Tony20100/langchain_full huggingface dataset:
get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None.  Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v
In either case, this is followed by: for k in F:  D[k] = F[k]
values() → an object providing a view on D's values¶
langchain.smith.evaluation.config.EvalConfig¶
class langchain.smith.evaluation.config.EvalConfig[source]¶
Bases: BaseModel
Configuration for a given run evaluator.
Parameters
evaluator_type (EvaluatorType) – The type of evaluator to use.
get_kwargs()[source]¶
Get the keyword arguments for the evaluator configuration.
Return type
Dict[str, Any]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param evaluator_type: EvaluatorType [Required]¶
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
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.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_kwargs() → Dict[str, Any][source]¶
Get the keyword arguments for the load_evaluator call.
Returns
The keyword arguments for the load_evaluator call.
Return type
Dict[str, Any]
langchain.smith.evaluation.progress.ProgressBarCallback¶
class langchain.smith.evaluation.progress.ProgressBarCallback(total: int, ncols: int = 50, **kwargs: Any)[source]¶
A simple progress bar for the console.
Initialize the progress bar.
Parameters
total (int) – int, the total number of items to be processed.
ncols (int) – int, the character width of the progress bar.
kwargs (Any) – 
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__(total[, ncols])
Initialize the progress bar.
increment()
Increment the counter and update the progress bar.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, parent_run_id])
Run when chain ends running.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id[, parent_run_id])
Run when LLM ends running.
on_llm_error(error, *, run_id[, parent_run_id])
on_llm_error(error, *, run_id[, parent_run_id])
Run when LLM errors. :param error: The error that occurred. :type error: BaseException :param kwargs: Additional keyword arguments. - response (LLMResult): The response which was generated before                    the error occurred. :type kwargs: Any.
on_llm_new_token(token, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__(total: int, ncols: int = 50, **kwargs: Any)[source]¶
Initialize the progress bar.
Parameters
total (int) – int, the total number of items to be processed.
ncols (int) – int, the character width of the progress bar.
kwargs (Any) – 
increment() → None[source]¶
kwargs (Any) – 
increment() → None[source]¶
Increment the counter and update the progress bar.
Return type
None
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
Parameters
action (AgentAction) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
Parameters
finish (AgentFinish) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when chain errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
kwargs (Any) – 
Return type
Any
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) – 
inputs (Dict[str, Any]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
Any
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
ATTENTION: This method is called for chat models. If you’re implementinga handler for a non-chat model, you should use on_llm_start instead.
Parameters
serialized (Dict[str, Any]) – 
messages (List[List[BaseMessage]]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
Any
on_llm_end(response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when LLM ends running.
Parameters
response (LLMResult) –
Run when LLM ends running.
Parameters
response (LLMResult) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when LLM errors.
:param error: The error that occurred.
:type error: BaseException
:param kwargs: Additional keyword arguments.
response (LLMResult): The response which was generated beforethe error occurred.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) – The new token.
chunk (GenerationChunk | ChatGenerationChunk) – The new generated chunk,
information. (containing content and other) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when LLM starts running.
Run when LLM starts running.
ATTENTION: This method is called for non-chat models (regular LLMs). Ifyou’re implementing a handler for a chat model,
you should use on_chat_model_start instead.
Parameters
serialized (Dict[str, Any]) – 
prompts (List[str]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
Any
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when Retriever ends running.
Parameters
documents (Sequence[Document]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when Retriever errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
Parameters
serialized (Dict[str, Any]) – 
query (str) – 
run_id (UUID) –
query (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
Any
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
Parameters
retry_state (RetryCallState) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
Parameters
text (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when tool ends running.
Parameters
output (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when tool errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
langchain.smith.evaluation.runner_utils.arun_on_dataset¶
async langchain.smith.evaluation.runner_utils.arun_on_dataset(client: Optional[Client], dataset_name: str, llm_or_chain_factory: Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel, Callable[[dict], Any], Runnable, Chain], *, evaluation: Optional[RunEvalConfig] = None, dataset_version: Optional[Union[datetime, str]] = None, concurrency_level: int = 5, project_name: Optional[str] = None, project_metadata: Optional[Dict[str, Any]] = None, verbose: bool = False, revision_id: Optional[str] = None, **kwargs: Any) → Dict[str, Any][source]¶
Run the Chain or language model on a dataset and store traces
to the specified project name.
