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classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.output_parser.BaseOutputParser.html
|
328b9e313def-0
|
langchain.schema.messages.AIMessage¶
class langchain.schema.messages.AIMessage[source]¶
Bases: BaseMessage
A Message from an AI.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
Any additional information.
param content: str [Required]¶
The string contents of the message.
param example: bool = False¶
Whether this Message is being passed in to the model as part of an example
conversation.
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/schema/langchain.schema.messages.AIMessage.html
|
328b9e313def-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/schema/langchain.schema.messages.AIMessage.html
|
328b9e313def-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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
Examples using AIMessage¶
Zep
Zep Memory
Anthropic
OpenAI
JinaChat
CAMEL Role-Playing Autonomous Cooperative Agents
Multi-agent decentralized speaker selection
Multi-agent authoritarian speaker selection
Multi-Player Dungeons & Dragons
Simulated Environment: Gymnasium
Agent Debates with Tools
Prompt Pipelining
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.AIMessage.html
|
ea8d4b1c058c-0
|
langchain.schema.runnable.RunnableBinding¶
class langchain.schema.runnable.RunnableBinding[source]¶
Bases: Serializable, Runnable[Input, Output]
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 bound: langchain.schema.runnable.Runnable[langchain.schema.runnable.Input, langchain.schema.runnable.Output] [Required]¶
param kwargs: Mapping[str, Any] [Required]¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output][source]¶
async ainvoke(input: Input, config: Optional[RunnableConfig] = None) → Output[source]¶
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output][source]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output][source]¶
bind(**kwargs: Any) → Runnable[Input, Output][source]¶
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
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableBinding.html
|
ea8d4b1c058c-1
|
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¶
invoke(input: Input, config: Optional[RunnableConfig] = None) → Output[source]¶
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableBinding.html
|
ea8d4b1c058c-2
|
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[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¶
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) → Iterator[Output][source]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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/schema/langchain.schema.runnable.RunnableBinding.html
|
ea8d4b1c058c-3
|
classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableBinding.html
|
81ce7b24fd20-0
|
langchain.schema.runnable.Runnable¶
class langchain.schema.runnable.Runnable[source]¶
Methods
__init__()
abatch(inputs[, config, max_concurrency])
ainvoke(input[, config])
astream(input[, config])
batch(inputs[, config, max_concurrency])
bind(**kwargs)
Bind arguments to a Runnable, returning a new Runnable.
invoke(input[, config])
stream(input[, config])
with_fallbacks(fallbacks, *[, ...])
__init__()¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output][source]¶
async ainvoke(input: Input, config: Optional[RunnableConfig] = None) → Output[source]¶
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output][source]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output][source]¶
bind(**kwargs: Any) → Runnable[Input, Output][source]¶
Bind arguments to a Runnable, returning a new Runnable.
abstract invoke(input: Input, config: Optional[RunnableConfig] = None) → Output[source]¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output][source]¶
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.Runnable.html
|
81ce7b24fd20-1
|
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output][source]¶
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.Runnable.html
|
e355dd528e67-0
|
langchain.schema.memory.BaseChatMessageHistory¶
class langchain.schema.memory.BaseChatMessageHistory[source]¶
Abstract base class for storing chat message history.
See ChatMessageHistory for default implementation.
Example
class FileChatMessageHistory(BaseChatMessageHistory):
storage_path: str
session_id: str
@property
def messages(self):
with open(os.path.join(storage_path, session_id), 'r:utf-8') as f:
messages = json.loads(f.read())
return messages_from_dict(messages)
def add_message(self, message: BaseMessage) -> None:
messages = self.messages.append(_message_to_dict(message))
with open(os.path.join(storage_path, session_id), 'w') as f:
json.dump(f, messages)
def clear(self):
with open(os.path.join(storage_path, session_id), 'w') as f:
f.write("[]")
Attributes
messages
A list of Messages stored in-memory.
Methods
__init__()
add_ai_message(message)
Convenience method for adding an AI message string to the store.
add_message(message)
Add a Message object to the store.
add_user_message(message)
Convenience method for adding a human message string to the store.
clear()
Remove all messages from the store
__init__()¶
add_ai_message(message: str) → None[source]¶
Convenience method for adding an AI message string to the store.
Parameters
message – The string contents of an AI message.
abstract add_message(message: BaseMessage) → None[source]¶
Add a Message object to the store.
Parameters
message – A BaseMessage object to store.
add_user_message(message: str) → None[source]¶
Convenience method for adding a human message string to the store.
Parameters
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.memory.BaseChatMessageHistory.html
|
e355dd528e67-1
|
Convenience method for adding a human message string to the store.
