Tony20100/langchain_full huggingface dataset:
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 [Required]¶
The value.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
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 executor.
Parameters
llm (BaseLanguageModel) – BaseLanguageModel
tools (List[BaseTool]) – List[BaseTool]
verbose (bool) – bool. Defaults to False.
include_task_in_prompt (bool) – bool. Defaults to False.
Returns
ChainExecutor
Return type
ChainExecutor
langchain_experimental.plan_and_execute.schema.ListStepContainer¶
class langchain_experimental.plan_and_execute.schema.ListStepContainer[source]¶
Bases: BaseStepContainer
Container for List of steps.
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: List[Tuple[Step, StepResponse]] [Optional]¶
The steps.
add_step(step: Step, step_response: StepResponse) → None[source]¶
Add step and step response to the container.
Parameters
step (Step) – 
step_response (StepResponse) – 
Return type
None
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_final_response() → str[source]¶
Return the final response based on steps taken.
Return type
str
get_steps() → List[Tuple[Step, StepResponse]][source]¶
Return type
List[Tuple[Step, StepResponse]]
Return type
List[Tuple[Step, StepResponse]]
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
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 form a valid model.
param llm_chain: LLMChain [Required]¶
The LLM chain to use.
param output_parser: PlanOutputParser [Required]¶
The output parser to use.
param stop: Optional[List] = None¶
The stop list to use.
async aplan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, asynchronously decide what to do.
Parameters
inputs (dict) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Plan
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
plan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decide what to do.
Parameters
inputs (dict) – 
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Plan
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
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 ValidationError if the input data cannot be parsed to form a valid model.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
List[Output]
async abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶
Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
def _lambda(x: int) -> int:
    return x + 1
runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]
Return type
Runnable[List[Input], List[Output]]
parse(text: str) → Plan[source]¶
Parse into a plan.
Parameters
text (str) – 
Return type
Plan
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
parse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, whichis assumed to be the highest-likelihood Generation.
Parameters
result (List[Generation]) – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
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 the header 'Plan:' and then followed by a numbered list of steps. Please make the plan the minimum number of steps required to accurately complete the task. If the task is a question, the final step should almost always be 'Given the above steps taken, please respond to the users original question'. At the end of your plan, say '<END_OF_PLAN>'") → LLMPlanner[source]¶
Load a chat planner.
Parameters
llm (BaseLanguageModel) – Language model.
system_prompt (str) – System prompt.
Returns
LLMPlanner
Return type
LLMPlanner
langchain_core.load.serializable.Serializable¶
class langchain_core.load.serializable.Serializable[source]¶
Bases: BaseModel, ABC
Serializable base class.
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.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
self (Model) – 
Returns
new model instance
Return type
Model
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod get_lc_namespace() → List[str][source]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
Return type
List[str]
classmethod is_lc_serializable() → bool[source]¶
Is this class serializable?
Return type
bool
Is this class serializable?
Return type
bool
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod lc_id() → List[str][source]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
Return type
List[str]
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) –
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
to_json() → Union[SerializedConstructor, SerializedNotImplemented][source]¶
Return type
Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented[source]¶
Return type
SerializedNotImplemented
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
property lc_attributes: Dict¶
langchain_core.load.serializable.SerializedConstructor¶
class langchain_core.load.serializable.SerializedConstructor[source]¶
Serialized constructor.
lc: int¶
id: List[str]¶
name: NotRequired[str]¶
graph: NotRequired[Dict[str, Any]]¶
type: Literal['constructor']¶
kwargs: Dict[str, Any]¶
langchain_core.load.load.loads¶
langchain_core.load.load.loads(text: str, *, secrets_map: Optional[Dict[str, str]] = None, valid_namespaces: Optional[List[str]] = None, secrets_from_env: bool = True) → Any[source]¶
[Beta] Revive a LangChain class from a JSON string.
Equivalent to load(json.loads(text)).
Parameters
text (str) – The string to load.
secrets_map (Optional[Dict[str, str]]) – A map of secrets to load.
valid_namespaces (Optional[List[str]]) – A list of additional namespaces (modules)
to allow to be deserialized.
secrets_from_env (bool) – 
Returns
Revived LangChain objects.
Return type
Any
Notes
langchain_core.load.load.Reviver¶
class langchain_core.load.load.Reviver(secrets_map: Optional[Dict[str, str]] = None, valid_namespaces: Optional[List[str]] = None, secrets_from_env: bool = True)[source]¶
Reviver for JSON objects.
Methods
__init__([secrets_map, valid_namespaces, ...])
Parameters
secrets_map (Optional[Dict[str, str]]) – 
valid_namespaces (Optional[List[str]]) – 
secrets_from_env (bool) – 
__init__(secrets_map: Optional[Dict[str, str]] = None, valid_namespaces: Optional[List[str]] = None, secrets_from_env: bool = True) → None[source]¶
Parameters
secrets_map (Optional[Dict[str, str]]) – 
valid_namespaces (Optional[List[str]]) – 
secrets_from_env (bool) – 
Return type
None
langchain_core.load.load.load¶
langchain_core.load.load.load(obj: Any, *, secrets_map: Optional[Dict[str, str]] = None, valid_namespaces: Optional[List[str]] = None, secrets_from_env: bool = True) → Any[source]¶
[Beta] Revive a LangChain class from a JSON object. Use this if you already
have a parsed JSON object, eg. from json.load or orjson.loads.
Parameters
obj (Any) – The object to load.
secrets_map (Optional[Dict[str, str]]) – A map of secrets to load.
valid_namespaces (Optional[List[str]]) – A list of additional namespaces (modules)
to allow to be deserialized.
secrets_from_env (bool) – 
Returns
Revived LangChain objects.
Return type
Any
Notes
langchain_core.load.serializable.try_neq_default¶
langchain_core.load.serializable.try_neq_default(value: Any, key: str, model: BaseModel) → bool[source]¶
Try to determine if a value is different from the default.
Parameters
value (Any) – The value.
key (str) – The key.
model (BaseModel) – The model.
Returns
Whether the value is different from the default.
Return type
bool
langchain_core.load.serializable.SerializedNotImplemented¶
class langchain_core.load.serializable.SerializedNotImplemented[source]¶
Serialized not implemented.
lc: int¶
id: List[str]¶
name: NotRequired[str]¶
graph: NotRequired[Dict[str, Any]]¶
type: Literal['not_implemented']¶
repr: Optional[str]¶
langchain_community.adapters.openai.aenumerate¶
async langchain_community.adapters.openai.aenumerate(iterable: AsyncIterator[Any], start: int = 0) → AsyncIterator[tuple[int, Any]][source]¶
Async version of enumerate function.
Parameters
iterable (AsyncIterator[Any]) – 
start (int) – 
Return type
AsyncIterator[tuple[int, Any]]
langchain_community.adapters.openai.ChatCompletions¶
class langchain_community.adapters.openai.ChatCompletions[source]¶
Bases: IndexableBaseModel
Chat completions.
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 choices: List[Choice] [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model