id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
bf5a1cd22a32-11 | Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Di... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-12 | Prepare prompts from inputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__c... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-13 | 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 – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definiti... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-14 | Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_liste... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-15 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces speci... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
2f650a7a2d3b-0 | langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain¶
class langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain[source]¶
Bases: LLMChain
Chain generating tasks.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationErr... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-1 | param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-2 | returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – 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.... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-3 | e.g., if the underlying runnable uses an API which supports a batch mode.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-4 | Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, 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 versi... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-5 | Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Ch... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-6 | # -> "The temperature in Boise is..."
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.
async astream_log(input: Any, conf... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-7 | 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.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-8 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-9 | methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-10 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defa... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-11 | Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Di... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-12 | Prepare prompts from inputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__c... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-13 | 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 – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definiti... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-14 | Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_liste... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
2f650a7a2d3b-15 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces speci... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_creation.TaskCreationChain.html |
24df683602da-0 | langchain_experimental.autonomous_agents.autogpt.prompt_generator.get_prompt¶
langchain_experimental.autonomous_agents.autogpt.prompt_generator.get_prompt(tools: List[BaseTool]) → str[source]¶
Generates a prompt string.
It includes various constraints, commands, resources, and performance evaluations.
Returns
The gener... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt_generator.get_prompt.html |
b3a5f6cf05dd-0 | langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser¶
class langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser[source]¶
Bases: BaseAutoGPTOutputParser
Output parser for AutoGPT.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[Ru... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
b3a5f6cf05dd-1 | to be different candidate outputs for a single model input.
Returns
Structured output.
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 str... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
b3a5f6cf05dd-2 | 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.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
b3a5f6cf05dd-3 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
b3a5f6cf05dd-4 | 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 – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input:... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
b3a5f6cf05dd-5 | The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
parse(text: str) → AutoGPTAction[source]¶
Return AutoGPTAction
classmethod parse_file... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
b3a5f6cf05dd-6 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override t... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
b3a5f6cf05dd-7 | fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
b3a5f6cf05dd-8 | The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser.html |
ad453bdae0c8-0 | langchain_experimental.autonomous_agents.hugginggpt.task_planner.Step¶
class langchain_experimental.autonomous_agents.hugginggpt.task_planner.Step(task: str, id: int, dep: List[int], args: Dict[str, str], tool: BaseTool)[source]¶
Methods
__init__(task, id, dep, args, tool)
__init__(task: str, id: int, dep: List[int], a... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.Step.html |
a497bff5e329-0 | langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory¶
class langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory[source]¶
Bases: BaseChatMemory
Memory for AutoGPT.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data c... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory.html |
a497bff5e329-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory.html |
a497bff5e329-2 | The unique identifier is a list of strings that describes the path
to the object.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]¶
Return key-value pairs given the text input to the chain.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.memory.AutoGPTMemory.html |
dff8cb845685-0 | langchain_experimental.autonomous_agents.hugginggpt.task_executor.TaskExecutor¶
class langchain_experimental.autonomous_agents.hugginggpt.task_executor.TaskExecutor(plan: Plan)[source]¶
Load tools to execute tasks.
Methods
__init__(plan)
check_dependency(task)
completed()
describe()
failed()
pending()
run()
update_args... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_executor.TaskExecutor.html |
28e239921655-0 | langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain¶
class langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain[source]¶
Bases: LLMChain
Chain to execute tasks.
Create a new model by parsing and validating input data from keyword arguments.
Raises Validatio... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-1 | param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-2 | returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – 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.... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-3 | e.g., if the underlying runnable uses an API which supports a batch mode.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-4 | Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, 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 versi... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-5 | Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Ch... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-6 | # -> "The temperature in Boise is..."
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.
async astream_log(input: Any, conf... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-7 | 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.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-8 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-9 | methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-10 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defa... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-11 | Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Di... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-12 | Prepare prompts from inputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__c... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-13 | 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 – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definiti... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-14 | Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_liste... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
28e239921655-15 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces speci... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain.html |
30e32d2a16b6-0 | langchain_experimental.autonomous_agents.hugginggpt.task_planner.PlanningOutputParser¶
class langchain_experimental.autonomous_agents.hugginggpt.task_planner.PlanningOutputParser[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.PlanningOutputParser.html |
30e32d2a16b6-1 | Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False,... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.PlanningOutputParser.html |
fd127fce4c26-0 | langchain_experimental.autonomous_agents.hugginggpt.task_planner.Plan¶
class langchain_experimental.autonomous_agents.hugginggpt.task_planner.Plan(steps: List[Step])[source]¶
Methods
__init__(steps)
__init__(steps: List[Step])[source]¶ | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.Plan.html |
c91ea208c010-0 | langchain_experimental.plan_and_execute.planners.base.BasePlanner¶
class langchain_experimental.plan_and_execute.planners.base.BasePlanner[source]¶
Bases: BaseModel
Base planner.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.BasePlanner.html |
c91ea208c010-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.BasePlanner.html |
c91ea208c010-2 | abstract plan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decide what to do.
