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如何配置运行时链的内部结构

前置条件

本指南假设您熟悉以下概念：

有时您可能希望尝试，甚至向最终用户展示链中执行任务的多种不同方法。 这包括调整参数（如温度），甚至替换不同的模型。 为了使这种体验尽可能简单，我们定义了两个方法。

  • A configurable_fields 方法。这允许您配置可运行对象的特定字段。
    • 这与可运行对象上的 .bind 方法相关，但允许您在运行时为链中的给定步骤指定参数，而不是预先指定它们。
  • 一个 configurable_alternatives 方法。使用此方法，您可以列出任何特定可运行项在运行时可设置的替代方案，并将其替换为您指定的那些替代方案。

可配置字段​

让我们通过一个示例来了解如何在运行时配置聊天模型的字段（如温度）：

%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()

配置聊天模型的字段​

如果使用 init_chat_model 创建聊天模型，您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

您随后可以在运行时使用 .with_config 设置该参数：

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

除了像 temperature 这样的调用参数外，通过这种方式配置字段还扩展到客户端和其他属性。

与工具配合使用​

此方法同样适用于绑定工具的情况：

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 之外，我们现在还可以在直接传递配置时包含该参数。见下方示例，我们允许在 LangGraph 智能体 内部可配置底层模型的温度：

! 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}},
)

配置任意 Runnables 的字段​

您也可以对任意可运行对象使用.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')

上面，我们将 temperature 定义为一个可以在运行时设置的 ConfigurableField。为此，我们使用如下所示的 with_config 方法：

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')

注意传入的字典中 llm_temperature 项与 ConfigurableField 中的 id 具有相同的键。

我们也可以这样做，以影响链中的某一步：

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')

可配置替代方案​

configurable_alternatives()方法允许我们用替代步骤替换链中的步骤。下面，我们将一个聊天模型替换为另一个：

%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')

使用提示词​

我们可以做类似的事情，但在提示之间交替

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')

使用提示词与大语言模型​

我们也可以配置多个内容！ 这里有一个同时使用提示词和大型语言模型（LLM）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​

我们也可以轻松地将配置好的链保存为独立的对象

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')

下一步​

你现在已经知道如何在运行时配置链的内部步骤。

若要了解更多，请查看本节中关于可运行对象的其他操操作指南，包括：

  • 使用 .bind() 作为设置可运行对象运行时参数的更简单方式