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130b2af82c32-1 | 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 – A list of Generations to be parsed. The Generations are assumed
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 streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex_dict.RegexDictParser.html |
130b2af82c32-2 | input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
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.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] = None, deep: bool = False) → Model¶
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 creating
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(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_orm(obj: Any) → Model¶
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex_dict.RegexDictParser.html |
130b2af82c32-3 | get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
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_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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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) → Dict[str, str][source]¶
Parse the output of an LLM call. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex_dict.RegexDictParser.html |
130b2af82c32-4 | Parse the output of an LLM call.
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: Protocol = None, allow_pickle: bool = False) → 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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
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
Structured output
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex_dict.RegexDictParser.html |
130b2af82c32-5 | Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex_dict.RegexDictParser.html |
130b2af82c32-6 | These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex_dict.RegexDictParser.html |
20334b9a6e56-0 | langchain.output_parsers.regex.RegexParser¶
class langchain.output_parsers.regex.RegexParser[source]¶
Bases: BaseOutputParser
Parse the output of an LLM call using a regex.
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 default_output_key: Optional[str] = None¶
The default key to use for the output.
param output_keys: List[str] [Required]¶
The keys to use for the output.
param regex: str [Required]¶
The regex to use to parse the output.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_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 | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
20334b9a6e56-1 | Parameters
result – A list of Generations to be parsed. The Generations are assumed
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 streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
20334b9a6e56-2 | Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
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.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] = None, deep: bool = False) → Model¶
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 creating
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(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_orm(obj: Any) → Model¶
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”] | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
20334b9a6e56-3 | namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
classmethod is_lc_serializable() → bool[source]¶
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_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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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) → Dict[str, str][source]¶
Parse the output of an LLM call.
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¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
20334b9a6e56-4 | 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¶
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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
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
Structured output
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
20334b9a6e56-5 | to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”} | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
20334b9a6e56-6 | For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
Examples using RegexParser¶
Multi-Agent Simulated Environment: Petting Zoo
Multi-agent decentralized speaker selection
Multi-agent authoritarian speaker selection
Simulated Environment: Gymnasium | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.regex.RegexParser.html |
88e4afc80ec0-0 | langchain.output_parsers.retry.RetryOutputParser¶
class langchain.output_parsers.retry.RetryOutputParser[source]¶
Bases: BaseOutputParser[T]
Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt and the completion to another
LLM, and telling it the completion did not satisfy criteria in the prompt.
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 parser: langchain.schema.output_parser.BaseOutputParser[langchain.output_parsers.retry.T] [Required]¶
The parser to use to parse the output.
param retry_chain: Any = None¶
The LLMChain to use to retry the completion.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html |
88e4afc80ec0-1 | 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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
async aparse_with_prompt(completion: str, prompt_value: PromptValue) → T[source]¶
Parse the output of an LLM call using a wrapped parser.
Parameters
completion – The chain completion to parse.
prompt_value – The prompt to use to parse the completion.
Returns
The parsed completion.
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, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html |
88e4afc80ec0-2 | The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
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.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] = None, deep: bool = False) → Model¶
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 creating
the new model: you should trust this data | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html |
88e4afc80ec0-3 | 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(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_llm(llm: BaseLanguageModel, parser: BaseOutputParser[T], prompt: BasePromptTemplate = PromptTemplate(input_variables=['completion', 'prompt'], template='Prompt:\n{prompt}\nCompletion:\n{completion}\n\nAbove, the Completion did not satisfy the constraints given in the Prompt.\nPlease try again:')) → RetryOutputParser[T][source]¶
classmethod from_orm(obj: Any) → Model¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
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_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(). | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html |
88e4afc80ec0-4 | 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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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(completion: str) → T[source]¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
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: Protocol = None, allow_pickle: bool = False) → 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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt_value: PromptValue) → T[source]¶
Parse the output of an LLM call using a wrapped parser.
Parameters
completion – The chain completion to parse. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html |
88e4afc80ec0-5 | Parameters
completion – The chain completion to parse.
prompt_value – The prompt to use to parse the completion.
Returns
The parsed completion.
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html |
88e4afc80ec0-6 | with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
Examples using RetryOutputParser¶
Retry parser | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html |
05098e362d09-0 | langchain.output_parsers.datetime.DatetimeOutputParser¶
class langchain.output_parsers.datetime.DatetimeOutputParser[source]¶
Bases: BaseOutputParser[datetime]
Parse the output of an LLM call to a datetime.
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 format: str = '%Y-%m-%dT%H:%M:%S.%fZ'¶
The string value that used as the datetime format.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html |
05098e362d09-1 | 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 streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html |
05098e362d09-2 | Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
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.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] = None, deep: bool = False) → Model¶
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 creating
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(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_orm(obj: Any) → Model¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”] | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html |
05098e362d09-3 | namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
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_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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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(response: str) → datetime[source]¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
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¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html |
05098e362d09-4 | 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¶
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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
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
Structured output
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html |
05098e362d09-5 | to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”} | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html |
05098e362d09-6 | For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
Examples using DatetimeOutputParser¶
Fallbacks
Datetime parser | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html |
abac67695a23-0 | langchain.output_parsers.pydantic.PydanticOutputParser¶
class langchain.output_parsers.pydantic.PydanticOutputParser[source]¶
Bases: BaseOutputParser[T]
Parse an output using a pydantic model.
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 pydantic_object: Type[langchain.output_parsers.pydantic.T] [Required]¶
The pydantic model to parse.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html |
abac67695a23-1 | 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 streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html |
abac67695a23-2 | Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
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.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] = None, deep: bool = False) → Model¶
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 creating
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(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_orm(obj: Any) → Model¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”] | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html |
abac67695a23-3 | namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
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_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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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) → T[source]¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
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¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html |
abac67695a23-4 | 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¶
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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
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
Structured output
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html |
abac67695a23-5 | to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”} | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html |
abac67695a23-6 | For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
Examples using PydanticOutputParser¶
Set env var OPENAI_API_KEY or load from a .env file:
MultiQueryRetriever
WebResearchRetriever
Retry parser
Pydantic (JSON) parser | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.pydantic.PydanticOutputParser.html |
c562ad5135e3-0 | langchain.output_parsers.openai_functions.OutputFunctionsParser¶
class langchain.output_parsers.openai_functions.OutputFunctionsParser[source]¶
Bases: BaseGenerationOutputParser[Any]
Parse an output that is one of sets of values.
