Runnable that can be dynamically configured.
A RunnableConfigurableFields should be initiated using the
configurable_fields method of a Runnable.
Here is an example of using a RunnableConfigurableFields with LLMs:
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="temperature",
name="LLM Temperature",
description="The temperature of the LLM",
)
)
# This creates a RunnableConfigurableFields for a chat model.
# When invoking the created RunnableSequence, you can pass in the
# value for your ConfigurableField's id which in this case
# will be change in temperature
prompt = PromptTemplate.from_template("Pick a random number above {x}")
chain = prompt | model
chain.invoke({"x": 0})
chain.invoke({"x": 0}, config={"configurable": {"temperature": 0.9}})Here is an example of using a RunnableConfigurableFields with HubRunnables:
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
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"})
# Invoking prompt with `with_config` method
prompt.invoke(
{"question": "foo", "context": "bar"},
config={"configurable": {"hub_commit": "rlm/rag-prompt-llama"}},
)Return True as this class is serializable.
Get the namespace of the LangChain object.
Prepare the Runnable for invocation.
Invoke the retriever to get relevant documents.
Asynchronously invoke the retriever to get relevant documents.
Return True as this class is serializable.
Get the namespace of the LangChain object.
Return a unique identifier for this class for serialization purposes.
Convert the graph to a JSON-serializable format.
Serialize a "not implemented" object.
Get a JSON schema that represents the input to the Runnable.
Get a JSON schema that represents the output of the Runnable.
The type of config this Runnable accepts specified as a Pydantic model.
Get a JSON schema that represents the config of the Runnable.
Return a list of prompts used by this Runnable.
Pipe Runnable objects.
Pick keys from the output dict of this Runnable.
Merge the Dict input with the output produced by the mapping argument.
Invoke the retriever to get relevant documents.
Asynchronously invoke the retriever to get relevant documents.
Run invoke in parallel on a list of inputs.
Run ainvoke in parallel on a list of inputs.
Stream all output from a Runnable, as reported to the callback system.
Generate a stream of events.
Bind arguments to a Runnable, returning a new Runnable.
Bind lifecycle listeners to a Runnable, returning a new Runnable.
Bind async lifecycle listeners to a Runnable.
Bind input and output types to a Runnable, returning a new Runnable.
Create a new Runnable that retries the original Runnable on exceptions.
Map a function to multiple iterables.
Add fallbacks to a Runnable, returning a new Runnable.
Create a BaseTool from a Runnable.