如何将LangGraph调用转换为LangGraph Cloud调用¶

你已经习惯了在本地与图形进行交互，但现在你已经将它部署到了LangGraph云上。如何将代码库中所有直接调用LangGraph的地方改为调用LangGraph Cloud呢？这个笔记本包含了并排比较，方便你从调用LangGraph过渡到调用LangGraph Cloud。

设置环境¶

我们将使用一个简单的ReAct代理来完成本指南。您还需要设置一个包含agent.py和langgraph.json文件的项目。请参阅快速入门以了解如何设置这些文件。

%%capture --no-stderr
%pip install -U langgraph langchain-openai

import getpass
import os


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


_set_env("OPENAI_API_KEY")

OPENAI_API_KEY:  ········

# this is all that's needed for the agent.py
from typing import Literal
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent


@tool
def get_weather(city: Literal["nyc", "sf"]):
    """Use this to get weather information."""
    if city == "nyc":
        return "It might be cloudy in nyc"
    elif city == "sf":
        return "It's always sunny in sf"
    else:
        raise AssertionError("Unknown city")


tools = [get_weather]

model = ChatOpenAI(model_name="gpt-4o", temperature=0)
graph = create_react_agent(model, tools)

API Reference: tool | ChatOpenAI | create_react_agent

现在我们将设置langgraph客户端。客户端假设LangGraph Cloud服务器运行在localhost:8123

from langgraph_sdk import get_client

client = get_client()

调用图形¶

以下示例展示了如何镜像LangGraph的CompiledGraph可运行对象的.invoke() / .ainvoke()方法，即创建一个阻塞的图执行。

使用LangGraph¶

inputs = {"messages": [("human", "what's the weather in sf")]}
invoke_output = await graph.ainvoke(inputs)

for m in invoke_output["messages"]:
    m.pretty_print()

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

what's the weather in sf
==================================[1m Ai Message [0m==================================
Tool Calls:
  get_weather (call_GOKlsBY2XKm7pZnmAzJweYDU)
 Call ID: call_GOKlsBY2XKm7pZnmAzJweYDU
  Args:
    city: sf
=================================[1m Tool Message [0m=================================
Name: get_weather

It's always sunny in sf
==================================[1m Ai Message [0m==================================

The weather in San Francisco is currently sunny.

使用LangGraph Cloud¶

# NOTE: We're not specifying the thread here -- this allows us to create a thread just for this run
wait_output = await client.runs.wait(None, "agent", input=inputs)

# we'll use this for pretty message formatting
from langchain_core.messages import convert_to_messages

API Reference: convert_to_messages

for m in convert_to_messages(wait_output["messages"]):
    m.pretty_print()

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

what's the weather in sf
==================================[1m Ai Message [0m==================================
Tool Calls:
  get_weather (call_pQJsT9uLG3nVppN8Dt2OhnFx)
 Call ID: call_pQJsT9uLG3nVppN8Dt2OhnFx
  Args:
    city: sf
=================================[1m Tool Message [0m=================================
Name: get_weather

It's always sunny in sf
==================================[1m Ai Message [0m==================================

The weather in San Francisco is currently sunny.

流式处理¶

以下示例展示了如何镜像.stream() / .astream()方法，以流式传输部分图执行结果。注意：LangGraph的stream_mode=values/updates/debug在LangGraph Cloud中的行为几乎相同（除了metadata / end事件类型的附加流式传输块外）。

使用LangGraph¶

inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in graph.astream(inputs, stream_mode="values"):
    chunk["messages"][-1].pretty_print()

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

what's the weather in sf
==================================[1m Ai Message [0m==================================
Tool Calls:
  get_weather (call_302y9671bqMkMcpLZOWLNAnq)
 Call ID: call_302y9671bqMkMcpLZOWLNAnq
  Args:
    city: sf
=================================[1m Tool Message [0m=================================
Name: get_weather

It's always sunny in sf
==================================[1m Ai Message [0m==================================

The weather in San Francisco is currently sunny.

使用LangGraph Cloud¶

inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in client.runs.stream(
    None, "agent", input=inputs, stream_mode="values"
):
    if chunk.event == "values":
        messages = convert_to_messages(chunk.data["messages"])
        messages[-1].pretty_print()

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

what's the weather in sf
==================================[1m Ai Message [0m==================================
Tool Calls:
  get_weather (call_NYVNSiBeF0oTAYnaDrlEAG7a)
 Call ID: call_NYVNSiBeF0oTAYnaDrlEAG7a
  Args:
    city: sf
=================================[1m Tool Message [0m=================================
Name: get_weather

It's always sunny in sf
==================================[1m Ai Message [0m==================================

The weather in San Francisco is currently sunny.

