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142e4c22d793-12 | Return type
Type[BaseModel]
configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
Configure alternatives for runnables that can be set at runtime.
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
ConfigurableField(id="llm"),
default_key="anthropic",
openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
# uses ChatOpenaAI
print(
model.with_config(
configurable={"llm": "openai"}
).invoke("which organization created you?").content
)
Parameters
which (ConfigurableField) –
default_key (str) –
prefix_keys (bool) –
kwargs (Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) –
Return type
RunnableSerializable[Input, Output]
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
Configure particular runnable fields at runtime.
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(max_tokens=20).configurable_fields(
max_tokens=ConfigurableField(
id="output_token_number", | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-13 | max_tokens=ConfigurableField(
id="output_token_number",
name="Max tokens in the output",
description="The maximum number of tokens in the output",
)
)
# max_tokens = 20
print(
"max_tokens_20: ",
model.invoke("tell me something about chess").content
)
# max_tokens = 200
print("max_tokens_200: ", model.with_config(
configurable={"output_token_number": 200}
).invoke("tell me something about chess").content
)
Parameters
kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –
Return type
RunnableSerializable[Input, Output]
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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-14 | 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
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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
get_graph(config: Optional[RunnableConfig] = None) → Graph¶
Return a graph representation of this runnable.
Parameters
config (Optional[RunnableConfig]) –
Return type
Graph
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-15 | 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 (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
Return type
Type[BaseModel]
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”, “openai”]
Return type
List[str]
get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶
Get the name of the runnable.
Parameters
suffix (Optional[str]) –
name (Optional[str]) –
Return type
str
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
Return type
Type[BaseModel]
get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶
Parameters
config (Optional[RunnableConfig]) –
Return type
List[BasePromptTemplate] | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-16 | config (Optional[RunnableConfig]) –
Return type
List[BasePromptTemplate]
get_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]¶
Retrieve documents relevant to a query.
: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.
Parameters
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.
query (str) –
callbacks (Callbacks) –
tags (Optional[List[str]]) –
run_name (Optional[str]) –
kwargs (Any) –
Returns
List of relevant documents
Return type
List[Document]
invoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
Transform a single input into an output. Override to implement.
Parameters
input (str) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
kwargs (Any) –
Returns
The output of the runnable.
Return type
List[Document] | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-17 | Returns
The output of the runnable.
Return type
List[Document]
classmethod is_lc_serializable() → bool¶
Is this class serializable?
Return type
bool
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_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
Return type
List[str]
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.
Example | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-18 | 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]]
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]¶
Pick keys from the dict output of this runnable.
Pick single key:import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)
chain.invoke("[1, 2, 3]") | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-19 | chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}
json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
Pick list of keys:from typing import Any
import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
return bytes(x, "utf-8")
chain = RunnableMap(
str=as_str,
json=as_json,
bytes=RunnableLambda(as_bytes)
)
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
Parameters
keys (Union[str, List[str]]) –
Return type
RunnableSerializable[Any, Any]
pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]¶
Compose this Runnable with Runnable-like objects to make a RunnableSequence.
Equivalent to RunnableSequence(self, *others) or self | others[0] | …
Example
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int: | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-20 | def add_one(x: int) -> int:
return x + 1
def mul_two(x: int) -> int:
return x * 2
runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4
sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]
Parameters
others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –
name (Optional[str]) –
Return type
RunnableSerializable[Input, Other]
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
Parameters
input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) – | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-21 | config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
Serialize the runnable to JSON.
Return type
Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented¶
Return type
SerializedNotImplemented
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
Parameters
input (Iterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
Parameters
config (Optional[RunnableConfig]) –
kwargs (Any) –
Return type
Runnable[Input, Output]
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Example
from typing import Iterator | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-22 | Add fallbacks to a runnable, returning a new Runnable.
Example
from typing import Iterator
from langchain_core.runnables import RunnableGenerator
def _generate_immediate_error(input: Iterator) -> Iterator[str]:
raise ValueError()
yield ""
def _generate(input: Iterator) -> Iterator[str]:
yield from "foo bar"
runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
[RunnableGenerator(_generate)]
)
print(''.join(runnable.stream({}))) #foo bar
Parameters
fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle (Tuple[Type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – If string is specified then handled exceptions will be passed
to fallbacks as part of the input under the specified key. If None,
exceptions will not be passed to fallbacks. If used, the base runnable
and its fallbacks must accept a dictionary as input.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
Return type
RunnableWithFallbacksT[Input, Output]
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 object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id, | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-23 | The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
Example:
Parameters
on_start (Optional[Listener]) –
on_end (Optional[Listener]) –
on_error (Optional[Listener]) –
Return type
Runnable[Input, Output]
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.
Example:
Parameters
retry_if_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on
wait_exponential_jitter (bool) – Whether to add jitter to the wait time
between retries
stop_after_attempt (int) – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
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]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
142e4c22d793-24 | 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 ParentDocumentRetriever¶
Parent Document Retriever | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html |
957c56882b88-0 | langchain.retrievers.document_compressors.flashrank_rerank.FlashrankRerank¶
class langchain.retrievers.document_compressors.flashrank_rerank.FlashrankRerank[source]¶
Bases: BaseDocumentCompressor
Document compressor using Flashrank interface.
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: Ranker [Required]¶
Flashrank client to use for compressing documents
param model: Optional[str] = None¶
Model to use for reranking.
param top_n: int = 3¶
Number of documents to return.
async acompress_documents(documents: Sequence[Document], query: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Document]¶
Compress retrieved documents given the query context.
Parameters
documents (Sequence[Document]) –
query (str) –
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –
Return type
Sequence[Document]
compress_documents(documents: Sequence[Document], query: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Document][source]¶
Compress retrieved documents given the query context.