Parameters
message – The string contents of a human message.
abstract clear() → None[source]¶
Remove all messages from the store
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.memory.BaseChatMessageHistory.html
|
e9dd7e595405-0
|
langchain.schema.messages.ChatMessage¶
class langchain.schema.messages.ChatMessage[source]¶
Bases: BaseMessage
A Message that can be assigned an arbitrary speaker (i.e. role).
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
Any additional information.
param content: str [Required]¶
The string contents of the message.
param role: str [Required]¶
The speaker / role of the Message.
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/schema/langchain.schema.messages.ChatMessage.html
|
e9dd7e595405-1
|
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/schema/langchain.schema.messages.ChatMessage.html
|
e9dd7e595405-2
|
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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.ChatMessage.html
|
0bda2995edbf-0
|
langchain.schema.output_parser.BaseGenerationOutputParser¶
class langchain.schema.output_parser.BaseGenerationOutputParser[source]¶
Bases: BaseLLMOutputParser, Runnable[Union[str, BaseMessage], T]
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, *, max_concurrency: Optional[int] = None) → List[Output]¶
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.RunnableConfig | None = None) → T[source]¶
async aparse_result(result: List[Generation]) → 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) → AsyncIterator[Output]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
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
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.output_parser.BaseGenerationOutputParser.html
|
0bda2995edbf-1
|
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¶
invoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.RunnableConfig | None = None) → T[source]¶
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.output_parser.BaseGenerationOutputParser.html
|
0bda2995edbf-2
|
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[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¶
abstract parse_result(result: List[Generation]) → 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.
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) → Iterator[Output]¶
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.output_parser.BaseGenerationOutputParser.html
|
0bda2995edbf-3
|
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.output_parser.BaseGenerationOutputParser.html
|
717d59c77482-0
|
langchain.schema.messages.FunctionMessageChunk¶
class langchain.schema.messages.FunctionMessageChunk[source]¶
Bases: FunctionMessage, BaseMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
Any additional information.
param content: str [Required]¶
The string contents of the message.
param name: str [Required]¶
The name of the function that was executed.
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
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.FunctionMessageChunk.html
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.FunctionMessageChunk.html
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717d59c77482-2
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.FunctionMessageChunk.html
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cea0c5307f06-0
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langchain.schema.messages.FunctionMessage¶
class langchain.schema.messages.FunctionMessage[source]¶
Bases: BaseMessage
A Message for passing the result of executing a function back to a 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 additional_kwargs: dict [Optional]¶
Any additional information.
param content: str [Required]¶
The string contents of the message.
param name: str [Required]¶
The name of the function that was executed.
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/schema/langchain.schema.messages.FunctionMessage.html
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cea0c5307f06-1
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.FunctionMessage.html
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cea0c5307f06-2
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.FunctionMessage.html
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c880c84dd5db-0
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langchain.schema.messages.messages_to_dict¶
langchain.schema.messages.messages_to_dict(messages: Sequence[BaseMessage]) → List[dict][source]¶
Convert a sequence of Messages to a list of dictionaries.
Parameters
messages – Sequence of messages (as BaseMessages) to convert.
Returns
List of messages as dicts.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.messages_to_dict.html
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f20525c64595-0
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langchain.schema.agent.AgentFinish¶
class langchain.schema.agent.AgentFinish(return_values: dict, log: str)[source]¶
The final return value of an ActionAgent.
Create new instance of AgentFinish(return_values, log)
Attributes
log
Additional information to log about the return value
return_values
Dictionary of return values.
Methods
__init__()
count(value, /)
Return number of occurrences of value.
index(value[, start, stop])
Return first index of value.
__init__()¶
count(value, /)¶
Return number of occurrences of value.
index(value, start=0, stop=9223372036854775807, /)¶
Return first index of value.
Raises ValueError if the value is not present.
Examples using AgentFinish¶
Plug-and-Plai
Wikibase Agent
SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base
Custom Agent with PlugIn Retrieval
Custom multi-action agent
Running Agent as an Iterator
Custom agent
Custom agent with tool retrieval
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.agent.AgentFinish.html
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a33d0c6cf7d3-0
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langchain.schema.output.GenerationChunk¶
class langchain.schema.output.GenerationChunk[source]¶
Bases: Generation
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 generation_info: Optional[Dict[str, Any]] = None¶
Raw response from the provider. May include things like the
reason for finishing or token log probabilities.
