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be4901f61e07-19 | 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
create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]¶
Create outputs from response.
Parameters
llm_result (LLMResult) –
Return type
List[Dict[str, Any]]
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs (Any) – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Return type
Dict
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, verbose: bool = True) → LLMChain[source]¶
Get the response parser.
Parameters
llm (BaseLanguageModel) –
verbose (bool) –
Return type
LLMChain
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
Parameters
llm (BaseLanguageModel) –
template (str) –
Return type
LLMChain
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-20 | Generate LLM result from inputs.
Parameters
input_list (List[Dict[str, Any]]) –
run_manager (Optional[CallbackManagerForChainRun]) –
Return type
LLMResult
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
methods will have a dynamic output schema that depends on which | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-21 | 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]
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Transform a single input into an output. Override to implement.
Parameters
input (Dict[str, Any]) – 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
Dict[str, Any]
classmethod is_lc_serializable() → bool¶
Is this class serializable?
Return type
bool | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-22 | 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
from langchain_core.runnables import RunnableLambda
def _lambda(x: int) -> int:
return x + 1 | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-23 | 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]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]} | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-24 | 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:
return x + 1
def mul_two(x: int) -> int:
return x * 2
runnable_1 = RunnableLambda(add_one) | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-25 | 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]
predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
Format prompt with kwargs and pass to LLM.
Parameters
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to pass to LLMChain
**kwargs (Any) – Keys to pass to prompt template.
Returns
Completion from LLM.
Return type
str
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, Any]]¶
Call predict and then parse the results.
Parameters
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –
kwargs (Any) –
Return type
Union[str, List[str], Dict[str, Any]]
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-26 | Prepare chain inputs, including adding inputs from memory.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
Return type
Dict[str, str]
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs (Dict[str, str]) – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs (Dict[str, str]) – Dictionary of initial chain outputs.
return_only_outputs (bool) – Whether to only return the chain outputs. If False,
inputs are also added to the final outputs.
Returns
A dict of the final chain outputs.
Return type
Dict[str, str]
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
Parameters
input_list (List[Dict[str, Any]]) –
run_manager (Optional[CallbackManagerForChainRun]) –
Return type
Tuple[List[PromptValue], Optional[List[str]]]
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
[Deprecated] Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-27 | The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args (Any) – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags (Optional[List[str]]) – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs (Any) – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
metadata (Optional[Dict[str, Any]]) –
**kwargs –
Returns
The chain output.
Return type
Any
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
Notes
Deprecated since version langchain==0.1.0: Use invoke instead.
save(file_path: Union[Path, str]) → None¶
Save the chain. | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-28 | save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path (Union[Path, str]) – Path to file to save the chain to.
Return type
None
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
Parameters
input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
Return type
Iterator[Output]
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
Serialize the runnable to JSON.
Return type
Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented¶
Return type
SerializedNotImplemented
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated. | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-29 | 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_alisteners(*, on_start: Optional[AsyncListener] = None, on_end: Optional[AsyncListener] = None, on_error: Optional[AsyncListener] = None) → Runnable[Input, Output]¶
Bind asynchronous lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Asynchronously called before the runnable starts running.
on_end: Asynchronously called after the runnable finishes running.
on_error: Asynchronously called if the runnable throws an error.
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[AsyncListener]) –
on_end (Optional[AsyncListener]) –
on_error (Optional[AsyncListener]) –
Return type
Runnable[Input, Output]
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] | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-30 | 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/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-31 | Return type
RunnableWithFallbacksT[Input, Output]
with_listeners(*, on_start: Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = None, on_end: Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = None, on_error: Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = 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:
from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run
import time
def test_runnable(time_to_sleep : int):
time.sleep(time_to_sleep)
def fn_start(run_obj: Run):
print("start_time:", run_obj.start_time)
def fn_end(run_obj: Run):
print("end_time:", run_obj.end_time)
chain = RunnableLambda(test_runnable).with_listeners(
on_start=fn_start,
on_end=fn_end
)
chain.invoke(2)
Parameters
on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) –
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-32 | on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) –
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:
from langchain_core.runnables import RunnableLambda
count = 0
def _lambda(x: int) -> None:
global count
count = count + 1
if x == 1:
raise ValueError("x is 1")
else:
pass
runnable = RunnableLambda(_lambda)
try:
runnable.with_retry(
stop_after_attempt=2,
retry_if_exception_type=(ValueError,),
).invoke(1)
except ValueError:
pass
assert (count == 2)
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]]) – | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
be4901f61e07-33 | 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/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
19f2a0535297-0 | langchain_experimental.autonomous_agents.autogpt.prompt_generator.PromptGenerator¶
class langchain_experimental.autonomous_agents.autogpt.prompt_generator.PromptGenerator[source]¶
Generator of custom prompt strings.
