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deep – set to True to make a deep copy of the model Returns new model instance dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kendra.AdditionalResultAttribute.html
f87c3ac7a53e-2
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶ classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶ classmethod update_forward_refs(**localns: Any) → None¶ Try to update ForwardRefs on...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kendra.AdditionalResultAttribute.html
7fde6f7c480b-0
langchain.retrievers.pinecone_hybrid_search.hash_text¶ langchain.retrievers.pinecone_hybrid_search.hash_text(text: str) → str[source]¶ Hash a text using SHA256. Parameters text – Text to hash. Returns Hashed text.
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.pinecone_hybrid_search.hash_text.html
dc18b9d6151e-0
langchain.retrievers.re_phraser.RePhraseQueryRetriever¶ class langchain.retrievers.re_phraser.RePhraseQueryRetriever[source]¶ Bases: BaseRetriever Given a query, use an LLM to re-phrase it. Then, retrieve docs for the re-phrased query. Create a new model by parsing and validating input data from keyword arguments. Rais...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-1
e.g., if the underlying runnable uses an API which supports a batch mode. async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) → List[Document]¶ Asynchronously get documents r...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-2
Subclasses should override this method if they support streaming output. async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-3
e.g., if the underlying runnable uses an API which supports a batch mode. bind(**kwargs: Any) → Runnable[Input, Output]¶ Bind arguments to a Runnable, returning a new Runnable. config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶ The type of config this runnable accepts specified as a pydantic m...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-4
exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*, i...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-5
Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the runnable is invoked with. This method allows to get an input schema for a specific configuration. Parameters config – A config to use when generating the schema....
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-6
Parameters metadata – Optional metadata associated with the retriever. Defaults to None This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Returns List of relevant documents invoke(input: str, config: Optional[RunnableConfig] = None) → List[D...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-7
to the object. map() → Runnable[List[Input], List[Output]]¶ Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input. classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool =...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-8
classmethod validate(value: Any) → Model¶ with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶ Bind config to a Runnable, returning a new Runnable. with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'E...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
dc18b9d6151e-9
Create a new Runnable that retries the original runnable on exceptions. Parameters retry_if_exception_type – A tuple of exception types to retry on wait_exponential_jitter – Whether to add jitter to the wait time between retries stop_after_attempt – The maximum number of attempts to make before giving up Returns A new ...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.re_phraser.RePhraseQueryRetriever.html
4594d0e3389a-0
langchain.retrievers.self_query.timescalevector.TimescaleVectorTranslator¶ class langchain.retrievers.self_query.timescalevector.TimescaleVectorTranslator[source]¶ Translate the internal query language elements to valid filters. Attributes COMPARATOR_MAP OPERATOR_MAP allowed_comparators allowed_operators Subset of allo...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.self_query.timescalevector.TimescaleVectorTranslator.html
e2cc0f83400a-0
langchain.retrievers.self_query.myscale.MyScaleTranslator¶ class langchain.retrievers.self_query.myscale.MyScaleTranslator(metadata_key: str = 'metadata')[source]¶ Translate MyScale internal query language elements to valid filters. Attributes allowed_comparators allowed_operators Subset of allowed logical operators. m...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.self_query.myscale.MyScaleTranslator.html
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langchain.retrievers.databerry.DataberryRetriever¶ class langchain.retrievers.databerry.DataberryRetriever[source]¶ Bases: BaseRetriever Databerry API retriever. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid mode...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
6a55126dfc6e-1
e.g., if the underlying runnable uses an API which supports a batch mode. async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) → List[Document]¶ Asynchronously get documents r...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
6a55126dfc6e-2
Subclasses should override this method if they support streaming output. async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
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e.g., if the underlying runnable uses an API which supports a batch mode. bind(**kwargs: Any) → Runnable[Input, Output]¶ Bind arguments to a Runnable, returning a new Runnable. config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶ The type of config this runnable accepts specified as a pydantic m...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
6a55126dfc6e-4
exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*, i...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
6a55126dfc6e-5
Get a pydantic model that can be used to validate output to the runnable. Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with. This method allows to get an output schema for a specific co...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
6a55126dfc6e-6
Returns The output of the runnable. classmethod is_lc_serializable() → bool¶ Is this class serializable? json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclu...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
6a55126dfc6e-7
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶ stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶ Default implementation of stream, which calls invoke. Subclasses should override t...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
6a55126dfc6e-8
fallback in order, upon failures. with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶ Bind lifecycle listeners to a Runnable, returning a new Runnable. on_start: Called before the runnable starts running, with the Run ...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
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The type of output this runnable produces specified as a type annotation. property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶ List configurable fields for this runnable. property input_schema: Type[pydantic.main.BaseModel]¶ The type of input this runnable accepts specified as a pydantic ...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.databerry.DataberryRetriever.html
8c0928ca14a3-0
langchain.retrievers.docarray.SearchType¶ class langchain.retrievers.docarray.SearchType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶ Enumerator of the types of search to perform. similarity = 'similarity'¶ mmr = 'mmr'¶
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.docarray.SearchType.html
7c9e3e7bc736-0
langchain.retrievers.svm.create_index¶ langchain.retrievers.svm.create_index(contexts: List[str], embeddings: Embeddings) → ndarray[source]¶ Create an index of embeddings for a list of contexts. Parameters contexts – List of contexts to embed. embeddings – Embeddings model to use. Returns Index of embeddings.
