id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
902718bd737b-7 | )
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_th... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
902718bd737b-8 | **kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, floa... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
902718bd737b-9 | “LangChainCollection”.
connection_args (dict[str, Any], optional) – Connection args to use. Defaults
to DEFAULT_MILVUS_CONNECTION.
consistency_level (str, optional) – Which consistency level to use. Defaults
to “Session”.
index_params (Optional[dict], optional) – Which index_params to use.
Defaults to None.
search_para... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
902718bd737b-10 | kwargs – Collection.search() keyword arguments.
Returns
Document results for search.
Return type
List[Document]
max_marginal_relevance_search_by_vector(embedding: list[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
902718bd737b-11 | Perform a similarity search against the query string.
Parameters
query (str) – The text to search.
k (int, optional) – How many results to return. Defaults to 4.
param (dict, optional) – The search params for the index type.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
902718bd737b-12 | query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_wit... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
902718bd737b-13 | For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Parameters
embedding (List[float]) – The embedding vector being searched.
k (int, optional) – The amount of results to return. Defaults to 4.
par... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
efe43098dd86-0 | langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.load_response_generator¶
langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.load_response_generator(llm: BaseLanguageModel) → ResponseGenerator[source]¶ | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.load_response_generator.html |
af724c6157fe-0 | langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerator¶
class langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerator(llm_chain: LLMChain, stop: Optional[List] = None)[source]¶
Methods
__init__(llm_chain[, stop])
generate(inputs[, callbacks])
Given inpu... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerator.html |
cb5741141b2a-0 | langchain_experimental.autonomous_agents.autogpt.output_parser.preprocess_json_input¶
langchain_experimental.autonomous_agents.autogpt.output_parser.preprocess_json_input(input_str: str) → str[source]¶
Preprocesses a string to be parsed as json.
Replace single backslashes with double backslashes,
while leaving already ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.preprocess_json_input.html |
14762ffd1837-0 | langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain¶
class langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain[source]¶
Bases: LLMChain
Chain to execute tasks.
Create a new model by parsing and validating input data from keyword argum... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-1 | param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-2 | returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-3 | e.g., if the underlying runnable uses an API which supports a batch mode.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-4 | Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async versi... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-5 | Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Ch... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-6 | # -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, conf... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-7 | Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-8 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-9 | 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_... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-10 | 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_defa... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-11 | Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Di... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-12 | Prepare prompts from inputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__c... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-13 | save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definiti... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-14 | 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.
with_liste... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
14762ffd1837-15 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces speci... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator.ResponseGenerationChain.html |
36c59cc5d271-0 | langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI¶
class langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI[source]¶
Bases: Chain, BaseModel
Controller model for the BabyAGI agent.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-1 | Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param task_creation_chain: langchain.chains.base... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-2 | addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to c... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-3 | response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-4 | Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expect... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-5 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-6 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic m... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-7 | 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(**kw... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-8 | 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_next_task(result: str, task_description: str, objective: str, **kwargs: Any) → List[Dict][source]¶
Get the next task.
get_output_schema(config: Optional[Runnabl... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-9 | 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_defa... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-10 | Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str,... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-11 | callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the c... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-12 | 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] = No... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-13 | 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... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
36c59cc5d271-14 | 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]¶
... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI.html |
971adc2e9ca8-0 | langchain_experimental.autonomous_agents.hugginggpt.task_executor.Task¶
class langchain_experimental.autonomous_agents.hugginggpt.task_executor.Task(task: str, id: int, dep: List[int], args: Dict, tool: BaseTool)[source]¶
Methods
__init__(task, id, dep, args, tool)
completed()
failed()
pending()
run()
save_product()
__... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_executor.Task.html |
013e5806335d-0 | langchain_experimental.autonomous_agents.hugginggpt.task_planner.BasePlanner¶
class langchain_experimental.autonomous_agents.hugginggpt.task_planner.BasePlanner[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be pa... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.BasePlanner.html |
013e5806335d-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.BasePlanner.html |
013e5806335d-2 | abstract plan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decide what to do.
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.BasePlanner.html |
18b51eea54f9-0 | langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt¶
class langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt[source]¶
Bases: BaseChatPromptTemplate, BaseModel
Prompt for AutoGPT.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
18b51eea54f9-1 | Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
async astream(input: Input, config: Optional[RunnableConfig... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
18b51eea54f9-2 | input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch w... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
18b51eea54f9-3 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
construct_full_prompt(goals: List[str]) → str[source]¶
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, dee... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
18b51eea54f9-4 | 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.... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
18b51eea54f9-5 | classmethod is_lc_serializable() → bool¶
Return whether this class is 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 = Fals... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
18b51eea54f9-6 | Save the prompt.