Does this based on constraints, commands, resources, and performance evaluations.
Initialize the PromptGenerator object.
Starts with empty lists of constraints, commands, resources,
and performance evaluations.
Methods
__init__()
Initialize the PromptGenerator object.
add_constraint(constraint)
Add a constraint to the constraints list.
add_performance_evaluation(evaluation)
Add a performance evaluation item to the performance_evaluation list.
add_resource(resource)
Add a resource to the resources list.
add_tool(tool)
generate_prompt_string()
Generate a prompt string.
__init__() → None[source]¶
Initialize the PromptGenerator object.
Starts with empty lists of constraints, commands, resources,
and performance evaluations.
Return type
None
add_constraint(constraint: str) → None[source]¶
Add a constraint to the constraints list.
Parameters
constraint (str) – The constraint to be added.
Return type
None
add_performance_evaluation(evaluation: str) → None[source]¶
Add a performance evaluation item to the performance_evaluation list.
Parameters
evaluation (str) – The evaluation item to be added.
Return type
None
add_resource(resource: str) → None[source]¶
Add a resource to the resources list.
Parameters
resource (str) – The resource to be added.
Return type
None
add_tool(tool: BaseTool) → None[source]¶
Parameters
tool (BaseTool) –
Return type
None
generate_prompt_string() → str[source]¶
Generate a prompt string.
Returns
The generated prompt string.
Return type
str | https://api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt_generator.PromptGenerator.html |
b9eff7b86a17-0 | langchain_google_community.vertex_rank.VertexAIRank¶
class langchain_google_community.vertex_rank.VertexAIRank[source]¶
Bases: BaseDocumentCompressor
Initializes the Vertex AI Ranker with configurable parameters.
Inherits from BaseDocumentCompressor for document processing
and validation features, respectively.
project_id¶
Google Cloud project ID
Type
str
location_id¶
Location ID for the ranking service.
Type
str
ranking_config¶
Required. The name of the rank service config, such as default_config.
It is set to default_config by default if unspecified.
Type
str
model¶
The identifier of the model to use. It is one of:
semantic-ranker-512@latest: Semantic ranking model
with maximum input token size 512.
It is set to semantic-ranker-512@latest by default if unspecified.
Type
str
top_n¶
The number of results to return. If this is
unset or no bigger than zero, returns all
results.
Type
int
ignore_record_details_in_response¶
If true, the response will contain only
record ID and score. By default, it is false,
the response will contain record details.
Type
bool
id_field¶
Specifies a unique document metadata field
Type
Optional[str]
to use as an id.
title_field¶
Specifies the document metadata field
Type
Optional[str]
to use as title.
credentials¶
Google Cloud credentials object.
Type
Optional[Credentials]
credentials_path¶
Path to the Google Cloud service
Type
Optional[str]
account credentials file.
Constructor for VertexAIRanker, allowing for specification of
ranking configuration and initialization of Google Cloud services.
The parameters accepted are the same as the attributes listed above.
param client: Any = None¶
param credentials: Optional[Credentials] = None¶ | https://api.python.langchain.com/en/latest/vertex_rank/langchain_google_community.vertex_rank.VertexAIRank.html |
b9eff7b86a17-1 | param client: Any = None¶
param credentials: Optional[Credentials] = None¶
param credentials_path: Optional[str] = None¶
param id_field: Optional[str] = None¶
param ignore_record_details_in_response: bool = False¶
param location_id: str = 'global'¶
param model: str = 'semantic-ranker-512@latest'¶
param project_id: str = None¶
param ranking_config: str = 'default_config'¶
param title_field: Optional[str] = None¶
param top_n: int = 10¶
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]¶
Compresses documents using Vertex AI’s rerank API.
Parameters
documents (Sequence[Document]) – List of Document instances to compress.
query (str) – Query string to use for compressing the documents.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to execute during compression (not used here).
Returns
A list of Document instances, compressed.
