mirror of
https://github.com/hwchase17/langchain.git
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langchain: summarize chain fix (#21266)
Issue: `load_summarize_chain` is placed in the __init__.py file. As a result, it doesn't listed in the API Reference docs. Change: moved code from __init__.py into a new file.
This commit is contained in:
Leonid Ganeline
GitHub
@@ -1,166 +1,6 @@
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"""Load summarizing chains."""
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from typing import Any, Mapping, Optional, Protocol
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from langchain.chains.summarize.chain import (
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LoadingCallable,
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load_summarize_chain,
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)
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from langchain_core.callbacks import Callbacks
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts import BasePromptTemplate
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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.reduce import ReduceDocumentsChain
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from langchain.chains.combine_documents.refine import RefineDocumentsChain
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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.chains.summarize import map_reduce_prompt, refine_prompts, stuff_prompt
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class LoadingCallable(Protocol):
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"""Interface for loading the combine documents chain."""
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def __call__(
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self, llm: BaseLanguageModel, **kwargs: Any
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) -> BaseCombineDocumentsChain:
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"""Callable to load the combine documents chain."""
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def _load_stuff_chain(
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llm: BaseLanguageModel,
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prompt: BasePromptTemplate = stuff_prompt.PROMPT,
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document_variable_name: str = "text",
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verbose: Optional[bool] = None,
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**kwargs: Any,
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) -> StuffDocumentsChain:
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llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose) # type: ignore[arg-type]
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# TODO: document prompt
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return StuffDocumentsChain(
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llm_chain=llm_chain,
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document_variable_name=document_variable_name,
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verbose=verbose, # type: ignore[arg-type]
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**kwargs,
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)
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def _load_map_reduce_chain(
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llm: BaseLanguageModel,
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map_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
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combine_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
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combine_document_variable_name: str = "text",
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map_reduce_document_variable_name: str = "text",
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collapse_prompt: Optional[BasePromptTemplate] = None,
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reduce_llm: Optional[BaseLanguageModel] = None,
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collapse_llm: Optional[BaseLanguageModel] = None,
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verbose: Optional[bool] = None,
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token_max: int = 3000,
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callbacks: Callbacks = None,
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*,
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collapse_max_retries: Optional[int] = None,
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**kwargs: Any,
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) -> MapReduceDocumentsChain:
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map_chain = LLMChain(
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llm=llm,
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prompt=map_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks, # type: ignore[arg-type]
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)
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_reduce_llm = reduce_llm or llm
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reduce_chain = LLMChain(
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llm=_reduce_llm,
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prompt=combine_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks, # type: ignore[arg-type]
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)
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# TODO: document prompt
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combine_documents_chain = StuffDocumentsChain(
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llm_chain=reduce_chain,
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document_variable_name=combine_document_variable_name,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks,
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)
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if collapse_prompt is None:
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collapse_chain = None
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if collapse_llm is not None:
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raise ValueError(
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"collapse_llm provided, but collapse_prompt was not: please "
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"provide one or stop providing collapse_llm."
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)
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else:
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_collapse_llm = collapse_llm or llm
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collapse_chain = StuffDocumentsChain(
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llm_chain=LLMChain(
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llm=_collapse_llm,
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prompt=collapse_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks,
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),
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document_variable_name=combine_document_variable_name,
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)
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reduce_documents_chain = ReduceDocumentsChain(
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combine_documents_chain=combine_documents_chain,
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collapse_documents_chain=collapse_chain,
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token_max=token_max,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks,
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collapse_max_retries=collapse_max_retries,
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)
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return MapReduceDocumentsChain(
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llm_chain=map_chain,
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reduce_documents_chain=reduce_documents_chain,
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document_variable_name=map_reduce_document_variable_name,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks,
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**kwargs,
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)
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def _load_refine_chain(
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llm: BaseLanguageModel,
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question_prompt: BasePromptTemplate = refine_prompts.PROMPT,
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refine_prompt: BasePromptTemplate = refine_prompts.REFINE_PROMPT,
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document_variable_name: str = "text",
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initial_response_name: str = "existing_answer",
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refine_llm: Optional[BaseLanguageModel] = None,
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verbose: Optional[bool] = None,
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**kwargs: Any,
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) -> RefineDocumentsChain:
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initial_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose) # type: ignore[arg-type]
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_refine_llm = refine_llm or llm
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refine_chain = LLMChain(llm=_refine_llm, prompt=refine_prompt, verbose=verbose) # type: ignore[arg-type]
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return RefineDocumentsChain(
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initial_llm_chain=initial_chain,
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refine_llm_chain=refine_chain,
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document_variable_name=document_variable_name,
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initial_response_name=initial_response_name,
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verbose=verbose, # type: ignore[arg-type]
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**kwargs,
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)
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def load_summarize_chain(
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llm: BaseLanguageModel,
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chain_type: str = "stuff",
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verbose: Optional[bool] = None,
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**kwargs: Any,
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) -> BaseCombineDocumentsChain:
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"""Load summarizing chain.
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Args:
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llm: Language Model to use in the chain.
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chain_type: Type of document combining chain to use. Should be one of "stuff",
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"map_reduce", and "refine".
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verbose: Whether chains should be run in verbose mode or not. Note that this
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applies to all chains that make up the final chain.
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Returns:
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A chain to use for summarizing.
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"""
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loader_mapping: Mapping[str, LoadingCallable] = {
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"stuff": _load_stuff_chain,
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"map_reduce": _load_map_reduce_chain,
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"refine": _load_refine_chain,
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}
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if chain_type not in loader_mapping:
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raise ValueError(
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f"Got unsupported chain type: {chain_type}. "
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f"Should be one of {loader_mapping.keys()}"
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)
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return loader_mapping[chain_type](llm, verbose=verbose, **kwargs)
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__all__ = ["LoadingCallable", "load_summarize_chain"]
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166
libs/langchain/langchain/chains/summarize/chain.py
Normal file
166
libs/langchain/langchain/chains/summarize/chain.py
Normal file
@@ -0,0 +1,166 @@
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"""Load summarizing chains."""