param text: str [Required]¶
Generated text output.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
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/schema/langchain.schema.output.GenerationChunk.html
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a33d0c6cf7d3-1
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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/schema/langchain.schema.output.GenerationChunk.html
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a33d0c6cf7d3-2
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.output.GenerationChunk.html
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langchain.schema.runnable.RunnableSequence¶
class langchain.schema.runnable.RunnableSequence[source]¶
Bases: Serializable, Runnable[Input, Output]
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 first: langchain.schema.runnable.Runnable[langchain.schema.runnable.Input, Any] [Required]¶
param last: langchain.schema.runnable.Runnable[Any, langchain.schema.runnable.Output] [Required]¶
param middle: List[langchain.schema.runnable.Runnable[Any, Any]] [Optional]¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output][source]¶
async ainvoke(input: Input, config: Optional[RunnableConfig] = None) → Output[source]¶
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output][source]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output][source]¶
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
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableSequence.html
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340e76597818-1
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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¶
invoke(input: Input, config: Optional[RunnableConfig] = None) → Output[source]¶
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableSequence.html
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340e76597818-2
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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¶
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) → Iterator[Output][source]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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/schema/langchain.schema.runnable.RunnableSequence.html
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340e76597818-3
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classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
property steps: List[langchain.schema.runnable.Runnable[Any, Any]]¶
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableSequence.html
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2d4d341db3b1-0
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langchain.schema.runnable.RunnableMap¶
class langchain.schema.runnable.RunnableMap[source]¶
Bases: Serializable, Runnable[Input, Dict[str, Any]]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param steps: Mapping[str, langchain.schema.runnable.Runnable[langchain.schema.runnable.Input, Any]] [Required]¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
async ainvoke(input: Input, config: Optional[RunnableConfig] = None) → Dict[str, Any][source]¶
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
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
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableMap.html
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2d4d341db3b1-1
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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¶
invoke(input: Input, config: Optional[RunnableConfig] = None) → Dict[str, Any][source]¶
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableMap.html
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2d4d341db3b1-2
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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¶
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) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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/schema/langchain.schema.runnable.RunnableMap.html
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2d4d341db3b1-3
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classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableMap.html
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302fbcd065b4-0
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langchain.schema.messages.AIMessageChunk¶
class langchain.schema.messages.AIMessageChunk[source]¶
Bases: AIMessage, BaseMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
Any additional information.
param content: str [Required]¶
The string contents of the message.
param example: bool = False¶
Whether this Message is being passed in to the model as part of an example
conversation.
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
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.AIMessageChunk.html
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.AIMessageChunk.html
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.AIMessageChunk.html
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b363471c2884-0
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langchain.schema.prompt_template.format_document¶
langchain.schema.prompt_template.format_document(doc: Document, prompt: BasePromptTemplate) → str[source]¶
Format a document into a string based on a prompt template.
First, this pulls information from the document from two sources:
page_content:This takes the information from the document.page_content
and assigns it to a variable named page_content.
metadata:This takes information from document.metadata and assigns
it to variables of the same name.
Those variables are then passed into the prompt to produce a formatted string.
Parameters
doc – Document, the page_content and metadata will be used to create
the final string.
prompt – BasePromptTemplate, will be used to format the page_content
and metadata into the final string.
Returns
string of the document formatted.
Example
from langchain.schema import Document
from langchain.prompts import PromptTemplate
doc = Document(page_content="This is a joke", metadata={"page": "1"})
prompt = PromptTemplate.from_template("Page {page}: {page_content}")
format_document(doc, prompt)
>>> "Page 1: This is a joke"
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https://api.python.langchain.com/en/latest/schema/langchain.schema.prompt_template.format_document.html
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73b3bf0bacc4-0
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langchain.schema.memory.BaseMemory¶
class langchain.schema.memory.BaseMemory[source]¶
Bases: Serializable, ABC
Abstract base class for memory in Chains.
Memory refers to state in Chains. Memory can be used to store information aboutpast executions of a Chain and inject that information into the inputs of
future executions of the Chain. For example, for conversational Chains Memory
can be used to store conversations and automatically add them to future model
prompts so that the model has the necessary context to respond coherently to
the latest input.
Example
class SimpleMemory(BaseMemory):
memories: Dict[str, Any] = dict()
@property
def memory_variables(self) -> List[str]:
return list(self.memories.keys())
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
return self.memories
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
pass
def clear(self) -> None:
pass
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.
abstract clear() → None[source]¶
Clear memory contents.
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
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https://api.python.langchain.com/en/latest/schema/langchain.schema.memory.BaseMemory.html
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73b3bf0bacc4-1
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.memory.BaseMemory.html
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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().
abstract load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]¶
Return key-value pairs given the text input to the chain.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', 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¶
abstract save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]¶
Save the context of this chain run to memory.
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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.memory.BaseMemory.html
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to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
abstract property memory_variables: List[str]¶
The string keys this memory class will add to chain inputs.
Examples using BaseMemory¶
How to create a custom Memory class
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https://api.python.langchain.com/en/latest/schema/langchain.schema.memory.BaseMemory.html
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langchain.schema.storage.BaseStore¶
class langchain.schema.storage.BaseStore[source]¶
Abstract interface for a key-value store.