Return type
Sequence[Document]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. | https://api.python.langchain.com/en/latest/vertex_rank/langchain_google_community.vertex_rank.VertexAIRank.html |
b9eff7b86a17-2 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/vertex_rank/langchain_google_community.vertex_rank.VertexAIRank.html |
b9eff7b86a17-3 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode | https://api.python.langchain.com/en/latest/vertex_rank/langchain_google_community.vertex_rank.VertexAIRank.html |
b9eff7b86a17-4 | dumps_kwargs (Any) –
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters | https://api.python.langchain.com/en/latest/vertex_rank/langchain_google_community.vertex_rank.VertexAIRank.html |
b9eff7b86a17-5 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/vertex_rank/langchain_google_community.vertex_rank.VertexAIRank.html |
c5316bb1aca0-0 | langchain_robocorp.toolkits.RunDetailsCallbackHandler¶
class langchain_robocorp.toolkits.RunDetailsCallbackHandler(run_details: dict)[source]¶
Callback handler to add run details to the run.
Initialize the callback handler.
Parameters
run_details (dict) – Run details.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__(run_details)
Initialize the callback handler.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, parent_run_id])
Run when chain ends running.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id[, parent_run_id])
Run when LLM ends running.
on_llm_error(error, *, run_id[, parent_run_id])
Run when LLM errors. :param error: The error that occurred. :type error: BaseException :param kwargs: Additional keyword arguments. - response (LLMResult): The response which was generated before the error occurred. :type kwargs: Any. | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.RunDetailsCallbackHandler.html |
c5316bb1aca0-1 | on_llm_new_token(token, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, **kwargs)
Run when tool starts running.
__init__(run_details: dict) → None[source]¶
Initialize the callback handler.
Parameters
run_details (dict) – Run details.
Return type
None
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
Parameters
action (AgentAction) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.RunDetailsCallbackHandler.html |
c5316bb1aca0-2 | Run on agent end.
Parameters
finish (AgentFinish) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain errors.
Parameters
error (BaseException) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) –
inputs (Dict[str, Any]) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
tags (Optional[List[str]]) –
metadata (Optional[Dict[str, Any]]) –
kwargs (Any) –
Return type
Any | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.RunDetailsCallbackHandler.html |
c5316bb1aca0-3 | kwargs (Any) –
Return type
Any
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
ATTENTION: This method is called for chat models. If you’re implementinga handler for a non-chat model, you should use on_llm_start instead.
Parameters
serialized (Dict[str, Any]) –
messages (List[List[BaseMessage]]) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
tags (Optional[List[str]]) –
metadata (Optional[Dict[str, Any]]) –
kwargs (Any) –
Return type
Any
on_llm_end(response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM ends running.
Parameters
response (LLMResult) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM errors.
:param error: The error that occurred.
:type error: BaseException
:param kwargs: Additional keyword arguments.
response (LLMResult): The response which was generated beforethe error occurred.
Parameters
error (BaseException) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) – | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.RunDetailsCallbackHandler.html |
c5316bb1aca0-4 | parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) – The new token.
chunk (GenerationChunk | ChatGenerationChunk) – The new generated chunk,
information. (containing content and other) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when LLM starts running.
ATTENTION: This method is called for non-chat models (regular LLMs). Ifyou’re implementing a handler for a chat model,
you should use on_chat_model_start instead.
Parameters
serialized (Dict[str, Any]) –
prompts (List[str]) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
tags (Optional[List[str]]) –
metadata (Optional[Dict[str, Any]]) –
kwargs (Any) –
Return type
Any
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
Parameters | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.RunDetailsCallbackHandler.html |
c5316bb1aca0-5 | Run when Retriever ends running.
Parameters
documents (Sequence[Document]) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
Parameters
error (BaseException) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
Parameters
serialized (Dict[str, Any]) –
query (str) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
tags (Optional[List[str]]) –
metadata (Optional[Dict[str, Any]]) –
kwargs (Any) –
Return type
Any
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
Parameters
retry_state (RetryCallState) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.RunDetailsCallbackHandler.html |
c5316bb1aca0-6 | Run on arbitrary text.
Parameters
text (str) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_tool_end(output: Any, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool ends running.
Parameters
output (Any) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool errors.
Parameters
error (BaseException) –
run_id (UUID) –
parent_run_id (Optional[UUID]) –
kwargs (Any) –
Return type
Any
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) –
input_str (str) –
kwargs (Any) –
Return type
None | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.RunDetailsCallbackHandler.html |
dc11471acf73-0 | langchain_robocorp.toolkits.ActionServerToolkit¶
class langchain_robocorp.toolkits.ActionServerToolkit[source]¶
Bases: BaseModel
Toolkit exposing Robocorp Action Server provided actions as individual tools.
