id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
4c4f2e5a6fa5-6 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.BaseThoughtGenerationStrategy.html |
4c4f2e5a6fa5-7 | Generate LLM result from inputs.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bo... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.BaseThoughtGenerationStrategy.html |
4c4f2e5a6fa5-8 | **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], Dict[str, Any]]¶
Call predict and then parse the result... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.BaseThoughtGenerationStrategy.html |
4c4f2e5a6fa5-9 | Convenience method for executing chain.
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 – If the chain expect... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.BaseThoughtGenerationStrategy.html |
4c4f2e5a6fa5-10 | Example
chain.save(file_path="path/chain.yaml")
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¶
stream(input: Input, config: Optiona... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.BaseThoughtGenerationStrategy.html |
728722c4d2f2-0 | langchain_experimental.tot.thought.ThoughtValidity¶
class langchain_experimental.tot.thought.ThoughtValidity(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
VALID_INTERMEDIATE = 0¶
VALID_FINAL = 1¶
INVALID = 2¶ | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought.ThoughtValidity.html |
3d8b3ba792a9-0 | langchain_experimental.tot.memory.ToTDFSMemory¶
class langchain_experimental.tot.memory.ToTDFSMemory(stack: Optional[List[Thought]] = None)[source]¶
Memory for the Tree of Thought (ToT) chain. Implemented as a stack of
thoughts. This allows for a depth first search (DFS) of the ToT.
Attributes
level
Return the current ... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.memory.ToTDFSMemory.html |
d2d95663ad72-0 | langchain_experimental.tot.prompts.JSONListOutputParser¶
class langchain_experimental.tot.prompts.JSONListOutputParser[source]¶
Bases: BaseOutputParser
Class to parse the output of a PROPOSE_PROMPT response.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the in... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.JSONListOutputParser.html |
d2d95663ad72-1 | Bind arguments to a Runnable, returning a new Runnable.
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... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.JSONListOutputParser.html |
d2d95663ad72-2 | 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[Cal... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.JSONListOutputParser.html |
d2d95663ad72-3 | Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – String output of a language model.
prompt – Input PromptValue.
Returns
Str... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.JSONListOutputParser.html |
d2d95663ad72-4 | Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable. | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.JSONListOutputParser.html |
ed4412c73b60-0 | langchain_experimental.tot.thought_generation.SampleCoTStrategy¶
class langchain_experimental.tot.thought_generation.SampleCoTStrategy[source]¶
Bases: BaseThoughtGenerationStrategy
Sample thoughts from a Chain-of-Thought (CoT) prompt.
This strategy works better when the thought space is rich, such as when each
thought ... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-1 | This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param output_key: str = 'text'¶
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defau... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-2 | will be printed to the console. Defaults to langchain.verbose value.
__call__(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, include_... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-3 | Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async abatch(inputs: List[Input], config:... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-4 | metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[A... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-5 | Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCal... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-6 | question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output]¶
batch(inputs: List[Inpu... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-7 | deep – set to True to make a deep copy of the model
Returns
new model instance
create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]¶
Create outputs from response.
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parame... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-8 | 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().
next_thought(problem_description: str, thoughts_path: Tuple[str, ...] = (), **kwargs: Any) → str[source]¶
Generate the next... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-9 | 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.
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... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-10 | 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.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The c... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
ed4412c73b60-11 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Ty... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.thought_generation.SampleCoTStrategy.html |
1d3d60296b05-0 | langchain_experimental.tot.prompts.CheckerOutputParser¶
class langchain_experimental.tot.prompts.CheckerOutputParser[source]¶
Bases: BaseOutputParser
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.
async aba... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.CheckerOutputParser.html |
1d3d60296b05-1 | 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
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.CheckerOutputParser.html |
1d3d60296b05-2 | 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[Cal... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.CheckerOutputParser.html |
1d3d60296b05-3 | Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – String output of a language model.
prompt – Input PromptValue.
Returns
Str... | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.CheckerOutputParser.html |
1d3d60296b05-4 | Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable. | https://api.python.langchain.com/en/latest/tot/langchain_experimental.tot.prompts.CheckerOutputParser.html |
18f2363f6050-0 | 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 ... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.ListStepContainer.html |
18f2363f6050-1 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.ListStepContainer.html |
18f2363f6050-2 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.ListStepContainer.html |
8c5f780b6056-0 | 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 ... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
8c5f780b6056-1 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
8c5f780b6056-2 | 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 chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Sho... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
8c5f780b6056-3 | these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvok... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
8c5f780b6056-4 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("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' s... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
8c5f780b6056-5 | 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 creating
the new model: you should trust this data
deep – set to True to make a deep co... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
8c5f780b6056-6 | classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
8c5f780b6056-7 | 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 – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this cha... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
8c5f780b6056-8 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedN... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
7d3127e9c98d-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 ... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.BasePlanner.html |
7d3127e9c98d-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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.BasePlanner.html |
7d3127e9c98d-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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.BasePlanner.html |
2447bd698b3f-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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
2447bd698b3f-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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
2447bd698b3f-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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
6b98c7d800c0-0 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.Step.html |
6b98c7d800c0-1 | 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,... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.Step.html |
00edb9209016-0 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.StepResponse.html |
00edb9209016-1 | 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,... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.StepResponse.html |
f267fc4d0e53-0 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
f267fc4d0e53-1 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
f267fc4d0e53-2 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
e8c17f16033f-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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.executors.agent_executor.load_agent_executor.html |
785950198ba9-0 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.BaseStepContainer.html |
785950198ba9-1 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.BaseStepContainer.html |
785950198ba9-2 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.BaseStepContainer.html |
68e58c6e8d36-0 | langchain_experimental.plan_and_execute.schema.Plan¶
class langchain_experimental.plan_and_execute.schema.Plan[source]¶
Bases: BaseModel
Plan.