Parameters
documents (Sequence[Document]) –
query (str) –
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –
Return type
Sequence[Document]
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. | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.flashrank_rerank.FlashrankRerank.html |
957c56882b88-1 | 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
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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.flashrank_rerank.FlashrankRerank.html |
957c56882b88-2 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
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_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.flashrank_rerank.FlashrankRerank.html |
957c56882b88-3 | dumps_kwargs (Any) –
Return type
unicode
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
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.flashrank_rerank.FlashrankRerank.html |
957c56882b88-4 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.flashrank_rerank.FlashrankRerank.html |
a57933f75724-0 | langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever¶
class langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever[source]¶
Bases: BaseRetriever
Tavily Search 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 api_key: Optional[str] = None¶
param exclude_domains: Optional[List[str]] = None¶
param include_domains: Optional[List[str]] = None¶
param include_generated_answer: bool = False¶
param include_images: bool = False¶
param include_raw_content: 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 search_depth: SearchDepth = SearchDepth.BASIC¶
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-1 | 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.
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: List[Input], config: Optional[Union[RunnableConfig, List[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 (List[Input]) –
config (Optional[Union[RunnableConfig, List[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]¶
Asynchronously get documents relevant to a query.
: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.
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-2 | and passed as arguments to the handlers defined in callbacks.
Parameters
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.
query (str) –
callbacks (Callbacks) –
tags (Optional[List[str]]) –
run_name (Optional[str]) –
kwargs (Any) –
Returns
List of relevant documents
Return type
List[Document]
async ainvoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
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.
Parameters
input (str) –
config (Optional[RunnableConfig]) –
kwargs (Any) –
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
from operator import itemgetter
prompt = (
SystemMessagePromptTemplate.from_template("You are a nice assistant.")
+ "{question}"
) | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-3 | + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])
chain: Runnable = prompt | llm | {"str": StrOutputParser()}
chain_with_assign = chain.assign(hello=itemgetter("str") | llm)
print(chain_with_assign.input_schema.schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.schema()) #
{'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}
Parameters
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]]]]) –
Return type
RunnableSerializable[Any, Any]
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
Parameters
input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
AsyncIterator[Output] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-4 | kwargs (Optional[Any]) –
Return type
AsyncIterator[Output]
astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → AsyncIterator[StreamEvent]¶
[Beta] Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information
about the progress of the runnable, including StreamEvents from intermediate
results.
A StreamEvent is a dictionary with the following schema:
event: str - Event names are of theformat: on_[runnable_type]_(start|stream|end).
name: str - The name of the runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution ofthe runnable that emitted the event.
A child runnable that gets invoked as part of the execution of a
parent runnable is assigned its own unique ID.
tags: Optional[List[str]] - The tags of the runnable that generatedthe event.
metadata: Optional[Dict[str, Any]] - The metadata of the runnablethat generated the event.
data: Dict[str, Any]
Below is a table that illustrates some evens that might be emitted by various
chains. Metadata fields have been omitted from the table for brevity.
Chain definitions have been included after the table.
event
name
chunk
input
output
on_chat_model_start
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=”hello”) | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-5 | on_chat_model_stream
[model name]
AIMessageChunk(content=”hello”)
on_chat_model_end
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
{“generations”: […], “llm_output”: None, …}
on_llm_start
[model name]
{‘input’: ‘hello’}
on_llm_stream
[model name]
‘Hello’
on_llm_end
[model name]
‘Hello human!’
on_chain_start
format_docs
on_chain_stream
format_docs
“hello world!, goodbye world!”
on_chain_end
format_docs
[Document(…)]
“hello world!, goodbye world!”
on_tool_start
some_tool
{“x”: 1, “y”: “2”}
on_tool_stream
some_tool
{“x”: 1, “y”: “2”}
on_tool_end
some_tool
{“x”: 1, “y”: “2”}
on_retriever_start
[retriever name]
{“query”: “hello”}
on_retriever_chunk
[retriever name]
{documents: […]}
on_retriever_end
[retriever name]
{“query”: “hello”}
{documents: […]}
on_prompt_start
[template_name]
{“question”: “hello”}
on_prompt_end
[template_name]
{“question”: “hello”}
ChatPromptValue(messages: [SystemMessage, …])
Here are declarations associated with the events shown above:
format_docs:
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
some_tool:
@tool | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-6 | format_docs = RunnableLambda(format_docs)
some_tool:
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
prompt:
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
from langchain_core.runnables import RunnableLambda
async def reverse(s: str) -> str:
return s[::-1]
chain = RunnableLambda(func=reverse)
events = [
event async for event in chain.astream_events("hello", version="v1")
]
# will produce the following events (run_id has been omitted for brevity):
[
{
"data": {"input": "hello"},
"event": "on_chain_start",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"chunk": "olleh"},
"event": "on_chain_stream",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"output": "olleh"},
"event": "on_chain_end",
"metadata": {},
"name": "reverse",
"tags": [],
},
]
Parameters
input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
version (Literal['v1']) – The version of the schema to use.
Currently only version 1 is available. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-7 | Currently only version 1 is available.
No default will be assigned until the API is stabilized.
include_names (Optional[Sequence[str]]) – Only include events from runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the runnable.
These will be passed to astream_log as this implementation
of astream_events is built on top of astream_log.
Returns
An async stream of StreamEvents.
Return type
AsyncIterator[StreamEvent]
Notes
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system.
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-8 | 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.
