langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain

class langchain_experimental.autonomous_agents.baby_agi.task_execution.TaskExecutionChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, prompt: BasePromptTemplate, llm: BaseLanguageModel, output_key: str = 'text', output_parser: BaseLLMOutputParser = None, return_final_only: bool = True, llm_kwargs: dict = None)

Bases: LLMChain

Chain to execute tasks.

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 callback_manager: Optional[BaseCallbackManager] = None

Deprecated, use callbacks instead.

param callbacks: Callbacks = None

Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details.

param llm: BaseLanguageModel [Required]

Language model to call.

param llm_kwargs: dict [Optional]

param memory: Optional[BaseMemory] = None

Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog.

param metadata: Optional[Dict[str, Any]] = None

Optional metadata associated with the chain. Defaults to None. This metadata 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 output_key: str = 'text'

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 to True. If false, will return a bunch of extra information about the generation.

param tags: Optional[List[str]] = None

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 verbose: bool [Optional]

Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value.

__call__(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, include_run_info: bool = False) → Dict[str, Any]

Execute the chain.

Parameters

• inputs – Dictionary of 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.

• return_only_outputs – Whether to return only outputs in the 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 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 calls to other objects.

• metadata – Optional metadata associated with the chain. Defaults to None

• include_run_info – Whether to include run info in the response. Defaults to False.

Returns

A dict of named outputs. Should contain all outputs specified in

Chain.output_keys.

async aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]

Utilize the LLM generate method for speed gains.

async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]

Call apply and then parse the results.

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, include_run_info: bool = False) → Dict[str, Any]

Asynchronously execute the chain.

Parameters

• inputs – Dictionary of 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.

• return_only_outputs – Whether to return only outputs in the 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 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 calls to other objects.

• metadata – Optional metadata associated with the chain. Defaults to None

• include_run_info – Whether to include run info in the response. Defaults to False.

Returns

A dict of named outputs. Should contain all outputs specified in

Chain.output_keys.

async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult

Generate LLM result from inputs.

async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]

apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]

Utilize the LLM generate method for speed gains.

apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]

Call apply and then parse the results.

async apredict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str

Format prompt with kwargs and pass to LLM.

Parameters

• callbacks – Callbacks to pass to LLMChain

• **kwargs – Keys to pass to prompt template.

Returns

Completion from LLM.

Example

completion = llm.predict(adjective="funny")

async apredict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, str]]

Call apredict and then parse the results.

async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]

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 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 expects a single input, it can be passed in as the sole positional argument.

• 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 chain during construction, but only these runtime tags will propagate to calls to other objects.

• **kwargs – If the chain expects multiple inputs, they can be passed in directly as keyword arguments.

Returns

The chain output.

Example

# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("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?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."

create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]

Create outputs from response.

dict(**kwargs: Any) → Dict

Dictionary representation of chain.

Expects Chain._chain_type property to be implemented and for memory to be

null.

Parameters

**kwargs – Keyword arguments passed to default pydantic.BaseModel.dict method.

Returns

A dictionary representation of the chain.

Example

..code-block:: python

chain.dict(exclude_unset=True) # -> {“_type”: “foo”, “verbose”: False, …}

classmethod from_llm(llm: BaseLanguageModel, verbose: bool = True) → LLMChain

Get the response parser.

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 inputs.

invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]

predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str

Format prompt with kwargs and pass to LLM.

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], Dict[str, Any]]

Call predict and then parse the results.

prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]

Validate and prepare chain inputs, including adding inputs from memory.

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, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]

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 final outputs.

Returns

A dict of the final chain outputs.

prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]

Prepare prompts from inputs.

validator raise_callback_manager_deprecation  »  all fields

Raise deprecation warning if callback_manager is used.

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.__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 expects a single input, it can be passed in as the sole positional argument.

• 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 chain during construction, but only these runtime tags will propagate to calls to other objects.

• **kwargs – If the chain expects multiple inputs, they can be passed in directly as keyword arguments.

Returns

The chain output.

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?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."

save(file_path: Union[Path, str]) → None

Save the chain.

Expects Chain._chain_type property to be implemented and for memory to be

null.

Parameters

file_path – Path to file to save the chain to.

Example

chain.save(file_path="path/chain.yaml")

validator set_verbose  »  verbose

Set the chain verbosity.

Defaults to the global setting if not specified by the user.

to_json() → Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() → SerializedNotImplemented

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.

model Config

Bases: object

Configuration for this pydantic object.

arbitrary_types_allowed = True

extra = 'forbid'