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26c725120512-9 | 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]¶
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]) – | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-10 | 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.'
# 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] | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-11 | 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.ensemble.EnsembleRetriever.html |
26c725120512-12 | 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.ensemble.EnsembleRetriever.html |
26c725120512-13 | 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.ensemble.EnsembleRetriever.html |
26c725120512-14 | 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.ensemble.EnsembleRetriever.html |
26c725120512-15 | 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][source]¶
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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-16 | 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]]) –
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. | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-17 | 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.ensemble.EnsembleRetriever.html |
26c725120512-18 | 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.ensemble.EnsembleRetriever.html |
26c725120512-19 | 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]
rank_fusion(query: str, run_manager: CallbackManagerForRetrieverRun, *, config: Optional[RunnableConfig] = None) → List[Document][source]¶
Retrieve the results of the retrievers and use rank_fusion_func to get
the final result.
Parameters
query (str) – The query to search for.
run_manager (CallbackManagerForRetrieverRun) –
config (Optional[RunnableConfig]) –
Returns
A list of reranked documents.
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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-20 | 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]) –
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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-21 | Parameters
value (Any) –
Return type
Model
weighted_reciprocal_rank(doc_lists: List[List[Document]]) → List[Document][source]¶
Perform weighted Reciprocal Rank Fusion on multiple rank lists.
You can find more details about RRF here:
https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf
Parameters
doc_lists (List[List[Document]]) – A list of rank lists, where each rank list contains unique items.
Returns
The final aggregated list of items sorted by their weighted RRFscores in descending order.
Return type
list
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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-22 | )
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
Runnable[Input, Output] | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-23 | 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.retrievers.ensemble.EnsembleRetriever.html |
26c725120512-24 | 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 EnsembleRetriever¶
Ensemble Retriever | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html |
ce8796dfb156-0 | langchain_community.retrievers.databerry.DataberryRetriever¶
class langchain_community.retrievers.databerry.DataberryRetriever[source]¶
Bases: BaseRetriever
Databerry API retriever.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_key: Optional[str] = None¶
param datastore_url: str [Required]¶
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param top_k: Optional[int] = 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]]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-1 | 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]]) –
run_name (Optional[str]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-2 | 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_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-3 | # {'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]
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-4 | 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”)
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-5 | “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
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 = [ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-6 | 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.
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-7 | 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
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-8 | 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]¶
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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-9 | 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.'
# 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]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-10 | 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"}
).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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-11 | 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.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-12 | 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]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-13 | 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]) –
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-14 | 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]]) –
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]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-15 | 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]]) –
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) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-16 | 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
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-17 | 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]")
# -> {"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]]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-18 | 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
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.databerry.DataberryRetriever.html |
ce8796dfb156-19 | 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.databerry.DataberryRetriever.html |
ce8796dfb156-20 | 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.databerry.DataberryRetriever.html |
ce8796dfb156-21 | 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.databerry.DataberryRetriever.html |
ce8796dfb156-22 | 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.databerry.DataberryRetriever.html |
e4f0d3f0923d-0 | langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter¶
class langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter[source]¶
Bases: BaseDocumentCompressor
Document compressor that uses embeddings to drop documents
unrelated to the query.
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 embeddings: Embeddings [Required]¶
Embeddings to use for embedding document contents and queries.
param k: Optional[int] = 20¶
The number of relevant documents to return. Can be set to None, in which case
similarity_threshold must be specified. Defaults to 20.
param similarity_fn: Callable = <function cosine_similarity>¶
Similarity function for comparing documents. Function expected to take as input
two matrices (List[List[float]]) and return a matrix of scores where higher values
indicate greater similarity.
param similarity_threshold: Optional[float] = None¶
Threshold for determining when two documents are similar enough
to be considered redundant. Defaults to None, must be specified if k is set
to None.
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]¶
Filter documents based on similarity of their embeddings to the query.
Parameters | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html |
e4f0d3f0923d-1 | Filter documents based on similarity of their embeddings to the query.
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.
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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html |
e4f0d3f0923d-2 | 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
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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html |
e4f0d3f0923d-3 | 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 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 | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html |
e4f0d3f0923d-4 | 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
value (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html |
8ab36dfaa96e-0 | langchain_community.retrievers.pinecone_hybrid_search.create_index¶
langchain_community.retrievers.pinecone_hybrid_search.create_index(contexts: List[str], index: Any, embeddings: Embeddings, sparse_encoder: Any, ids: Optional[List[str]] = None, metadatas: Optional[List[dict]] = None, namespace: Optional[str] = None) → None[source]¶
Create an index from a list of contexts.
It modifies the index argument in-place!
Parameters
contexts (List[str]) – List of contexts to embed.
index (Any) – Index to use.
embeddings (Embeddings) – Embeddings model to use.
sparse_encoder (Any) – Sparse encoder to use.
ids (Optional[List[str]]) – List of ids to use for the documents.
metadatas (Optional[List[dict]]) – List of metadata to use for the documents.
namespace (Optional[str]) –
Return type
None | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.pinecone_hybrid_search.create_index.html |
77ee96c276cd-0 | langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever¶
class langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever[source]¶
Bases: AzureAISearchRetriever
Azure Cognitive Search service retriever.