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 additional_headers: dict [Optional]¶
Additional headers to be passed to the Action Server
param api_key: str = ''¶
Action Server request API key
param report_trace: bool = False¶
Enable reporting Langsmith trace to Action Server runs
param url: str [Required]¶
Action Server URL
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/toolkits/langchain_robocorp.toolkits.ActionServerToolkit.html |
dc11471acf73-1 | 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_tools(llm: Optional[BaseChatModel] = None, callback_manager: Optional[CallbackManager] = None) → List[BaseTool][source]¶
Get Action Server actions as a toolkit
Parameters
llm (Optional[BaseChatModel]) – Optionally pass a model to return single input tools
callback_manager (Optional[CallbackManager]) – Callback manager to be passed to tools
Return type
List[BaseTool] | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerToolkit.html |
dc11471acf73-2 | Return type
List[BaseTool]
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 parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerToolkit.html |
dc11471acf73-3 | Parameters
obj (Any) –
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerToolkit.html |
ed24743d6d42-0 | langchain_robocorp.toolkits.ActionServerRequestTool¶
class langchain_robocorp.toolkits.ActionServerRequestTool[source]¶
Bases: BaseTool
Requests POST tool with LLM-instructed extraction of truncated responses.
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 action_request: Callable[[str], str] [Required]¶
Action request execution
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Useful to make requests to Action Server API'¶
Tool description.
param endpoint: str [Required]¶
“Action API endpoint
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param handle_validation_error: Optional[Union[bool, str, Callable[[ValidationError], str]]] = False¶
Handle the content of the ValidationError thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param tags: Optional[List[str]] = None¶ | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-1 | param tags: Optional[List[str]] = None¶
Optional list of tags associated with the tool. Defaults to None
These tags will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
[Deprecated] Make tool callable.
Notes
Deprecated since version langchain-core==0.1.47: Use invoke instead.
Parameters
tool_input (str) –
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –
Return type
str
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
Parameters
inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –
Return type
List[Output]
async abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶ | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-2 | Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) –
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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 (Union[str, Dict]) –
config (Optional[RunnableConfig]) –
kwargs (Any) –
Return type
Any
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, run_id: Optional[UUID] = None, config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.
Parameters
tool_input (Union[str, Dict]) –
verbose (Optional[bool]) –
start_color (Optional[str]) –
color (Optional[str]) –
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –
tags (Optional[List[str]]) –
metadata (Optional[Dict[str, Any]]) – | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-3 | metadata (Optional[Dict[str, Any]]) –
run_name (Optional[str]) –
run_id (Optional[UUID]) –
config (Optional[RunnableConfig]) –
kwargs (Any) –
Return type
Any
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 | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-4 | 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', 'v2'], 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. | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-5 | parent runnable is assigned its own unique ID.
parent_ids: List[str] - The IDs of the parent runnables thatgenerated the event. The root runnable will have an empty list.
The order of the parent IDs is from the root to the immediate parent.
Only available for v2 version of the API. The v1 version of the API
will return an empty list.
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.
ATTENTION This reference table is for the V2 version of the schema.
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]]}
AIMessageChunk(content=”hello world”)
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_end
some_tool
{“x”: 1, “y”: “2”} | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-6 | some_tool
{“x”: 1, “y”: “2”}
on_retriever_start
[retriever name]
{“query”: “hello”}
on_retriever_end
[retriever name]
{“query”: “hello”}
[Document(…), ..]
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 = [
event async for event in chain.astream_events("hello", version="v2")
]
# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
{
"data": {"input": "hello"},
"event": "on_chain_start",
"metadata": {}, | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-7 | "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', 'v2']) – The version of the schema to use either v2 or v1.
Users should use v2.
v1 is for backwards compatibility and will be deprecated
in 0.4.0.
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 | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-8 | 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.
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. | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-9 | 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]
batch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[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. | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-10 | Run invoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) –
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –
Return type
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 | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-11 | 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 ChatOpenAI
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( | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-12 | 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
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 | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-13 | 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) –
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]¶
The tool’s input schema.
Parameters | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-14 | The tool’s input schema.
Parameters
config (Optional[RunnableConfig]) –
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]
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
Transform a single input into an output. Override to implement.
Parameters
input (Union[str, Dict]) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable. | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-15 | 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
Any
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. | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-16 | 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
pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]¶
Pick keys from the dict output of this runnable.