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 args_only: bool = True¶
Whether to only return the arguments to the function call.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
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 streaming output. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.OutputFunctionsParser.html |
c562ad5135e3-1 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.OutputFunctionsParser.html |
c562ad5135e3-2 | 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, 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 – 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 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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”] | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.OutputFunctionsParser.html |
c562ad5135e3-3 | namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
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_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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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.
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: Protocol = None, allow_pickle: bool = False) → Model¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.OutputFunctionsParser.html |
c562ad5135e3-4 | parse_result(result: List[Generation], *, partial: bool = False) → Any[source]¶
Parse a list of candidate model Generations into a specific format.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.OutputFunctionsParser.html |
c562ad5135e3-5 | Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.OutputFunctionsParser.html |
8c8dd5251a2a-0 | langchain.output_parsers.structured.ResponseSchema¶
class langchain.output_parsers.structured.ResponseSchema[source]¶
Bases: BaseModel
A schema for a response from a structured 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.
param description: str [Required]¶
The description of the schema.
param name: str [Required]¶
The name of the schema.
param type: str = 'string'¶
The type of the response.
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
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 – 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 copy of the model
Returns
new model instance | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.structured.ResponseSchema.html |
8c8dd5251a2a-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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
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, 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().
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: Protocol = None, allow_pickle: bool = False) → Model¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.structured.ResponseSchema.html |
8c8dd5251a2a-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 fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.structured.ResponseSchema.html |
ce6ed4dd5247-0 | langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser¶
class langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser[source]¶
Bases: OutputFunctionsParser
Parse an output as a pydantic object.
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 args_only: bool = True¶
Whether to only return the arguments to the function call.
param pydantic_schema: Union[Type[pydantic.main.BaseModel], Dict[str, Type[pydantic.main.BaseModel]]] [Required]¶
The pydantic schema to parse the output with.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
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]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser.html |
ce6ed4dd5247-1 | Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser.html |
ce6ed4dd5247-2 | 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.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] = None, deep: bool = False) → Model¶
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 creating
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[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.
classmethod from_orm(obj: Any) → Model¶
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser.html |
ce6ed4dd5247-3 | Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
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_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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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.
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¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser.html |
ce6ed4dd5247-4 | 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¶
parse_result(result: List[Generation], *, partial: bool = False) → Any[source]¶
Parse a list of candidate model Generations into a specific format.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser.html |
ce6ed4dd5247-5 | Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser.html |
10f45cd39df6-0 | langchain.output_parsers.list.NumberedListOutputParser¶
class langchain.output_parsers.list.NumberedListOutputParser[source]¶
Bases: ListOutputParser
Parse a numbered list.
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 of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_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 – A list of Generations to be parsed. The Generations are assumed
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. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.NumberedListOutputParser.html |
10f45cd39df6-1 | Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.NumberedListOutputParser.html |
10f45cd39df6-2 | 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.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] = None, deep: bool = False) → Model¶
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 creating
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(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_orm(obj: Any) → Model¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
classmethod is_lc_serializable() → bool¶
Is this class serializable? | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.NumberedListOutputParser.html |
10f45cd39df6-3 | 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_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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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) → List[str][source]¶
Parse the output of an LLM call.
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: Protocol = None, allow_pickle: bool = False) → Model¶
parse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.NumberedListOutputParser.html |
10f45cd39df6-4 | 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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
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
Structured output
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.NumberedListOutputParser.html |
10f45cd39df6-5 | classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.list.NumberedListOutputParser.html |
54c79ef56bb5-0 | langchain.output_parsers.combining.CombiningOutputParser¶
class langchain.output_parsers.combining.CombiningOutputParser[source]¶
Bases: BaseOutputParser
Combine multiple output parsers into one.
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 parsers: List[langchain.schema.output_parser.BaseOutputParser] [Required]¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html |
54c79ef56bb5-1 | 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 streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times. | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html |
54c79ef56bb5-2 | Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
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.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] = None, deep: bool = False) → Model¶
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 creating
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(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_orm(obj: Any) → Model¶
get_format_instructions() → str[source]¶
Instructions on how the LLM output should be formatted.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”] | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html |
54c79ef56bb5-3 | namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
classmethod is_lc_serializable() → bool[source]¶
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_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().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
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) → Dict[str, Any][source]¶
Parse the output of an LLM call.
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¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html |
54c79ef56bb5-4 | 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¶
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 – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
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
Structured output
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: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html |
54c79ef56bb5-5 | to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”} | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html |
54c79ef56bb5-6 | For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/output_parsers/langchain.output_parsers.combining.CombiningOutputParser.html |
c966971fa5b0-0 | langchain.document_loaders.dataframe.BaseDataFrameLoader¶
class langchain.document_loaders.dataframe.BaseDataFrameLoader(data_frame: Any, *, page_content_column: str = 'text')[source]¶
Initialize with dataframe object.
Parameters
data_frame – DataFrame object.
page_content_column – Name of the column containing the page content.
Defaults to “text”.
Methods
__init__(data_frame, *[, page_content_column])
Initialize with dataframe object.
lazy_load()
Lazy load records from dataframe.
load()
Load full dataframe.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(data_frame: Any, *, page_content_column: str = 'text')[source]¶
Initialize with dataframe object.
Parameters
data_frame – DataFrame object.
page_content_column – Name of the column containing the page content.
Defaults to “text”.
lazy_load() → Iterator[Document][source]¶
Lazy load records from dataframe.
load() → List[Document][source]¶
Load full dataframe.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.dataframe.BaseDataFrameLoader.html |
78e90c2acd40-0 | langchain.document_loaders.diffbot.DiffbotLoader¶
class langchain.document_loaders.diffbot.DiffbotLoader(api_token: str, urls: List[str], continue_on_failure: bool = True)[source]¶
Load Diffbot json file.
Initialize with API token, ids, and key.
