Tony20100/langchain_full huggingface dataset:
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
List[Output]
async abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶
Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
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]¶
[Deprecated] Asynchronously get documents relevant to a query.
Users should favor using .ainvoke or .abatch rather than
aget_relevant_documents directly.
Parameters
query (str) – string to find relevant documents for
callbacks (Callbacks) – Callback manager or list of callbacks
tags (Optional[List[str]]) – 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.
metadata (Optional[Dict[str, Any]]) – 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.
and passed as arguments to the handlers defined in callbacks.
run_name (Optional[str]) – Optional name for the run.
kwargs (Any) – 
Returns
List of relevant documents
Return type
List[Document]
Notes
Deprecated since version langchain-core==0.1.46: Use ainvoke instead.
async ainvoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
Asynchronously invoke the retriever to get relevant documents.
Main entry point for asynchronous retriever invocations.
Parameters
input (str) – The query string
config (Optional[RunnableConfig]) – Configuration for the retriever
**kwargs (Any) – Additional arguments to pass to the retriever
Returns
List of relevant documents
Return type
List[Document]
Examples:
await retriever.ainvoke("query")
assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) → RunnableSerializable[Any, Any]¶
Assigns new fields to the dict output of this runnable.
Returns a new runnable.
from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter
prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])
chain: Runnable = prompt | llm | {"str": StrOutputParser()}
chain_with_assign = chain.assign(hello=itemgetter("str") | llm)
langchain_aws.retrievers.bedrock.RetrievalConfig¶
class langchain_aws.retrievers.bedrock.RetrievalConfig[source]¶
Bases: BaseModel
Configuration for retrieval.
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 vectorSearchConfiguration: VectorSearchConfig [Required]¶
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
langchain_aws.retrievers.kendra.Highlight¶
class langchain_aws.retrievers.kendra.Highlight[source]¶
Bases: BaseModel
Information that highlights the keywords in the excerpt.
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 BeginOffset: int [Required]¶
The zero-based location in the excerpt where the highlight starts.
param EndOffset: int [Required]¶
The zero-based location in the excerpt where the highlight ends.
param TopAnswer: Optional[bool] = None¶
Indicates whether the result is the best one.
param Type: Optional[str] = None¶
The highlight type: STANDARD or THESAURUS_SYNONYM.
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
langchain_community.retrievers.kendra.Highlight¶
class langchain_community.retrievers.kendra.Highlight[source]¶
Bases: BaseModel
Information that highlights the keywords in the excerpt.
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 BeginOffset: int [Required]¶
The zero-based location in the excerpt where the highlight starts.
param EndOffset: int [Required]¶
The zero-based location in the excerpt where the highlight ends.
param TopAnswer: Optional[bool] = None¶
Indicates whether the result is the best one.
param Type: Optional[str] = None¶
The highlight type: STANDARD or THESAURUS_SYNONYM.
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
langchain.retrievers.document_compressors.cross_encoder.BaseCrossEncoder¶
class langchain.retrievers.document_compressors.cross_encoder.BaseCrossEncoder[source]¶
Interface for cross encoder models.
Methods
__init__()
score(text_pairs)
Score pairs' similarity.
__init__()¶
abstract score(text_pairs: List[Tuple[str, str]]) → List[float][source]¶
Score pairs’ similarity.
Parameters
text_pairs (List[Tuple[str, str]]) – List of pairs of texts.
Returns
List of scores.
Return type
List[float]
langchain.retrievers.merger_retriever.MergerRetriever¶
class langchain.retrievers.merger_retriever.MergerRetriever[source]¶
Bases: BaseRetriever
Retriever that merges the results of multiple retrievers.
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 metadata: Optional[Dict[str, Any]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
use case.
param retrievers: List[BaseRetriever] [Required]¶
A list of retrievers to merge.
param tags: Optional[List[str]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
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 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.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
and passed as arguments to the handlers defined in callbacks.
run_name (Optional[str]) – Optional name for the run.
kwargs (Any) – 
Returns
List of relevant documents
Return type
List[Document]
Notes
Deprecated since version langchain-core==0.1.46: Use ainvoke instead.
async ainvoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
Asynchronously invoke the retriever to get relevant documents.
Main entry point for asynchronous retriever invocations.
Parameters
input (str) – The query string
config (Optional[RunnableConfig]) – Configuration for the retriever
**kwargs (Any) – Additional arguments to pass to the retriever
Returns
List of relevant documents
Return type
List[Document]
Examples:
await retriever.ainvoke("query")
async amerge_documents(query: str, run_manager: AsyncCallbackManagerForRetrieverRun) → List[Document][source]¶
Asynchronously merge the results of the retrievers.