Pick single key:import json | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-17 | 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]]) –
Return type
RunnableSerializable[Any, Any] | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-18 | 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] | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-19 | name (Optional[str]) –
Return type
RunnableSerializable[Input, Other]
run(tool_input: Union[str, Dict[str, Any]], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, run_id: Optional[UUID] = None, config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
Run the tool.
Parameters
tool_input (Union[str, Dict[str, Any]]) –
verbose (Optional[bool]) –
start_color (Optional[str]) –
color (Optional[str]) –
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –
tags (Optional[List[str]]) –
metadata (Optional[Dict[str, Any]]) –
run_name (Optional[str]) –
run_id (Optional[UUID]) –
config (Optional[RunnableConfig]) –
kwargs (Any) –
Return type
Any
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 | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-20 | 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_alisteners(*, on_start: Optional[AsyncListener] = None, on_end: Optional[AsyncListener] = None, on_error: Optional[AsyncListener] = None) → Runnable[Input, Output]¶
Bind asynchronous lifecycle listeners to a Runnable, returning a new Runnable. | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-21 | Bind asynchronous lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Asynchronously called before the runnable starts running.
on_end: Asynchronously called after the runnable finishes running.
on_error: Asynchronously called if the runnable throws an error.
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[AsyncListener]) –
on_end (Optional[AsyncListener]) –
on_error (Optional[AsyncListener]) –
Return type
Runnable[Input, Output]
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/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-22 | )
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[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = None, on_end: Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = None, on_error: Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]] = 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:
from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run
import time | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-23 | from langchain_core.tracers.schemas import Run
import time
def test_runnable(time_to_sleep : int):
time.sleep(time_to_sleep)
def fn_start(run_obj: Run):
print("start_time:", run_obj.start_time)
def fn_end(run_obj: Run):
print("end_time:", run_obj.end_time)
chain = RunnableLambda(test_runnable).with_listeners(
on_start=fn_start,
on_end=fn_end
)
chain.invoke(2)
Parameters
on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) –
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) –
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) –
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:
from langchain_core.runnables import RunnableLambda
count = 0
def _lambda(x: int) -> None:
global count
count = count + 1
if x == 1:
raise ValueError("x is 1")
else:
pass
runnable = RunnableLambda(_lambda)
try:
runnable.with_retry(
stop_after_attempt=2,
retry_if_exception_type=(ValueError,),
).invoke(1)
except ValueError:
pass
assert (count == 2)
Parameters | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-24 | except ValueError:
pass
assert (count == 2)
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 args: dict¶
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 is_single_input: bool¶
Whether the tool only accepts a single input.
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”}
property output_schema: Type[BaseModel]¶ | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
ed24743d6d42-25 | property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ActionServerRequestTool.html |
6c333f7c273b-0 | langchain_robocorp.toolkits.ToolInputSchema¶
class langchain_robocorp.toolkits.ToolInputSchema[source]¶
Bases: BaseModel
Tool input schema.
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 question: str [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ToolInputSchema.html |
6c333f7c273b-1 | 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/toolkits/langchain_robocorp.toolkits.ToolInputSchema.html |
6c333f7c273b-2 | 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/toolkits/langchain_robocorp.toolkits.ToolInputSchema.html |
6c333f7c273b-3 | 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/toolkits/langchain_robocorp.toolkits.ToolInputSchema.html |
0925bab0498e-0 | langchain_robocorp.toolkits.ToolArgs¶
class langchain_robocorp.toolkits.ToolArgs[source]¶
Tool arguments.
name: str¶
description: str¶
callback_manager: CallbackManager¶ | https://api.python.langchain.com/en/latest/toolkits/langchain_robocorp.toolkits.ToolArgs.html |
b611094d3027-0 | langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings¶
class langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings[source]¶
Bases: BaseModel, Embeddings
TensorflowHub embedding models.
To use, you should have the tensorflow_text python package installed.
Example
from langchain_community.embeddings import TensorflowHubEmbeddings
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
tf = TensorflowHubEmbeddings(model_url=url)
Initialize the tensorflow_hub and tensorflow_text.
param model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3'¶
Model name to use.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
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¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings.html |
b611094d3027-1 | 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) –
exclude_none (bool) –
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Compute doc embeddings using a TensorflowHub embedding model.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings.html |
b611094d3027-2 | Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Compute query embeddings using a TensorflowHub embedding model.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings.html |
b611094d3027-3 | dumps_kwargs (Any) –
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings.html |
b611094d3027-4 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using TensorflowHubEmbeddings¶
TensorFlow Hub | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings.html |
08d2515375ef-0 | langchain_community.embeddings.solar.embed_with_retry¶
langchain_community.embeddings.solar.embed_with_retry(embeddings: SolarEmbeddings, *args: Any, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call.