Parameters
api_token – Diffbot API token.
urls – List of URLs to load.
continue_on_failure – Whether to continue loading other URLs if one fails.
Defaults to True.
Methods
__init__(api_token, urls[, continue_on_failure])
Initialize with API token, ids, and key.
lazy_load()
A lazy loader for Documents.
load()
Extract text from Diffbot on all the URLs and return Documents
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(api_token: str, urls: List[str], continue_on_failure: bool = True)[source]¶
Initialize with API token, ids, and key.
Parameters
api_token – Diffbot API token.
urls – List of URLs to load.
continue_on_failure – Whether to continue loading other URLs if one fails.
Defaults to True.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Extract text from Diffbot on all the URLs and return Documents
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using DiffbotLoader¶
Diffbot | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.diffbot.DiffbotLoader.html |
d43609a6c50c-0 | langchain.document_loaders.blockchain.BlockchainType¶
class langchain.document_loaders.blockchain.BlockchainType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enumerator of the supported blockchains.
ETH_MAINNET = 'eth-mainnet'¶
ETH_GOERLI = 'eth-goerli'¶
POLYGON_MAINNET = 'polygon-mainnet'¶
POLYGON_MUMBAI = 'polygon-mumbai'¶
Examples using BlockchainType¶
Blockchain | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.blockchain.BlockchainType.html |
360ce3d783a7-0 | langchain.document_loaders.azlyrics.AZLyricsLoader¶
class langchain.document_loaders.azlyrics.AZLyricsLoader(web_path: Union[str, Sequence[str]] = '', header_template: Optional[dict] = None, verify_ssl: bool = True, proxies: Optional[dict] = None, continue_on_failure: bool = False, autoset_encoding: bool = True, encoding: Optional[str] = None, web_paths: Sequence[str] = (), requests_per_second: int = 2, default_parser: str = 'html.parser', requests_kwargs: Optional[Dict[str, Any]] = None, raise_for_status: bool = False, bs_get_text_kwargs: Optional[Dict[str, Any]] = None, bs_kwargs: Optional[Dict[str, Any]] = None, session: Any = None)[source]¶
Load AZLyrics webpages.
Initialize loader.
Parameters
web_paths – Web paths to load from.
requests_per_second – Max number of concurrent requests to make.
default_parser – Default parser to use for BeautifulSoup.
requests_kwargs – kwargs for requests
raise_for_status – Raise an exception if http status code denotes an error.
bs_get_text_kwargs – kwargs for beatifulsoup4 get_text
bs_kwargs – kwargs for beatifulsoup4 web page parsing
Attributes
web_path
Methods
__init__([web_path, header_template, ...])
Initialize loader.
aload()
Load text from the urls in web_path async into Documents.
fetch_all(urls)
Fetch all urls concurrently with rate limiting.
lazy_load()
Lazy load text from the url(s) in web_path.
load()
Load webpages into Documents.
load_and_split([text_splitter])
Load Documents and split into chunks.
scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.azlyrics.AZLyricsLoader.html |
360ce3d783a7-1 | scrape([parser])
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls[, parser])
Fetch all urls, then return soups for all results.
__init__(web_path: Union[str, Sequence[str]] = '', header_template: Optional[dict] = None, verify_ssl: bool = True, proxies: Optional[dict] = None, continue_on_failure: bool = False, autoset_encoding: bool = True, encoding: Optional[str] = None, web_paths: Sequence[str] = (), requests_per_second: int = 2, default_parser: str = 'html.parser', requests_kwargs: Optional[Dict[str, Any]] = None, raise_for_status: bool = False, bs_get_text_kwargs: Optional[Dict[str, Any]] = None, bs_kwargs: Optional[Dict[str, Any]] = None, session: Any = None) → None¶
Initialize loader.
Parameters
web_paths – Web paths to load from.
requests_per_second – Max number of concurrent requests to make.
default_parser – Default parser to use for BeautifulSoup.
requests_kwargs – kwargs for requests
raise_for_status – Raise an exception if http status code denotes an error.
bs_get_text_kwargs – kwargs for beatifulsoup4 get_text
bs_kwargs – kwargs for beatifulsoup4 web page parsing
aload() → List[Document]¶
Load text from the urls in web_path async into Documents.
async fetch_all(urls: List[str]) → Any¶
Fetch all urls concurrently with rate limiting.
lazy_load() → Iterator[Document]¶
Lazy load text from the url(s) in web_path.
load() → List[Document][source]¶
Load webpages into Documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶ | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.azlyrics.AZLyricsLoader.html |
360ce3d783a7-2 | Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
scrape(parser: Optional[str] = None) → Any¶
Scrape data from webpage and return it in BeautifulSoup format.
scrape_all(urls: List[str], parser: Optional[str] = None) → List[Any]¶
Fetch all urls, then return soups for all results.
Examples using AZLyricsLoader¶
AZLyrics | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.azlyrics.AZLyricsLoader.html |
6ae2048d1ad3-0 | langchain.document_loaders.nuclia.NucliaLoader¶
class langchain.document_loaders.nuclia.NucliaLoader(path: str, nuclia_tool: NucliaUnderstandingAPI)[source]¶
Load from any file type using Nuclia Understanding API.
Methods
__init__(path, nuclia_tool)
lazy_load()
A lazy loader for Documents.
load()
Load documents.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(path: str, nuclia_tool: NucliaUnderstandingAPI)[source]¶
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using NucliaLoader¶
Nuclia Understanding API document loader | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.nuclia.NucliaLoader.html |
506c8fda4fe3-0 | langchain.document_loaders.url_playwright.PlaywrightURLLoader¶
class langchain.document_loaders.url_playwright.PlaywrightURLLoader(urls: List[str], continue_on_failure: bool = True, headless: bool = True, remove_selectors: Optional[List[str]] = None, evaluator: Optional[PlaywrightEvaluator] = None)[source]¶
Load HTML pages with Playwright and parse with Unstructured.
This is useful for loading pages that require javascript to render.
urls¶
List of URLs to load.
Type
List[str]
continue_on_failure¶
If True, continue loading other URLs on failure.
Type
bool
headless¶
If True, the browser will run in headless mode.
Type
bool
Load a list of URLs using Playwright.