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.svm.create_index.html
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langchain.retrievers.multi_query.MultiQueryRetriever¶ class langchain.retrievers.multi_query.MultiQueryRetriever[source]¶ Bases: BaseRetriever Given a query, use an LLM to write a set of queries. Retrieve docs for each query. Return the unique union of all retrieved docs. Create a new model by parsing and validating in...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
9027fff0e8a9-1
Default implementation runs ainvoke in parallel using asyncio.gather. The default implementation of batch works well for IO bound runnables. Subclasses should override this method if they can batch more efficiently; e.g., if the underlying runnable uses an API which supports a batch mode. async agenerate_queries(questi...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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Subclasses should override this method if they can run asynchronously. async aretrieve_documents(queries: List[str], run_manager: AsyncCallbackManagerForRetrieverRun) → List[Document][source]¶ Run all LLM generated queries. Parameters queries – query list Returns List of retrieved Documents async astream(input: Input, ...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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input is still being generated. batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶ Default implementation runs invoke in parallel using a thread pool executor. The default implementation of batch w...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶ Duplicate a model, optionally...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. classmethod from_llm(retriever: BaseRetriever, llm: BaseLLM, prompt: PromptTemplate = PromptTemplate(input_variables=['question'], template='You are an AI language model assistant. Your task is \n    to generate...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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Parameters config – A config to use when generating the schema. Returns A pydantic model that can be used to validate input. classmethod get_lc_namespace() → List[str]¶ Get the namespace of the langchain object. For example, if the class is langchain.llms.openai.OpenAI, then the namespace is [“langchain”, “llms”, “open...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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and passed as arguments to the handlers defined in callbacks. Returns List of relevant documents invoke(input: str, config: Optional[RunnableConfig] = None) → List[Document]¶ Transform a single input into an output. Override to implement. Parameters input – The input to the runnable. config – A config to use when invok...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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by calling invoke() with each input. classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶ classmethod parse_obj(obj: Any) → Model¶ classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = No...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
9027fff0e8a9-9
Parameters documents – List of retrieved Documents Returns List of unique retrieved Documents classmethod update_forward_refs(**localns: Any) → None¶ Try to update ForwardRefs on fields based on this Model, globalns and localns. classmethod validate(value: Any) → Model¶ with_config(config: Optional[RunnableConfig] = No...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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added to the run. with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶ Create a new Runnable that retries the original runnable on exceptions. Parameters retry_if_exc...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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property output_schema: Type[pydantic.main.BaseModel]¶ The type of output this runnable produces specified as a pydantic model. Examples using MultiQueryRetriever¶ Question Answering MultiQueryRetriever
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html
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langchain.retrievers.kay.KayAiRetriever¶ class langchain.retrievers.kay.KayAiRetriever[source]¶ Bases: BaseRetriever Retriever for Kay.ai datasets. To work properly, expects you to have KAY_API_KEY env variable set. You can get one for free at https://kay.ai/. Create a new model by parsing and validating input data fro...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-1
e.g., if the underlying runnable uses an API which supports a batch mode. async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) → List[Document]¶ Asynchronously get documents r...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-2
Subclasses should override this method if they support streaming output. async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-3
e.g., if the underlying runnable uses an API which supports a batch mode. bind(**kwargs: Any) → Runnable[Input, Output]¶ Bind arguments to a Runnable, returning a new Runnable. config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶ The type of config this runnable accepts specified as a pydantic m...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-4
exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance classmeth...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-5
methods will have a dynamic input schema that depends on which configuration the runnable is invoked with. This method allows to get an input schema for a specific configuration. Parameters config – A config to use when generating the schema. Returns A pydantic model that can be used to validate input. classmethod get_...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-6
This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Returns List of relevant documents invoke(input: str, config: Optional[RunnableConfig] = None) → List[Document]¶ Transform a single input into an output. Override to implement. Parameters inp...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-7
by calling invoke() with each input. classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶ classmethod parse_obj(obj: Any) → Model¶ classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = No...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-8
Bind config to a Runnable, returning a new Runnable. with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶ Add fallbacks to a runnable, returning a new Runnable. Parameters fallbacks – A se...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
5b6b36a8b1bd-9
between retries stop_after_attempt – The maximum number of attempts to make before giving up Returns A new Runnable that retries the original runnable on exceptions. with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶ Bind input and output types...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html