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 steps: Lis... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.Plan.html |
68e58c6e8d36-1 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.Plan.html |
68e58c6e8d36-2 | 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¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.Plan.html |
188d22081311-0 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.load_chat_planner.html |
0af654e7256c-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.
Create a new model by parsing and validating input data from keyword arguments.
Raises Val... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
0af654e7256c-1 | Bind arguments to a Runnable, returning a new Runnable.
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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
0af654e7256c-2 | 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[Cal... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
0af654e7256c-3 | The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – String output of a language model.
prompt – Input PromptValue.
Returns
Structured output
classmethod schema(by_alias: bool = True, ref_templa... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
0af654e7256c-4 | eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable. | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
9792b7199e1a-0 | langchain_experimental.plan_and_execute.schema.PlanOutputParser¶
class langchain_experimental.plan_and_execute.schema.PlanOutputParser[source]¶
Bases: BaseOutputParser
Plan output parser.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be p... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
9792b7199e1a-1 | 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
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
9792b7199e1a-2 | 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[Cal... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
9792b7199e1a-3 | The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – String output of a language model.
prompt – Input PromptValue.
Returns
Structured output
classmethod schema(by_alias: bool = True, ref_templa... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
9792b7199e1a-4 | eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable. | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.PlanOutputParser.html |
d2d47bdb0755-0 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.LLMPlanner.html |
d2d47bdb0755-1 | 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... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.LLMPlanner.html |
d2d47bdb0755-2 | 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, ... | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.base.LLMPlanner.html |
a8216c3da165-0 | langchain_experimental.cpal.constants.Constant¶
class langchain_experimental.cpal.constants.Constant(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enum for constants used in the CPAL.
narrative_input = 'narrative_input'¶
chain_answer = 'chain_answer'¶
chain_data = 'chain_... | https://api.python.langchain.com/en/latest/cpal/langchain_experimental.cpal.constants.Constant.html |
730f221f8f44-0 | langchain_experimental.sql.base.SQLDatabaseChain¶
class langchain_experimental.sql.base.SQLDatabaseChain[source]¶
Bases: Chain
Chain for interacting with SQL Database.
Example
from langchain_experimental.sql import SQLDatabaseChain
from langchain import OpenAI, SQLDatabase
db = SQLDatabase(...)
db_chain = SQLDatabaseCh... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-1 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param prompt: Optional[BasePromptTemplate] = None¶
[Deprecated] Prompt to use to translate natural language to SQL.
param query_checker_prompt: Optional[BasePromptTemplate] = ... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-2 | Execute the chain.
Parameters
inputs – Dictionary of 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.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys gen... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-3 | only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by thi... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-4 | 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 – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this cha... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-5 | Bind arguments to a Runnable, returning a new Runnable.
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... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-6 | classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Opti... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-7 | Returns
A dictionary of all inputs, including those added by the chain’s memory.
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 – Dictionary of chain inputs, ... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-8 | directly as keyword arguments.
Returns
The chain output.
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:
... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
730f221f8f44-9 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseChain.html |
ebaf2bac1465-0 | langchain_experimental.sql.base.SQLDatabaseSequentialChain¶
class langchain_experimental.sql.base.SQLDatabaseSequentialChain[source]¶
Bases: Chain
Chain for querying SQL database that is a sequential chain.
The chain is as follows:
1. Based on the query, determine which tables to use.
2. Based on those tables, call the... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-1 | You can use these to eg identify a specific instance of a chain with its use case.
param return_intermediate_steps: bool = False¶
param sql_chain: SQLDatabaseChain [Required]¶
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with ea... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-2 | 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 chain. Defaults to None
include_run_info – Whether to include run ... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-3 | 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 chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Sho... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-4 | **kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we ha... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-5 | 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... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-6 | # -> {“_type”: “foo”, “verbose”: False, …}
classmethod from_llm(llm: BaseLanguageModel, database: SQLDatabase, query_prompt: BasePromptTemplate = PromptTemplate(input_variables=['input', 'table_info', 'dialect', 'top_k'], output_parser=None, partial_variables={}, template='Given an input question, first create a syntac... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-7 | Load the necessary chains.
classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool =... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-8 | Returns
A dictionary of all inputs, including those added by the chain’s memory.
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 – Dictionary of chain inputs, ... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-9 | directly as keyword arguments.
Returns
The chain output.
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:
... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
ebaf2bac1465-10 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/sql/langchain_experimental.sql.base.SQLDatabaseSequentialChain.html |
013143b4ddd1-0 | langchain.model_laboratory.ModelLaboratory¶
class langchain.model_laboratory.ModelLaboratory(chains: Sequence[Chain], names: Optional[List[str]] = None)[source]¶
Experiment with different models.
Initialize with chains to experiment with.
Parameters
chains – list of chains to experiment with.
Methods
__init__(chains[, ... | https://api.python.langchain.com/en/latest/model_laboratory/langchain.model_laboratory.ModelLaboratory.html |
0b5c55164a55-0 | langchain_experimental.generative_agents.generative_agent.GenerativeAgent¶
class langchain_experimental.generative_agents.generative_agent.GenerativeAgent[source]¶
Bases: BaseModel
An Agent as a character with memory and innate characteristics.
Create a new model by parsing and validating input data from keyword argume... | https://api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.generative_agent.GenerativeAgent.html |
0b5c55164a55-1 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny... | https://api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.generative_agent.GenerativeAgent.html |
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