Parameters
input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
diff (bool) – Whether to yield diffs between each step, or the current state.
with_streamed_output_list (bool) – Whether to yield the streamed_output list.
include_names (Optional[Sequence[str]]) – Only include logs with these names.
include_types (Optional[Sequence[str]]) – Only include logs with these types.
include_tags (Optional[Sequence[str]]) – Only include logs with these tags.
exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.
exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.
exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.
kwargs (Any) –
Return type
Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]
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 they can start producing output while
input is still being generated.
Parameters
input (AsyncIterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
AsyncIterator[Output]
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-9 | Default implementation runs invoke in parallel using a thread pool executor.
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]
batch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → Iterator[Tuple[int, Union[Output, Exception]]]¶
Run invoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –
Return type
Iterator[Tuple[int, Union[Output, Exception]]]
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
Useful when a runnable in a chain requires an argument that is not
in the output of the previous runnable or included in the user input.
Example:
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
llm = ChatOllama(model='llama2')
# Without bind.
chain = (
llm
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.' | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-10 | # Output is 'One two three four five.'
# With bind.
chain = (
llm.bind(stop=["three"])
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
Parameters
kwargs (Any) –
Return type
Runnable[Input, Output]
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include (Optional[Sequence[str]]) – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
Return type
Type[BaseModel]
configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
Configure alternatives for runnables that can be set at runtime.
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
ConfigurableField(id="llm"),
default_key="anthropic",
openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
# uses ChatOpenaAI
print(
model.with_config(
configurable={"llm": "openai"} | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-11 | print(
model.with_config(
configurable={"llm": "openai"}
).invoke("which organization created you?").content
)
Parameters
which (ConfigurableField) –
default_key (str) –
prefix_keys (bool) –
kwargs (Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) –
Return type
RunnableSerializable[Input, Output]
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
Configure particular runnable fields at runtime.
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(max_tokens=20).configurable_fields(
max_tokens=ConfigurableField(
id="output_token_number",
name="Max tokens in the output",
description="The maximum number of tokens in the output",
)
)
# max_tokens = 20
print(
"max_tokens_20: ",
model.invoke("tell me something about chess").content
)
# max_tokens = 200
print("max_tokens_200: ", model.with_config(
configurable={"output_token_number": 200}
).invoke("tell me something about chess").content
)
Parameters
kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –
Return type
RunnableSerializable[Input, Output]
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-12 | 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
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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-13 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
get_graph(config: Optional[RunnableConfig] = None) → Graph¶
Return a graph representation of this runnable.
Parameters
config (Optional[RunnableConfig]) –
Return type
Graph
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
Return type
Type[BaseModel]
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”, “openai”]
Return type
List[str]
get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶
Get the name of the runnable.
Parameters
suffix (Optional[str]) –
name (Optional[str]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-14 | Parameters
suffix (Optional[str]) –
name (Optional[str]) –
Return type
str
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
Return type
Type[BaseModel]
get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶
Parameters
config (Optional[RunnableConfig]) –
Return type
List[BasePromptTemplate]
get_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]¶
Retrieve documents relevant to a query.
: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.
Parameters
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.
query (str) –
callbacks (Callbacks) –
tags (Optional[List[str]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-15 | callbacks (Callbacks) –
tags (Optional[List[str]]) –
run_name (Optional[str]) –
kwargs (Any) –
Returns
List of relevant documents
Return type
List[Document]
invoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
Transform a single input into an output. Override to implement.
Parameters
input (str) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
kwargs (Any) –
Returns
The output of the runnable.
Return type
List[Document]
classmethod is_lc_serializable() → bool¶
Is this class serializable?
Return type
bool
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_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-16 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
Return type
List[str]
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.
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]]
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-17 | 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]¶
Pick keys from the dict output of this runnable.
Pick single key:import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}
json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
Pick list of keys:from typing import Any
import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
return bytes(x, "utf-8")
chain = RunnableMap(
str=as_str,
json=as_json,
bytes=RunnableLambda(as_bytes)
)
chain.invoke("[1, 2, 3]") | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-18 | )
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
Parameters
keys (Union[str, List[str]]) –
Return type
RunnableSerializable[Any, Any]
pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]¶
Compose this Runnable with Runnable-like objects to make a RunnableSequence.
Equivalent to RunnableSequence(self, *others) or self | others[0] | …
Example
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
def mul_two(x: int) -> int:
return x * 2
runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4
sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-19 | # -> [4, 6, 8]
Parameters
others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –
name (Optional[str]) –
Return type
RunnableSerializable[Input, Other]
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
Parameters
input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
Serialize the runnable to JSON.
Return type
Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented¶
Return type
SerializedNotImplemented
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
Parameters
input (Iterator[Input]) –
config (Optional[RunnableConfig]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-20 | input (Iterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
Parameters
config (Optional[RunnableConfig]) –
kwargs (Any) –
Return type
Runnable[Input, Output]
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Example
from typing import Iterator
from langchain_core.runnables import RunnableGenerator
def _generate_immediate_error(input: Iterator) -> Iterator[str]:
raise ValueError()
yield ""
def _generate(input: Iterator) -> Iterator[str]:
yield from "foo bar"
runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
[RunnableGenerator(_generate)]
)
print(''.join(runnable.stream({}))) #foo bar
Parameters
fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original runnable fails. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-21 | exceptions_to_handle (Tuple[Type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – If string is specified then handled exceptions will be passed
to fallbacks as part of the input under the specified key. If None,
exceptions will not be passed to fallbacks. If used, the base runnable
and its fallbacks must accept a dictionary as input.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
Return type
RunnableWithFallbacksT[Input, Output]
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 object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
Example:
Parameters
on_start (Optional[Listener]) –
on_end (Optional[Listener]) –
on_error (Optional[Listener]) –
Return type
Runnable[Input, Output]
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.
Example:
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-22 | Create a new Runnable that retries the original runnable on exceptions.