Methods
__init__(urls[, continue_on_failure, ...])
Load a list of URLs using Playwright.
aload()
Load the specified URLs with Playwright and create Documents asynchronously.
lazy_load()
A lazy loader for Documents.
load()
Load the specified URLs using Playwright and create Document instances.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(urls: List[str], continue_on_failure: bool = True, headless: bool = True, remove_selectors: Optional[List[str]] = None, evaluator: Optional[PlaywrightEvaluator] = None)[source]¶
Load a list of URLs using Playwright.
async aload() → List[Document][source]¶
Load the specified URLs with Playwright and create Documents asynchronously.
Use this function when in a jupyter notebook environment.
Returns
A list of Document instances with loaded content.
Return type
List[Document]
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶ | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.url_playwright.PlaywrightURLLoader.html |
506c8fda4fe3-1 | A lazy loader for Documents.
load() → List[Document][source]¶
Load the specified URLs using Playwright and create Document instances.
Returns
A list of Document instances with loaded content.
Return type
List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using PlaywrightURLLoader¶
URL | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.url_playwright.PlaywrightURLLoader.html |
dd1d26b6f1c9-0 | langchain.document_loaders.onedrive.OneDriveLoader¶
class langchain.document_loaders.onedrive.OneDriveLoader[source]¶
Bases: O365BaseLoader
Load from Microsoft OneDrive.
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 auth_with_token: bool = False¶
Whether to authenticate with a token or not. Defaults to False.
param chunk_size: Union[int, str] = 5242880¶
Number of bytes to retrieve from each api call to the server. int or ‘auto’.
param drive_id: str [Required]¶
The ID of the OneDrive drive to load data from.
param folder_path: Optional[str] = None¶
The path to the folder to load data from.
param object_ids: Optional[List[str]] = None¶
The IDs of the objects to load data from.
param settings: _O365Settings [Optional]¶
Settings for the Office365 API client.
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
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 – fields to include in new model | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.onedrive.OneDriveLoader.html |
dd1d26b6f1c9-1 | 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 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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
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, 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().
lazy_load() → Iterator[Document][source]¶
Load documents lazily. Use this when working at a large scale.
load() → List[Document][source]¶
Load all documents. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.onedrive.OneDriveLoader.html |
dd1d26b6f1c9-2 | load() → List[Document][source]¶
Load all documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
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: Protocol = None, allow_pickle: bool = False) → Model¶
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 fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
Examples using OneDriveLoader¶
Microsoft OneDrive | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.onedrive.OneDriveLoader.html |
88e93997c740-0 | langchain.document_loaders.base_o365.O365BaseLoader¶
class langchain.document_loaders.base_o365.O365BaseLoader[source]¶
Bases: BaseLoader, BaseModel
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 auth_with_token: bool = False¶
Whether to authenticate with a token or not. Defaults to False.
param chunk_size: Union[int, str] = 5242880¶
Number of bytes to retrieve from each api call to the server. int or ‘auto’.
param settings: langchain.document_loaders.base_o365._O365Settings [Optional]¶
Settings for the Office365 API client.
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
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 – 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 copy of the model
Returns
new model instance | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.base_o365.O365BaseLoader.html |
88e93997c740-1 | 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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
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, 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().
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
abstract load() → List[Document]¶
Load data into Document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.base_o365.O365BaseLoader.html |
88e93997c740-2 | Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
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: Protocol = None, allow_pickle: bool = False) → Model¶
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 fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶ | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.base_o365.O365BaseLoader.html |
7c556cd5a17c-0 | langchain.document_loaders.snowflake_loader.SnowflakeLoader¶
class langchain.document_loaders.snowflake_loader.SnowflakeLoader(query: str, user: str, password: str, account: str, warehouse: str, role: str, database: str, schema: str, parameters: Optional[Dict[str, Any]] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None)[source]¶
Load from Snowflake API.
Each document represents one row of the result. The page_content_columns
are written into the page_content of the document. The metadata_columns
are written into the metadata of the document. By default, all columns
are written into the page_content and none into the metadata.
Initialize Snowflake document loader.
Parameters
query – The query to run in Snowflake.
user – Snowflake user.
password – Snowflake password.
account – Snowflake account.
warehouse – Snowflake warehouse.
role – Snowflake role.
database – Snowflake database
schema – Snowflake schema
parameters – Optional. Parameters to pass to the query.
page_content_columns – Optional. Columns written to Document page_content.
metadata_columns – Optional. Columns written to Document metadata.
Methods
__init__(query, user, password, account, ...)
Initialize Snowflake document loader.
lazy_load()
A lazy loader for Documents.
load()
Load data into document objects.
load_and_split([text_splitter])
Load Documents and split into chunks. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.snowflake_loader.SnowflakeLoader.html |
7c556cd5a17c-1 | load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(query: str, user: str, password: str, account: str, warehouse: str, role: str, database: str, schema: str, parameters: Optional[Dict[str, Any]] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None)[source]¶
Initialize Snowflake document loader.
Parameters
query – The query to run in Snowflake.
user – Snowflake user.
password – Snowflake password.
account – Snowflake account.
warehouse – Snowflake warehouse.
role – Snowflake role.
database – Snowflake database
schema – Snowflake schema
parameters – Optional. Parameters to pass to the query.
page_content_columns – Optional. Columns written to Document page_content.
metadata_columns – Optional. Columns written to Document metadata.
lazy_load() → Iterator[Document][source]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load data into document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using SnowflakeLoader¶
Snowflake | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.snowflake_loader.SnowflakeLoader.html |
f1e4deebf4f9-0 | langchain.document_loaders.duckdb_loader.DuckDBLoader¶
class langchain.document_loaders.duckdb_loader.DuckDBLoader(query: str, database: str = ':memory:', read_only: bool = False, config: Optional[Dict[str, str]] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None)[source]¶
Load from DuckDB.
Each document represents one row of the result. The page_content_columns
are written into the page_content of the document. The metadata_columns
are written into the metadata of the document. By default, all columns
are written into the page_content and none into the metadata.
Parameters
query – The query to execute.
database – The database to connect to. Defaults to “:memory:”.
read_only – Whether to open the database in read-only mode.
Defaults to False.
config – A dictionary of configuration options to pass to the database.
