field retriever: BaseRetriever [Required]#
Index to connect to.
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, retriever: langchain.schema.BaseRetriever, condense_question_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone question:', template_format='f-string', validate_template=True), chain_type: str = 'stuff', verbose: bool = False, condense_question_llm: Optional[langchain.base_language.BaseLanguageModel] = None, combine_docs_chain_kwargs: Optional[Dict] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → langchain.chains.conversational_retrieval.base.BaseConversationalRetrievalChain[source]#
Load chain from LLM.
pydantic model langchain.chains.FlareChain[source]#
Validators
raise_deprecation » all fields
set_verbose » verbose
field max_iter: int = 10#
field min_prob: float = 0.2#
field min_token_gap: int = 5#
field num_pad_tokens: int = 2#
field output_parser: FinishedOutputParser [Optional]#
field question_generator_chain: QuestionGeneratorChain [Required]#
field response_chain: _ResponseChain [Optional]#
field retriever: BaseRetriever [Required]#
field start_with_retrieval: bool = True#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html