Parameters
embeddings (SolarEmbeddings) –
args (Any) –
kwargs (Any) –
Return type
Any | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.solar.embed_with_retry.html |
1fe08917acea-0 | langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings¶
class langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings[source]¶
Bases: BaseModel, Embeddings
Client to NVIDIA embeddings models.
Fields:
- model: str, the name of the model to use
- truncate: “NONE”, “START”, “END”, truncate input text if it exceeds the model’s
maximum token length. Default is “NONE”, which raises an error if an input is
too long.
Create a new NVIDIAEmbeddings embedder.
This class provides access to a NVIDIA NIM for embedding. By default, it
connects to a hosted NIM, but can be configured to connect to a local NIM
using the base_url parameter. An API key is required to connect to the
hosted NIM.
Parameters
model (str) – The model to use for embedding.
nvidia_api_key (str) – The API key to use for connecting to the hosted NIM.
api_key (str) – Alternative to nvidia_api_key.
base_url (str) – The base URL of the NIM to connect to.
trucate (str) – “NONE”, “START”, “END”, truncate input text if it exceeds
the model’s context length. Default is “NONE”, which raises
an error if an input is too long.
API Key:
- The recommended way to provide the API key is through the NVIDIA_API_KEY
environment variable.
param base_url: str = 'https://integrate.api.nvidia.com/v1'¶
Base url for model listing an invocation
param max_batch_size: int = 50¶
param model: str = 'NV-Embed-QA'¶
Name of the model to invoke
param model_type: Optional[Literal['passage', 'query']] = None¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings.html |
1fe08917acea-1 | param model_type: Optional[Literal['passage', 'query']] = None¶
(DEPRECATED) The type of text to be embedded.
param truncate: Literal['NONE', 'START', 'END'] = 'NONE'¶
Truncate input text if it exceeds the model’s maximum token length. Default is ‘NONE’, which raises an error if an input is too long.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
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/embeddings/langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings.html |
1fe08917acea-2 | 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
embed_documents(texts: List[str]) → List[List[float]][source]¶
Input pathway for document embeddings.
Parameters
texts (List[str]) –
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Input pathway for query embeddings.
Parameters
text (str) –
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
classmethod get_available_models(**kwargs: Any) → List[Model][source]¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings.html |
1fe08917acea-3 | Model
classmethod get_available_models(**kwargs: Any) → List[Model][source]¶
Get a list of available models that work with NVIDIAEmbeddings.
Parameters
kwargs (Any) –
Return type
List[Model]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
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) – | https://api.python.langchain.com/en/latest/embeddings/langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings.html |
1fe08917acea-4 | encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
property available_models: List[Model]¶
Get a list of available models that work with NVIDIAEmbeddings. | https://api.python.langchain.com/en/latest/embeddings/langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings.html |
4fa9aba23fde-0 | langchain_community.embeddings.laser.LaserEmbeddings¶
class langchain_community.embeddings.laser.LaserEmbeddings[source]¶
Bases: BaseModel, Embeddings
LASER Language-Agnostic SEntence Representations.
LASER is a Python library developed by the Meta AI Research team
and used for creating multilingual sentence embeddings for over 147 languages
as of 2/25/2024
See more documentation at:
* https://github.com/facebookresearch/LASER/
* https://github.com/facebookresearch/LASER/tree/main/laser_encoders
* https://arxiv.org/abs/2205.12654
To use this class, you must install the laser_encoders Python package.
pip install laser_encoders
.. rubric:: Example
from laser_encoders import LaserEncoderPipeline
encoder = LaserEncoderPipeline(lang=”eng_Latn”)
embeddings = encoder.encode_sentences([“Hello”, “World”])
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 lang: Optional[str] = None¶
The language or language code you’d like to use
If empty, this implementation will default
to using a multilingual earlier LASER encoder model (called laser2)
Find the list of supported languages at
https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float] | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html |
4fa9aba23fde-1 | Parameters
text (str) –
Return type
List[float]
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/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html |
4fa9aba23fde-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
embed_documents(texts: List[str]) → List[List[float]][source]¶
Generate embeddings for documents using LASER.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Generate single query text embeddings using LASER.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html |
4fa9aba23fde-3 | Parameters
obj (Any) –
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html |
4fa9aba23fde-4 | Parameters
obj (Any) –
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using LaserEmbeddings¶
Facebook - Meta
LASER Language-Agnostic SEntence Representations Embeddings by Meta AI | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html |
e87e1aa1508d-0 | langchain_community.embeddings.yandex.YandexGPTEmbeddings¶
class langchain_community.embeddings.yandex.YandexGPTEmbeddings[source]¶
Bases: BaseModel, Embeddings
YandexGPT Embeddings models.