Example:
Parameters
retry_if_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on
wait_exponential_jitter (bool) – Whether to add jitter to the wait time
between retries
stop_after_attempt (int) – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
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]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
a57933f75724-23 | property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html |
00de430ce002-0 | langchain.retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker¶
class langchain.retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker[source]¶
Bases: BaseDocumentCompressor
Document compressor that uses CrossEncoder for reranking.
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 model: BaseCrossEncoder [Required]¶
CrossEncoder model to use for scoring similarity
between the query and documents.
param top_n: int = 3¶
Number of documents to return.
async acompress_documents(documents: Sequence[Document], query: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Document]¶
Compress retrieved documents given the query context.
Parameters
documents (Sequence[Document]) –
query (str) –
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –
Return type
Sequence[Document]
compress_documents(documents: Sequence[Document], query: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Document][source]¶
Rerank documents using CrossEncoder.
Parameters
documents (Sequence[Document]) – A sequence of documents to compress.
query (str) – The query to use for compressing the documents.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to run during the compression process.
Returns
A sequence of compressed documents.
Return type
Sequence[Document]
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. | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker.html |
00de430ce002-1 | 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
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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker.html |
00de430ce002-2 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
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_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker.html |
00de430ce002-3 | dumps_kwargs (Any) –
Return type
unicode
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
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker.html |
00de430ce002-4 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.cross_encoder_rerank.CrossEncoderReranker.html |
a795abaab9fa-0 | langchain_community.retrievers.arxiv.ArxivRetriever¶
class langchain_community.retrievers.arxiv.ArxivRetriever[source]¶
Bases: BaseRetriever, ArxivAPIWrapper
Arxiv retriever.
It wraps load() to get_relevant_documents().
It uses all ArxivAPIWrapper arguments without any change.
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 ARXIV_MAX_QUERY_LENGTH: int = 300¶
param arxiv_exceptions: Any = None¶
param doc_content_chars_max: Optional[int] = 4000¶
param get_full_documents: bool = False¶
param load_all_available_meta: bool = False¶
param load_max_docs: int = 100¶
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.
param top_k_results: int = 3¶
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-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.
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: List[Input], config: Optional[Union[RunnableConfig, List[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 (List[Input]) –
config (Optional[Union[RunnableConfig, List[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]¶
Asynchronously get documents relevant to a query.
: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.
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-2 | and passed as arguments to the handlers defined in callbacks.
Parameters
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.
query (str) –
callbacks (Callbacks) –
tags (Optional[List[str]]) –
run_name (Optional[str]) –
kwargs (Any) –
Returns
List of relevant documents
Return type
List[Document]
async ainvoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
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.
Parameters
input (str) –
config (Optional[RunnableConfig]) –
kwargs (Any) –
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
from operator import itemgetter
prompt = (
SystemMessagePromptTemplate.from_template("You are a nice assistant.")
+ "{question}"
) | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-3 | + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])
chain: Runnable = prompt | llm | {"str": StrOutputParser()}
chain_with_assign = chain.assign(hello=itemgetter("str") | llm)
print(chain_with_assign.input_schema.schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.schema()) #
{'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}
Parameters
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]]]]) –
Return type
RunnableSerializable[Any, Any]
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
Parameters
input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
AsyncIterator[Output] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-4 | kwargs (Optional[Any]) –
Return type
AsyncIterator[Output]
astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → AsyncIterator[StreamEvent]¶
[Beta] Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information
about the progress of the runnable, including StreamEvents from intermediate
results.
A StreamEvent is a dictionary with the following schema:
event: str - Event names are of theformat: on_[runnable_type]_(start|stream|end).
name: str - The name of the runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution ofthe runnable that emitted the event.
A child runnable that gets invoked as part of the execution of a
parent runnable is assigned its own unique ID.
tags: Optional[List[str]] - The tags of the runnable that generatedthe event.
metadata: Optional[Dict[str, Any]] - The metadata of the runnablethat generated the event.
data: Dict[str, Any]
Below is a table that illustrates some evens that might be emitted by various
chains. Metadata fields have been omitted from the table for brevity.
Chain definitions have been included after the table.
event
name
chunk
input
output
on_chat_model_start
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=”hello”) | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-5 | on_chat_model_stream
[model name]
AIMessageChunk(content=”hello”)
on_chat_model_end
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
{“generations”: […], “llm_output”: None, …}
on_llm_start
[model name]
{‘input’: ‘hello’}
on_llm_stream
[model name]
‘Hello’
on_llm_end
[model name]
‘Hello human!’
on_chain_start
format_docs
on_chain_stream
format_docs
“hello world!, goodbye world!”
on_chain_end
format_docs
[Document(…)]
“hello world!, goodbye world!”
on_tool_start
some_tool
{“x”: 1, “y”: “2”}
on_tool_stream
some_tool
{“x”: 1, “y”: “2”}
on_tool_end
some_tool
{“x”: 1, “y”: “2”}
on_retriever_start
[retriever name]
{“query”: “hello”}
on_retriever_chunk
[retriever name]
{documents: […]}
on_retriever_end
[retriever name]
{“query”: “hello”}
{documents: […]}
on_prompt_start
[template_name]
{“question”: “hello”}
on_prompt_end
[template_name]
{“question”: “hello”}
ChatPromptValue(messages: [SystemMessage, …])
Here are declarations associated with the events shown above:
format_docs:
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
some_tool:
@tool | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-6 | format_docs = RunnableLambda(format_docs)
some_tool:
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
prompt:
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
from langchain_core.runnables import RunnableLambda
async def reverse(s: str) -> str:
return s[::-1]
chain = RunnableLambda(func=reverse)
events = [
event async for event in chain.astream_events("hello", version="v1")
]
# will produce the following events (run_id has been omitted for brevity):
[
{
"data": {"input": "hello"},
"event": "on_chain_start",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"chunk": "olleh"},
"event": "on_chain_stream",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"output": "olleh"},
"event": "on_chain_end",
"metadata": {},
"name": "reverse",
"tags": [],
},
]
Parameters
input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
version (Literal['v1']) – The version of the schema to use.
