}]})
StreamPart(event='values', data={'messages': [{'content': 'Multiply 2 and 3', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '9aaa247f-1e6e-4451-af25-ac678fe46d82'}, {'content': '', 'additional_kwargs': {'refusal': None}, 'response_metadata': {'token_usage': {'completion_tokens': 17, 'prompt_tokens': 134, 'total_tokens': 151, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb3c3cb84d', 'id': 'chatcmpl-CSqw4HhOXHahA4tIr4mIdEhQB1QB4', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, 'type': 'ai', 'name': None, 'id': 'lc_run--4a3794f6-52c3-41b5-9620-1710d6e8392d-0', 'tool_calls': [{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'call_3sCWZZ89AoUe91MYc3ZBtJ0P', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 134, 'output_tokens': 17, 'total_tokens': 151, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}}]})
StreamPart(event='values', data={'messages': [{'content': 'Multiply 2 and 3', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '9aaa247f-1e6e-4451-af25-ac678fe46d82'}, {'content': '', 'additional_kwargs': {'refusal': None}, 'response_metadata': {'token_usage': {'completion_tokens': 17, 'prompt_tokens': 134, 'total_tokens': 151, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb3c3cb84d', 'id': 'chatcmpl-CSqw4HhOXHahA4tIr4mIdEhQB1QB4', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, 'type': 'ai', 'name': None, 'id': 'lc_run--4a3794f6-52c3-41b5-9620-1710d6e8392d-0', 'tool_calls': [{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'call_3sCWZZ89AoUe91MYc3ZBtJ0P', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 134, 'output_tokens': 17, 'total_tokens': 151, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}}, {'content': '6', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'multiply', 'id': 'f05da6b3-27be-4896-967a-3ff60aa06d85', 'tool_call_id': 'call_3sCWZZ89AoUe91MYc3ZBtJ0P', 'artifact': None, 'status': 'success'}]})
StreamPart(event='values', data={'messages': [{'content': 'Multiply 2 and 3', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'human', 'name': None, 'id': '9aaa247f-1e6e-4451-af25-ac678fe46d82'}, {'content': '', 'additional_kwargs': {'refusal': None}, 'response_metadata': {'token_usage': {'completion_tokens': 17, 'prompt_tokens': 134, 'total_tokens': 151, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb3c3cb84d', 'id': 'chatcmpl-CSqw4HhOXHahA4tIr4mIdEhQB1QB4', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None}, 'type': 'ai', 'name': None, 'id': 'lc_run--4a3794f6-52c3-41b5-9620-1710d6e8392d-0', 'tool_calls': [{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'call_3sCWZZ89AoUe91MYc3ZBtJ0P', 'type': 'tool_call'}], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 134, 'output_tokens': 17, 'total_tokens': 151, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}}, {'content': '6', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'tool', 'name': 'multiply', 'id': 'f05da6b3-27be-4896-967a-3ff60aa06d85', 'tool_call_id': 'call_3sCWZZ89AoUe91MYc3ZBtJ0P', 'artifact': None, 'status': 'success'}, {'content': 'The result of multiplying 2 and 3 is 6.', 'additional_kwargs': {'refusal': None}, 'response_metadata': {'token_usage': {'completion_tokens': 14, 'prompt_tokens': 159, 'total_tokens': 173, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb3c3cb84d', 'id': 'chatcmpl-CSqw4xk8t3sPODL5beks5TlJBozgB', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, 'type': 'ai', 'name': None, 'id': 'lc_run--33a25ab0-f748-4c7f-a086-d9249e25fdc0-0', 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 159, 'output_tokens': 14, 'total_tokens': 173, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}}]})

The streamed objects have:

流式传输的对象包含：

• event: Type

  • event：类型

• data: State

  • data：状态

from langchain_core.messages import convert_to_messages
thread = await client.threads.create()
input_message = HumanMessage(content="Multiply 2 and 3")
async for event in client.runs.stream(thread["thread_id"], assistant_id="agent", input={"messages": [input_message]}, stream_mode="values"):
    messages = event.data.get('messages',None)
    if messages:
        print(convert_to_messages(messages)[-1])
    print('='*25)

=========================
content='Multiply 2 and 3' additional_kwargs={} response_metadata={} id='c3ec872a-99a1-4eec-bcb6-a04973f48ac5'
=========================
content='' additional_kwargs={'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 134, 'output_tokens': 17, 'total_tokens': 151, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}, 'refusal': None} response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 134, 'total_tokens': 151, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_f64f290af2', 'id': 'chatcmpl-CSqw6HYoyCI7z2AuKAAfSTQGbvzla', 'service_tier': 'default', 'finish_reason': 'tool_calls', 'logprobs': None} id='lc_run--c91028e7-7a0a-4746-a4f5-edcff5380abc-0' tool_calls=[{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'call_AFChrxIQGbr7mmzr8buxymY0', 'type': 'tool_call'}]
=========================
content='6' name='multiply' id='f69a844b-5f82-4256-96dd-92b044e888d9' tool_call_id='call_AFChrxIQGbr7mmzr8buxymY0'
=========================
content='The result of multiplying 2 and 3 is 6.' additional_kwargs={'invalid_tool_calls': [], 'usage_metadata': {'input_tokens': 159, 'output_tokens': 14, 'total_tokens': 173, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}, 'refusal': None} response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 159, 'total_tokens': 173, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_provider': 'openai', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb3c3cb84d', 'id': 'chatcmpl-CSqw7xBeinGuHlx0upkmo2tryppco', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None} id='lc_run--dba1c4af-ed8f-4fed-8770-2c3429603cd0-0'
=========================

There are some new streaming mode that are only supported via the API.

