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80af13ff513b-6 | Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, 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
dict(**kwargs: Any) → Dict¶
Return a dictionary of the LLM.
classmethod from_orm(obj: Any) → Model¶ | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
80af13ff513b-7 | classmethod from_orm(obj: Any) → Model¶
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, **kwargs: Any) → LLMResult¶
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched
API.
Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptValue is an object that can be
converted to match the format of any language model (string for pure
text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional
functionality, such as logging or streaming, throughout generation. | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
80af13ff513b-8 | functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_num_tokens(text: str) → int¶
Get the number of tokens present in the text.
Useful for checking if an input will fit in a model’s context window.
Parameters
text – The string input to tokenize.
Returns
The integer number of tokens in the text.
get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶
Get the number of tokens in the messages.
Useful for checking if an input will fit in a model’s context window.
Parameters
messages – The message inputs to tokenize.
Returns
The sum of the number of tokens across the messages.
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶ | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
80af13ff513b-9 | Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
get_token_ids(text: str) → List[int]¶
Return the ordered ids of the tokens in a text.
Parameters
text – The string input to tokenize.
Returns
A list of ids corresponding to the tokens in the text, in order they occurin the text.
invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable? | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
80af13ff513b-10 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
Pass a single string input to the model and return a string prediction. | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
80af13ff513b-11 | Pass a single string input to the model and return a string prediction.
Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages.
Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.
predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
Pass a message sequence to the model and return a message prediction.
Use this method when passing in chat messages. If you want to pass in raw text,use predict.
Parameters
messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
save(file_path: Union[Path, str]) → None¶
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶ | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
80af13ff513b-12 | stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures. | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
80af13ff513b-13 | fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶ | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
80af13ff513b-14 | property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using GPT4All¶
PromptLayer
GPT4All
Run LLMs locally
Use local LLMs | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html |
23e3e3edbba9-0 | langchain.llms.bedrock.LLMInputOutputAdapter¶
class langchain.llms.bedrock.LLMInputOutputAdapter[source]¶
Adapter class to prepare the inputs from Langchain to a format
that LLM model expects.
It also provides helper function to extract
the generated text from the model response.
Attributes
provider_to_output_key_map
Methods
__init__()
prepare_input(provider, prompt, model_kwargs)
prepare_output(provider, response)
prepare_output_stream(provider, response[, stop])
__init__()¶
classmethod prepare_input(provider: str, prompt: str, model_kwargs: Dict[str, Any]) → Dict[str, Any][source]¶
classmethod prepare_output(provider: str, response: Any) → str[source]¶
classmethod prepare_output_stream(provider: str, response: Any, stop: Optional[List[str]] = None) → Iterator[GenerationChunk][source]¶ | lang/api.python.langchain.com/en/latest/llms/langchain.llms.bedrock.LLMInputOutputAdapter.html |
e74707f2ebbe-0 | langchain.llms.gradient_ai.TrainResult¶
class langchain.llms.gradient_ai.TrainResult[source]¶
Train result.
loss: float¶ | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.TrainResult.html |
5c81f3a01aad-0 | langchain_experimental.graph_transformers.diffbot.NodesList¶
class langchain_experimental.graph_transformers.diffbot.NodesList[source]¶
Manages a list of nodes with associated properties.
nodes¶
Stores nodes as keys and their properties as values.
Each key is a tuple where the first element is the
node ID and the second is the node type.
Type
Dict[Tuple, Any]
Methods
__init__()
add_node_property(node, properties)
Adds or updates node properties.
return_node_list()
Returns the nodes as a list of Node objects.
__init__() → None[source]¶
add_node_property(node: Tuple[Union[str, int], str], properties: Dict[str, Any]) → None[source]¶
Adds or updates node properties.
If the node does not exist in the list, it’s added along with its properties.
If the node already exists, its properties are updated with the new values.
Parameters
node (Tuple) – A tuple containing the node ID and node type.
properties (Dict) – A dictionary of properties to add or update for the node.
return_node_list() → List[Node][source]¶
Returns the nodes as a list of Node objects.
Each Node object will have its ID, type, and properties populated.
Returns
A list of Node objects.
Return type
List[Node] | lang/api.python.langchain.com/en/latest/graph_transformers/langchain_experimental.graph_transformers.diffbot.NodesList.html |
99b2056ba95d-0 | langchain_experimental.graph_transformers.diffbot.SimplifiedSchema¶
class langchain_experimental.graph_transformers.diffbot.SimplifiedSchema[source]¶
Provides functionality for working with a simplified schema mapping.
schema¶
A dictionary containing the mapping to simplified schema types.
Type
Dict
Initializes the schema dictionary based on the predefined list.
Methods
__init__()
Initializes the schema dictionary based on the predefined list.
get_type(type)
Retrieves the simplified schema type for a given original type.
