Skip to main content
Open In Colab在 GitHub 上打开

如何配置运行时链内部

先决条件

本指南假定您熟悉以下概念：

有时，您可能希望尝试甚至向最终用户展示链中的多种不同作方式。 这可能包括调整参数，例如温度，甚至将一个模型换成另一个模型。 为了尽可能简化此体验，我们定义了两种方法。

  • 一个configurable_fields方法。这允许您配置可运行对象的特定字段。
    • 这与.bindmethod 的 onnables 上，但允许您在运行时为链中的给定步骤指定参数，而不是事先指定它们。
  • 一个configurable_alternatives方法。使用此方法，您可以列出可在运行时设置的任何特定 runnable 的替代项，并将它们交换为那些指定的替代项。

可配置字段

让我们来看一个在运行时配置聊天模型字段（如 temperature）的示例：

%pip install --upgrade --quiet langchain langchain-openai

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()

在聊天模型上配置字段

如果使用 init_chat_model 创建聊天模型，则可以在构造函数中指定可配置字段：

from langchain.chat_models import init_chat_model

llm = init_chat_model(
    "openai:gpt-4o-mini",
    configurable_fields=("temperature",),
)
API 参考：init_chat_model

然后，您可以在运行时使用.with_config:

response = llm.with_config({"temperature": 0}).invoke("Hello")
print(response.content)
Hello! How can I assist you today?
提示

除了 temperature 等调用参数之外，以这种方式配置字段还会扩展到 clients 和其他属性。

与工具一起使用

此方法也适用于绑定工具：

from langchain_core.tools import tool


@tool
def get_weather(location: str):
    """Get the weather."""
    return "It's sunny."


llm_with_tools = llm.bind_tools([get_weather])
response = llm_with_tools.with_config({"temperature": 0}).invoke(
    "What's the weather in SF?"
)
response.tool_calls
API 参考：工具
[{'name': 'get_weather',
  'args': {'location': 'San Francisco'},
  'id': 'call_B93EttzlGyYUhzbIIiMcl3bE',
  'type': 'tool_call'}]

除了.with_config，我们现在可以在直接传递配置时包含该参数。请参阅下面的示例，其中我们允许在 langgraph 代理内部配置底层模型温度：

! pip install --upgrade langgraph
from langgraph.prebuilt import create_react_agent

agent = create_react_agent(llm, [get_weather])

response = agent.invoke(
    {"messages": "What's the weather in Boston?"},
    {"configurable": {"temperature": 0}},
)
API 参考：create_react_agent

在任意 Runnables 上配置字段

您还可以使用.configurable_fields方法，如下所示：

from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(temperature=0).configurable_fields(
    temperature=ConfigurableField(
        id="llm_temperature",
        name="LLM Temperature",
        description="The temperature of the LLM",
    )
)

model.invoke("pick a random number")
AIMessage(content='17', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 11, 'total_tokens': 12}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-ba26a0da-0a69-4533-ab7f-21178a73d303-0')

在上面，我们定义了temperature作为ConfigurableField我们可以在运行时设置。为此，我们使用with_config方法如下：

model.with_config(configurable={"llm_temperature": 0.9}).invoke("pick a random number")
AIMessage(content='12', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 11, 'total_tokens': 12}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-ba8422ad-be77-4cb1-ac45-ad0aae74e3d9-0')

请注意，传递的llm_temperatureentry 的 key 与id的ConfigurableField.

我们也可以这样做来只影响链中的一个步骤：

prompt = PromptTemplate.from_template("Pick a random number above {x}")
chain = prompt | model

chain.invoke({"x": 0})
AIMessage(content='27', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 14, 'total_tokens': 15}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-ecd4cadd-1b72-4f92-b9a0-15e08091f537-0')
chain.with_config(configurable={"llm_temperature": 0.9}).invoke({"x": 0})
AIMessage(content='35', response_metadata={'token_usage': {'completion_tokens': 1, 'prompt_tokens': 14, 'total_tokens': 15}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-a916602b-3460-46d3-a4a8-7c926ec747c0-0')