To use, you should have the yandexcloud python package installed.
There are two authentication options for the service account
with the ai.languageModels.user role:
You can specify the token in a constructor parameter iam_token
or in an environment variable YC_IAM_TOKEN.
- You can specify the key in a constructor parameter api_key
or in an environment variable YC_API_KEY.
To use the default model specify the folder ID in a parameter folder_id
or in an environment variable YC_FOLDER_ID.
Example
from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", folder_id=<folder-id>)
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: SecretStr = ''¶
Yandex Cloud Api Key for service account
with the ai.languageModels.user role
Constraints
type = string
writeOnly = True
format = password
param disable_request_logging: bool = False¶
YandexGPT API logs all request data by default.
If you provide personal data, confidential information, disable logging.
param doc_model_name: str = 'text-search-doc'¶
Doc model name to use.
param doc_model_uri: str = ''¶
Doc model uri to use.
param folder_id: str = ''¶
Yandex Cloud folder ID
param iam_token: SecretStr = ''¶
Yandex Cloud IAM token for service account
with the ai.languageModels.user role
Constraints
type = string | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.yandex.YandexGPTEmbeddings.html |
e87e1aa1508d-1 | with the ai.languageModels.user role
Constraints
type = string
writeOnly = True
format = password
param max_retries: int = 6¶
Maximum number of retries to make when generating.
param model_name: str = 'text-search-query' (alias 'query_model_name')¶
Query model name to use.
param model_uri: str = '' (alias 'query_model_uri')¶
Query model uri to use.
param model_version: str = 'latest'¶
Model version to use.
param sleep_interval: float = 0.0¶
Delay between API requests
param url: str = 'llm.api.cloud.yandex.net:443'¶
The url of the API.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
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¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.yandex.YandexGPTEmbeddings.html |
e87e1aa1508d-2 | 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) –
exclude_none (bool) –
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed documents using a YandexGPT embeddings models.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.yandex.YandexGPTEmbeddings.html |
e87e1aa1508d-3 | Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed a query using a YandexGPT embeddings models.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.yandex.YandexGPTEmbeddings.html |
e87e1aa1508d-4 | dumps_kwargs (Any) –
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
classmethod validate(value: Any) → Model¶
Parameters | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.yandex.YandexGPTEmbeddings.html |
e87e1aa1508d-5 | Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) –
Return type
Model
Examples using YandexGPTEmbeddings¶
YandexGPT | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.yandex.YandexGPTEmbeddings.html |
2bb449468342-0 | langchain_community.embeddings.ernie.ErnieEmbeddings¶
class langchain_community.embeddings.ernie.ErnieEmbeddings[source]¶
Bases: BaseModel, Embeddings
[Deprecated] Ernie Embeddings V1 embedding models.
Notes
Deprecated since version 0.0.13: Use langchain_community.embeddings.QianfanEmbeddingsEndpoint instead.
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 access_token: Optional[str] = None¶
param chunk_size: int = 16¶
param ernie_api_base: Optional[str] = None¶
param ernie_client_id: Optional[str] = None¶
param ernie_client_secret: Optional[str] = None¶
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) – The list of texts to embed
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
async aembed_query(text: str) → List[float][source]¶
Asynchronous Embed query text.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
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/embeddings/langchain_community.embeddings.ernie.ErnieEmbeddings.html |
2bb449468342-1 | 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/embeddings/langchain_community.embeddings.ernie.ErnieEmbeddings.html |
2bb449468342-2 | exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed search docs.
Parameters
texts (List[str]) – The list of texts to embed
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Embed query text.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ernie.ErnieEmbeddings.html |
2bb449468342-3 | 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
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¶ | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ernie.ErnieEmbeddings.html |
2bb449468342-4 | 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
Examples using ErnieEmbeddings¶
ERNIE | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.ernie.ErnieEmbeddings.html |
afd562a44c33-0 | langchain_community.embeddings.awa.AwaEmbeddings¶
class langchain_community.embeddings.awa.AwaEmbeddings[source]¶
Bases: BaseModel, Embeddings
Embedding documents and queries with Awa DB.
client¶
The AwaEmbedding client.
model¶
The name of the model used for embedding.