Currently only version 1 is available. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-7 | Currently only version 1 is available.
No default will be assigned until the API is stabilized.
include_names (Optional[Sequence[str]]) – Only include events from runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the runnable.
These will be passed to astream_log as this implementation
of astream_events is built on top of astream_log.
Returns
An async stream of StreamEvents.
Return type
AsyncIterator[StreamEvent]
Notes
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system.
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-8 | 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.
Parameters
input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
diff (bool) – Whether to yield diffs between each step, or the current state.
with_streamed_output_list (bool) – Whether to yield the streamed_output list.
include_names (Optional[Sequence[str]]) – Only include logs with these names.
include_types (Optional[Sequence[str]]) – Only include logs with these types.
include_tags (Optional[Sequence[str]]) – Only include logs with these tags.
exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.
exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.
exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.
kwargs (Any) –
Return type
Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]
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 they can start producing output while
input is still being generated.
Parameters
input (AsyncIterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
AsyncIterator[Output]
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-9 | Default implementation runs invoke in parallel using a thread pool executor.
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]
batch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → Iterator[Tuple[int, Union[Output, Exception]]]¶
Run invoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –
Return type
Iterator[Tuple[int, Union[Output, Exception]]]
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
Useful when a runnable in a chain requires an argument that is not
in the output of the previous runnable or included in the user input.
Example:
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
llm = ChatOllama(model='llama2')
# Without bind.
chain = (
llm
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.' | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-10 | # Output is 'One two three four five.'
# With bind.
chain = (
llm.bind(stop=["three"])
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
Parameters
kwargs (Any) –
Return type
Runnable[Input, Output]
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include (Optional[Sequence[str]]) – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
Return type
Type[BaseModel]
configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
Configure alternatives for runnables that can be set at runtime.
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
ConfigurableField(id="llm"),
default_key="anthropic",
openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
# uses ChatOpenaAI
print(
model.with_config(
configurable={"llm": "openai"} | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-11 | print(
model.with_config(
configurable={"llm": "openai"}
).invoke("which organization created you?").content
)
Parameters
which (ConfigurableField) –
default_key (str) –
prefix_keys (bool) –
kwargs (Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) –
Return type
RunnableSerializable[Input, Output]
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
Configure particular runnable fields at runtime.
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(max_tokens=20).configurable_fields(
max_tokens=ConfigurableField(
id="output_token_number",
name="Max tokens in the output",
description="The maximum number of tokens in the output",
)
)
# max_tokens = 20
print(
"max_tokens_20: ",
model.invoke("tell me something about chess").content
)
# max_tokens = 200
print("max_tokens_200: ", model.with_config(
configurable={"output_token_number": 200}
).invoke("tell me something about chess").content
)
Parameters
kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –
Return type
RunnableSerializable[Input, Output]
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-12 | 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
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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-13 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
get_graph(config: Optional[RunnableConfig] = None) → Graph¶
Return a graph representation of this runnable.
Parameters
config (Optional[RunnableConfig]) –
Return type
Graph
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
Return type
Type[BaseModel]
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”, “openai”]
Return type
List[str]
get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶
Get the name of the runnable.
Parameters
suffix (Optional[str]) –
name (Optional[str]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-14 | Parameters
suffix (Optional[str]) –
name (Optional[str]) –
Return type
str
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
Return type
Type[BaseModel]
get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶
Parameters
config (Optional[RunnableConfig]) –
Return type
List[BasePromptTemplate]
get_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]¶
Retrieve documents relevant to a query.
: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.
Parameters
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.
query (str) –
callbacks (Callbacks) –
tags (Optional[List[str]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-15 | callbacks (Callbacks) –
tags (Optional[List[str]]) –
run_name (Optional[str]) –
kwargs (Any) –
Returns
List of relevant documents
Return type
List[Document]
get_summaries_as_docs(query: str) → List[Document]¶
Performs an arxiv search and returns list of
documents, with summaries as the content.
If an error occurs or no documents found, error text
is returned instead. Wrapper for
https://lukasschwab.me/arxiv.py/index.html#Search
Parameters
query (str) – a plaintext search query
Return type
List[Document]
invoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
Transform a single input into an output. Override to implement.
Parameters
input (str) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
kwargs (Any) –
Returns
The output of the runnable.
Return type
List[Document]
is_arxiv_identifier(query: str) → bool¶
Check if a query is an arxiv identifier.
Parameters
query (str) –
Return type
bool
classmethod is_lc_serializable() → bool¶
Is this class serializable?
Return type
bool | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-16 | Is this class serializable?
Return type
bool
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_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
lazy_load(query: str) → Iterator[Document]¶
Run Arxiv search and get the article texts plus the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
Returns: documents with the document.page_content in text format
Performs an arxiv search, downloads the top k results as PDFs, loads
them as Documents, and returns them.
Parameters
query (str) – a plaintext search query
Return type
Iterator[Document]
classmethod lc_id() → List[str]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-17 | Return type
Iterator[Document]
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
Return type
List[str]
load(query: str) → List[Document]¶
Run Arxiv search and get the article texts plus the article meta information.
See https://lukasschwab.me/arxiv.py/index.html#Search
Returns: a list of documents with the document.page_content in text format
Performs an arxiv search, downloads the top k results as PDFs, loads
them as Documents, and returns them in a List.
