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How to create a dynamic (self-constructing) chain


Sometimes we want to construct parts of a chain at runtime, depending on the chain inputs (routing is the most common example of this). We can create dynamic chains like this using a very useful property of RunnableLambda's, which is that if a RunnableLambda returns a Runnable, that Runnable is itself invoked. Let's see an example.

pip install -qU langchain-openai
import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
# | echo: false

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-sonnet-20240229")
API Reference:ChatAnthropic
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough, chain

contextualize_instructions = """Convert the latest user question into a standalone question given the chat history. Don't answer the question, return the question and nothing else (no descriptive text)."""
contextualize_prompt = ChatPromptTemplate.from_messages(
("system", contextualize_instructions),
("placeholder", "{chat_history}"),
("human", "{question}"),
contextualize_question = contextualize_prompt | llm | StrOutputParser()

qa_instructions = (
"""Answer the user question given the following context:\n\n{context}."""
qa_prompt = ChatPromptTemplate.from_messages(
[("system", qa_instructions), ("human", "{question}")]

def contextualize_if_needed(input_: dict) -> Runnable:
if input_.get("chat_history"):
# NOTE: This is returning another Runnable, not an actual output.
return contextualize_question
return RunnablePassthrough()

def fake_retriever(input_: dict) -> str:
return "egypt's population in 2024 is about 111 million"

full_chain = (
| qa_prompt
| llm
| StrOutputParser()

"question": "what about egypt",
"chat_history": [
("human", "what's the population of indonesia"),
("ai", "about 276 million"),
"According to the context provided, Egypt's population in 2024 is estimated to be about 111 million."

The key here is that contextualize_if_needed returns another Runnable and not an actual output. This returned Runnable is itself run when the full chain is executed.

Looking at the trace we can see that, since we passed in chat_history, we executed the contextualize_question chain as part of the full chain:

Note that the streaming, batching, etc. capabilities of the returned Runnable are all preserved

for chunk in
"question": "what about egypt",
"chat_history": [
("human", "what's the population of indonesia"),
("ai", "about 276 million"),

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