Methods
__init__()
mdelete(keys)
Delete the given keys and their associated values.
mget(keys)
Get the values associated with the given keys.
mset(key_value_pairs)
Set the values for the given keys.
yield_keys(*[, prefix])
Get an iterator over keys that match the given prefix.
__init__()¶
abstract mdelete(keys: Sequence[K]) → None[source]¶
Delete the given keys and their associated values.
Parameters
keys (Sequence[K]) – A sequence of keys to delete.
abstract mget(keys: Sequence[K]) → List[Optional[V]][source]¶
Get the values associated with the given keys.
Parameters
keys (Sequence[K]) – A sequence of keys.
Returns
A sequence of optional values associated with the keys.
If a key is not found, the corresponding value will be None.
abstract mset(key_value_pairs: Sequence[Tuple[K, V]]) → None[source]¶
Set the values for the given keys.
Parameters
key_value_pairs (Sequence[Tuple[K, V]]) – A sequence of key-value pairs.
abstract yield_keys(*, prefix: Optional[str] = None) → Union[Iterator[K], Iterator[str]][source]¶
Get an iterator over keys that match the given prefix.
Parameters
prefix (str) – The prefix to match.
Returns
An iterator over keys that match the given prefix.
This method is allowed to return an iterator over either K or str
depending on what makes more sense for the given store.
Return type
Iterator[K | str]
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https://api.python.langchain.com/en/latest/schema/langchain.schema.storage.BaseStore.html
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langchain.schema.messages.HumanMessage¶
class langchain.schema.messages.HumanMessage[source]¶
Bases: BaseMessage
A Message from a human.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
Any additional information.
param content: str [Required]¶
The string contents of the message.
param example: bool = False¶
Whether this Message is being passed in to the model as part of an example
conversation.
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
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.HumanMessage.html
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.HumanMessage.html
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
Examples using HumanMessage¶
Zep
Zep Memory
Anthropic
OpenAI
Google Cloud Platform Vertex AI PaLM
JinaChat
Azure
PromptLayer ChatOpenAI
Context
PromptLayer
MLflow AI Gateway
Flyte
Arthur
Structure answers with OpenAI functions
CAMEL Role-Playing Autonomous Cooperative Agents
Multi-Agent Simulated Environment: Petting Zoo
Multi-agent decentralized speaker selection
Multi-agent authoritarian speaker selection
Two-Player Dungeons & Dragons
Multi-Player Dungeons & Dragons
Simulated Environment: Gymnasium
Agent Debates with Tools
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.HumanMessage.html
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Simulated Environment: Gymnasium
Agent Debates with Tools
Custom callback handlers
Async callbacks
Tools as OpenAI Functions
Prompt Pipelining
Using OpenAI functions
Retrieval QA using OpenAI functions
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.HumanMessage.html
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langchain.schema.agent.AgentAction¶
class langchain.schema.agent.AgentAction(tool: str, tool_input: Union[str, dict], log: str)[source]¶
A full description of an action for an ActionAgent to execute.
Attributes
tool
The name of the Tool to execute.
tool_input
The input to pass in to the Tool.
log
Additional information to log about the action.
Methods
__init__(tool, tool_input, log)
__init__(tool: str, tool_input: Union[str, dict], log: str) → None¶
Examples using AgentAction¶
Custom Trajectory Evaluator
Plug-and-Plai
Wikibase Agent
SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base
Custom Agent with PlugIn Retrieval
Multiple callback handlers
Custom multi-action agent
Custom agent
Custom agent with tool retrieval
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https://api.python.langchain.com/en/latest/schema/langchain.schema.agent.AgentAction.html
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ce360b81e8a2-0
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langchain.schema.messages.ChatMessageChunk¶
class langchain.schema.messages.ChatMessageChunk[source]¶
Bases: ChatMessage, BaseMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
Any additional information.
param content: str [Required]¶
The string contents of the message.
param role: str [Required]¶
The speaker / role of the Message.
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
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.ChatMessageChunk.html
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ce360b81e8a2-1
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.ChatMessageChunk.html
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ce360b81e8a2-2
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.ChatMessageChunk.html
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9e1f63b45cc8-0
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langchain.schema.runnable.RunnableConfig¶
class langchain.schema.runnable.RunnableConfig[source]¶
tags: List[str]¶
Tags for this call and any sub-calls (eg. a Chain calling an LLM).
You can use these to filter calls.
metadata: Dict[str, Any]¶
Metadata for this call and any sub-calls (eg. a Chain calling an LLM).
Keys should be strings, values should be JSON-serializable.
callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]¶
Callbacks for this call and any sub-calls (eg. a Chain calling an LLM).
