Source code for langchain.chains.combine_documents.map_reduce

"""Combining documents by mapping a chain over them first, then combining results."""

from __future__ import annotations

from typing import Any, Dict, List, Optional, Tuple, Type

from langchain_core.callbacks import Callbacks
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from langchain_core.runnables.config import RunnableConfig
from langchain_core.runnables.utils import create_model

from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.reduce import ReduceDocumentsChain
from langchain.chains.llm import LLMChain


[docs]class MapReduceDocumentsChain(BaseCombineDocumentsChain): """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: .. code-block:: python from langchain.chains import ( StuffDocumentsChain, LLMChain, ReduceDocumentsChain, MapReduceDocumentsChain, ) from langchain_core.prompts import PromptTemplate from langchain_community.llms 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" llm = 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=llm, prompt=prompt) # We now define how to combine these summaries reduce_prompt = PromptTemplate.from_template( "Combine these summaries: {context}" ) reduce_llm_chain = LLMChain(llm=llm, 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=llm, 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, ) """ llm_chain: LLMChain """Chain to apply to each document individually.""" reduce_documents_chain: BaseCombineDocumentsChain """Chain to use to reduce the results of applying `llm_chain` to each doc. This typically either a ReduceDocumentChain or StuffDocumentChain.""" document_variable_name: str """The variable name in the llm_chain to put the documents in. If only one variable in the llm_chain, this need not be provided.""" return_intermediate_steps: bool = False """Return the results of the map steps in the output."""
[docs] def get_output_schema( self, config: Optional[RunnableConfig] = None ) -> Type[BaseModel]: if self.return_intermediate_steps: return create_model( "MapReduceDocumentsOutput", **{ self.output_key: (str, None), "intermediate_steps": (List[str], None), }, # type: ignore[call-overload] ) return super().get_output_schema(config)
@property def output_keys(self) -> List[str]: """Expect input key. :meta private: """ _output_keys = super().output_keys if self.return_intermediate_steps: _output_keys = _output_keys + ["intermediate_steps"] return _output_keys class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @root_validator(pre=True) def get_reduce_chain(cls, values: Dict) -> Dict: """For backwards compatibility.""" if "combine_document_chain" in values: if "reduce_documents_chain" in values: raise ValueError( "Both `reduce_documents_chain` and `combine_document_chain` " "cannot be provided at the same time. `combine_document_chain` " "is deprecated, please only provide `reduce_documents_chain`" ) combine_chain = values["combine_document_chain"] collapse_chain = values.get("collapse_document_chain") reduce_chain = ReduceDocumentsChain( combine_documents_chain=combine_chain, collapse_documents_chain=collapse_chain, ) values["reduce_documents_chain"] = reduce_chain del values["combine_document_chain"] if "collapse_document_chain" in values: del values["collapse_document_chain"] return values @root_validator(pre=True) def get_return_intermediate_steps(cls, values: Dict) -> Dict: """For backwards compatibility.""" if "return_map_steps" in values: values["return_intermediate_steps"] = values["return_map_steps"] del values["return_map_steps"] return values @root_validator(pre=True) def get_default_document_variable_name(cls, values: Dict) -> Dict: """Get default document variable name, if not provided.""" if "document_variable_name" not in values: llm_chain_variables = values["llm_chain"].prompt.input_variables if len(llm_chain_variables) == 1: values["document_variable_name"] = llm_chain_variables[0] else: raise ValueError( "document_variable_name must be provided if there are " "multiple llm_chain input_variables" ) else: llm_chain_variables = values["llm_chain"].prompt.input_variables if values["document_variable_name"] not in llm_chain_variables: raise ValueError( f"document_variable_name {values['document_variable_name']} was " f"not found in llm_chain input_variables: {llm_chain_variables}" ) return values @property def collapse_document_chain(self) -> BaseCombineDocumentsChain: """Kept for backward compatibility.""" if isinstance(self.reduce_documents_chain, ReduceDocumentsChain): if self.reduce_documents_chain.collapse_documents_chain: return self.reduce_documents_chain.collapse_documents_chain else: return self.reduce_documents_chain.combine_documents_chain else: raise ValueError( f"`reduce_documents_chain` is of type " f"{type(self.reduce_documents_chain)} so it does not have " f"this attribute." ) @property def combine_document_chain(self) -> BaseCombineDocumentsChain: """Kept for backward compatibility.""" if isinstance(self.reduce_documents_chain, ReduceDocumentsChain): return self.reduce_documents_chain.combine_documents_chain else: raise ValueError( f"`reduce_documents_chain` is of type " f"{type(self.reduce_documents_chain)} so it does not have " f"this attribute." )
[docs] def combine_docs( self, docs: List[Document], token_max: Optional[int] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> Tuple[str, dict]: """Combine documents in a map reduce manner. Combine by mapping first chain over all documents, then reducing the results. This reducing can be done recursively if needed (if there are many documents). """ map_results = self.llm_chain.apply( # FYI - this is parallelized and so it is fast. [{self.document_variable_name: d.page_content, **kwargs} for d in docs], callbacks=callbacks, ) question_result_key = self.llm_chain.output_key result_docs = [ Document(page_content=r[question_result_key], metadata=docs[i].metadata) # This uses metadata from the docs, and the textual results from `results` for i, r in enumerate(map_results) ] result, extra_return_dict = self.reduce_documents_chain.combine_docs( result_docs, token_max=token_max, callbacks=callbacks, **kwargs ) if self.return_intermediate_steps: intermediate_steps = [r[question_result_key] for r in map_results] extra_return_dict["intermediate_steps"] = intermediate_steps return result, extra_return_dict
[docs] async def acombine_docs( self, docs: List[Document], token_max: Optional[int] = None, callbacks: Callbacks = None, **kwargs: Any, ) -> Tuple[str, dict]: """Combine documents in a map reduce manner. Combine by mapping first chain over all documents, then reducing the results. This reducing can be done recursively if needed (if there are many documents). """ map_results = await self.llm_chain.aapply( # FYI - this is parallelized and so it is fast. [{**{self.document_variable_name: d.page_content}, **kwargs} for d in docs], callbacks=callbacks, ) question_result_key = self.llm_chain.output_key result_docs = [ Document(page_content=r[question_result_key], metadata=docs[i].metadata) # This uses metadata from the docs, and the textual results from `results` for i, r in enumerate(map_results) ] result, extra_return_dict = await self.reduce_documents_chain.acombine_docs( result_docs, token_max=token_max, callbacks=callbacks, **kwargs ) if self.return_intermediate_steps: intermediate_steps = [r[question_result_key] for r in map_results] extra_return_dict["intermediate_steps"] = intermediate_steps return result, extra_return_dict
@property def _chain_type(self) -> str: return "map_reduce_documents_chain"