可配置的替代方案

这configurable_alternatives()方法允许我们将链中的步骤换成替代方案。下面，我们将一种聊天模式换成另一种聊天模式：

%pip install --upgrade --quiet langchain-anthropic

import os
from getpass import getpass

if "ANTHROPIC_API_KEY" not in os.environ:
    os.environ["ANTHROPIC_API_KEY"] = getpass()
WARNING: You are using pip version 22.0.4; however, version 24.0 is available.
You should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

llm = ChatAnthropic(
    model="claude-3-haiku-20240307", temperature=0
).configurable_alternatives(
    # This gives this field an id
    # When configuring the end runnable, we can then use this id to configure this field
    ConfigurableField(id="llm"),
    # This sets a default_key.
    # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used
    default_key="anthropic",
    # This adds a new option, with name `openai` that is equal to `ChatOpenAI()`
    openai=ChatOpenAI(),
    # This adds a new option, with name `gpt4` that is equal to `ChatOpenAI(model="gpt-4")`
    gpt4=ChatOpenAI(model="gpt-4"),
    # You can add more configuration options here
)
prompt = PromptTemplate.from_template("Tell me a joke about {topic}")
chain = prompt | llm

# By default it will call Anthropic
chain.invoke({"topic": "bears"})
AIMessage(content="Here's a bear joke for you:\n\nWhy don't bears wear socks? \nBecause they have bear feet!\n\nHow's that? I tried to come up with a simple, silly pun-based joke about bears. Puns and wordplay are a common way to create humorous bear jokes. Let me know if you'd like to hear another one!", response_metadata={'id': 'msg_018edUHh5fUbWdiimhrC3dZD', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 13, 'output_tokens': 80}}, id='run-775bc58c-28d7-4e6b-a268-48fa6661f02f-0')
# We can use `.with_config(configurable={"llm": "openai"})` to specify an llm to use
chain.with_config(configurable={"llm": "openai"}).invoke({"topic": "bears"})
AIMessage(content="Why don't bears like fast food?\n\nBecause they can't catch it!", response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 13, 'total_tokens': 28}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-7bdaa992-19c9-4f0d-9a0c-1f326bc992d4-0')
# If we use the `default_key` then it uses the default
chain.with_config(configurable={"llm": "anthropic"}).invoke({"topic": "bears"})
AIMessage(content="Here's a bear joke for you:\n\nWhy don't bears wear socks? \nBecause they have bear feet!\n\nHow's that? I tried to come up with a simple, silly pun-based joke about bears. Puns and wordplay are a common way to create humorous bear jokes. Let me know if you'd like to hear another one!", response_metadata={'id': 'msg_01BZvbmnEPGBtcxRWETCHkct', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 13, 'output_tokens': 80}}, id='run-59b6ee44-a1cd-41b8-a026-28ee67cdd718-0')

带提示

我们可以做类似的事情，但在 prompt 之间交替

llm = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
prompt = PromptTemplate.from_template(
    "Tell me a joke about {topic}"
).configurable_alternatives(
    # This gives this field an id
    # When configuring the end runnable, we can then use this id to configure this field
    ConfigurableField(id="prompt"),
    # This sets a default_key.
    # If we specify this key, the default prompt (asking for a joke, as initialized above) will be used
    default_key="joke",
    # This adds a new option, with name `poem`
    poem=PromptTemplate.from_template("Write a short poem about {topic}"),
    # You can add more configuration options here
)
chain = prompt | llm