Tags are passed to all callbacks, metadata is passed to handle*Start callbacks.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableConfig.html
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langchain.schema.runnable.RunnableWithFallbacks¶
class langchain.schema.runnable.RunnableWithFallbacks[source]¶
Bases: Serializable, Runnable[Input, Output]
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 exceptions_to_handle: Tuple[Type[BaseException]] = (<class 'Exception'>,)¶
param fallbacks: Sequence[langchain.schema.runnable.Runnable[langchain.schema.runnable.Input, langchain.schema.runnable.Output]] [Required]¶
param runnable: langchain.schema.runnable.Runnable[langchain.schema.runnable.Input, langchain.schema.runnable.Output] [Required]¶
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output][source]¶
async ainvoke(input: Input, config: Optional[RunnableConfig] = None) → Output[source]¶
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output][source]¶
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
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableWithFallbacks.html
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8dbdece815eb-1
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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¶
invoke(input: Input, config: Optional[RunnableConfig] = None) → Output[source]¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableWithFallbacks.html
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8dbdece815eb-2
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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¶
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) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableWithFallbacks.html
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classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
property runnables: Iterator[langchain.schema.runnable.Runnable[langchain.schema.runnable.Input, langchain.schema.runnable.Output]]¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnableWithFallbacks.html
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langchain.schema.prompt.PromptValue¶
class langchain.schema.prompt.PromptValue[source]¶
Bases: Serializable, ABC
Base abstract class for inputs to any language model.
PromptValues can be converted to both LLM (pure text-generation) inputs andChatModel inputs.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
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
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https://api.python.langchain.com/en/latest/schema/langchain.schema.prompt.PromptValue.html
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.prompt.PromptValue.html
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
abstract to_messages() → List[BaseMessage][source]¶
Return prompt as a list of Messages.
abstract to_string() → str[source]¶
Return prompt value as string.
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
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https://api.python.langchain.com/en/latest/schema/langchain.schema.prompt.PromptValue.html
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langchain.schema.document.Document¶
class langchain.schema.document.Document[source]¶
Bases: Serializable
Class for storing a piece of text and associated metadata.
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 metadata: dict [Optional]¶
Arbitrary metadata about the page content (e.g., source, relationships to other
documents, etc.).
param page_content: str [Required]¶
String text.
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
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https://api.python.langchain.com/en/latest/schema/langchain.schema.document.Document.html
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.document.Document.html
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6c3483b36e9c-2
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
Examples using Document¶
ChatGPT Plugin
Weaviate Hybrid Search
BM25
TF-IDF
Apify
Vectara Text Generation
PGVector
Annoy
pg_embedding
FAISS
OpenAI Functions Metadata Tagger
Doctran Extract Properties
Doctran Interrogate Documents
Doctran Translate Documents
Copy Paste
Apify Dataset
Docugami
SageMakerEndpoint
Caching integrations
!pip install bs4
Retrieve from vector stores directly
Retrieve as you generate with FLARE
Plug-and-Plai
Custom Agent with PlugIn Retrieval
Weaviate self-querying
Chroma self-querying
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https://api.python.langchain.com/en/latest/schema/langchain.schema.document.Document.html
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6c3483b36e9c-3
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Weaviate self-querying
Chroma self-querying
DeepLake self-querying
Self-querying with Pinecone
Self-querying with MyScale
Qdrant self-querying
How to add memory to a Multi-Input Chain
Custom agent with tool retrieval
Vector store-augmented text generation
FLARE
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https://api.python.langchain.com/en/latest/schema/langchain.schema.document.Document.html
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27612997e5b0-0
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langchain.schema.runnable.RunnablePassthrough¶
class langchain.schema.runnable.RunnablePassthrough[source]¶
Bases: Serializable, Runnable[Input, Input]
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, *, max_concurrency: Optional[int] = None) → List[Output]¶
async ainvoke(input: Input, config: Optional[RunnableConfig] = None) → Output¶
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
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
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https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnablePassthrough.html
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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¶
invoke(input: Input, config: Optional[RunnableConfig] = None) → Input[source]¶
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().
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https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnablePassthrough.html
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27612997e5b0-2
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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¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
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https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnablePassthrough.html
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eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.RunnablePassthrough.html
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639f700f5b4d-0
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langchain.schema.output.Generation¶
class langchain.schema.output.Generation[source]¶
Bases: Serializable
A single text generation output.