Optional.
page_content_columns – The columns to write into the page_content
of the document. Optional.
metadata_columns – The columns to write into the metadata of the document.
Optional.
Methods
__init__(query[, database, read_only, ...])
param query
The query to execute.
lazy_load()
A lazy loader for Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(query: str, database: str = ':memory:', read_only: bool = False, config: Optional[Dict[str, str]] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None)[source]¶
Parameters
query – The query to execute.
database – The database to connect to. Defaults to “:memory:”. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.duckdb_loader.DuckDBLoader.html |
f1e4deebf4f9-1 | database – The database to connect to. Defaults to “:memory:”.
read_only – Whether to open the database in read-only mode.
Defaults to False.
config – A dictionary of configuration options to pass to the database.
Optional.
page_content_columns – The columns to write into the page_content
of the document. Optional.
metadata_columns – The columns to write into the metadata of the document.
Optional.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load data into Document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using DuckDBLoader¶
DuckDB | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.duckdb_loader.DuckDBLoader.html |
3634eecaad2d-0 | langchain.document_loaders.ifixit.IFixitLoader¶
class langchain.document_loaders.ifixit.IFixitLoader(web_path: str)[source]¶
Load iFixit repair guides, device wikis and answers.
iFixit is the largest, open repair community on the web. The site contains nearly
100k repair manuals, 200k Questions & Answers on 42k devices, and all the data is
licensed under CC-BY.
This loader will allow you to download the text of a repair guide, text of Q&A’s
and wikis from devices on iFixit using their open APIs and web scraping.
Initialize with a web path.
Methods
__init__(web_path)
Initialize with a web path.
lazy_load()
A lazy loader for Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
load_device([url_override, include_guides])
Loads a device
load_guide([url_override])
Load a guide
load_questions_and_answers([url_override])
Load a list of questions and answers.
load_suggestions([query, doc_type])
Load suggestions.
__init__(web_path: str)[source]¶
Initialize with a web path.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load data into Document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.ifixit.IFixitLoader.html |
3634eecaad2d-1 | Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
load_device(url_override: Optional[str] = None, include_guides: bool = True) → List[Document][source]¶
Loads a device
Parameters
url_override – A URL to override the default URL.
include_guides – Whether to include guides linked to from the device.
Defaults to True.
Returns:
load_guide(url_override: Optional[str] = None) → List[Document][source]¶
Load a guide
Parameters
url_override – A URL to override the default URL.
Returns: List[Document]
load_questions_and_answers(url_override: Optional[str] = None) → List[Document][source]¶
Load a list of questions and answers.
Parameters
url_override – A URL to override the default URL.
Returns: List[Document]
static load_suggestions(query: str = '', doc_type: str = 'all') → List[Document][source]¶
Load suggestions.
Parameters
query – A query string
doc_type – The type of document to search for. Can be one of “all”,
“device”, “guide”, “teardown”, “answer”, “wiki”.
Returns:
Examples using IFixitLoader¶
iFixit | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.ifixit.IFixitLoader.html |
fe5ad81d5f23-0 | langchain.document_loaders.directory.DirectoryLoader¶
class langchain.document_loaders.directory.DirectoryLoader(path: str, glob: str = '**/[!.]*', silent_errors: bool = False, load_hidden: bool = False, loader_cls: ~typing.Union[~typing.Type[~langchain.document_loaders.unstructured.UnstructuredFileLoader], ~typing.Type[~langchain.document_loaders.text.TextLoader], ~typing.Type[~langchain.document_loaders.html_bs.BSHTMLLoader]] = <class 'langchain.document_loaders.unstructured.UnstructuredFileLoader'>, loader_kwargs: ~typing.Optional[dict] = None, recursive: bool = False, show_progress: bool = False, use_multithreading: bool = False, max_concurrency: int = 4, *, sample_size: int = 0, randomize_sample: bool = False, sample_seed: ~typing.Optional[int] = None)[source]¶
Load from a directory.
Initialize with a path to directory and how to glob over it.
Parameters
path – Path to directory.
glob – Glob pattern to use to find files. Defaults to “**/[!.]*”
(all files except hidden).
silent_errors – Whether to silently ignore errors. Defaults to False.
load_hidden – Whether to load hidden files. Defaults to False.
loader_cls – Loader class to use for loading files.
Defaults to UnstructuredFileLoader.
loader_kwargs – Keyword arguments to pass to loader_cls. Defaults to None.
recursive – Whether to recursively search for files. Defaults to False.
show_progress – Whether to show a progress bar. Defaults to False.
use_multithreading – Whether to use multithreading. Defaults to False.
max_concurrency – The maximum number of threads to use. Defaults to 4.
sample_size – The maximum number of files you would like to load from the
directory. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.directory.DirectoryLoader.html |
fe5ad81d5f23-1 | sample_size – The maximum number of files you would like to load from the
directory.
randomize_sample – Suffle the files to get a random sample.
sample_seed – set the seed of the random shuffle for reporoducibility.
Methods
__init__(path[, glob, silent_errors, ...])
Initialize with a path to directory and how to glob over it.
lazy_load()
A lazy loader for Documents.
load()
Load documents.
load_and_split([text_splitter])
Load Documents and split into chunks.
load_file(item, path, docs, pbar)
Load a file.
__init__(path: str, glob: str = '**/[!.]*', silent_errors: bool = False, load_hidden: bool = False, loader_cls: ~typing.Union[~typing.Type[~langchain.document_loaders.unstructured.UnstructuredFileLoader], ~typing.Type[~langchain.document_loaders.text.TextLoader], ~typing.Type[~langchain.document_loaders.html_bs.BSHTMLLoader]] = <class 'langchain.document_loaders.unstructured.UnstructuredFileLoader'>, loader_kwargs: ~typing.Optional[dict] = None, recursive: bool = False, show_progress: bool = False, use_multithreading: bool = False, max_concurrency: int = 4, *, sample_size: int = 0, randomize_sample: bool = False, sample_seed: ~typing.Optional[int] = None)[source]¶
Initialize with a path to directory and how to glob over it.