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langchain.retrievers.parent_document_retriever.ParentDocumentRetriever¶ class langchain.retrievers.parent_document_retriever.ParentDocumentRetriever[source]¶ Bases: MultiVectorRetriever Retrieve small chunks then retrieve their parent documents. When splitting documents for retrieval, there are often conflicting desire...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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docstore=store, child_splitter=child_splitter, parent_splitter=parent_splitter, ) Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param child_splitter: langchain.text_splitter.TextSplitter [Required]...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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The underlying vectorstore to use to store small chunks and their embedding vectors async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶ Default implementation runs ainvoke in parallel using a...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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:param query: string to find relevant documents for :param callbacks: Callback manager or list of callbacks :param tags: Optional list of tags associated with the retriever. Defaults to None These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Par...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state. async atransform(input: As...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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Returns A pydantic model that can be used to validate config. configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶ configurable_fields(**kwargs: Union[ConfigurableField, C...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
24d339a7146e-6
deep – set to True to make a deep copy of the model Returns new model instance dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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This method allows to get an output schema for a specific configuration. Parameters config – A config to use when generating the schema. Returns A pydantic model that can be used to validate output. get_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[D...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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classmethod is_lc_serializable() → bool¶ Is this class serializable? json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defa...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
24d339a7146e-9
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶ stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶ Default implementation of stream, which calls invoke. Subclasses should override t...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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fallback in order, upon failures. with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶ Bind lifecycle listeners to a Runnable, returning a new Runnable. on_start: Called before the runnable starts running, with the Run ...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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The type of output this runnable produces specified as a type annotation. property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶ List configurable fields for this runnable. property input_schema: Type[pydantic.main.BaseModel]¶ The type of input this runnable accepts specified as a pydantic ...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html
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langchain.retrievers.document_compressors.chain_filter.LLMChainFilter¶ class langchain.retrievers.document_compressors.chain_filter.LLMChainFilter[source]¶ Bases: BaseDocumentCompressor Filter that drops documents that aren’t relevant to the query. Create a new model by parsing and validating input data from keyword ar...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.chain_filter.LLMChainFilter.html
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Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶ Duplicate a model, optionally...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.chain_filter.LLMChainFilter.html
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classmethod from_orm(obj: Any) → Model¶ json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_n...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.chain_filter.LLMChainFilter.html
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langchain.retrievers.kendra.AdditionalResultAttributeValue¶ class langchain.retrievers.kendra.AdditionalResultAttributeValue[source]¶ Bases: BaseModel Value of an additional result attribute. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot ...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kendra.AdditionalResultAttributeValue.html
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deep – set to True to make a deep copy of the model Returns new model instance dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kendra.AdditionalResultAttributeValue.html
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classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶ classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶ classmethod update_forward_refs(**localns: Any) → None¶ Try to update ForwardRefs on...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kendra.AdditionalResultAttributeValue.html
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langchain.retrievers.wikipedia.WikipediaRetriever¶ class langchain.retrievers.wikipedia.WikipediaRetriever[source]¶ Bases: BaseRetriever, WikipediaAPIWrapper Wikipedia API retriever. It wraps load() to get_relevant_documents(). It uses all WikipediaAPIWrapper arguments without any change. Create a new model by parsing ...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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e.g., if the underlying runnable uses an API which supports a batch mode. async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) → List[Document]¶ Asynchronously get documents r...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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Subclasses should override this method if they support streaming output. async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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e.g., if the underlying runnable uses an API which supports a batch mode. bind(**kwargs: Any) → Runnable[Input, Output]¶ Bind arguments to a Runnable, returning a new Runnable. config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶ The type of config this runnable accepts specified as a pydantic m...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*, i...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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Get a pydantic model that can be used to validate output to the runnable. Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with. This method allows to get an output schema for a specific co...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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Returns The output of the runnable. classmethod is_lc_serializable() → bool¶ Is this class serializable? json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclu...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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run(query: str) → str¶ Run Wikipedia search and get page summaries. classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶ classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶ stream(input: In...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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exceptions_to_handle – A tuple of exception types to handle. Returns A new Runnable that will try the original runnable, and then each fallback in order, upon failures. with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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The type of input this runnable accepts specified as a type annotation. property OutputType: Type[langchain.schema.runnable.utils.Output]¶ The type of output this runnable produces specified as a type annotation. property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶ List configurable field...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.wikipedia.WikipediaRetriever.html
