mirror of https://github.com/hwchase17/langchain
add documentation on how to load different chain types (#595)
Harrison Chase
1 year ago
committed by
GitHub
parent
956416c150
commit
d574bf0a27
@ -1,169 +1,4 @@
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"""Load question answering with sources chains."""
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from typing import Any, Mapping, Optional, Protocol
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from langchain.chains.qa_with_sources.loading import load_qa_with_sources_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.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.qa_with_sources 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 import map_rerank_prompt
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from langchain.llms.base import BaseLLM
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from langchain.prompts.base import BasePromptTemplate
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class LoadingCallable(Protocol):
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"""Interface for loading the combine documents chain."""
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def __call__(self, llm: BaseLLM, **kwargs: Any) -> 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: BaseLLM,
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prompt: BasePromptTemplate = map_rerank_prompt.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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**kwargs: Any,
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) -> MapRerankDocumentsChain:
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llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
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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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**kwargs,
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)
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def _load_stuff_chain(
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llm: BaseLLM,
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prompt: BasePromptTemplate = stuff_prompt.PROMPT,
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document_prompt: BasePromptTemplate = stuff_prompt.EXAMPLE_PROMPT,
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document_variable_name: str = "summaries",
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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)
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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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document_prompt=document_prompt,
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verbose=verbose,
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**kwargs,
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)
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def _load_map_reduce_chain(
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llm: BaseLLM,
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question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
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combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
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document_prompt: BasePromptTemplate = map_reduce_prompt.EXAMPLE_PROMPT,
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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[BaseLLM] = None,
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collapse_llm: Optional[BaseLLM] = None,
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verbose: Optional[bool] = None,
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**kwargs: Any,
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) -> MapReduceDocumentsChain:
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map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
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_reduce_llm = reduce_llm or llm
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reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)
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combine_document_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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document_prompt=document_prompt,
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verbose=verbose,
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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,
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),
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document_variable_name=combine_document_variable_name,
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document_prompt=document_prompt,
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)
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return MapReduceDocumentsChain(
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llm_chain=map_chain,
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combine_document_chain=combine_document_chain,
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document_variable_name=map_reduce_document_variable_name,
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collapse_document_chain=collapse_chain,
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verbose=verbose,
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**kwargs,
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)
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def _load_refine_chain(
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llm: BaseLLM,
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question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
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refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
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document_prompt: BasePromptTemplate = refine_prompts.EXAMPLE_PROMPT,
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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[BaseLLM] = 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)
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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)
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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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document_prompt=document_prompt,
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verbose=verbose,
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**kwargs,
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)
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def load_qa_with_sources_chain(
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llm: BaseLLM,
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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 question answering with sources 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 question answering with sources.
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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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_func: LoadingCallable = loader_mapping[chain_type]
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return _func(llm, verbose=verbose, **kwargs)
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__all__ = ["load_qa_with_sources_chain"]
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@ -0,0 +1,169 @@
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"""Load question answering with sources chains."""
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from typing import Any, Mapping, Optional, Protocol
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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.qa_with_sources 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 import map_rerank_prompt
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from langchain.llms.base import BaseLLM
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from langchain.prompts.base import BasePromptTemplate
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class LoadingCallable(Protocol):
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"""Interface for loading the combine documents chain."""
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def __call__(self, llm: BaseLLM, **kwargs: Any) -> 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: BaseLLM,
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prompt: BasePromptTemplate = map_rerank_prompt.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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**kwargs: Any,
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) -> MapRerankDocumentsChain:
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llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
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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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**kwargs,
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)
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def _load_stuff_chain(
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llm: BaseLLM,
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prompt: BasePromptTemplate = stuff_prompt.PROMPT,
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document_prompt: BasePromptTemplate = stuff_prompt.EXAMPLE_PROMPT,
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document_variable_name: str = "summaries",
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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)
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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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document_prompt=document_prompt,
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verbose=verbose,
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**kwargs,
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)
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def _load_map_reduce_chain(
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llm: BaseLLM,
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question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
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combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
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document_prompt: BasePromptTemplate = map_reduce_prompt.EXAMPLE_PROMPT,
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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[BaseLLM] = None,
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collapse_llm: Optional[BaseLLM] = None,
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verbose: Optional[bool] = None,
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**kwargs: Any,
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) -> MapReduceDocumentsChain:
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map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose)
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_reduce_llm = reduce_llm or llm
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reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose)
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combine_document_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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document_prompt=document_prompt,
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verbose=verbose,
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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,
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),
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document_variable_name=combine_document_variable_name,
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document_prompt=document_prompt,
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)
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return MapReduceDocumentsChain(
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llm_chain=map_chain,
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combine_document_chain=combine_document_chain,
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document_variable_name=map_reduce_document_variable_name,
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collapse_document_chain=collapse_chain,
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verbose=verbose,
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**kwargs,
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)
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def _load_refine_chain(
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llm: BaseLLM,
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question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
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refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
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document_prompt: BasePromptTemplate = refine_prompts.EXAMPLE_PROMPT,
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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[BaseLLM] = 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)
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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)
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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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document_prompt=document_prompt,
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verbose=verbose,
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**kwargs,
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)
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def load_qa_with_sources_chain(
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llm: BaseLLM,
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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 question answering with sources 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 question answering with sources.
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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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_func: LoadingCallable = loader_mapping[chain_type]
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return _func(llm, verbose=verbose, **kwargs)
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