持久化¶

在LangGraph中，当你编译图时需要提供一个checkpointer对象，以便在与图的每次交互（例如线程）之间持久化状态。在LangGraph Cloud中，你不需要创建一个checkpointer——服务器已经为你实现了一个。你也可以直接从客户端管理线程。

使用LangGraph¶

from langgraph.checkpoint.memory import MemorySaver

API Reference: MemorySaver

checkpointer = MemorySaver()
graph_with_memory = create_react_agent(model, tools, checkpointer=checkpointer)

inputs = {"messages": [("human", "what's the weather in nyc")]}
invoke_output = await graph_with_memory.ainvoke(
    inputs, config={"configurable": {"thread_id": "1"}}
)
invoke_output["messages"][-1].pretty_print()

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

The weather in NYC might be cloudy.

inputs = {"messages": [("human", "what's it known for?")]}
invoke_output = await graph_with_memory.ainvoke(
    inputs, config={"configurable": {"thread_id": "1"}}
)
invoke_output["messages"][-1].pretty_print()

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

New York City (NYC) is known for a variety of iconic landmarks, cultural institutions, and vibrant neighborhoods. Some of the most notable things NYC is known for include:

1. **Statue of Liberty**: A symbol of freedom and democracy.
2. **Times Square**: Famous for its bright lights, Broadway theaters, and bustling atmosphere.
3. **Central Park**: A large urban park offering a green oasis in the middle of the city.
4. **Empire State Building**: An iconic skyscraper with an observation deck offering panoramic views of the city.
5. **Broadway**: Renowned for its world-class theater productions.
6. **Wall Street**: The financial hub of the United States.
7. **Museums**: Including the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), and the American Museum of Natural History.
8. **Diverse Cuisine**: A melting pot of culinary experiences from around the world.
9. **Cultural Diversity**: A rich tapestry of cultures, languages, and traditions.
10. **Fashion**: A global fashion capital, home to numerous designers and fashion events.

These are just a few highlights, but NYC offers countless other attractions and experiences.

inputs = {"messages": [("human", "what's it known for?")]}
invoke_output = await graph_with_memory.ainvoke(
    inputs, config={"configurable": {"thread_id": "2"}}
)
invoke_output["messages"][-1].pretty_print()

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

Could you please specify what "it" refers to? Are you asking about a specific city, person, object, or something else?

# get the state of the thread
checkpointer.get({"configurable": {"thread_id": "2"}})

{'v': 1,
 'ts': '2024-06-22T02:31:49.722569+00:00',
 'id': '1ef303f9-4149-6b56-8001-a80d1e3c9dc6',
 'channel_values': {'messages': [HumanMessage(content="what's it known for?", id='ea0d1672-05e9-4d77-9dff-b33bd5c824e7'),
   AIMessage(content='Could you please specify what "it" refers to? Are you asking about a specific city, person, object, or something else?', response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 57, 'total_tokens': 85}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'stop', 'logprobs': None}, id='run-f0381dc0-d891-4203-8f77-3155ba17998c-0', usage_metadata={'input_tokens': 57, 'output_tokens': 28, 'total_tokens': 85})],
  'agent': 'agent'},
 'channel_versions': {'__start__': 2,
  'messages': 3,
  'start:agent': 3,
  'agent': 3},
 'versions_seen': {'__start__': {'__start__': 1},
  'agent': {'start:agent': 2},
  'tools': {}},
 'pending_sends': []}

使用LangGraph Cloud¶

现在让我们使用LangGraph Cloud来重现相同的操作。请注意，我们不再使用检查点器，而是直接在后端创建一个新的线程，并将该线程的ID传递给API。

thread = await client.threads.create()

inputs = {"messages": [("human", "what's the weather in nyc")]}
wait_output = await client.runs.wait(thread["thread_id"], "agent", input=inputs)
convert_to_messages(wait_output["messages"])[-1].pretty_print()

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

The weather in NYC might be cloudy.

inputs = {"messages": [("human", "what's it known for?")]}
wait_output = await client.runs.wait(thread["thread_id"], "agent", input=inputs)
convert_to_messages(wait_output["messages"])[-1].pretty_print()

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

New York City (NYC) is known for a variety of iconic landmarks, cultural institutions, and vibrant neighborhoods. Some of the most notable things NYC is known for include:

1. **Statue of Liberty**: A symbol of freedom and democracy, located on Liberty Island.
2. **Times Square**: Known for its bright lights, Broadway theaters, and bustling atmosphere.
3. **Central Park**: A large urban park offering a green oasis in the middle of the city.
4. **Empire State Building**: An iconic skyscraper with an observation deck offering panoramic views of the city.
5. **Broadway**: Famous for its world-class theater productions and musicals.
6. **Wall Street**: The financial hub of the United States, home to the New York Stock Exchange.
7. **Museums**: Including the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), and the American Museum of Natural History.
8. **Diverse Cuisine**: A melting pot of culinary experiences, from street food to Michelin-starred restaurants.
9. **Cultural Diversity**: A rich tapestry of cultures and communities from around the world.
10. **Skyscrapers**: A skyline filled with iconic buildings and modern architecture.

