MapReduceDocumentsChain()Optional memory object.
Optional list of callback handlers (or callback manager).
Whether or not run in verbose mode. In verbose mode, some intermediate logs
Optional list of tags associated with the chain.
Optional metadata associated with the chain.
[DEPRECATED] Use callbacks instead.
Keys expected to be in the chain input.
Combining documents by mapping a chain over them, then combining results.
We first call llm_chain on each document individually, passing in the
page_content and any other kwargs. This is the map step.
We then process the results of that map step in a reduce step. This should
likely be a ReduceDocumentsChain.
Example:
from langchain_classic.chains import (
StuffDocumentsChain,
LLMChain,
ReduceDocumentsChain,
MapReduceDocumentsChain,
)
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
# This controls how each document will be formatted. Specifically,
# it will be passed to `format_document` - see that function for more
# details.
document_prompt = PromptTemplate(
input_variables=["page_content"], template="{page_content}"
)
document_variable_name = "context"
model = OpenAI()
# The prompt here should take as an input variable the
# `document_variable_name`
prompt = PromptTemplate.from_template("Summarize this content: {context}")
llm_chain = LLMChain(llm=model, prompt=prompt)
# We now define how to combine these summaries
reduce_prompt = PromptTemplate.from_template(
"Combine these summaries: {context}"
)
reduce_llm_chain = LLMChain(llm=model, prompt=reduce_prompt)
combine_documents_chain = StuffDocumentsChain(
llm_chain=reduce_llm_chain,
document_prompt=document_prompt,
document_variable_name=document_variable_name,
)
reduce_documents_chain = ReduceDocumentsChain(
combine_documents_chain=combine_documents_chain,
)
chain = MapReduceDocumentsChain(
llm_chain=llm_chain,
reduce_documents_chain=reduce_documents_chain,
)
# If we wanted to, we could also pass in collapse_documents_chain
# which is specifically aimed at collapsing documents BEFORE
# the final call.
prompt = PromptTemplate.from_template("Collapse this content: {context}")
llm_chain = LLMChain(llm=model, prompt=prompt)
collapse_documents_chain = StuffDocumentsChain(
llm_chain=llm_chain,
document_prompt=document_prompt,
document_variable_name=document_variable_name,
)
reduce_documents_chain = ReduceDocumentsChain(
combine_documents_chain=combine_documents_chain,
collapse_documents_chain=collapse_documents_chain,
)
chain = MapReduceDocumentsChain(
llm_chain=llm_chain,
reduce_documents_chain=reduce_documents_chain,
)Asynchronously execute the chain.