Parameters
query (str) – The query to search for.
run_manager (AsyncCallbackManagerForRetrieverRun) – 
Returns
A list of merged documents.
Return type
List[Document]
assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) → RunnableSerializable[Any, Any]¶
Assigns new fields to the dict output of this runnable.
Returns a new runnable.
from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
by calling invoke() with each input.
Example
from langchain_core.runnables import RunnableLambda
def _lambda(x: int) -> int:
    return x + 1
runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]
Return type
Runnable[List[Input], List[Output]]
merge_documents(query: str, run_manager: CallbackManagerForRetrieverRun) → List[Document][source]¶
Merge the results of the retrievers.
Parameters
query (str) – The query to search for.
run_manager (CallbackManagerForRetrieverRun) – 
Returns
A list of merged documents.
Return type
List[Document]
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]¶
langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever¶
class langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever[source]¶
Bases: BaseRetriever
Azure AI Search service 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 aiosession: Optional[aiohttp.ClientSession] = None¶
ClientSession, in case we want to reuse connection for better performance.
param api_key: str = ''¶
API Key. Both Admin and Query keys work, but for reading data it’s
recommended to use a Query key.
param api_version: str = '2023-11-01'¶
API version
param content_key: str = 'content'¶
Key in a retrieved result to set as the Document page_content.
param filter: Optional[str] = None¶
OData $filter expression to apply to the search query.
param index_name: str = ''¶
Name of Index inside Azure AI Search service
param metadata: Optional[Dict[str, Any]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
use case.
param service_name: str = ''¶
Name of Azure AI Search service
param tags: Optional[List[str]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
use case.
use case.
param top_k: Optional[int] = None¶
Number of results to retrieve. Set to None to retrieve all results.
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 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.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
List[Output]
async abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶
Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
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]¶
Return type
Runnable[Input, Output]
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
Parameters
input_type (Optional[Type[Input]]) – 
output_type (Optional[Type[Output]]) – 
Return type
Runnable[Input, Output]
property InputType: Type[Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
name: Optional[str] = None¶
The name of the runnable. Used for debugging and tracing.
property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using AzureAISearchRetriever¶
Azure AI Search
Microsoft
langchain_community.retrievers.metal.MetalRetriever¶
class langchain_community.retrievers.metal.MetalRetriever[source]¶
Bases: BaseRetriever
Metal 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 client: Any = None¶
The Metal client to use.
param metadata: Optional[Dict[str, Any]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
use case.
param params: Optional[dict] = None¶
The parameters to pass to the Metal client.
param tags: Optional[List[str]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
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 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.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
name: Optional[str] = None¶
The name of the runnable. Used for debugging and tracing.
property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using MetalRetriever¶
Metal
langchain_community.retrievers.asknews.AskNewsRetriever¶
class langchain_community.retrievers.asknews.AskNewsRetriever[source]¶
Bases: BaseRetriever
AskNews 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 categories: List[Literal['All', 'Business', 'Crime', 'Politics', 'Science', 'Sports', 'Technology', 'Military', 'Health', 'Entertainment', 'Finance', 'Culture', 'Climate', 'Environment', 'World']] = ['All']¶
param client_id: Optional[str] = None¶
param client_secret: Optional[str] = None¶
param end_timestamp: Optional[int] = None¶
param historical: bool = False¶
param k: int = 10¶
param kwargs: Optional[Dict[str, Any]] = {}¶
param metadata: Optional[Dict[str, Any]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
use case.
param method: Literal['nl', 'kw'] = 'nl'¶
param offset: int = 0¶
param similarity_score_threshold: float = 0.5¶
param start_timestamp: Optional[int] = None¶
param tags: Optional[List[str]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
use case.
Return type
Runnable[Input, Output]
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
Parameters
input_type (Optional[Type[Input]]) – 
output_type (Optional[Type[Output]]) – 
Return type
Runnable[Input, Output]
property InputType: Type[Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
name: Optional[str] = None¶
The name of the runnable. Used for debugging and tracing.
property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using AskNewsRetriever¶
AskNews
langchain_community.retrievers.breebs.BreebsRetriever¶
class langchain_community.retrievers.breebs.BreebsRetriever[source]¶
Bases: BaseRetriever
A retriever class for Breebs.
See https://www.breebs.com/ for more info.
:param breeb_key: The key to trigger the breeb
:param (specialized knowledge pill on a specific topic).:
To retrieve the list of all available Breebs : you can call https://breebs.promptbreeders.com/web/listbreebs
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 breeb_key: str [Required]¶
param metadata: Optional[Dict[str, Any]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
use case.
param tags: Optional[List[str]] = None¶
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.
You can use these to eg identify a specific instance of a retriever with its
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 runnables.