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75 lines
2.5 KiB
Python
75 lines
2.5 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 Dict, List
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from pydantic import BaseModel, Extra
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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.llms.base import BaseLLM
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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, BaseModel):
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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, llm: BaseLLM, prompt: BasePromptTemplate, text_splitter: TextSplitter
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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)
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reduce_chain = StuffDocumentsChain(llm_chain=llm_chain)
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combine_documents_chain = MapReduceDocumentsChain(
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llm_chain=llm_chain, combine_document_chain=reduce_chain
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)
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return cls(
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combine_documents_chain=combine_documents_chain, text_splitter=text_splitter
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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(self, inputs: Dict[str, str]) -> Dict[str, str]:
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# Split the larger text into smaller chunks.
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texts = self.text_splitter.split_text(inputs[self.input_key])
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docs = [Document(page_content=text) for text in texts]
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outputs = self.combine_documents_chain.combine_docs(docs)
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return {self.output_key: outputs}
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