Default is “all-mpnet-base-v2”.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param model: str = 'all-mpnet-base-v2'¶
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
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 | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.awa.AwaEmbeddings.html |
afd562a44c33-1 | 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) –
exclude_none (bool) –
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed a list of documents using AwaEmbedding.
Parameters
texts (List[str]) – The list of texts need to be embedded
Returns
List of embeddings, one for each text. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.awa.AwaEmbeddings.html |
afd562a44c33-2 | Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Compute query embeddings using AwaEmbedding.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.awa.AwaEmbeddings.html |
afd562a44c33-3 | dumps_kwargs (Any) –
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) –
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) –
ref_template (unicode) –
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
Return type
unicode
set_model(model_name: str) → None[source]¶
Set the model used for embedding.
The default model used is all-mpnet-base-v2
Parameters
model_name (str) – A string which represents the name of model.
Return type
None | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.awa.AwaEmbeddings.html |
afd562a44c33-4 | Return type
None
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
Examples using AwaEmbeddings¶
AwaDB | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.awa.AwaEmbeddings.html |
edb5a5180289-0 | langchain_core.embeddings.embeddings.Embeddings¶
class langchain_core.embeddings.embeddings.Embeddings[source]¶
Interface for embedding models.
Methods
__init__()
aembed_documents(texts)
Asynchronous Embed search docs.
aembed_query(text)
Asynchronous Embed query text.
embed_documents(texts)
Embed search docs.
embed_query(text)
Embed query text.
__init__()¶
async aembed_documents(texts: List[str]) → List[List[float]][source]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float][source]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
abstract embed_documents(texts: List[str]) → List[List[float]][source]¶
Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
abstract embed_query(text: str) → List[float][source]¶
Embed query text.
Parameters
text (str) –
Return type
List[float]
Examples using Embeddings¶
Elasticsearch
Infinispan | https://api.python.langchain.com/en/latest/embeddings/langchain_core.embeddings.embeddings.Embeddings.html |
70abc908f9ed-0 | langchain_community.embeddings.openvino.OpenVINOEmbeddings¶
class langchain_community.embeddings.openvino.OpenVINOEmbeddings[source]¶
Bases: BaseModel, Embeddings
OpenVINO embedding models.
Example
from langchain_community.embeddings import OpenVINOEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'CPU'}
encode_kwargs = {'normalize_embeddings': True}
ov = OpenVINOEmbeddings(
model_name_or_path=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
Initialize the sentence_transformer.
param encode_kwargs: Dict[str, Any] [Optional]¶
Keyword arguments to pass when calling the encode method of the model.
param model_kwargs: Dict[str, Any] [Optional]¶
Keyword arguments to pass to the model.
param model_name_or_path: str [Required]¶
HuggingFace model id.
param ov_model: Any = None¶
OpenVINO model object.
param show_progress: bool = False¶
Whether to show a progress bar.
param tokenizer: Any = None¶
Tokenizer for embedding model.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
Parameters
texts (List[str]) –
Return type
List[List[float]]
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
Parameters
text (str) –
Return type
List[float]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openvino.OpenVINOEmbeddings.html |
70abc908f9ed-1 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openvino.OpenVINOEmbeddings.html |
70abc908f9ed-2 | include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
Return type
DictStrAny
embed_documents(texts: List[str]) → List[List[float]][source]¶
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts (List[str]) – The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text: str) → List[float][source]¶
Compute query embeddings using a HuggingFace transformer model.
Parameters
text (str) – The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
encode(sentences: Any, batch_size: int = 4, show_progress_bar: bool = False, convert_to_numpy: bool = True, convert_to_tensor: bool = False, mean_pooling: bool = False, normalize_embeddings: bool = True) → Any[source]¶
Computes sentence embeddings.
Parameters
sentences (Any) – the sentences to embed.
batch_size (int) – the batch size used for the computation.
show_progress_bar (bool) – Whether to output a progress bar.
convert_to_numpy (bool) – Whether the output should be a list of numpy vectors.
convert_to_tensor (bool) – Whether the output should be one large tensor.
mean_pooling (bool) – Whether to pool returned vectors.
normalize_embeddings (bool) – Whether to normalize returned vectors.
Returns
By default, a 2d numpy array with shape [num_inputs, output_dimension]. | https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.openvino.OpenVINOEmbeddings.html |
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