__init__() → None[source]¶
Initializes the schema dictionary based on the predefined list.
get_type(type: str) → str[source]¶
Retrieves the simplified schema type for a given original type.
Parameters
type (str) – The original schema type to find the simplified type for.
Returns
The simplified schema type if it exists;otherwise, returns the original type.
Return type
str | lang/api.python.langchain.com/en/latest/graph_transformers/langchain_experimental.graph_transformers.diffbot.SimplifiedSchema.html |
ce0aa7fae373-0 | langchain_experimental.graph_transformers.diffbot.DiffbotGraphTransformer¶
class langchain_experimental.graph_transformers.diffbot.DiffbotGraphTransformer(diffbot_api_key: Optional[str] = None, fact_confidence_threshold: float = 0.7, include_qualifiers: bool = True, include_evidence: bool = True, simplified_schema: bool = True)[source]¶
Transforms documents into graph documents using Diffbot’s NLP API.
A graph document transformation system takes a sequence of Documents and returns a
sequence of Graph Documents.
Example
class DiffbotGraphTransformer(BaseGraphDocumentTransformer):
def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[GraphDocument]:
results = []
for document in documents:
raw_results = self.nlp_request(document.page_content)
graph_document = self.process_response(raw_results, document)
results.append(graph_document)
return results
async def atransform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
raise NotImplementedError
Initialize the graph transformer with various options.
Parameters
diffbot_api_key (str) – The API key for Diffbot’s NLP services.
fact_confidence_threshold (float) – Minimum confidence level for facts to be included.
include_qualifiers (bool) – Whether to include qualifiers in the relationships.
include_evidence (bool) – Whether to include evidence for the relationships.
simplified_schema (bool) – Whether to use a simplified schema for relationships.
Methods
__init__([diffbot_api_key, ...])
Initialize the graph transformer with various options.
convert_to_graph_documents(documents)
Convert a sequence of documents into graph documents.
nlp_request(text)
Make an API request to the Diffbot NLP endpoint. | lang/api.python.langchain.com/en/latest/graph_transformers/langchain_experimental.graph_transformers.diffbot.DiffbotGraphTransformer.html |
ce0aa7fae373-1 | nlp_request(text)
Make an API request to the Diffbot NLP endpoint.
process_response(payload, document)
Transform the Diffbot NLP response into a GraphDocument.
__init__(diffbot_api_key: Optional[str] = None, fact_confidence_threshold: float = 0.7, include_qualifiers: bool = True, include_evidence: bool = True, simplified_schema: bool = True) → None[source]¶
Initialize the graph transformer with various options.
Parameters
diffbot_api_key (str) – The API key for Diffbot’s NLP services.
fact_confidence_threshold (float) – Minimum confidence level for facts to be included.
include_qualifiers (bool) – Whether to include qualifiers in the relationships.
include_evidence (bool) – Whether to include evidence for the relationships.
simplified_schema (bool) – Whether to use a simplified schema for relationships.
convert_to_graph_documents(documents: Sequence[Document]) → List[GraphDocument][source]¶
Convert a sequence of documents into graph documents.
Parameters
documents (Sequence[Document]) – The original documents.
**kwargs – Additional keyword arguments.
Returns
The transformed documents as graphs.
Return type
Sequence[GraphDocument]
nlp_request(text: str) → Dict[str, Any][source]¶
Make an API request to the Diffbot NLP endpoint.
Parameters
text (str) – The text to be processed.
Returns
The JSON response from the API.
Return type
Dict[str, Any]
process_response(payload: Dict[str, Any], document: Document) → GraphDocument[source]¶
Transform the Diffbot NLP response into a GraphDocument.
Parameters
payload (Dict[str, Any]) – The JSON response from Diffbot’s NLP API.
document (Document) – The original document.
Returns | lang/api.python.langchain.com/en/latest/graph_transformers/langchain_experimental.graph_transformers.diffbot.DiffbotGraphTransformer.html |
ce0aa7fae373-2 | document (Document) – The original document.
Returns
The transformed document as a graph.
Return type
GraphDocument | lang/api.python.langchain.com/en/latest/graph_transformers/langchain_experimental.graph_transformers.diffbot.DiffbotGraphTransformer.html |
508ed3ea8828-0 | langchain_experimental.graph_transformers.diffbot.format_property_key¶
langchain_experimental.graph_transformers.diffbot.format_property_key(s: str) → str[source]¶ | lang/api.python.langchain.com/en/latest/graph_transformers/langchain_experimental.graph_transformers.diffbot.format_property_key.html |
a5be391302b0-0 | langchain_experimental.generative_agents.memory.GenerativeAgentMemory¶
class langchain_experimental.generative_agents.memory.GenerativeAgentMemory[source]¶
Bases: BaseMemory
Memory for the generative agent.