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 generation_info: Optional[Dict[str, Any]] = None¶
Raw response from the provider. May include things like the
reason for finishing or token log probabilities.
param text: str [Required]¶
Generated text output.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
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/schema/langchain.schema.output.Generation.html
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639f700f5b4d-1
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.output.Generation.html
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639f700f5b4d-2
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
|
https://api.python.langchain.com/en/latest/schema/langchain.schema.output.Generation.html
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5da9952fbe58-0
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langchain.schema.messages.SystemMessageChunk¶
class langchain.schema.messages.SystemMessageChunk[source]¶
Bases: SystemMessage, BaseMessageChunk
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param additional_kwargs: dict [Optional]¶
Any additional information.
param content: str [Required]¶
The string contents of the message.
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/schema/langchain.schema.messages.SystemMessageChunk.html
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5da9952fbe58-1
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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¶
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.SystemMessageChunk.html
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5da9952fbe58-2
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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¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Whether this class is LangChain serializable.
property type: str¶
Type of the message, used for serialization.
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https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.SystemMessageChunk.html
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0930c9231d91-0
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langchain.document_transformers.long_context_reorder.LongContextReorder¶
class langchain.document_transformers.long_context_reorder.LongContextReorder[source]¶
Bases: BaseDocumentTransformer, BaseModel
Lost in the middle:
Performance degrades when models must access relevant information
in the middle of long contexts.
See: https://arxiv.org/abs//2307.03172
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 atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Asynchronously transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
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
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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¶
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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¶
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Reorders documents.
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 LongContextReorder¶
LOTR (Merger Retriever)
Lost in the middle: The problem with long contexts
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.long_context_reorder.LongContextReorder.html
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langchain.document_transformers.openai_functions.create_metadata_tagger¶
langchain.document_transformers.openai_functions.create_metadata_tagger(metadata_schema: Union[Dict[str, Any], Type[BaseModel]], llm: BaseLanguageModel, prompt: Optional[ChatPromptTemplate] = None, *, tagging_chain_kwargs: Optional[Dict] = None) → OpenAIMetadataTagger[source]¶
Create a DocumentTransformer that uses an OpenAI function chain to automatically
tag documents with metadata based on their content and an input schema.
Args:
metadata_schema: Either a dictionary or pydantic.BaseModel class. If a dictionaryis passed in, it’s assumed to already be a valid JsonSchema.
For best results, pydantic.BaseModels should have docstrings describing what
the schema represents and descriptions for the parameters.
llm: Language model to use, assumed to support the OpenAI function-calling API.Defaults to use “gpt-3.5-turbo-0613”
prompt: BasePromptTemplate to pass to the model.
Returns:An LLMChain that will pass the given function to the model.
Example:from langchain.chat_models import ChatOpenAI
from langchain.document_transformers import create_metadata_tagger
from langchain.schema import Document
schema = {
"properties": {
"movie_title": { "type": "string" },
"critic": { "type": "string" },
"tone": {
"type": "string",
"enum": ["positive", "negative"]
},
"rating": {
"type": "integer",
"description": "The number of stars the critic rated the movie"
}
},
"required": ["movie_title", "critic", "tone"]
}
# Must be an OpenAI model that supports functions
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}
# Must be an OpenAI model that supports functions
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
document_transformer = create_metadata_tagger(schema, llm)
original_documents = [
Document(page_content="Review of The Bee Movie
By Roger Ebert
This is the greatest movie ever made. 4 out of 5 stars.”),
Document(page_content=”Review of The Godfather
By Anonymous
This movie was super boring. 1 out of 5 stars.”, metadata={“reliable”: False}),]
enhanced_documents = document_transformer.transform_documents(original_documents)
Examples using create_metadata_tagger¶
OpenAI Functions Metadata Tagger
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.openai_functions.create_metadata_tagger.html
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langchain.document_transformers.doctran_text_translate.DoctranTextTranslator¶
class langchain.document_transformers.doctran_text_translate.DoctranTextTranslator(openai_api_key: Optional[str] = None, language: str = 'english', openai_api_model: Optional[str] = None)[source]¶
Translate text documents using doctran.
Parameters
openai_api_key – OpenAI API key. Can also be specified via environment variable
OPENAI_API_KEY. –
language – The language to translate to.
Example
from langchain.document_transformers import DoctranTextTranslator
# Pass in openai_api_key or set env var OPENAI_API_KEY
qa_translator = DoctranTextTranslator(language=”spanish”)
translated_document = await qa_translator.atransform_documents(documents)
Methods
__init__([openai_api_key, language, ...])
atransform_documents(documents, **kwargs)
Translates text documents using doctran.
transform_documents(documents, **kwargs)
Transform a list of documents.
__init__(openai_api_key: Optional[str] = None, language: str = 'english', openai_api_model: Optional[str] = None) → None[source]¶
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Translates text documents using doctran.
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
Examples using DoctranTextTranslator¶
Doctran Translate Documents
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.doctran_text_translate.DoctranTextTranslator.html
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langchain.document_transformers.embeddings_redundant_filter.EmbeddingsRedundantFilter¶
class langchain.document_transformers.embeddings_redundant_filter.EmbeddingsRedundantFilter[source]¶
Bases: BaseDocumentTransformer, BaseModel
Filter that drops redundant documents by comparing their embeddings.
