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在运行时配置链内部

通常，您可能想要尝试多种不同的做事方式，甚至向最终用户展示多种不同的做事方式。为了使这种体验尽可能简单，我们定义了两种方法。

首先是可配置字段方法。这允许您配置可运行的特定字段。

其次，configurable_alternatives 方法。使用此方法，您可以列出可以在运行时设置的任何特定可运行对象的替代方案。

配置字段​

用LLMs​

借助法学硕士，我们可以配置温度等内容

%pip install --upgrade --quiet  langchain langchain-openai
from langchain.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='7')

model.with_config(configurable={"llm_temperature": 0.9}).invoke("pick a random number")

AIMessage(content='34')

当它用作链的一部分时我们也可以这样做

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

chain.invoke({"x": 0})

AIMessage(content='57')

chain.with_config(configurable={"llm_temperature": 0.9}).invoke({"x": 0})

AIMessage(content='6')

使用 HubRunnable​

这对于允许切换提示很有用

from langchain.runnables.hub import HubRunnable

prompt = HubRunnable("rlm/rag-prompt").configurable_fields(
    owner_repo_commit=ConfigurableField(
        id="hub_commit",
        name="Hub Commit",
        description="The Hub commit to pull from",
    )
)



prompt.invoke({"question": "foo", "context": "bar"})



ChatPromptValue(messages=[HumanMessage(content="You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: foo \nContext: bar \nAnswer:")])



prompt.with_config(configurable={"hub_commit": "rlm/rag-prompt-llama"}).invoke(
    {"question": "foo", "context": "bar"}
)



ChatPromptValue(messages=[HumanMessage(content="[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \nQuestion: foo \nContext: bar \nAnswer: [/INST]")])


可配置的替代方案​

用 LLMs​

让我们看看法学硕士如何做到这一点

from langchain.prompts import PromptTemplate
from langchain_community.chat_models import ChatAnthropic
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI


llm = ChatAnthropic(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 silly joke about bears:\n\nWhat do you call a bear with no teeth?\nA gummy bear!")


# 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="Sure, here's a bear joke for you:\n\nWhy don't bears wear shoes?\n\nBecause they already have bear feet!")


# 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 silly joke about bears:\n\nWhat do you call a bear with no teeth?\nA gummy bear!")


用Prompts​

我们可以做类似的事情，但是在提示之间交替

llm = ChatAnthropic(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 LLM (ChatAnthropic 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 silly joke about bears:\n\nWhat do you call a bear with no teeth?\nA gummy bear!")


# 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\nThe bears awaken from their sleep\nAnd lumber out into the deep\nForests filled with trees so tall\nForaging for food before nightfall \nTheir furry coats and claws so sharp\nSniffing for berries and fish to nab\nLumbering about without a care\nThe mighty grizzly and black bear\nProud creatures, wild and free\nRuling their domain majestically\nWandering the woods they call their own\nBefore returning to their dens alone')


用 Prompts 和 LLMs​

我们还可以配置多项内容！这是一个使用提示和LLMs来实现这一点的示例。

llm = ChatAnthropic(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 LLM (ChatAnthropic 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, where tall trees sway,\nA creature roams, both fierce and gray.\nWith mighty paws and piercing eyes,\nThe bear, a symbol of strength, defies.\n\nThrough snow-kissed mountains, it does roam,\nA guardian of its woodland home.\nWith fur so thick, a shield of might,\nIt braves the coldest winter night.\n\nA gentle giant, yet wild and free,\nThe bear commands respect, you see.\nWith every step, it leaves a trace,\nOf untamed power and ancient grace.\n\nFrom honeyed feast to salmon's leap,\nIt takes its place, in nature's keep.\nA symbol of untamed delight,\nThe bear, a wonder, day and night.\n\nSo let us honor this noble beast,\nIn forests where its soul finds peace.\nFor in its presence, we come to know,\nThe untamed spirit that in us also flows.")


# We can always just configure only one if we want
chain.with_config(configurable={"llm": "openai"}).invoke({"topic": "bears"})

AIMessage(content="Sure, here's a bear joke for you:\n\nWhy don't bears wear shoes?\n\nBecause they have bear feet!")

保存配置​

我们还可以轻松地将配置的链保存为自己的对象

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

openai_joke.invoke({"topic": "bears"})

AIMessage(content="Why don't bears wear shoes?\n\nBecause they have bear feet!")