Parameters
query (str) – a plaintext search query
Return type
List[Document]
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.
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]]
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-18 | 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]¶
Pick keys from the dict output of this runnable.
Pick single key:import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}
json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
Pick list of keys:from typing import Any
import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
return bytes(x, "utf-8")
chain = RunnableMap(
str=as_str,
json=as_json,
bytes=RunnableLambda(as_bytes)
)
chain.invoke("[1, 2, 3]") | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-19 | )
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
Parameters
keys (Union[str, List[str]]) –
Return type
RunnableSerializable[Any, Any]
pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]¶
Compose this Runnable with Runnable-like objects to make a RunnableSequence.
Equivalent to RunnableSequence(self, *others) or self | others[0] | …
Example
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
def mul_two(x: int) -> int:
return x * 2
runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4
sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-20 | # -> [4, 6, 8]
Parameters
others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –
name (Optional[str]) –
Return type
RunnableSerializable[Input, Other]
run(query: str) → str¶
Performs an arxiv search and A single string
with the publish date, title, authors, and summary
for each article separated by two newlines.
If an error occurs or no documents found, error text
is returned instead. Wrapper for
https://lukasschwab.me/arxiv.py/index.html#Search
Parameters
query (str) – a plaintext search query
Return type
str
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
Parameters
input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
Serialize the runnable to JSON.
Return type
Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented¶
Return type | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-21 | to_json_not_implemented() → SerializedNotImplemented¶
Return type
SerializedNotImplemented
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
Parameters
input (Iterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
Parameters
config (Optional[RunnableConfig]) –
kwargs (Any) –
Return type
Runnable[Input, Output]
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Example
from typing import Iterator
from langchain_core.runnables import RunnableGenerator
def _generate_immediate_error(input: Iterator) -> Iterator[str]:
raise ValueError()
yield ""
def _generate(input: Iterator) -> Iterator[str]:
yield from "foo bar" | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-22 | def _generate(input: Iterator) -> Iterator[str]:
yield from "foo bar"
runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
[RunnableGenerator(_generate)]
)
print(''.join(runnable.stream({}))) #foo bar
Parameters
fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle (Tuple[Type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – If string is specified then handled exceptions will be passed
to fallbacks as part of the input under the specified key. If None,
exceptions will not be passed to fallbacks. If used, the base runnable
and its fallbacks must accept a dictionary as input.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
Return type
RunnableWithFallbacksT[Input, Output]
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 object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
Example:
Parameters
on_start (Optional[Listener]) –
on_end (Optional[Listener]) –
on_error (Optional[Listener]) –
Return type | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-23 | on_error (Optional[Listener]) –
Return type
Runnable[Input, Output]
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.
Example:
Parameters
retry_if_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on
wait_exponential_jitter (bool) – Whether to add jitter to the wait time
between retries
stop_after_attempt (int) – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
a795abaab9fa-24 | 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 ArxivRetriever¶
Arxiv | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html |
1c4dafa708a3-0 | langchain_community.retrievers.you.YouRetriever¶
class langchain_community.retrievers.you.YouRetriever[source]¶
Bases: BaseRetriever, YouSearchAPIWrapper
You retriever that uses You.com’s search API.
It wraps results() to get_relevant_documents
It uses all YouSearchAPIWrapper arguments without any change.
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 country: Optional[str] = None¶
param endpoint_type: Literal['search', 'news', 'rag', 'snippet'] = 'search'¶
param k: Optional[int] = None¶
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 n_hits: Optional[int] = None¶
param n_snippets_per_hit: Optional[int] = None¶
param num_web_results: Optional[int] = None¶
param safesearch: Optional[str] = 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.
param ydc_api_key: Optional[str] = None¶ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-1 | use case.
param ydc_api_key: Optional[str] = None¶
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: List[Input], config: Optional[Union[RunnableConfig, List[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 (List[Input]) –
config (Optional[Union[RunnableConfig, List[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]¶
Asynchronously get documents relevant to a query.
:param query: string to find relevant documents for | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-2 | Asynchronously get documents relevant to a query.
: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.
Parameters
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.
query (str) –
callbacks (Callbacks) –
tags (Optional[List[str]]) –
run_name (Optional[str]) –
kwargs (Any) –
Returns
List of relevant documents
Return type
List[Document]
async ainvoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
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.
Parameters
input (str) –
config (Optional[RunnableConfig]) –
kwargs (Any) –
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-3 | 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)
print(chain_with_assign.input_schema.schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.schema()) #
{'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}
Parameters
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]]]]) –
Return type
RunnableSerializable[Any, Any]
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
Parameters
input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
AsyncIterator[Output] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-4 | kwargs (Optional[Any]) –
Return type
AsyncIterator[Output]
astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → AsyncIterator[StreamEvent]¶
[Beta] Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information
about the progress of the runnable, including StreamEvents from intermediate
results.
A StreamEvent is a dictionary with the following schema:
event: str - Event names are of theformat: on_[runnable_type]_(start|stream|end).
name: str - The name of the runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution ofthe runnable that emitted the event.
A child runnable that gets invoked as part of the execution of a
parent runnable is assigned its own unique ID.
tags: Optional[List[str]] - The tags of the runnable that generatedthe event.
metadata: Optional[Dict[str, Any]] - The metadata of the runnablethat generated the event.
data: Dict[str, Any]
Below is a table that illustrates some evens that might be emitted by various
chains. Metadata fields have been omitted from the table for brevity.
