Branching and Parallel Chains
Build RunnableParallel and RunnableBranch constructs to run multiple chains simultaneously or route input to different chains based on dynamic conditions.
Why Parallel and Branching Matter
Real-world LLM pipelines often need to do multiple things at once or route requests differently based on content. Parallel chains run multiple branches simultaneously, reducing latency when tasks are independent. Branching chains route input to different specialized chains based on dynamic conditions. LCEL supports both patterns natively with RunnableParallel and RunnableBranch.
RunnableParallel Basics
RunnableParallel takes a dictionary where each key maps to a Runnable. When invoked, it runs all branches concurrently and returns a dictionary with each key containing the result of its branch. This is ideal when you want to generate multiple outputs from the same input — for example, generating a summary and extracting keywords simultaneously.
from langchain_core.runnables import RunnableParallel
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
model = ChatOpenAI(model='gpt-4o-mini')
parser = StrOutputParser()
parallel = RunnableParallel(
summary=(
ChatPromptTemplate.from_template('Summarize: {text}') | model | parser
),
keywords=(
ChatPromptTemplate.from_template('Extract keywords from: {text}') | model | parser
)
)
result = parallel.invoke({'text': 'Long document text here...'})
print(result['summary'])
print(result['keywords'])All lessons in this course
- LangChain Architecture and Core Abstractions
- Building Chains with LCEL
- Branching and Parallel Chains
- Streaming Output in LangChain