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.BasePlanner.html |
cd9e549b1f51-0 | langchain_experimental.plan_and_execute.planners.chat_planner.load_chat_planner¶
langchain_experimental.plan_and_execute.planners.chat_planner.load_chat_planner(llm: BaseLanguageModel, system_prompt: str = "Let's first understand the problem and devise a plan to solve the problem. Please output the plan starting with t... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.load_chat_planner.html |
c5e0a224f23d-0 | langchain_experimental.plan_and_execute.schema.BaseStepContainer¶
class langchain_experimental.plan_and_execute.schema.BaseStepContainer[source]¶
Bases: BaseModel
Base step container.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parse... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.BaseStepContainer.html |
c5e0a224f23d-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.BaseStepContainer.html |
c5e0a224f23d-2 | classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmet... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.BaseStepContainer.html |
f3c741a3e2cf-0 | langchain_experimental.plan_and_execute.schema.PlanOutputParser¶
class langchain_experimental.plan_and_execute.schema.PlanOutputParser[source]¶
Bases: BaseOutputParser
Plan output parser.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool =... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
f3c741a3e2cf-1 | to be different candidate outputs for a single model input.
Returns
Structured output.
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 str... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
f3c741a3e2cf-2 | 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.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
f3c741a3e2cf-3 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
f3c741a3e2cf-4 | 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 – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input:... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
f3c741a3e2cf-5 | The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
abstract parse(text: str) → Plan[source]¶
Parse into a plan.
classmethod parse_file(p... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
f3c741a3e2cf-6 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override t... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
f3c741a3e2cf-7 | fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
f3c741a3e2cf-8 | The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
64b99234be11-0 | langchain_experimental.plan_and_execute.executors.base.BaseExecutor¶
class langchain_experimental.plan_and_execute.executors.base.BaseExecutor[source]¶
Bases: BaseModel
Base executor.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parse... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
64b99234be11-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
64b99234be11-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
abstract step(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], Bas... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
b6433dc0b222-0 | langchain_experimental.plan_and_execute.schema.ListStepContainer¶
class langchain_experimental.plan_and_execute.schema.ListStepContainer[source]¶
Bases: BaseStepContainer
List step container.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.ListStepContainer.html |
b6433dc0b222-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.ListStepContainer.html |
b6433dc0b222-2 | classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmet... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.ListStepContainer.html |
d24e78dcd7b5-0 | langchain_experimental.plan_and_execute.schema.Step¶
class langchain_experimental.plan_and_execute.schema.Step[source]¶
Bases: BaseModel
Step.
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 value: str... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.Step.html |
d24e78dcd7b5-1 | Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False,... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.Step.html |
639e7390cf04-0 | langchain_experimental.plan_and_execute.executors.agent_executor.load_agent_executor¶
langchain_experimental.plan_and_execute.executors.agent_executor.load_agent_executor(llm: BaseLanguageModel, tools: List[BaseTool], verbose: bool = False, include_task_in_prompt: bool = False) → ChainExecutor[source]¶
Load an agent ex... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.agent_executor.load_agent_executor.html |
3aac5f77c4a3-0 | langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser¶
class langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser[source]¶
Bases: PlanOutputParser
Planning output parser.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConf... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
3aac5f77c4a3-1 | to be different candidate outputs for a single model input.
Returns
Structured output.
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 str... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
3aac5f77c4a3-2 | 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.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
3aac5f77c4a3-3 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
3aac5f77c4a3-4 | 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 – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input:... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
3aac5f77c4a3-5 | The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
parse(text: str) → Plan[source]¶
Parse into a plan.
classmethod parse_file(path: Unio... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
3aac5f77c4a3-6 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override t... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
3aac5f77c4a3-7 | fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
3aac5f77c4a3-8 | The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
dbb06b635548-0 | langchain_experimental.plan_and_execute.executors.base.ChainExecutor¶
class langchain_experimental.plan_and_execute.executors.base.ChainExecutor[source]¶
Bases: BaseExecutor
Chain executor.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
dbb06b635548-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
dbb06b635548-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
step(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallback... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
764e3cdb8347-0 | langchain_experimental.plan_and_execute.planners.base.LLMPlanner¶
class langchain_experimental.plan_and_execute.planners.base.LLMPlanner[source]¶
Bases: BasePlanner
LLM planner.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to f... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.LLMPlanner.html |
764e3cdb8347-1 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[boo... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.LLMPlanner.html |
764e3cdb8347-2 | plan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decide what to do.
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.LLMPlanner.html |
71ac42396996-0 | langchain_experimental.plan_and_execute.schema.StepResponse¶
class langchain_experimental.plan_and_execute.schema.StepResponse[source]¶
Bases: BaseModel
Step response.
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 vali... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.StepResponse.html |
71ac42396996-1 | Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False,... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.StepResponse.html |
6331aa868e0e-0 | langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute¶
class langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute[source]¶
Bases: Chain
Plan and execute a chain of steps.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
6331aa868e0e-1 | The planner to use.
param step_container: langchain_experimental.plan_and_execute.schema.BaseStepContainer [Optional]¶
The step container to use.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and pas... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
6331aa868e0e-2 | these runtime callbacks will propagate to calls to other objects.
tags – 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.
metadata – Optional metadata associated with the ... | lang/api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
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