有一些新的流式传输模式仅通过 API 支持。

For example, we can use messages mode to better handle the above case!

例如，我们可以使用 messages 模式更好地处理上述情况！

This mode currently assumes that you have a messages key in your graph, which is a list of messages.

该模式当前假设您的图中存在一个 messages 键，其值为消息列表。

All events emitted using messages mode have two attributes:

所有使用 messages 模式发出的事件均具有两个属性：

• event: This is the name of the event

  • event：事件名称

• data: This is data associated with the event

  • data：与此事件关联的数据

thread = await client.threads.create()
input_message = HumanMessage(content="Multiply 2 and 3")
async for event in client.runs.stream(thread["thread_id"], 
                                      assistant_id="agent", 
                                      input={"messages": [input_message]}, 
                                      stream_mode="messages"):
    print(event.event)

metadata
messages/metadata
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/metadata
messages/complete
messages/metadata
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial
messages/partial

We can see a few events:

我们可以看到若干事件：

• metadata: metadata about the run

  • metadata：关于该运行的元数据

• messages/complete: fully formed message

  • messages/complete：已完整构建的消息

• messages/partial: chat model tokens

  • messages/partial：聊天模型生成的 token

Now, let’s show how to stream these messages.

接下来，我们演示如何流式传输这些消息。

We’ll define a helper function for better formatting of the tool calls in messages.

我们将定义一个辅助函数，以更好地格式化消息中的工具调用。

thread = await client.threads.create()
input_message = HumanMessage(content="Multiply 2 and 3")

def format_tool_calls(tool_calls):
    """
    Format a list of tool calls into a readable string.

    Args:
        tool_calls (list): A list of dictionaries, each representing a tool call.
            Each dictionary should have 'id', 'name', and 'args' keys.

    Returns:
        str: A formatted string of tool calls, or "No tool calls" if the list is empty.

    """

    if tool_calls:
        formatted_calls = []
        for call in tool_calls:
            formatted_calls.append(
                f"Tool Call ID: {call['id']}, Function: {call['name']}, Arguments: {call['args']}"
            )
        return "\n".join(formatted_calls)
    return "No tool calls"

async for event in client.runs.stream(
    thread["thread_id"],
    assistant_id="agent",
    input={"messages": [input_message]},
    stream_mode="messages",):
    
    # Handle metadata events
    if event.event == "metadata":
        print(f"Metadata: Run ID - {event.data['run_id']}")
        print("-" * 50)
    
    # Handle partial message events
    elif event.event == "messages/partial":
        for data_item in event.data:
            # Process user messages
            if "role" in data_item and data_item["role"] == "user":
                print(f"Human: {data_item['content']}")
            else:
                # Extract relevant data from the event
                tool_calls = data_item.get("tool_calls", [])
                invalid_tool_calls = data_item.get("invalid_tool_calls", [])
                content = data_item.get("content", "")
                response_metadata = data_item.get("response_metadata", {})

                if content:
                    print(f"AI: {content}")

                if tool_calls:
                    print("Tool Calls:")
                    print(format_tool_calls(tool_calls))

                if invalid_tool_calls:
                    print("Invalid Tool Calls:")
                    print(format_tool_calls(invalid_tool_calls))

                if response_metadata and response_metadata.get("finish_reason"):
                    print(f"Response Metadata: Finish Reason - {response_metadata['finish_reason']}")                    
        print("-" * 50)

Metadata: Run ID - 019a0358-57dc-76f9-bc63-633eee467a86
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2, 'b': 3}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2, 'b': 3}
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2, 'b': 3}
Response Metadata: Finish Reason - tool_calls
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2, 'b': 3}
Response Metadata: Finish Reason - tool_calls
--------------------------------------------------
Tool Calls:
Tool Call ID: call_mDtKBiuzkpN5ITWykhEFN0XU, Function: multiply, Arguments: {'a': 2, 'b': 3}
Response Metadata: Finish Reason - tool_calls
--------------------------------------------------
--------------------------------------------------
AI: The
--------------------------------------------------
AI: The result
--------------------------------------------------
AI: The result of
--------------------------------------------------
AI: The result of multiplying
--------------------------------------------------
AI: The result of multiplying 
--------------------------------------------------
AI: The result of multiplying 2
--------------------------------------------------
AI: The result of multiplying 2 and
--------------------------------------------------
AI: The result of multiplying 2 and 
--------------------------------------------------
AI: The result of multiplying 2 and 3
--------------------------------------------------
AI: The result of multiplying 2 and 3 is
--------------------------------------------------
AI: The result of multiplying 2 and 3 is 
--------------------------------------------------
AI: The result of multiplying 2 and 3 is 6
--------------------------------------------------
AI: The result of multiplying 2 and 3 is 6.
--------------------------------------------------
AI: The result of multiplying 2 and 3 is 6.
Response Metadata: Finish Reason - stop
--------------------------------------------------
AI: The result of multiplying 2 and 3 is 6.
Response Metadata: Finish Reason - stop
--------------------------------------------------
AI: The result of multiplying 2 and 3 is 6.
Response Metadata: Finish Reason - stop
--------------------------------------------------