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Showing content from https://python.langchain.com/v0.1/docs/expression_language/primitives/assign/ below:

Assign: Add values to state

The RunnablePassthrough.assign(...) static method takes an input value and adds the extra arguments passed to the assign function.

This is useful when additively creating a dictionary to use as input to a later step, which is a common LCEL pattern.

Here's an example:

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

WARNING: You are using pip version 22.0.4; however, version 24.0 is available.
You should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.

from langchain_core.runnables import RunnableParallel, RunnablePassthrough

runnable = RunnableParallel(
    extra=RunnablePassthrough.assign(mult=lambda x: x["num"] * 3),
    modified=lambda x: x["num"] + 1,
)

runnable.invoke({"num": 1})

{'extra': {'num': 1, 'mult': 3}, 'modified': 2}

Let's break down what's happening here.

• The input to the chain is {"num": 1}. This is passed into a RunnableParallel, which invokes the runnables it is passed in parallel with that input.
• The value under the extra key is invoked. RunnablePassthrough.assign() keeps the original keys in the input dict ({"num": 1}), and assigns a new key called mult. The value is lambda x: x["num"] * 3), which is 3. Thus, the result is {"num": 1, "mult": 3}.
• {"num": 1, "mult": 3} is returned to the RunnableParallel call, and is set as the value to the key extra.
• At the same time, the modified key is called. The result is 2, since the lambda extracts a key called "num" from its input and adds one.

Thus, the result is {'extra': {'num': 1, 'mult': 3}, 'modified': 2}.

One nice feature of this method is that it allows values to pass through as soon as they are available. To show this off, we'll use RunnablePassthrough.assign() to immediately return source docs in a retrieval chain:

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}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()

generation_chain = prompt | model | StrOutputParser()

retrieval_chain = {
    "context": retriever,
    "question": RunnablePassthrough(),
} | RunnablePassthrough.assign(output=generation_chain)

stream = retrieval_chain.stream("where did harrison work?")

for chunk in stream:
    print(chunk)

{'question': 'where did harrison work?'}
{'context': [Document(page_content='harrison worked at kensho')]}
{'output': ''}
{'output': 'H'}
{'output': 'arrison'}
{'output': ' worked'}
{'output': ' at'}
{'output': ' Kens'}
{'output': 'ho'}
{'output': '.'}
{'output': ''}

We can see that the first chunk contains the original "question" since that is immediately available. The second chunk contains "context" since the retriever finishes second. Finally, the output from the generation_chain streams in chunks as soon as it is available.

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