mirror of
https://github.com/hwchase17/langchain.git
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langchain: qa_chain fix (#21279)
Issue: `load_qa_chain` is placed in the __init__.py file. As a result, it is not listed in the API Reference docs. BTW `load_qa_chain` is heavily presented in the doc examples, but is missed in API Ref. Change: moved code from init.py into a new file. Related: #21266
This commit is contained in:
Leonid Ganeline
GitHub
parent
7ecf9996f1
commit
8c13e8a79b
@ -1,251 +1,6 @@
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"""Load question answering chains."""
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from typing import Any, Mapping, Optional, Protocol
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from langchain.chains.question_answering.chain import LoadingCallable, load_qa_chain
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from langchain_core.callbacks import BaseCallbackManager, 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 import ReduceDocumentsChain
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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.map_rerank import MapRerankDocumentsChain
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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.question_answering import (
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map_reduce_prompt,
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refine_prompts,
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stuff_prompt,
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)
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from langchain.chains.question_answering.map_rerank_prompt import (
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PROMPT as MAP_RERANK_PROMPT,
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)
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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_map_rerank_chain(
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llm: BaseLanguageModel,
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prompt: BasePromptTemplate = MAP_RERANK_PROMPT,
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verbose: bool = False,
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document_variable_name: str = "context",
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rank_key: str = "score",
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answer_key: str = "answer",
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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> MapRerankDocumentsChain:
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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verbose=verbose,
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callback_manager=callback_manager,
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callbacks=callbacks,
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)
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return MapRerankDocumentsChain(
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llm_chain=llm_chain,
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rank_key=rank_key,
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answer_key=answer_key,
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document_variable_name=document_variable_name,
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verbose=verbose,
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callback_manager=callback_manager,
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**kwargs,
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)
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def _load_stuff_chain(
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llm: BaseLanguageModel,
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prompt: Optional[BasePromptTemplate] = None,
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document_variable_name: str = "context",
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verbose: Optional[bool] = None,
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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> StuffDocumentsChain:
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_prompt = prompt or stuff_prompt.PROMPT_SELECTOR.get_prompt(llm)
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llm_chain = LLMChain(
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llm=llm,
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prompt=_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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callbacks=callbacks,
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)
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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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callback_manager=callback_manager,
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callbacks=callbacks,
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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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question_prompt: Optional[BasePromptTemplate] = None,
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combine_prompt: Optional[BasePromptTemplate] = None,
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combine_document_variable_name: str = "summaries",
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map_reduce_document_variable_name: str = "context",
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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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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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token_max: int = 3000,
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**kwargs: Any,
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) -> MapReduceDocumentsChain:
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_question_prompt = (
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question_prompt or map_reduce_prompt.QUESTION_PROMPT_SELECTOR.get_prompt(llm)
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)
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_combine_prompt = (
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combine_prompt or map_reduce_prompt.COMBINE_PROMPT_SELECTOR.get_prompt(llm)
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)
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map_chain = LLMChain(
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llm=llm,
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prompt=_question_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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callbacks=callbacks,
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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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callback_manager=callback_manager,
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callbacks=callbacks,
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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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callback_manager=callback_manager,
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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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callback_manager=callback_manager,
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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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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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)
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reduce_documents_chain = ReduceDocumentsChain( # type: ignore[misc]
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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,
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)
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return MapReduceDocumentsChain(
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llm_chain=map_chain,
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document_variable_name=map_reduce_document_variable_name,
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reduce_documents_chain=reduce_documents_chain,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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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: Optional[BasePromptTemplate] = None,
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refine_prompt: Optional[BasePromptTemplate] = None,
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document_variable_name: str = "context_str",
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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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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> RefineDocumentsChain:
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_question_prompt = (
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question_prompt or refine_prompts.QUESTION_PROMPT_SELECTOR.get_prompt(llm)
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)
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_refine_prompt = refine_prompt or refine_prompts.REFINE_PROMPT_SELECTOR.get_prompt(
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llm
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)
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initial_chain = LLMChain(
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llm=llm,
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prompt=_question_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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callbacks=callbacks,
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)
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_refine_llm = refine_llm or llm
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refine_chain = LLMChain(
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llm=_refine_llm,
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prompt=_refine_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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callbacks=callbacks,
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)
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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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callback_manager=callback_manager,
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callbacks=callbacks,
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**kwargs,
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)
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def load_qa_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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callback_manager: Optional[BaseCallbackManager] = None,
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**kwargs: Any,
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) -> BaseCombineDocumentsChain:
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"""Load question answering 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", "map_rerank", 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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callback_manager: Callback manager to use for the chain.
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Returns:
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A chain to use for question answering.
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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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"map_rerank": _load_map_rerank_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](
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llm, verbose=verbose, callback_manager=callback_manager, **kwargs
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)
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__all__ = [
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"LoadingCallable",
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"load_qa_chain",
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]
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251
libs/langchain/langchain/chains/question_answering/chain.py
Normal file
251
libs/langchain/langchain/chains/question_answering/chain.py
Normal file
@ -0,0 +1,251 @@
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"""Load question answering chains."""
