How to invoke runnables in parallel
This guide assumes familiarity with the following concepts:
The RunnableParallel primitive is essentially a dict whose values are runnables (or things that can be coerced to runnables, like functions). It runs all of its values in parallel, and each value is called with the overall input of the RunnableParallel. The final return value is a dict with the results of each value under its appropriate key.
Formatting with RunnableParallels
RunnableParallels are useful for parallelizing operations, but can also be useful for manipulating the output of one Runnable to match the input format of the next Runnable in a sequence. You can use them to split or fork the chain so that multiple components can process the input in parallel. Later, other components can join or merge the results to synthesize a final response. This type of chain creates a computation graph that looks like the following:
Input
/ \
/ \
Branch1 Branch2
\ /
\ /
Combine
Below, the input to prompt is expected to be a map with keys "context" and "question". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the "question" key.
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
vectorstore = FAISS.from_texts(
["harrison worked at kensho"], embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
# The prompt expects input with keys for "context" and "question"
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()
retrieval_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
retrieval_chain.invoke("where did harrison work?")
'Harrison worked at Kensho.'
Note that when composing a RunnableParallel with another Runnable we don't even need to wrap our dictionary in the RunnableParallel class — the type conversion is handled for us. In the context of a chain, these are equivalent:
{"context": retriever, "question": RunnablePassthrough()}
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
RunnableParallel(context=retriever, question=RunnablePassthrough())
See the section on coercion for more.
Using itemgetter as shorthand
Note that you can use Python's itemgetter as shorthand to extract data from the map when combining with RunnableParallel. You can find more information about itemgetter in the Python Documentation.
In the example below, we use itemgetter to extract specific keys from the map:
from operator import itemgetter
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
vectorstore = FAISS.from_texts(
["harrison worked at kensho"], embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
Answer in the following language: {language}
"""
prompt = ChatPromptTemplate.from_template(template)
chain = (
{
"context": itemgetter("question") | retriever,
"question": itemgetter("question"),
"language": itemgetter("language"),
}
| prompt
| model
| StrOutputParser()
)
chain.invoke({"question": "where did harrison work", "language": "italian"})
'Harrison ha lavorato a Kensho.'
Parallelize steps
RunnableParallels make it easy to execute multiple Runnables in parallel, and to return the output of these Runnables as a map.
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableParallel
from langchain_openai import ChatOpenAI
model = ChatOpenAI()
joke_chain = ChatPromptTemplate.from_template("tell me a joke about {topic}") | model
poem_chain = (
ChatPromptTemplate.from_template("write a 2-line poem about {topic}") | model
)
map_chain = RunnableParallel(joke=joke_chain, poem=poem_chain)
map_chain.invoke({"topic": "bear"})
{'joke': AIMessage(content="Why don't bears like fast food? Because they can't catch it!", response_metadata={'token_usage': {'completion_tokens': 15, 'prompt_tokens': 13, 'total_tokens': 28}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_d9767fc5b9', 'finish_reason': 'stop', 'logprobs': None}, id='run-fe024170-c251-4b7a-bfd4-64a3737c67f2-0'),
'poem': AIMessage(content='In the quiet of the forest, the bear roams free\nMajestic and wild, a sight to see.', response_metadata={'token_usage': {'completion_tokens': 24, 'prompt_tokens': 15, 'total_tokens': 39}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-2707913e-a743-4101-b6ec-840df4568a76-0')}
Parallelism
RunnableParallel are also useful for running independent processes in parallel, since each Runnable in the map is executed in parallel. For example, we can see our earlier joke_chain, poem_chain and map_chain all have about the same runtime, even though map_chain executes both of the other two.
%%timeit
joke_chain.invoke({"topic": "bear"})
610 ms ± 64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%%timeit
poem_chain.invoke({"topic": "bear"})
599 ms ± 73.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%%timeit
map_chain.invoke({"topic": "bear"})
643 ms ± 77.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Next steps
You now know some ways to format and parallelize chain steps with RunnableParallel.
To learn more, see the other how-to guides on runnables in this section.