Configure chain internals at runtime

Oftentimes you may want to experiment with, or even expose to the end user, multiple different ways of doing things. In order to make this experience as easy as possible, we have defined two methods.

First, a configurable_fields method. This lets you configure particular fields of a runnable.

Second, a configurable_alternatives method. With this method, you can list out alternatives for any particular runnable that can be set during runtime.

Configuration Fields

With LLMs

With LLMs we can configure things like temperature

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

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",
    )
)

API Reference:

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')

We can also do this when its used as part of a chain

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')

With HubRunnables

This is useful to allow for switching of prompts

from langchain.runnables.hub import HubRunnable

API Reference:

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]")])

Configurable Alternatives

With LLMs

Let's take a look at doing this with LLMs

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

API Reference:

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!")

With Prompts

We can do a similar thing, but alternate between prompts

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')

With Prompts and LLMs

We can also have multiple things configurable! Here's an example doing that with both prompts and LLMs.

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!")

Saving configurations

We can also easily save configured chains as their own objects

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!")

Configure chain internals at runtime

Configuration Fields​

With LLMs​

API Reference:

With HubRunnables​

API Reference:

Configurable Alternatives​

With LLMs​

API Reference:

With Prompts​

With Prompts and LLMs​

Saving configurations​

Help us out by providing feedback on this documentation page:

Configuration Fields

With LLMs

With HubRunnables

Configurable Alternatives

With LLMs

With Prompts

With Prompts and LLMs

Saving configurations