Parameters
file_path – Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
18b51eea54f9-7 | 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.
with_liste... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
18b51eea54f9-8 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Any¶
The type of output this runnable produces specified as a type annotation.
property confi... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt.html |
2a662d0ef6a1-0 | langchain_experimental.autonomous_agents.hugginggpt.hugginggpt.HuggingGPT¶
class langchain_experimental.autonomous_agents.hugginggpt.hugginggpt.HuggingGPT(llm: BaseLanguageModel, tools: List[BaseTool])[source]¶
Methods
__init__(llm, tools)
run(input)
__init__(llm: BaseLanguageModel, tools: List[BaseTool])[source]¶
run(... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.hugginggpt.HuggingGPT.html |
a3c116f13179-0 | langchain_experimental.autonomous_agents.autogpt.prompt_generator.PromptGenerator¶
class langchain_experimental.autonomous_agents.autogpt.prompt_generator.PromptGenerator[source]¶
A class for generating custom prompt strings.
Does this based on constraints, commands, resources, and performance evaluations.
Initialize t... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.prompt_generator.PromptGenerator.html |
6d9fb02de1be-0 | langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlanner¶
class langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlanner[source]¶
Bases: BasePlanner
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlanner.html |
6d9fb02de1be-1 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[boo... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlanner.html |
6d9fb02de1be-2 | plan(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Plan[source]¶
Given input, decided what to do.
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True,... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlanner.html |
672fd7f3eff2-0 | langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser¶
class langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser[source]¶
Bases: BaseOutputParser
Base Output parser for AutoGPT.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, L... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
672fd7f3eff2-1 | to be different candidate outputs for a single model input.
Returns
Structured output.
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support str... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
672fd7f3eff2-2 | Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
672fd7f3eff2-3 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
672fd7f3eff2-4 | 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.
invoke(input:... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
672fd7f3eff2-5 | 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.
abstract parse(text: str) → AutoGPTAction[source]¶
Return AutoGPTAction
classmethod p... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
672fd7f3eff2-6 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override t... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
672fd7f3eff2-7 | 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 ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
672fd7f3eff2-8 | The type of output this runnable produces specified as a type annotation.
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 ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser.html |
215a52b1a532-0 | langchain_experimental.autonomous_agents.hugginggpt.task_planner.load_chat_planner¶
langchain_experimental.autonomous_agents.hugginggpt.task_planner.load_chat_planner(llm: BaseLanguageModel) → TaskPlanner[source]¶ | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.load_chat_planner.html |
288d37b34980-0 | langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTAction¶
class langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTAction(name: str, args: Dict)[source]¶
Action returned by AutoGPTOutputParser.
Create new instance of AutoGPTAction(name, args)
Attributes
args
Alias for field number... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.output_parser.AutoGPTAction.html |
67565d446f61-0 | langchain_experimental.autonomous_agents.autogpt.agent.AutoGPT¶
class langchain_experimental.autonomous_agents.autogpt.agent.AutoGPT(ai_name: str, memory: VectorStoreRetriever, chain: LLMChain, output_parser: BaseAutoGPTOutputParser, tools: List[BaseTool], feedback_tool: Optional[HumanInputRun] = None, chat_history_mem... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.autogpt.agent.AutoGPT.html |
5830e06cf4b1-0 | langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain¶
class langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain[source]¶
Bases: LLMChain
Chain to execute tasks.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationErr... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-1 | param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-2 | returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-3 | e.g., if the underlying runnable uses an API which supports a batch mode.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-4 | Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async versi... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-5 | Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Ch... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-6 | # -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, conf... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-7 | Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-8 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-9 | # -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, demos: List[Dict] = [{'role': 'user', 'content': "please show me a video and an image of (based on the text) 'a boy is running' and dub it"}, {'role': 'assistant', 'content': '[{{"task": "video_generator", "id": 0, "dep": [-1], "a... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-10 | Get the response parser.
classmethod from_orm(obj: Any) → Model¶
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from in... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-11 | Returns
A pydantic model that can be used to validate output.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
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 r... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-12 | 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 = No... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-13 | Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the fi... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-14 | Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
cont... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-15 | 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 Runna... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-16 | 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_exc... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
5830e06cf4b1-17 | property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.hugginggpt.task_planner.TaskPlaningChain.html |
bf5a1cd22a32-0 | langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain¶
class langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain[source]¶
Bases: LLMChain
Chain to prioritize tasks.
Create a new model by parsing and validating input data from keyword argu... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-1 | You can use these to eg identify a specific instance of a chain with its use case.
param output_parser: BaseLLMOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param retur... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-2 | response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-3 | e.g., if the underlying runnable uses an API which supports a batch mode.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, ... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-4 | Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async versi... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-5 | Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Ch... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-6 | # -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, conf... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-7 | Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-8 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-9 | 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_... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
bf5a1cd22a32-10 | 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_defa... | lang/api.python.langchain.com/en/latest/autonomous_agents/langchain_experimental.autonomous_agents.baby_agi.task_prioritization.TaskPrioritizationChain.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.