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 embeddings: langchain.embeddings.base.Embeddings [Required]¶
Embeddings to use for embedding document contents.
param similarity_fn: Callable = <function cosine_similarity>¶
Similarity function for comparing documents. Function expected to take as input
two matrices (List[List[float]]) and return a matrix of scores where higher values
indicate greater similarity.
param similarity_threshold: float = 0.95¶
Threshold for determining when two documents are similar enough
to be considered redundant.
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Asynchronously transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
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¶
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.embeddings_redundant_filter.EmbeddingsRedundantFilter.html
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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¶
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().
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.embeddings_redundant_filter.EmbeddingsRedundantFilter.html
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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¶
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Filter down documents.
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 EmbeddingsRedundantFilter¶
LOTR (Merger Retriever)
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.embeddings_redundant_filter.EmbeddingsRedundantFilter.html
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langchain.document_transformers.doctran_text_qa.DoctranQATransformer¶
class langchain.document_transformers.doctran_text_qa.DoctranQATransformer(openai_api_key: Optional[str] = None, openai_api_model: Optional[str] = None)[source]¶
Extract QA from text documents using doctran.
Parameters
openai_api_key – OpenAI API key. Can also be specified via environment variable
OPENAI_API_KEY.
Example
from langchain.document_transformers import DoctranQATransformer
# Pass in openai_api_key or set env var OPENAI_API_KEY
qa_transformer = DoctranQATransformer()
transformed_document = await qa_transformer.atransform_documents(documents)
Methods
__init__([openai_api_key, openai_api_model])
atransform_documents(documents, **kwargs)
Extracts QA from text documents using doctran.
transform_documents(documents, **kwargs)
Transform a list of documents.
__init__(openai_api_key: Optional[str] = None, openai_api_model: Optional[str] = None) → None[source]¶
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Extracts QA from text documents using doctran.
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
Examples using DoctranQATransformer¶
Doctran Interrogate Documents
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.doctran_text_qa.DoctranQATransformer.html
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langchain.document_transformers.openai_functions.OpenAIMetadataTagger¶
class langchain.document_transformers.openai_functions.OpenAIMetadataTagger[source]¶
Bases: BaseDocumentTransformer, BaseModel
Extract metadata tags from document contents using OpenAI functions.
Example:from langchain.chat_models import ChatOpenAI
from langchain.document_transformers import OpenAIMetadataTagger
from langchain.schema import Document
schema = {
"properties": {
"movie_title": { "type": "string" },
"critic": { "type": "string" },
"tone": {
"type": "string",
"enum": ["positive", "negative"]
},
"rating": {
"type": "integer",
"description": "The number of stars the critic rated the movie"
}
},
"required": ["movie_title", "critic", "tone"]
}
# Must be an OpenAI model that supports functions
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
tagging_chain = create_tagging_chain(schema, llm)
document_transformer = OpenAIMetadataTagger(tagging_chain=tagging_chain)
original_documents = [
Document(page_content="Review of The Bee Movie
By Roger Ebert
This is the greatest movie ever made. 4 out of 5 stars.”),
Document(page_content=”Review of The Godfather
By Anonymous
This movie was super boring. 1 out of 5 stars.”, metadata={“reliable”: False}),]
enhanced_documents = document_transformer.transform_documents(original_documents)
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.
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.openai_functions.OpenAIMetadataTagger.html
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Raises ValidationError if the input data cannot be parsed to form a valid model.
param tagging_chain: langchain.chains.llm.LLMChain [Required]¶
The chain used to extract metadata from each document.
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Asynchronously transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
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
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.openai_functions.OpenAIMetadataTagger.html
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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¶
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.openai_functions.OpenAIMetadataTagger.html
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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¶
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Automatically extract and populate metadata
for each document according to the provided schema.
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¶
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.openai_functions.OpenAIMetadataTagger.html
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langchain.document_transformers.doctran_text_extract.DoctranPropertyExtractor¶
class langchain.document_transformers.doctran_text_extract.DoctranPropertyExtractor(properties: List[dict], openai_api_key: Optional[str] = None, openai_api_model: Optional[str] = None)[source]¶
Extract properties from text documents using doctran.
Parameters
properties – A list of the properties to extract.
openai_api_key – OpenAI API key. Can also be specified via environment variable
OPENAI_API_KEY.