This version of the retriever will soon be
depreciated. Please switch to AzureAISearchRetriever
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param aiosession: Optional[aiohttp.ClientSession] = None¶
ClientSession, in case we want to reuse connection for better performance.
param api_key: str = ''¶
API Key. Both Admin and Query keys work, but for reading data it’s
recommended to use a Query key.
param api_version: str = '2023-11-01'¶
API version
param content_key: str = 'content'¶
Key in a retrieved result to set as the Document page_content.
param index_name: str = ''¶
Name of Index inside Azure AI Search service
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param service_name: str = ''¶
Name of Azure AI Search service
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-1 | You can use these to eg identify a specific instance of a retriever with its
use case.
param top_k: Optional[int] = None¶
Number of results to retrieve. Set to None to retrieve all results.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
Parameters
inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –
Return type
List[Output]
async abatch_as_completed(inputs: 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]]] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-2 | 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]]) –
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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-3 | 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':
{'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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-4 | 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]
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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-5 | 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”)
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”} | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-6 | 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
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": {}, | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-7 | "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.
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-8 | 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
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-9 | 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]¶
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]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-10 | 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.'
# 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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-11 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-12 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-13 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-14 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-15 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-16 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-17 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-18 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-19 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-20 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-21 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-22 | 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_community.retrievers.azure_ai_search.AzureCognitiveSearchRetriever.html |
77ee96c276cd-23 | 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.azure_ai_search.AzureCognitiveSearchRetriever.html |
55705ccc8abc-0 | langchain_community.retrievers.zep.SearchScope¶
class langchain_community.retrievers.zep.SearchScope(value)[source]¶
Which documents to search. Messages or Summaries?
messages = 'messages'¶
Search chat history messages.
summary = 'summary'¶
Search chat history summaries. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.zep.SearchScope.html |
5885614c85c7-0 | langchain_community.retrievers.docarray.DocArrayRetriever¶
class langchain_community.retrievers.docarray.DocArrayRetriever[source]¶
Bases: BaseRetriever
DocArray Document Indices retriever.
Currently, it supports 5 backends:
InMemoryExactNNIndex, HnswDocumentIndex, QdrantDocumentIndex,
ElasticDocIndex, and WeaviateDocumentIndex.
Parameters
index – One of the above-mentioned index instances
embeddings – Embedding model to represent text as vectors
search_field – Field to consider for searching in the documents.
Should be an embedding/vector/tensor.
content_field – Field that represents the main content in your document schema.
Will be used as a page_content. Everything else will go into metadata.
search_type – Type of search to perform (similarity / mmr)
filters – Filters applied for document retrieval.
top_k – Number of documents to return
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 content_field: str [Required]¶
param embeddings: Embeddings [Required]¶
param filters: Optional[Any] = None¶
param index: Any = 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 search_field: str [Required]¶
param search_type: SearchType = SearchType.similarity¶
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the retriever. Defaults to None | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-1 | 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: int = 1¶
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]]] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-2 | 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]]) –
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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-3 | 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':
{'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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-4 | 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]
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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-5 | 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”)
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”} | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-6 | 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
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": {}, | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-7 | "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.
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 | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-8 | 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
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. | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-9 | 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]¶
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]) – | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-10 | 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.'
# 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] | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-11 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-12 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-13 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-14 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-15 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-16 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-17 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-18 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-19 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-20 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-21 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-22 | 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_community.retrievers.docarray.DocArrayRetriever.html |
5885614c85c7-23 | 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 DocArrayRetriever¶
DocArray Retriever | https://api.python.langchain.com/en/latest/retrievers/langchain_community.retrievers.docarray.DocArrayRetriever.html |
ab04e907ed98-0 | langchain.retrievers.self_query.base.SelfQueryRetriever¶
class langchain.retrievers.self_query.base.SelfQueryRetriever[source]¶
Bases: BaseRetriever
Retriever that uses a vector store and an LLM to generate
the vector store queries.
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 query_constructor: Runnable[dict, StructuredQuery] [Required] (alias 'llm_chain')¶
The query constructor chain for generating the vector store queries.
llm_chain is legacy name kept for backwards compatibility.
param search_kwargs: dict [Optional]¶
Keyword arguments to pass in to the vector store search.
param search_type: str = 'similarity'¶
The search type to perform on the vector store.
param structured_query_translator: Visitor [Required]¶
Translator for turning internal query language into vectorstore search params.
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 use_original_query: bool = False¶
Use original query instead of the revised new query from LLM
param vectorstore: VectorStore [Required]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.self_query.base.SelfQueryRetriever.html |
ab04e907ed98-1 | param vectorstore: VectorStore [Required]¶
The underlying vector store from which documents will be retrieved.
param verbose: bool = False¶
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]¶ | https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.self_query.base.SelfQueryRetriever.html |
ab04e907ed98-2 | 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.retrievers.self_query.base.SelfQueryRetriever.html |
ab04e907ed98-3 | 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.retrievers.self_query.base.SelfQueryRetriever.html |
ab04e907ed98-4 | 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.retrievers.self_query.base.SelfQueryRetriever.html |
ab04e907ed98-5 | 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.retrievers.self_query.base.SelfQueryRetriever.html |
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