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langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever¶ class langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever[source]¶ Bases: BaseRetriever Retriever that combines embedding similarity with recency in retrieving values. Create a new model by parsing and validating in...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case. param vectorstore: langchain.schema.vectorstore.VectorStore [Required]¶ The vectorstore to store documents and determine salience. async aadd_documents(documents: List[Doc...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Returns List of relevant documents async ainvoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → List[Document]¶ Default implementation of ainvoke, calls invok...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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The jsonpatch ops can be applied in order to construct state. async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶ Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if th...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶ Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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Get a pydantic model that can be used to validate input to the runnable. Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the runnable is invoked with. This method allows to get an input schema for a specific confi...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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and passed as arguments to the handlers defined in callbacks. Parameters metadata – Optional metadata associated with the retriever. Defaults to None This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Returns List of relevant documents get_sa...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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A unique identifier for this class for serialization purposes. The unique identifier is a list of strings that describes the path to the object. map() → Runnable[List[Input], List[Output]]¶ Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input. classmethod parse_file...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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classmethod update_forward_refs(**localns: Any) → None¶ Try to update ForwardRefs on fields based on this Model, globalns and localns. classmethod validate(value: Any) → Model¶ with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶ Bind config to a Runnable, returning a new Runna...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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added to the run. with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶ Create a new Runnable that retries the original runnable on exceptions. Parameters retry_if_exc...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever.html
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property output_schema: Type[pydantic.main.BaseModel]¶ The type of output this runnable produces specified as a pydantic model. Examples using TimeWeightedVectorStoreRetriever¶ Generative Agents in LangChain
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langchain.retrievers.zep.SearchType¶ class langchain.retrievers.zep.SearchType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶ Enumerator of the types of search to perform. similarity = 'similarity'¶ Similarity search. mmr = 'mmr'¶ Maximal Marginal Relevance reranking of si...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zep.SearchType.html
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langchain.retrievers.document_compressors.chain_extract.LLMChainExtractor¶ class langchain.retrievers.document_compressors.chain_extract.LLMChainExtractor[source]¶ Bases: BaseDocumentCompressor Document compressor that uses an LLM chain to extract the relevant parts of documents. Create a new model by parsing and valid...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.chain_extract.LLMChainExtractor.html
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Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creat...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.chain_extract.LLMChainExtractor.html
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Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.chain_extract.LLMChainExtractor.html
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langchain.retrievers.zilliz.ZillizRetriever¶ class langchain.retrievers.zilliz.ZillizRetriever[source]¶ Bases: BaseRetriever Zilliz API retriever. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param collec...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zilliz.ZillizRetriever.html
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use case. async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶ Default implementation runs ainvoke in parallel using asyncio.gather. The default implementation of batch works well for IO bound...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zilliz.ZillizRetriever.html
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Default implementation of ainvoke, calls invoke from a thread. The default implementation allows usage of async code even if the runnable did not implement a native async version of invoke. Subclasses should override this method if they can run asynchronously. async astream(input: Input, config: Optional[RunnableConfig...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zilliz.ZillizRetriever.html
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input is still being generated. batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶ Default implementation runs invoke in parallel using a thread pool executor. The default implementation of batch w...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zilliz.ZillizRetriever.html
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Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶ Duplicate a model, optionally...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zilliz.ZillizRetriever.html
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Parameters config – A config to use when generating the schema. Returns A pydantic model that can be used to validate input. classmethod get_lc_namespace() → List[str]¶ Get the namespace of the langchain object. For example, if the class is langchain.llms.openai.OpenAI, then the namespace is [“langchain”, “llms”, “open...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zilliz.ZillizRetriever.html
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and passed as arguments to the handlers defined in callbacks. Returns List of relevant documents invoke(input: str, config: Optional[RunnableConfig] = None) → List[Document]¶ Transform a single input into an output. Override to implement. Parameters input – The input to the runnable. config – A config to use when invok...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zilliz.ZillizRetriever.html
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by calling invoke() with each input. classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶ classmethod parse_obj(obj: Any) → Model¶ classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = No...
lang/api.python.langchain.com/en/latest/retrievers/langchain.retrievers.zilliz.ZillizRetriever.html