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https://github.com/kennethreitz/langchain.git
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Chandan Routray
bc875a9df1
# Fixed multi input prompt for MapReduceChain Added `kwargs` support for inner chains of `MapReduceChain` via `from_params` method Currently the `from_method` method of intialising `MapReduceChain` chain doesn't work if prompt has multiple inputs. It happens because it uses `StuffDocumentsChain` and `MapReduceDocumentsChain` underneath, both of them require specifying `document_variable_name` if `prompt` of their `llm_chain` has more than one `input`. With this PR, I have added support for passing their respective `kwargs` via the `from_params` method. ## Fixes https://github.com/hwchase17/langchain/issues/4752 ## Who can review? @dev2049 @hwchase17 @agola11 --------- Co-authored-by: imeckr <chandanroutray2012@gmail.com>
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
"""Map-reduce chain.
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Splits up a document, sends the smaller parts to the LLM with one prompt,
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then combines the results with another one.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Mapping, Optional
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from pydantic import Extra
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
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from langchain.chains.base import Chain
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from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
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from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
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from langchain.chains.combine_documents.stuff import StuffDocumentsChain
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from langchain.chains.llm import LLMChain
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from langchain.docstore.document import Document
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from langchain.prompts.base import BasePromptTemplate
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from langchain.text_splitter import TextSplitter
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class MapReduceChain(Chain):
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"""Map-reduce chain."""
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combine_documents_chain: BaseCombineDocumentsChain
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"""Chain to use to combine documents."""
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text_splitter: TextSplitter
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"""Text splitter to use."""
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input_key: str = "input_text" #: :meta private:
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output_key: str = "output_text" #: :meta private:
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@classmethod
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def from_params(
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cls,
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llm: BaseLanguageModel,
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prompt: BasePromptTemplate,
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text_splitter: TextSplitter,
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callbacks: Callbacks = None,
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combine_chain_kwargs: Optional[Mapping[str, Any]] = None,
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reduce_chain_kwargs: Optional[Mapping[str, Any]] = None,
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**kwargs: Any,
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) -> MapReduceChain:
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"""Construct a map-reduce chain that uses the chain for map and reduce."""
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llm_chain = LLMChain(llm=llm, prompt=prompt, callbacks=callbacks)
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reduce_chain = StuffDocumentsChain(
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llm_chain=llm_chain,
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callbacks=callbacks,
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**(reduce_chain_kwargs if reduce_chain_kwargs else {}),
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)
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combine_documents_chain = MapReduceDocumentsChain(
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llm_chain=llm_chain,
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combine_document_chain=reduce_chain,
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callbacks=callbacks,
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**(combine_chain_kwargs if combine_chain_kwargs else {}),
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)
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return cls(
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combine_documents_chain=combine_documents_chain,
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text_splitter=text_splitter,
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callbacks=callbacks,
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**kwargs,
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)
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return output key.
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:meta private:
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"""
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return [self.output_key]
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def _call(
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self,
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inputs: Dict[str, str],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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# Split the larger text into smaller chunks.
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doc_text = inputs.pop(self.input_key)
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texts = self.text_splitter.split_text(doc_text)
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docs = [Document(page_content=text) for text in texts]
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_inputs: Dict[str, Any] = {
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**inputs,
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self.combine_documents_chain.input_key: docs,
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}
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outputs = self.combine_documents_chain.run(
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_inputs, callbacks=_run_manager.get_child()
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)
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return {self.output_key: outputs}
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