Parameters
path – Path to directory.
glob – Glob pattern to use to find files. Defaults to “**/[!.]*”
(all files except hidden).
silent_errors – Whether to silently ignore errors. Defaults to False.
load_hidden – Whether to load hidden files. Defaults to False. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.directory.DirectoryLoader.html |
fe5ad81d5f23-2 | load_hidden – Whether to load hidden files. Defaults to False.
loader_cls – Loader class to use for loading files.
Defaults to UnstructuredFileLoader.
loader_kwargs – Keyword arguments to pass to loader_cls. Defaults to None.
recursive – Whether to recursively search for files. Defaults to False.
show_progress – Whether to show a progress bar. Defaults to False.
use_multithreading – Whether to use multithreading. Defaults to False.
max_concurrency – The maximum number of threads to use. Defaults to 4.
sample_size – The maximum number of files you would like to load from the
directory.
randomize_sample – Suffle the files to get a random sample.
sample_seed – set the seed of the random shuffle for reporoducibility.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
load_file(item: Path, path: Path, docs: List[Document], pbar: Optional[Any]) → None[source]¶
Load a file.
Parameters
item – File path.
path – Directory path.
docs – List of documents to append to.
pbar – Progress bar. Defaults to None.
Examples using DirectoryLoader¶
StarRocks | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.directory.DirectoryLoader.html |
2a4b88792418-0 | langchain.document_loaders.confluence.ConfluenceLoader¶
class langchain.document_loaders.confluence.ConfluenceLoader(url: str, api_key: Optional[str] = None, username: Optional[str] = None, session: Optional[Session] = None, oauth2: Optional[dict] = None, token: Optional[str] = None, cloud: Optional[bool] = True, number_of_retries: Optional[int] = 3, min_retry_seconds: Optional[int] = 2, max_retry_seconds: Optional[int] = 10, confluence_kwargs: Optional[dict] = None)[source]¶
Load Confluence pages.
Port of https://llamahub.ai/l/confluence
This currently supports username/api_key, Oauth2 login or personal access token
authentication.
Specify a list page_ids and/or space_key to load in the corresponding pages into
Document objects, if both are specified the union of both sets will be returned.
You can also specify a boolean include_attachments to include attachments, this
is set to False by default, if set to True all attachments will be downloaded and
ConfluenceReader will extract the text from the attachments and add it to the
Document object. Currently supported attachment types are: PDF, PNG, JPEG/JPG,
SVG, Word and Excel.
Confluence API supports difference format of page content. The storage format is the
raw XML representation for storage. The view format is the HTML representation for
viewing with macros are rendered as though it is viewed by users. You can pass
a enum content_format argument to load() to specify the content format, this is
set to ContentFormat.STORAGE by default, the supported values are:
ContentFormat.EDITOR, ContentFormat.EXPORT_VIEW,
ContentFormat.ANONYMOUS_EXPORT_VIEW, ContentFormat.STORAGE,
and ContentFormat.VIEW. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html |
2a4b88792418-1 | and ContentFormat.VIEW.
Hint: space_key and page_id can both be found in the URL of a page in Confluence
- https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/<page_id>
Example
from langchain.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(
url="https://yoursite.atlassian.com/wiki",
username="me",
api_key="12345"
)
documents = loader.load(space_key="SPACE",limit=50)
# Server on perm
loader = ConfluenceLoader(
url="https://confluence.yoursite.com/",
username="me",
api_key="your_password",
cloud=False
)
documents = loader.load(space_key="SPACE",limit=50)
Parameters
url (str) – _description_
api_key (str, optional) – _description_, defaults to None
username (str, optional) – _description_, defaults to None
oauth2 (dict, optional) – _description_, defaults to {}
token (str, optional) – _description_, defaults to None
cloud (bool, optional) – _description_, defaults to True
number_of_retries (Optional[int], optional) – How many times to retry, defaults to 3
min_retry_seconds (Optional[int], optional) – defaults to 2
max_retry_seconds (Optional[int], optional) – defaults to 10
confluence_kwargs (dict, optional) – additional kwargs to initialize confluence with
Raises
ValueError – Errors while validating input
ImportError – Required dependencies not installed.
Methods
__init__(url[, api_key, username, session, ...])
is_public_page(page)
Check if a page is publicly accessible.
lazy_load()
A lazy loader for Documents. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html |
2a4b88792418-2 | Check if a page is publicly accessible.
lazy_load()
A lazy loader for Documents.
load([space_key, page_ids, label, cql, ...])
param space_key
Space key retrieved from a confluence URL, defaults to None
load_and_split([text_splitter])
Load Documents and split into chunks.
paginate_request(retrieval_method, **kwargs)
Paginate the various methods to retrieve groups of pages.
process_attachment(page_id[, ocr_languages])
process_doc(link)
process_image(link[, ocr_languages])
process_page(page, include_attachments, ...)
process_pages(pages, ...[, ocr_languages, ...])
Process a list of pages into a list of documents.
process_pdf(link[, ocr_languages])
process_svg(link[, ocr_languages])
process_xls(link)
validate_init_args([url, api_key, username, ...])
Validates proper combinations of init arguments
__init__(url: str, api_key: Optional[str] = None, username: Optional[str] = None, session: Optional[Session] = None, oauth2: Optional[dict] = None, token: Optional[str] = None, cloud: Optional[bool] = True, number_of_retries: Optional[int] = 3, min_retry_seconds: Optional[int] = 2, max_retry_seconds: Optional[int] = 10, confluence_kwargs: Optional[dict] = None)[source]¶
is_public_page(page: dict) → bool[source]¶
Check if a page is publicly accessible.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html |
2a4b88792418-3 | lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load(space_key: Optional[str] = None, page_ids: Optional[List[str]] = None, label: Optional[str] = None, cql: Optional[str] = None, include_restricted_content: bool = False, include_archived_content: bool = False, include_attachments: bool = False, include_comments: bool = False, content_format: ContentFormat = ContentFormat.STORAGE, limit: Optional[int] = 50, max_pages: Optional[int] = 1000, ocr_languages: Optional[str] = None, keep_markdown_format: bool = False, keep_newlines: bool = False) → List[Document][source]¶
Parameters
space_key (Optional[str], optional) – Space key retrieved from a confluence URL, defaults to None
page_ids (Optional[List[str]], optional) – List of specific page IDs to load, defaults to None
label (Optional[str], optional) – Get all pages with this label, defaults to None
cql (Optional[str], optional) – CQL Expression, defaults to None
include_restricted_content (bool, optional) – defaults to False
include_archived_content (bool, optional) – Whether to include archived content,
defaults to False
include_attachments (bool, optional) – defaults to False
include_comments (bool, optional) – defaults to False
content_format (ContentFormat) – Specify content format, defaults to
ContentFormat.STORAGE, the supported values are:
ContentFormat.EDITOR, ContentFormat.EXPORT_VIEW,
ContentFormat.ANONYMOUS_EXPORT_VIEW,
ContentFormat.STORAGE, and ContentFormat.VIEW.