NYC is also known for its influence in fashion, media, and entertainment, making it one of the most dynamic and influential cities in the world.

thread = await client.threads.create()

inputs = {"messages": [("human", "what's it known for?")]}
wait_output = await client.runs.wait(thread["thread_id"], "agent", input=inputs)
convert_to_messages(wait_output["messages"])[-1].pretty_print()

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

Could you please specify what "it" refers to? Are you asking about a specific city, person, object, or something else?

# get the state of the thread
await client.threads.get_state(thread["thread_id"])

{'values': {'messages': [{'content': "what's it known for?",
    'additional_kwargs': {},
    'response_metadata': {},
    'type': 'human',
    'name': None,
    'id': '381cd144-b360-4c7d-8177-e2634446993c',
    'example': False},
   {'content': 'Could you please specify what "it" refers to? Are you asking about a specific city, person, object, or something else?',
    'additional_kwargs': {'refusal': None},
    'response_metadata': {'token_usage': {'completion_tokens': 28,
      'prompt_tokens': 57,
      'total_tokens': 85,
      'completion_tokens_details': {'reasoning_tokens': 0}},
     'model_name': 'gpt-4o-2024-05-13',
     'system_fingerprint': 'fp_3537616b13',
     'finish_reason': 'stop',
     'logprobs': None},
    'type': 'ai',
    'name': None,
    'id': 'run-b23c6a05-d6c7-46fd-8b09-443a334feb6b-0',
    'example': False,
    'tool_calls': [],
    'invalid_tool_calls': [],
    'usage_metadata': {'input_tokens': 57,
     'output_tokens': 28,
     'total_tokens': 85}}]},
 'next': [],
 'tasks': [],
 'metadata': {'step': 1,
  'run_id': '1ef78309-0a79-6667-a00b-1a04034cabff',
  'source': 'loop',
  'writes': {'agent': {'messages': [{'id': 'run-b23c6a05-d6c7-46fd-8b09-443a334feb6b-0',
      'name': None,
      'type': 'ai',
      'content': 'Could you please specify what "it" refers to? Are you asking about a specific city, person, object, or something else?',
      'example': False,
      'tool_calls': [],
      'usage_metadata': {'input_tokens': 57,
       'total_tokens': 85,
       'output_tokens': 28},
      'additional_kwargs': {'refusal': None},
      'response_metadata': {'logprobs': None,
       'model_name': 'gpt-4o-2024-05-13',
       'token_usage': {'total_tokens': 85,
        'prompt_tokens': 57,
        'completion_tokens': 28,
        'completion_tokens_details': {'reasoning_tokens': 0}},
       'finish_reason': 'stop',
       'system_fingerprint': 'fp_3537616b13'},
      'invalid_tool_calls': []}]}},
  'parents': {},
  'user_id': '',
  'graph_id': 'agent',
  'thread_id': '14162c42-ab85-404f-a2f9-e7207493f74b',
  'created_by': 'system',
  'run_attempt': 1,
  'assistant_id': 'fe096781-5601-53d2-b2f6-0d3403f7e9ca'},
 'created_at': '2024-09-21T15:45:46.150887+00:00',
 'checkpoint_id': '1ef78309-131f-673f-8001-55ea44389e6a',
 'parent_checkpoint_id': '1ef78309-0aab-6271-8000-caaf708a2185'}

断点¶

使用LangGraph¶

inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in graph_with_memory.astream(
    inputs,
    stream_mode="values",
    interrupt_before=["tools"],
    config={"configurable": {"thread_id": "3"}},
):
    chunk["messages"][-1].pretty_print()

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

what's the weather in sf
==================================[1m Ai Message [0m==================================
Tool Calls:
  get_weather (call_cYp3BijeW2JNQ9RqJRdkrbMu)
 Call ID: call_cYp3BijeW2JNQ9RqJRdkrbMu
  Args:
    city: sf

async for chunk in graph_with_memory.astream(
    None,
    stream_mode="values",
    interrupt_before=["tools"],
    config={"configurable": {"thread_id": "3"}},
):
    chunk["messages"][-1].pretty_print()

=================================[1m Tool Message [0m=================================
Name: get_weather

It's always sunny in sf
==================================[1m Ai Message [0m==================================

The weather in San Francisco is sunny!