Chain definitions have been included after the table.
event
name
chunk
input
output
on_chat_model_start
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=”hello”) | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-5 | on_chat_model_stream
[model name]
AIMessageChunk(content=”hello”)
on_chat_model_end
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
{“generations”: […], “llm_output”: None, …}
on_llm_start
[model name]
{‘input’: ‘hello’}
on_llm_stream
[model name]
‘Hello’
on_llm_end
[model name]
‘Hello human!’
on_chain_start
format_docs
on_chain_stream
format_docs
“hello world!, goodbye world!”
on_chain_end
format_docs
[Document(…)]
“hello world!, goodbye world!”
on_tool_start
some_tool
{“x”: 1, “y”: “2”}
on_tool_stream
some_tool
{“x”: 1, “y”: “2”}
on_tool_end
some_tool
{“x”: 1, “y”: “2”}
on_retriever_start
[retriever name]
{“query”: “hello”}
on_retriever_chunk
[retriever name]
{documents: […]}
on_retriever_end
[retriever name]
{“query”: “hello”}
{documents: […]}
on_prompt_start
[template_name]
{“question”: “hello”}
on_prompt_end
[template_name]
{“question”: “hello”}
ChatPromptValue(messages: [SystemMessage, …])
Here are declarations associated with the events shown above:
format_docs:
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
some_tool:
@tool | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-6 | format_docs = RunnableLambda(format_docs)
some_tool:
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
prompt:
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
from langchain_core.runnables import RunnableLambda
async def reverse(s: str) -> str:
return s[::-1]
chain = RunnableLambda(func=reverse)
events = [
event async for event in chain.astream_events("hello", version="v1")
]
# will produce the following events (run_id has been omitted for brevity):
[
{
"data": {"input": "hello"},
"event": "on_chain_start",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"chunk": "olleh"},
"event": "on_chain_stream",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"output": "olleh"},
"event": "on_chain_end",
"metadata": {},
"name": "reverse",
"tags": [],
},
]
Parameters
input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
version (Literal['v1']) – The version of the schema to use.
Currently only version 1 is available. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-7 | Currently only version 1 is available.
No default will be assigned until the API is stabilized.
include_names (Optional[Sequence[str]]) – Only include events from runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the runnable.
These will be passed to astream_log as this implementation
of astream_events is built on top of astream_log.
Returns
An async stream of StreamEvents.
Return type
AsyncIterator[StreamEvent]
Notes
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system.
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-8 | 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.
Parameters
input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
diff (bool) – Whether to yield diffs between each step, or the current state.
with_streamed_output_list (bool) – Whether to yield the streamed_output list.
include_names (Optional[Sequence[str]]) – Only include logs with these names.
include_types (Optional[Sequence[str]]) – Only include logs with these types.
include_tags (Optional[Sequence[str]]) – Only include logs with these tags.
exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.
exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.
exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.
kwargs (Any) –
Return type
Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]
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 they can start producing output while
input is still being generated.
Parameters
input (AsyncIterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
AsyncIterator[Output]
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-9 | Default implementation runs invoke in parallel using a thread pool executor.
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]
batch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → Iterator[Tuple[int, Union[Output, Exception]]]¶
Run invoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –
Return type
Iterator[Tuple[int, Union[Output, Exception]]]
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
Useful when a runnable in a chain requires an argument that is not
in the output of the previous runnable or included in the user input.
Example:
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
llm = ChatOllama(model='llama2')
# Without bind.
chain = (
llm
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.' | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-10 | # Output is 'One two three four five.'
# With bind.
chain = (
llm.bind(stop=["three"])
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
Parameters
kwargs (Any) –
Return type
Runnable[Input, Output]
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include (Optional[Sequence[str]]) – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
Return type
Type[BaseModel]
configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
Configure alternatives for runnables that can be set at runtime.
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
ConfigurableField(id="llm"),
default_key="anthropic",
openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
# uses ChatOpenaAI
print(
model.with_config(
configurable={"llm": "openai"} | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-11 | print(
model.with_config(
configurable={"llm": "openai"}
).invoke("which organization created you?").content
)
Parameters
which (ConfigurableField) –
default_key (str) –
prefix_keys (bool) –
kwargs (Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) –
Return type
RunnableSerializable[Input, Output]
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
Configure particular runnable fields at runtime.
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(max_tokens=20).configurable_fields(
max_tokens=ConfigurableField(
id="output_token_number",
name="Max tokens in the output",
description="The maximum number of tokens in the output",
)
)
# max_tokens = 20
print(
"max_tokens_20: ",
model.invoke("tell me something about chess").content
)
# max_tokens = 200
print("max_tokens_200: ", model.with_config(
configurable={"output_token_number": 200}
).invoke("tell me something about chess").content
)
Parameters
kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –
Return type
RunnableSerializable[Input, Output]
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-12 | 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
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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-13 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
get_graph(config: Optional[RunnableConfig] = None) → Graph¶
Return a graph representation of this runnable.
Parameters
config (Optional[RunnableConfig]) –
Return type
Graph
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
Return type
Type[BaseModel]
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”, “openai”]
Return type
List[str]
get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶
Get the name of the runnable.
Parameters
suffix (Optional[str]) –
name (Optional[str]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-14 | Parameters
suffix (Optional[str]) –
name (Optional[str]) –
Return type
str
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
Return type
Type[BaseModel]
get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶
Parameters
config (Optional[RunnableConfig]) –
Return type
List[BasePromptTemplate]
get_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]¶
Retrieve documents relevant to a query.
: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.
Parameters
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.
query (str) –
callbacks (Callbacks) –
tags (Optional[List[str]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-15 | callbacks (Callbacks) –
tags (Optional[List[str]]) –
run_name (Optional[str]) –
kwargs (Any) –
Returns
List of relevant documents
Return type
List[Document]
invoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
Transform a single input into an output. Override to implement.
Parameters
input (str) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
kwargs (Any) –
Returns
The output of the runnable.