Example
from langchain.document_transformers import DoctranPropertyExtractor
properties = [
{
"name": "category",
"description": "What type of email this is.",
"type": "string",
"enum": ["update", "action_item", "customer_feedback", "announcement", "other"],
"required": True,
},
{
"name": "mentions",
"description": "A list of all people mentioned in this email.",
"type": "array",
"items": {
"name": "full_name",
"description": "The full name of the person mentioned.",
"type": "string",
},
"required": True,
},
{
"name": "eli5",
"description": "Explain this email to me like I'm 5 years old.",
"type": "string",
"required": True,
},
]
# Pass in openai_api_key or set env var OPENAI_API_KEY
property_extractor = DoctranPropertyExtractor(properties)
transformed_document = await qa_transformer.atransform_documents(documents)
Methods
__init__(properties[, openai_api_key, ...])
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.doctran_text_extract.DoctranPropertyExtractor.html
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Methods
__init__(properties[, openai_api_key, ...])
atransform_documents(documents, **kwargs)
Extracts properties from text documents using doctran.
transform_documents(documents, **kwargs)
Transform a list of documents.
__init__(properties: List[dict], openai_api_key: Optional[str] = None, openai_api_model: Optional[str] = None) → None[source]¶
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Extracts properties from text documents using doctran.
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
Examples using DoctranPropertyExtractor¶
Doctran Extract Properties
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.doctran_text_extract.DoctranPropertyExtractor.html
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langchain.document_transformers.embeddings_redundant_filter.get_stateful_documents¶
langchain.document_transformers.embeddings_redundant_filter.get_stateful_documents(documents: Sequence[Document]) → Sequence[_DocumentWithState][source]¶
Convert a list of documents to a list of documents with state.
Parameters
documents – The documents to convert.
Returns
A list of documents with state.
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.embeddings_redundant_filter.get_stateful_documents.html
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langchain.document_transformers.nuclia_text_transform.NucliaTextTransformer¶
class langchain.document_transformers.nuclia_text_transform.NucliaTextTransformer(nua: NucliaUnderstandingAPI)[source]¶
The Nuclia Understanding API splits into paragraphs and sentences,
identifies entities, provides a summary of the text and generates
embeddings for all the sentences.
Methods
__init__(nua)
atransform_documents(documents, **kwargs)
Asynchronously transform a list of documents.
transform_documents(documents, **kwargs)
Transform a list of documents.
__init__(nua: NucliaUnderstandingAPI)[source]¶
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Asynchronously transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.nuclia_text_transform.NucliaTextTransformer.html
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langchain.document_transformers.html2text.Html2TextTransformer¶
class langchain.document_transformers.html2text.Html2TextTransformer[source]¶
Replace occurrences of a particular search pattern with a replacement string
.. rubric:: Example
Methods
__init__()
atransform_documents(documents, **kwargs)
Asynchronously transform a list of documents.
transform_documents(documents, **kwargs)
Transform a list of documents.
__init__()¶
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Asynchronously transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
Examples using Html2TextTransformer¶
html2text
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.html2text.Html2TextTransformer.html
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langchain.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter¶
class langchain.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter[source]¶
Bases: BaseDocumentTransformer, BaseModel
Perform K-means clustering on document vectors.
Returns an arbitrary number of documents closest to center.
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 embeddings: langchain.embeddings.base.Embeddings [Required]¶
Embeddings to use for embedding document contents.
param num_closest: int = 1¶
The number of closest vectors to return for each cluster center.
param num_clusters: int = 5¶
Number of clusters. Groups of documents with similar meaning.
param random_state: int = 42¶
Controls the random number generator used to initialize the cluster centroids.
If you set the random_state parameter to None, the KMeans algorithm will use a
random number generator that is seeded with the current time. This means
that the results of the KMeans algorithm will be different each time you
run it.
param remove_duplicates = False¶
By default duplicated results are skipped and replaced by the next closest
vector in the cluster. If remove_duplicates is true no replacement will be done:
This could dramatically reduce results when there is a lot of overlap between
clusters.
param sorted: bool = False¶
By default results are re-ordered “grouping” them by cluster, if sorted is true
result will be ordered by the original position from the retriever
async atransform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Asynchronously transform a list of documents.
Parameters
documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter.html
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documents – A sequence of Documents to be transformed.
Returns
A list of transformed Documents.
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¶
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter.html
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1f84d30e52eb-2
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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¶
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¶
transform_documents(documents: Sequence[Document], **kwargs: Any) → Sequence[Document][source]¶
Filter down documents.
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 EmbeddingsClusteringFilter¶
|
https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter.html
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1f84d30e52eb-3
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classmethod validate(value: Any) → Model¶
Examples using EmbeddingsClusteringFilter¶
LOTR (Merger Retriever)
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https://api.python.langchain.com/en/latest/document_transformers/langchain.document_transformers.embeddings_redundant_filter.EmbeddingsClusteringFilter.html
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