# By default it will write a joke
chain.invoke({"topic": "bears"})
AIMessage(content="Here's a bear joke for you:\n\nWhy don't bears wear socks? \nBecause they have bear feet!", response_metadata={'id': 'msg_01DtM1cssjNFZYgeS3gMZ49H', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 13, 'output_tokens': 28}}, id='run-8199af7d-ea31-443d-b064-483693f2e0a1-0')
# We can configure it write a poem
chain.with_config(configurable={"prompt": "poem"}).invoke({"topic": "bears"})
AIMessage(content="Here is a short poem about bears:\n\nMajestic bears, strong and true,\nRoaming the forests, wild and free.\nPowerful paws, fur soft and brown,\nCommanding respect, nature's crown.\n\nForaging for berries, fishing streams,\nProtecting their young, fierce and keen.\nMighty bears, a sight to behold,\nGuardians of the wilderness, untold.\n\nIn the wild they reign supreme,\nEmbodying nature's grand theme.\nBears, a symbol of strength and grace,\nCaptivating all who see their face.", response_metadata={'id': 'msg_01Wck3qPxrjURtutvtodaJFn', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 13, 'output_tokens': 134}}, id='run-69414a1e-51d7-4bec-a307-b34b7d61025e-0')

使用 Prompts 和 LLM

我们还可以配置多个东西！ 下面是一个使用 prompts 和 LLM 执行此作的示例。

llm = ChatAnthropic(
    model="claude-3-haiku-20240307", temperature=0
).configurable_alternatives(
    # This gives this field an id
    # When configuring the end runnable, we can then use this id to configure this field
    ConfigurableField(id="llm"),
    # This sets a default_key.
    # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used
    default_key="anthropic",
    # This adds a new option, with name `openai` that is equal to `ChatOpenAI()`
    openai=ChatOpenAI(),
    # This adds a new option, with name `gpt4` that is equal to `ChatOpenAI(model="gpt-4")`
    gpt4=ChatOpenAI(model="gpt-4"),
    # You can add more configuration options here
)
prompt = PromptTemplate.from_template(
    "Tell me a joke about {topic}"
).configurable_alternatives(
    # This gives this field an id
    # When configuring the end runnable, we can then use this id to configure this field
    ConfigurableField(id="prompt"),
    # This sets a default_key.
    # If we specify this key, the default prompt (asking for a joke, as initialized above) will be used
    default_key="joke",
    # This adds a new option, with name `poem`
    poem=PromptTemplate.from_template("Write a short poem about {topic}"),
    # You can add more configuration options here
)
chain = prompt | llm

# We can configure it write a poem with OpenAI
chain.with_config(configurable={"prompt": "poem", "llm": "openai"}).invoke(
    {"topic": "bears"}
)
AIMessage(content="In the forest deep and wide,\nBears roam with grace and pride.\nWith fur as dark as night,\nThey rule the land with all their might.\n\nIn winter's chill, they hibernate,\nIn spring they emerge, hungry and great.\nWith claws sharp and eyes so keen,\nThey hunt for food, fierce and lean.\n\nBut beneath their tough exterior,\nLies a gentle heart, warm and superior.\nThey love their cubs with all their might,\nProtecting them through day and night.\n\nSo let us admire these majestic creatures,\nIn awe of their strength and features.\nFor in the wild, they reign supreme,\nThe mighty bears, a timeless dream.", response_metadata={'token_usage': {'completion_tokens': 133, 'prompt_tokens': 13, 'total_tokens': 146}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-5eec0b96-d580-49fd-ac4e-e32a0803b49b-0')
# We can always just configure only one if we want
chain.with_config(configurable={"llm": "openai"}).invoke({"topic": "bears"})
AIMessage(content="Why don't bears wear shoes?\n\nBecause they have bear feet!", response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 13, 'total_tokens': 26}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-c1b14c9c-4988-49b8-9363-15bfd479973a-0')

保存配置

我们还可以轻松地将配置的链保存为它们自己的对象

openai_joke = chain.with_config(configurable={"llm": "openai"})

openai_joke.invoke({"topic": "bears"})
AIMessage(content="Why did the bear break up with his girlfriend? \nBecause he couldn't bear the relationship anymore!", response_metadata={'token_usage': {'completion_tokens': 20, 'prompt_tokens': 13, 'total_tokens': 33}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-391ebd55-9137-458b-9a11-97acaff6a892-0')

后续步骤

您现在知道如何在运行时配置链的内部步骤。

要了解更多信息，请参阅本节中有关 runnables 的其他操作指南，包括：

  • 使用 .bind（） 作为设置可运行对象的运行时参数的更简单方法