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 add_memory_key: str = 'add_memory'¶
param aggregate_importance: float = 0.0¶
Track the sum of the ‘importance’ of recent memories.
Triggers reflection when it reaches reflection_threshold.
param current_plan: List[str] = []¶
The current plan of the agent.
param importance_weight: float = 0.15¶
How much weight to assign the memory importance.
param llm: langchain.schema.language_model.BaseLanguageModel [Required]¶
The core language model.
param max_tokens_limit: int = 1200¶
param memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever [Required]¶
The retriever to fetch related memories.
param most_recent_memories_key: str = 'most_recent_memories'¶
param most_recent_memories_token_key: str = 'recent_memories_token'¶
param now_key: str = 'now'¶
param queries_key: str = 'queries'¶
param reflecting: bool = False¶
param reflection_threshold: Optional[float] = None¶
When aggregate_importance exceeds reflection_threshold, stop to reflect.
param relevant_memories_key: str = 'relevant_memories'¶
param relevant_memories_simple_key: str = 'relevant_memories_simple'¶
param verbose: bool = False¶
add_memories(memory_content: str, now: Optional[datetime] = None) → List[str][source]¶ | lang/api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.memory.GenerativeAgentMemory.html |
a5be391302b0-1 | Add an observations or memories to the agent’s memory.
add_memory(memory_content: str, now: Optional[datetime] = None) → List[str][source]¶
Add an observation or memory to the agent’s memory.
chain(prompt: PromptTemplate) → LLMChain[source]¶
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
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¶ | lang/api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.memory.GenerativeAgentMemory.html |
a5be391302b0-2 | Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
fetch_memories(observation: str, now: Optional[datetime] = None) → List[Document][source]¶
Fetch related memories.
format_memories_detail(relevant_memories: List[Document]) → str[source]¶
format_memories_simple(relevant_memories: List[Document]) → str[source]¶
classmethod from_orm(obj: Any) → Model¶
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]¶
Return key-value pairs given the text input to the chain. | lang/api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.memory.GenerativeAgentMemory.html |
a5be391302b0-3 | 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¶
pause_to_reflect(now: Optional[datetime] = None) → List[str][source]¶
Reflect on recent observations and generate ‘insights’.
save_context(inputs: Dict[str, Any], outputs: Dict[str, Any]) → None[source]¶
Save the context of this model 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¶
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¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property memory_variables: List[str]¶
Input keys this memory class will load dynamically. | lang/api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.memory.GenerativeAgentMemory.html |
62b4d8483fa0-0 | langchain_experimental.generative_agents.generative_agent.GenerativeAgent¶
class langchain_experimental.generative_agents.generative_agent.GenerativeAgent[source]¶
Bases: BaseModel
An Agent as a character with memory and innate characteristics.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param age: Optional[int] = None¶
The optional age of the character.
param daily_summaries: List[str] [Optional]¶
Summary of the events in the plan that the agent took.
param last_refreshed: datetime.datetime [Optional]¶
The last time the character’s summary was regenerated.
param llm: langchain.schema.language_model.BaseLanguageModel [Required]¶
The underlying language model.
param memory: langchain_experimental.generative_agents.memory.GenerativeAgentMemory [Required]¶
The memory object that combines relevance, recency, and ‘importance’.
param name: str [Required]¶
The character’s name.
param status: str [Required]¶
The traits of the character you wish not to change.
param summary: str = ''¶
Stateful self-summary generated via reflection on the character’s memory.
param summary_refresh_seconds: int = 3600¶
How frequently to re-generate the summary.
param traits: str = 'N/A'¶
Permanent traits to ascribe to the character.
param verbose: bool = False¶
chain(prompt: PromptTemplate) → LLMChain[source]¶
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. | lang/api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.generative_agent.GenerativeAgent.html |
62b4d8483fa0-1 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = 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¶
generate_dialogue_response(observation: str, now: Optional[datetime] = None) → Tuple[bool, str][source]¶
React to a given observation.
generate_reaction(observation: str, now: Optional[datetime] = None) → Tuple[bool, str][source]¶
React to a given observation.
get_full_header(force_refresh: bool = False, now: Optional[datetime] = None) → str[source]¶ | lang/api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.generative_agent.GenerativeAgent.html |
62b4d8483fa0-2 | Return a full header of the agent’s status, summary, and current time.
get_summary(force_refresh: bool = False, now: Optional[datetime] = None) → str[source]¶
Return a descriptive summary of the agent.
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¶
summarize_related_memories(observation: str) → str[source]¶
Summarize memories that are most relevant to an observation.
classmethod update_forward_refs(**localns: Any) → None¶ | lang/api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.generative_agent.GenerativeAgent.html |
62b4d8483fa0-3 | 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¶ | lang/api.python.langchain.com/en/latest/generative_agents/langchain_experimental.generative_agents.generative_agent.GenerativeAgent.html |
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