使用LangGraph Cloud¶

类似于持久化示例，我们需要创建一个线程，以便可以持久化状态并从断点处继续执行。

thread = await client.threads.create()

async for chunk in client.runs.stream(
    thread["thread_id"],
    "agent",
    input=inputs,
    stream_mode="values",
    interrupt_before=["tools"],
):
    if chunk.event == "values":
        messages = convert_to_messages(chunk.data["messages"])
        messages[-1].pretty_print()

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

what's the weather in sf
==================================[1m Ai Message [0m==================================
Tool Calls:
  get_weather (call_MVQEJtPYAj1nJ7J6YaCeLX8a)
 Call ID: call_MVQEJtPYAj1nJ7J6YaCeLX8a
  Args:
    city: sf

async for chunk in client.runs.stream(
    thread["thread_id"],
    "agent",
    input=None,
    stream_mode="values",
    interrupt_before=["tools"],
):
    if chunk.event == "values":
        messages = convert_to_messages(chunk.data["messages"])
        messages[-1].pretty_print()

=================================[1m Tool Message [0m=================================
Name: get_weather

It's always sunny in sf
==================================[1m Ai Message [0m==================================

The weather in San Francisco is currently sunny.

直播事件¶

对于流式事件，在LangGraph中需要在CompiledGraph上使用.astream方法。在LangGraph Cloud中，这是通过传递stream_mode="events"来完成的。

使用LangGraph¶

from langchain_core.messages import AIMessageChunk

inputs = {"messages": [("human", "what's the weather in sf")]}
first = True
async for msg, metadata in graph.astream(inputs, stream_mode="messages"):
    if msg.content:
        print(msg.content, end="|", flush=True)

    if isinstance(msg, AIMessageChunk):
        if first:
            gathered = msg
            first = False
        else:
            gathered = gathered + msg

        if msg.tool_call_chunks:
            print(gathered.tool_calls)

API Reference: AIMessageChunk

[{'name': 'get_weather', 'args': {}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]
[{'name': 'get_weather', 'args': {}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]
[{'name': 'get_weather', 'args': {}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]
[{'name': 'get_weather', 'args': {'city': ''}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]
[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]
[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_G6boP6Hj21glqPTqtFdTllUd', 'type': 'tool_call'}]
It's always sunny in sf|The| weather| in| San| Francisco| is| currently| sunny|.|

使用LangGraph Cloud¶

inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in client.runs.stream(
    None, "agent", input=inputs, stream_mode="events"
):
    if chunk.event == "events" and chunk.data["event"] == "on_chat_model_stream":
        print(chunk.data["data"]["chunk"])

{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'function': {'arguments': '', 'name': 'get_weather'}, 'type': 'function'}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': 'get_weather', 'args': '', 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': 'get_weather', 'args': '', 'id': 'call_JYWaAecaAV92cOlZwRHi9B7M', 'index': 0}]}
{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '{"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [{'name': '', 'args': {}, 'id': None}], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '{"', 'id': None, 'index': 0}]}
{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'city', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'city', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'city', 'id': None, 'index': 0}]}
{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '":"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '":"', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '":"', 'id': None, 'index': 0}]}
{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'sf', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'sf', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'sf', 'id': None, 'index': 0}]}
{'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '"}', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '"}', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '"}', 'id': None, 'index': 0}]}
{'content': '', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'tool_calls'}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-855fec3d-15df-4ae8-b74d-208a0e463be9', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': '', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': 'The', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' weather', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' in', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' San', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' Francisco', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' is', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' currently', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' sunny', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': '.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' Enjoy', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' the', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': ' sunshine', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': '!', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
{'content': '', 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop'}, 'type': 'AIMessageChunk', 'name': None, 'id': 'run-19a0bdff-8724-4730-8052-c3ac89525461', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}

如何将LangGraph调用转换为LangGraph Cloud调用¶

设置环境¶

调用图形¶

使用LangGraph¶

使用LangGraph Cloud¶

流式处理¶

使用LangGraph¶

使用LangGraph Cloud¶

持久化¶

使用LangGraph¶

使用LangGraph Cloud¶

断点¶

使用LangGraph¶

使用LangGraph Cloud¶

直播事件¶

使用LangGraph¶

使用LangGraph Cloud¶

Comments