limit (int, optional) – Maximum number of pages to retrieve per request, defaults to 50
max_pages (int, optional) – Maximum number of pages to retrieve in total, defaults 1000 | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html |
2a4b88792418-4 | ocr_languages (str, optional) – The languages to use for the Tesseract agent. To use a
language, you’ll first need to install the appropriate
Tesseract language pack.
keep_markdown_format (bool) – Whether to keep the markdown format, defaults to
False
keep_newlines (bool) – Whether to keep the newlines format, defaults to
False
Raises
ValueError – _description_
ImportError – _description_
Returns
_description_
Return type
List[Document]
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
paginate_request(retrieval_method: Callable, **kwargs: Any) → List[source]¶
Paginate the various methods to retrieve groups of pages.
Unfortunately, due to page size, sometimes the Confluence API
doesn’t match the limit value. If limit is >100 confluence
seems to cap the response to 100. Also, due to the Atlassian Python
package, we don’t get the “next” values from the “_links” key because
they only return the value from the result key. So here, the pagination
starts from 0 and goes until the max_pages, getting the limit number
of pages with each request. We have to manually check if there
are more docs based on the length of the returned list of pages, rather than
just checking for the presence of a next key in the response like this page
would have you do:
https://developer.atlassian.com/server/confluence/pagination-in-the-rest-api/
Parameters
retrieval_method (callable) – Function used to retrieve docs
Returns | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html |
2a4b88792418-5 | Parameters
retrieval_method (callable) – Function used to retrieve docs
Returns
List of documents
Return type
List
process_attachment(page_id: str, ocr_languages: Optional[str] = None) → List[str][source]¶
process_doc(link: str) → str[source]¶
process_image(link: str, ocr_languages: Optional[str] = None) → str[source]¶
process_page(page: dict, include_attachments: bool, include_comments: bool, content_format: ContentFormat, ocr_languages: Optional[str] = None, keep_markdown_format: Optional[bool] = False, keep_newlines: bool = False) → Document[source]¶
process_pages(pages: List[dict], include_restricted_content: bool, include_attachments: bool, include_comments: bool, content_format: ContentFormat, ocr_languages: Optional[str] = None, keep_markdown_format: Optional[bool] = False, keep_newlines: bool = False) → List[Document][source]¶
Process a list of pages into a list of documents.
process_pdf(link: str, ocr_languages: Optional[str] = None) → str[source]¶
process_svg(link: str, ocr_languages: Optional[str] = None) → str[source]¶
process_xls(link: str) → str[source]¶
static validate_init_args(url: Optional[str] = None, api_key: Optional[str] = None, username: Optional[str] = None, session: Optional[Session] = None, oauth2: Optional[dict] = None, token: Optional[str] = None) → Optional[List][source]¶
Validates proper combinations of init arguments
Examples using ConfluenceLoader¶
Confluence | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.confluence.ConfluenceLoader.html |
6ea745f1e92d-0 | langchain.document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader¶
class langchain.document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader(conf: Any, bucket: str, prefix: str = '')[source]¶
Load from Tencent Cloud COS directory.
Initialize with COS config, bucket and prefix.
:param conf(CosConfig): COS config.
:param bucket(str): COS bucket.
:param prefix(str): prefix.
Methods
__init__(conf, bucket[, prefix])
Initialize with COS config, bucket and prefix.
lazy_load()
Load documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(conf: Any, bucket: str, prefix: str = '')[source]¶
Initialize with COS config, bucket and prefix.
:param conf(CosConfig): COS config.
:param bucket(str): COS bucket.
:param prefix(str): prefix.
lazy_load() → Iterator[Document][source]¶
Load documents.
load() → List[Document][source]¶
Load data into Document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using TencentCOSDirectoryLoader¶
Tencent COS Directory | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader.html |
f4d2103c9a8f-0 | langchain.document_loaders.airbyte_json.AirbyteJSONLoader¶
class langchain.document_loaders.airbyte_json.AirbyteJSONLoader(file_path: str)[source]¶
Load local Airbyte json files.
Initialize with a file path. This should start with ‘/tmp/airbyte_local/’.
Attributes
file_path
Path to the directory containing the json files.
Methods
__init__(file_path)
Initialize with a file path.
lazy_load()
A lazy loader for Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(file_path: str)[source]¶
Initialize with a file path. This should start with ‘/tmp/airbyte_local/’.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load data into Document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using AirbyteJSONLoader¶
Airbyte
Airbyte JSON | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.airbyte_json.AirbyteJSONLoader.html |
083d3dd399d8-0 | langchain.document_loaders.obs_file.OBSFileLoader¶
class langchain.document_loaders.obs_file.OBSFileLoader(bucket: str, key: str, client: Any = None, endpoint: str = '', config: Optional[dict] = None)[source]¶
Load from the Huawei OBS file.
Initialize the OBSFileLoader with the specified settings.
Parameters
bucket (str) – The name of the OBS bucket to be used.
key (str) – The name of the object in the OBS bucket.
client (ObsClient, optional) – An instance of the ObsClient to connect to OBS.
endpoint (str, optional) – The endpoint URL of your OBS bucket. This parameter is mandatory if client is not provided.
config (dict, optional) – The parameters for connecting to OBS, provided as a dictionary. This parameter is ignored if client is provided. The dictionary could have the following keys:
- “ak” (str, optional): Your OBS access key (required if get_token_from_ecs is False and bucket policy is not public read).