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from typing import Any, Mapping, Optional, Protocol
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from langchain_core.callbacks import BaseCallbackManager, 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 import ReduceDocumentsChain
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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.map_rerank import MapRerankDocumentsChain
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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.question_answering import (
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map_reduce_prompt,
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refine_prompts,
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stuff_prompt,
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)
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from langchain.chains.question_answering.map_rerank_prompt import (
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PROMPT as MAP_RERANK_PROMPT,
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)
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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_map_rerank_chain(
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llm: BaseLanguageModel,
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prompt: BasePromptTemplate = MAP_RERANK_PROMPT,
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verbose: bool = False,
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document_variable_name: str = "context",
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rank_key: str = "score",
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answer_key: str = "answer",
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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> MapRerankDocumentsChain:
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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verbose=verbose,
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callback_manager=callback_manager,
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callbacks=callbacks,
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)
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return MapRerankDocumentsChain(
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llm_chain=llm_chain,
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rank_key=rank_key,
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answer_key=answer_key,
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document_variable_name=document_variable_name,
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verbose=verbose,
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callback_manager=callback_manager,
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**kwargs,
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)
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def _load_stuff_chain(
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llm: BaseLanguageModel,
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prompt: Optional[BasePromptTemplate] = None,
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document_variable_name: str = "context",
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verbose: Optional[bool] = None,
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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> StuffDocumentsChain:
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_prompt = prompt or stuff_prompt.PROMPT_SELECTOR.get_prompt(llm)
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llm_chain = LLMChain(
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llm=llm,
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prompt=_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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callbacks=callbacks,
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)
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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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callback_manager=callback_manager,
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callbacks=callbacks,
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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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question_prompt: Optional[BasePromptTemplate] = None,
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combine_prompt: Optional[BasePromptTemplate] = None,
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combine_document_variable_name: str = "summaries",
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map_reduce_document_variable_name: str = "context",
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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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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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token_max: int = 3000,
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**kwargs: Any,
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) -> MapReduceDocumentsChain:
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_question_prompt = (
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question_prompt or map_reduce_prompt.QUESTION_PROMPT_SELECTOR.get_prompt(llm)
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)
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_combine_prompt = (
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combine_prompt or map_reduce_prompt.COMBINE_PROMPT_SELECTOR.get_prompt(llm)
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)
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map_chain = LLMChain(
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llm=llm,
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prompt=_question_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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callbacks=callbacks,
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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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callback_manager=callback_manager,
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callbacks=callbacks,
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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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callback_manager=callback_manager,
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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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callback_manager=callback_manager,
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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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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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)
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reduce_documents_chain = ReduceDocumentsChain( # type: ignore[misc]
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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,
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)
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return MapReduceDocumentsChain(
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llm_chain=map_chain,
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document_variable_name=map_reduce_document_variable_name,
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reduce_documents_chain=reduce_documents_chain,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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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: Optional[BasePromptTemplate] = None,
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refine_prompt: Optional[BasePromptTemplate] = None,
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document_variable_name: str = "context_str",
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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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callback_manager: Optional[BaseCallbackManager] = None,
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> RefineDocumentsChain:
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_question_prompt = (
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question_prompt or refine_prompts.QUESTION_PROMPT_SELECTOR.get_prompt(llm)
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)
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_refine_prompt = refine_prompt or refine_prompts.REFINE_PROMPT_SELECTOR.get_prompt(
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llm
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)
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initial_chain = LLMChain(
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llm=llm,
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prompt=_question_prompt,
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verbose=verbose, # type: ignore[arg-type]
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callback_manager=callback_manager,
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callbacks=callbacks,
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)
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_refine_llm = refine_llm or llm
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refine_chain = LLMChain(
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llm=_refine_llm,
|
||||
prompt=_refine_prompt,
|
||||
verbose=verbose, # type: ignore[arg-type]
|
||||
callback_manager=callback_manager,
|
||||
callbacks=callbacks,
|
||||
)
|
||||
return RefineDocumentsChain(
|
||||
initial_llm_chain=initial_chain,
|
||||
refine_llm_chain=refine_chain,
|
||||
document_variable_name=document_variable_name,
|
||||
initial_response_name=initial_response_name,
|
||||
verbose=verbose, # type: ignore[arg-type]
|
||||
callback_manager=callback_manager,
|
||||
callbacks=callbacks,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def load_qa_chain(
|
||||
llm: BaseLanguageModel,
|
||||
chain_type: str = "stuff",
|
||||
verbose: Optional[bool] = None,
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
**kwargs: Any,
|
||||
) -> BaseCombineDocumentsChain:
|
||||
"""Load question answering chain.
|
||||
|
||||
Args:
|
||||
llm: Language Model to use in the chain.
|
||||
chain_type: Type of document combining chain to use. Should be one of "stuff",
|
||||
"map_reduce", "map_rerank", and "refine".
|
||||
verbose: Whether chains should be run in verbose mode or not. Note that this
|
||||
applies to all chains that make up the final chain.
|
||||
callback_manager: Callback manager to use for the chain.
|
||||
|
||||
Returns:
|
||||
A chain to use for question answering.
|
||||
"""
|
||||
loader_mapping: Mapping[str, LoadingCallable] = {
|
||||
"stuff": _load_stuff_chain,
|
||||
"map_reduce": _load_map_reduce_chain,
|
||||
"refine": _load_refine_chain,
|
||||
"map_rerank": _load_map_rerank_chain,
|
||||
}
|
||||
if chain_type not in loader_mapping:
|
||||
raise ValueError(
|
||||
f"Got unsupported chain type: {chain_type}. "
|
||||
f"Should be one of {loader_mapping.keys()}"
|
||||
)
|
||||
return loader_mapping[chain_type](
|
||||
llm, verbose=verbose, callback_manager=callback_manager, **kwargs
|
||||
)
|
||||
Loading…
Reference in New Issue
Block a user