Return type
List[Document]
classmethod is_lc_serializable() → bool¶
Is this class serializable?
Return type
bool
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_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-16 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
Return type
List[str]
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.
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]]
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-17 | 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]¶
Pick keys from the dict output of this runnable.
Pick single key:import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}
json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]
Pick list of keys:from typing import Any
import json
from langchain_core.runnables import RunnableLambda, RunnableMap
as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
return bytes(x, "utf-8")
chain = RunnableMap(
str=as_str,
json=as_json,
bytes=RunnableLambda(as_bytes)
)
chain.invoke("[1, 2, 3]") | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-18 | )
chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
Parameters
keys (Union[str, List[str]]) –
Return type
RunnableSerializable[Any, Any]
pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]¶
Compose this Runnable with Runnable-like objects to make a RunnableSequence.
Equivalent to RunnableSequence(self, *others) or self | others[0] | …
Example
from langchain_core.runnables import RunnableLambda
def add_one(x: int) -> int:
return x + 1
def mul_two(x: int) -> int:
return x * 2
runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4
sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-19 | # -> [4, 6, 8]
Parameters
others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –
name (Optional[str]) –
Return type
RunnableSerializable[Input, Other]
raw_results(query: str, **kwargs: Any) → Dict¶
Run query through you.com Search and return hits.
Parameters
query (str) – The query to search for.
num_web_results – The maximum number of results to return.
safesearch – Safesearch settings,
one of off, moderate, strict, defaults to moderate
country – Country code
kwargs (Any) –
Return type
Dict
Returns: YouAPIOutput
async raw_results_async(query: str, **kwargs: Any) → Dict¶
Get results from the you.com Search API asynchronously.
Parameters
query (str) –
kwargs (Any) –
Return type
Dict
results(query: str, **kwargs: Any) → List[Document]¶
Run query through you.com Search and parses results into Documents.
Parameters
query (str) –
kwargs (Any) –
Return type
List[Document]
async results_async(query: str, **kwargs: Any) → List[Document]¶
Parameters
query (str) –
kwargs (Any) –
Return type
List[Document]
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-20 | Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
Parameters
input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
Serialize the runnable to JSON.
Return type
Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented¶
Return type
SerializedNotImplemented
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
Parameters
input (Iterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
Parameters
config (Optional[RunnableConfig]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-21 | Parameters
config (Optional[RunnableConfig]) –
kwargs (Any) –
Return type
Runnable[Input, Output]
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Example
from typing import Iterator
from langchain_core.runnables import RunnableGenerator
def _generate_immediate_error(input: Iterator) -> Iterator[str]:
raise ValueError()
yield ""
def _generate(input: Iterator) -> Iterator[str]:
yield from "foo bar"
runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
[RunnableGenerator(_generate)]
)
print(''.join(runnable.stream({}))) #foo bar
Parameters
fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle (Tuple[Type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – If string is specified then handled exceptions will be passed
to fallbacks as part of the input under the specified key. If None,
exceptions will not be passed to fallbacks. If used, the base runnable
and its fallbacks must accept a dictionary as input.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
Return type
RunnableWithFallbacksT[Input, Output] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-22 | Return type
RunnableWithFallbacksT[Input, Output]
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 object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
Example:
Parameters
on_start (Optional[Listener]) –
on_end (Optional[Listener]) –
on_error (Optional[Listener]) –
Return type
Runnable[Input, Output]
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.
Example:
Parameters
retry_if_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on
wait_exponential_jitter (bool) – Whether to add jitter to the wait time
between retries
stop_after_attempt (int) – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
Return type
Runnable[Input, Output] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
1c4dafa708a3-23 | 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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.you.YouRetriever.html |
3362d7d9fe13-0 | langchain.retrievers.ensemble.unique_by_key¶
langchain.retrievers.ensemble.unique_by_key(iterable: Iterable[T], key: Callable[[T], H]) → Iterator[T][source]¶
Parameters
iterable (Iterable[T]) –
key (Callable[[T], H]) –
Return type
Iterator[T] | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.unique_by_key.html |
c0c44cde3240-0 | langchain_experimental.retrievers.vector_sql_database.VectorSQLDatabaseChainRetriever¶
class langchain_experimental.retrievers.vector_sql_database.VectorSQLDatabaseChainRetriever[source]¶
Bases: BaseRetriever
Retriever that uses Vector SQL Database.
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 page_content_key: str = 'content'¶
column name for page content of documents
param sql_db_chain: VectorSQLDatabaseChain [Required]¶
SQL Database Chain
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]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_experimental.retrievers.vector_sql_database.VectorSQLDatabaseChainRetriever.html |
c0c44cde3240-1 | 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: List[Input], config: Optional[Union[RunnableConfig, List[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 (List[Input]) –
config (Optional[Union[RunnableConfig, List[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]¶
Asynchronously get documents relevant to a query.
: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.
Parameters
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.
query (str) –
callbacks (Callbacks) –
tags (Optional[List[str]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_experimental.retrievers.vector_sql_database.VectorSQLDatabaseChainRetriever.html |
c0c44cde3240-2 | callbacks (Callbacks) –
tags (Optional[List[str]]) –
run_name (Optional[str]) –
kwargs (Any) –
Returns
List of relevant documents
Return type
List[Document]
async ainvoke(input: str, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶
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.
Parameters
input (str) –
config (Optional[RunnableConfig]) –
kwargs (Any) –
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
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)
print(chain_with_assign.input_schema.schema())
# {'title': 'PromptInput', 'type': 'object', 'properties': | https://api.python.langchain.com/en/latest/retrievers/langchain_experimental.retrievers.vector_sql_database.VectorSQLDatabaseChainRetriever.html |
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