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from typing import Any, Mapping, Optional, Protocol
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from langchain_core.callbacks import Callbacks
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts import BasePromptTemplate
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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.reduce import ReduceDocumentsChain
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from langchain.chains.combine_documents.refine import RefineDocumentsChain
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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.chains.summarize import map_reduce_prompt, refine_prompts, stuff_prompt
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class LoadingCallable(Protocol):
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"""Interface for loading the combine documents chain."""
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def __call__(
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self, llm: BaseLanguageModel, **kwargs: Any
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) -> BaseCombineDocumentsChain:
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"""Callable to load the combine documents chain."""
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def _load_stuff_chain(
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llm: BaseLanguageModel,
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prompt: BasePromptTemplate = stuff_prompt.PROMPT,
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document_variable_name: str = "text",
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verbose: Optional[bool] = None,
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**kwargs: Any,
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) -> StuffDocumentsChain:
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llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose) # type: ignore[arg-type]
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# TODO: document prompt
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return StuffDocumentsChain(
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llm_chain=llm_chain,
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document_variable_name=document_variable_name,
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verbose=verbose, # type: ignore[arg-type]
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**kwargs,
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)
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def _load_map_reduce_chain(
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llm: BaseLanguageModel,
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map_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
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combine_prompt: BasePromptTemplate = map_reduce_prompt.PROMPT,
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combine_document_variable_name: str = "text",
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map_reduce_document_variable_name: str = "text",
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collapse_prompt: Optional[BasePromptTemplate] = None,
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reduce_llm: Optional[BaseLanguageModel] = None,
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collapse_llm: Optional[BaseLanguageModel] = None,
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verbose: Optional[bool] = None,
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token_max: int = 3000,
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callbacks: Callbacks = None,
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*,
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collapse_max_retries: Optional[int] = None,
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**kwargs: Any,
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) -> MapReduceDocumentsChain:
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map_chain = LLMChain(
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llm=llm,
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prompt=map_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks, # type: ignore[arg-type]
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)
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_reduce_llm = reduce_llm or llm
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reduce_chain = LLMChain(
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llm=_reduce_llm,
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prompt=combine_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks, # type: ignore[arg-type]
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)
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# TODO: document prompt
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combine_documents_chain = StuffDocumentsChain(
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llm_chain=reduce_chain,
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document_variable_name=combine_document_variable_name,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks,
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)
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if collapse_prompt is None:
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collapse_chain = None
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if collapse_llm is not None:
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raise ValueError(
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"collapse_llm provided, but collapse_prompt was not: please "
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"provide one or stop providing collapse_llm."
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)
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else:
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_collapse_llm = collapse_llm or llm
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collapse_chain = StuffDocumentsChain(
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llm_chain=LLMChain(
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llm=_collapse_llm,
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prompt=collapse_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks,
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),
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document_variable_name=combine_document_variable_name,
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)
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reduce_documents_chain = ReduceDocumentsChain(
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combine_documents_chain=combine_documents_chain,
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collapse_documents_chain=collapse_chain,
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token_max=token_max,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks,
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collapse_max_retries=collapse_max_retries,
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)
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return MapReduceDocumentsChain(
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llm_chain=map_chain,
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reduce_documents_chain=reduce_documents_chain,
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document_variable_name=map_reduce_document_variable_name,
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verbose=verbose, # type: ignore[arg-type]
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callbacks=callbacks,
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**kwargs,
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)
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def _load_refine_chain(
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llm: BaseLanguageModel,
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question_prompt: BasePromptTemplate = refine_prompts.PROMPT,
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refine_prompt: BasePromptTemplate = refine_prompts.REFINE_PROMPT,
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document_variable_name: str = "text",
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initial_response_name: str = "existing_answer",
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refine_llm: Optional[BaseLanguageModel] = None,
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verbose: Optional[bool] = None,
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**kwargs: Any,
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) -> RefineDocumentsChain:
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initial_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose) # type: ignore[arg-type]
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_refine_llm = refine_llm or llm
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refine_chain = LLMChain(llm=_refine_llm, prompt=refine_prompt, verbose=verbose) # type: ignore[arg-type]
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return RefineDocumentsChain(
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initial_llm_chain=initial_chain,
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refine_llm_chain=refine_chain,
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document_variable_name=document_variable_name,
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initial_response_name=initial_response_name,
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verbose=verbose, # type: ignore[arg-type]
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**kwargs,
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)
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def load_summarize_chain(
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llm: BaseLanguageModel,
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chain_type: str = "stuff",
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verbose: Optional[bool] = None,
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**kwargs: Any,
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) -> BaseCombineDocumentsChain:
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"""Load summarizing chain.
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Args:
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llm: Language Model to use in the chain.
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chain_type: Type of document combining chain to use. Should be one of "stuff",
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"map_reduce", and "refine".
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verbose: Whether chains should be run in verbose mode or not. Note that this
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applies to all chains that make up the final chain.
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Returns:
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A chain to use for summarizing.
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"""
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loader_mapping: Mapping[str, LoadingCallable] = {
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"stuff": _load_stuff_chain,
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"map_reduce": _load_map_reduce_chain,
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"refine": _load_refine_chain,
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}
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if chain_type not in loader_mapping:
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raise ValueError(
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f"Got unsupported chain type: {chain_type}. "
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f"Should be one of {loader_mapping.keys()}"
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)
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return loader_mapping[chain_type](llm, verbose=verbose, **kwargs)
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