langchain_experimental.tot.base.ToTChain

class langchain_experimental.tot.base.ToTChain[source]

Bases: Chain

A Chain implementing the Tree of Thought (ToT).

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 c: int = 3

The number of children to explore at each node

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 checker: ToTChecker [Required]

ToT Checker to use.

param k: int = 10

The maximum number of conversation rounds

param llm: BaseLanguageModel [Required]

Language model to use. It must be set to produce different variations for the same prompt.

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 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 tot_controller: ToTController = <langchain_experimental.tot.controller.ToTController object>
param tot_memory: ToTDFSMemory = <langchain_experimental.tot.memory.ToTDFSMemory object>
param tot_strategy_class: Type[BaseThoughtGenerationStrategy] = <class 'langchain_experimental.tot.thought_generation.ProposePromptStrategy'>
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 the global verbose value, accessible via langchain.globals.get_verbose().

param verbose_llm: bool = False
__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, run_name: Optional[str] = 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 abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) List[Output]

Default implementation runs ainvoke in parallel using asyncio.gather.

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.

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, run_name: Optional[str] = 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 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 version of invoke.

Subclasses should override this method if they can run asynchronously.

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

Call the chain on all inputs in the list.

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..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) AsyncIterator[Output]

Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]

Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc.

Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.

The jsonpatch ops can be applied in order to construct state.

Parameters
  • input – The input to the runnable.

  • config – The config to use for the runnable.

  • diff – Whether to yield diffs between each step, or the current state.

  • with_streamed_output_list – Whether to yield the streamed_output list.

  • include_names – Only include logs with these names.

  • include_types – Only include logs with these types.

  • include_tags – Only include logs with these tags.

  • exclude_names – Exclude logs with these names.

  • exclude_types – Exclude logs with these types.

  • exclude_tags – Exclude logs with these tags.

async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) AsyncIterator[Output]

Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.

batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) List[Output]

Default implementation 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) 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 model.

To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.

Parameters

include – A list of fields to include in the config schema.

Returns

A pydantic model that can be used to validate config.

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **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

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

chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, **kwargs: Any) ToTChain[source]

Create a ToTChain from a language model.

Parameters
  • llm – The language model to use.

  • kwargs – Additional arguments to pass to the ToTChain constructor.

classmethod from_orm(obj: Any) Model
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_output_schema(config: Optional[RunnableConfig] = None) Type[BaseModel]

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.

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 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?

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.

log_thought(thought: Thought, level: int, run_manager: Optional[CallbackManagerForChainRun] = None) None[source]
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
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.

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")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) Iterator[Output]

Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

to_json() Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() SerializedNotImplemented
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) Iterator[Output]

Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.

classmethod update_forward_refs(**localns: Any) None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

with_fallbacks(fallbacks: 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.

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: Type[langchain_core.runnables.utils.Input]

The type of input this runnable accepts specified as a type annotation.

property OutputType: Type[langchain_core.runnables.utils.Output]

The type of output this runnable produces specified as a type annotation.

property config_specs: List[langchain_core.runnables.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.