- “sk” (str, optional): Your OBS secret key (required if get_token_from_ecs is False and bucket policy is not public read).
- “token” (str, optional): Your security token (required if using temporary credentials).
- “get_token_from_ecs” (bool, optional): Whether to retrieve the security token from ECS. Defaults to False if not provided. If set to True, ak, sk, and token will be ignored.
Raises
ValueError – If the esdk-obs-python package is not installed.
TypeError – If the provided client is not an instance of ObsClient.
ValueError – If client is not provided, but endpoint is missing.
Note | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.obs_file.OBSFileLoader.html |
083d3dd399d8-1 | ValueError – If client is not provided, but endpoint is missing.
Note
Before using this class, make sure you have registered with OBS and have the necessary credentials. The ak, sk, and endpoint values are mandatory unless get_token_from_ecs is True or the bucket policy is public read. token is required when using temporary credentials.
Example
To create a new OBSFileLoader with a new client:
```
config = {
“ak”: “your-access-key”,
“sk”: “your-secret-key”
}
obs_loader = OBSFileLoader(“your-bucket-name”, “your-object-key”, config=config)
```
To create a new OBSFileLoader with an existing client:
```
from obs import ObsClient
# Assuming you have an existing ObsClient object ‘obs_client’
obs_loader = OBSFileLoader(“your-bucket-name”, “your-object-key”, client=obs_client)
```
To create a new OBSFileLoader without an existing client:
`
obs_loader = OBSFileLoader("your-bucket-name", "your-object-key", endpoint="your-endpoint-url")
`
Methods
__init__(bucket, key[, client, endpoint, config])
Initialize the OBSFileLoader with the specified settings.
lazy_load()
A lazy loader for Documents.
load()
Load documents.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(bucket: str, key: str, client: Any = None, endpoint: str = '', config: Optional[dict] = None) → None[source]¶
Initialize the OBSFileLoader with the specified settings.
Parameters
bucket (str) – The name of the OBS bucket to be used.
key (str) – The name of the object in the OBS bucket. | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.obs_file.OBSFileLoader.html |
083d3dd399d8-2 | key (str) – The name of the object in the OBS bucket.
client (ObsClient, optional) – An instance of the ObsClient to connect to OBS.
endpoint (str, optional) – The endpoint URL of your OBS bucket. This parameter is mandatory if client is not provided.
config (dict, optional) – The parameters for connecting to OBS, provided as a dictionary. This parameter is ignored if client is provided. The dictionary could have the following keys:
- “ak” (str, optional): Your OBS access key (required if get_token_from_ecs is False and bucket policy is not public read).
- “sk” (str, optional): Your OBS secret key (required if get_token_from_ecs is False and bucket policy is not public read).
- “token” (str, optional): Your security token (required if using temporary credentials).
- “get_token_from_ecs” (bool, optional): Whether to retrieve the security token from ECS. Defaults to False if not provided. If set to True, ak, sk, and token will be ignored.
Raises
ValueError – If the esdk-obs-python package is not installed.
TypeError – If the provided client is not an instance of ObsClient.
ValueError – If client is not provided, but endpoint is missing.
Note
Before using this class, make sure you have registered with OBS and have the necessary credentials. The ak, sk, and endpoint values are mandatory unless get_token_from_ecs is True or the bucket policy is public read. token is required when using temporary credentials.
Example
To create a new OBSFileLoader with a new client:
```
config = {
“ak”: “your-access-key”,
“sk”: “your-secret-key”
}
obs_loader = OBSFileLoader(“your-bucket-name”, “your-object-key”, config=config)
``` | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.obs_file.OBSFileLoader.html |
083d3dd399d8-3 | ```
To create a new OBSFileLoader with an existing client:
```
from obs import ObsClient
# Assuming you have an existing ObsClient object ‘obs_client’
obs_loader = OBSFileLoader(“your-bucket-name”, “your-object-key”, client=obs_client)
```
To create a new OBSFileLoader without an existing client:
`
obs_loader = OBSFileLoader("your-bucket-name", "your-object-key", endpoint="your-endpoint-url")
`
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load documents.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using OBSFileLoader¶
Huawei OBS File | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.obs_file.OBSFileLoader.html |
6df79ddf8407-0 | langchain.document_loaders.git.GitLoader¶
class langchain.document_loaders.git.GitLoader(repo_path: str, clone_url: Optional[str] = None, branch: Optional[str] = 'main', file_filter: Optional[Callable[[str], bool]] = None)[source]¶
Load Git repository files.
The Repository can be local on disk available at repo_path,
or remote at clone_url that will be cloned to repo_path.
Currently, supports only text files.
Each document represents one file in the repository. The path points to
the local Git repository, and the branch specifies the branch to load
files from. By default, it loads from the main branch.
Parameters
repo_path – The path to the Git repository.
clone_url – Optional. The URL to clone the repository from.
branch – Optional. The branch to load files from. Defaults to main.
file_filter – Optional. A function that takes a file path and returns
a boolean indicating whether to load the file. Defaults to None.
Methods
__init__(repo_path[, clone_url, branch, ...])
param repo_path
The path to the Git repository.
lazy_load()
A lazy loader for Documents.
load()
Load data into Document objects.
load_and_split([text_splitter])
Load Documents and split into chunks.
__init__(repo_path: str, clone_url: Optional[str] = None, branch: Optional[str] = 'main', file_filter: Optional[Callable[[str], bool]] = None)[source]¶
Parameters
repo_path – The path to the Git repository.
clone_url – Optional. The URL to clone the repository from.
branch – Optional. The branch to load files from. Defaults to main.
file_filter – Optional. A function that takes a file path and returns | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.git.GitLoader.html |
6df79ddf8407-1 | file_filter – Optional. A function that takes a file path and returns
a boolean indicating whether to load the file. Defaults to None.
lazy_load() → Iterator[Document]¶
A lazy loader for Documents.
load() → List[Document][source]¶
Load data into Document objects.
load_and_split(text_splitter: Optional[TextSplitter] = None) → List[Document]¶
Load Documents and split into chunks. Chunks are returned as Documents.
Parameters
text_splitter – TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns
List of Documents.
Examples using GitLoader¶
Git | https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.git.GitLoader.html |