Parameters
dataset_name (str) – Name of the dataset to run the chain on.
llm_or_chain_factory (Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel, Callable[[dict], Any], Runnable, Chain]) – Language model or Chain constructor to run
over the dataset. The Chain constructor is used to permit
independent calls on each example without carrying over state.
evaluation (Optional[RunEvalConfig]) – Configuration for evaluators to run on the
results of the chain
concurrency_level (int) – The number of async tasks to run concurrently.
project_name (Optional[str]) – Name of the project to store the traces in.
Defaults to {dataset_name}-{chain class name}-{datetime}.
project_metadata (Optional[Dict[str, Any]]) – Optional metadata to add to the project.
Useful for storing information the test variant.
(prompt version, model version, etc.)
client (Optional[Client]) – LangSmith client to use to access the dataset and to
log feedback and run traces.
log feedback and run traces.
verbose (bool) – Whether to print progress.
tags – Tags to add to each run in the project.
revision_id (Optional[str]) – Optional revision identifier to assign this test run to
track the performance of different versions of your system.
dataset_version (Optional[Union[datetime, str]]) – 
kwargs (Any) – 
Returns
A dictionary containing the run’s project name and the resulting model outputs.
Return type
Dict[str, Any]
For the (usually faster) async version of this function, see arun_on_dataset().
Examples
from langsmith import Client
from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from langchain.smith import smith_eval.RunEvalConfig, run_on_dataset
# Chains may have memory. Passing in a constructor function lets the
# evaluation framework avoid cross-contamination between runs.
def construct_chain():
    llm = ChatOpenAI(temperature=0)
    chain = LLMChain.from_string(
        llm,
        "What's the answer to {your_input_key}"
    )
    return chain
# Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
evaluation_config = smith_eval.RunEvalConfig(
    evaluators=[
        "qa",  # "Correctness" against a reference answer
        "embedding_distance",
        smith_eval.RunEvalConfig.Criteria("helpfulness"),
        smith_eval.RunEvalConfig.Criteria({
            "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
        }),
    ]
)
client = Client()
await arun_on_dataset(
    client,
    dataset_name="<my_dataset_name>",
    llm_or_chain_factory=construct_chain,
dataset_name="<my_dataset_name>",
    llm_or_chain_factory=construct_chain,
    evaluation=evaluation_config,
)
You can also create custom evaluators by subclassing the
StringEvaluator
or LangSmith’s RunEvaluator classes.
from typing import Optional
from langchain.evaluation import StringEvaluator
class MyStringEvaluator(StringEvaluator):
    @property
    def requires_input(self) -> bool:
        return False
    @property
    def requires_reference(self) -> bool:
        return True
    @property
    def evaluation_name(self) -> str:
        return "exact_match"
    def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict:
        return {"score": prediction == reference}
evaluation_config = smith_eval.RunEvalConfig(
    custom_evaluators = [MyStringEvaluator()],
)
await arun_on_dataset(
    client,
    dataset_name="<my_dataset_name>",
    llm_or_chain_factory=construct_chain,
    evaluation=evaluation_config,
)
langchain.smith.evaluation.string_run_evaluator.LLMStringRunMapper¶
class langchain.smith.evaluation.string_run_evaluator.LLMStringRunMapper[source]¶
Bases: StringRunMapper
Extract items to evaluate from the run object.
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.
__call__(run: Run) → Dict[str, str]¶
Maps the Run to a dictionary.
Parameters
run (Run) – 
Return type
Dict[str, str]
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) – 
Returns
new model instance
Return type
Model
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.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
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”]
Return type
List[str]
classmethod is_lc_serializable() → bool¶
Is this class serializable?
Return type
bool
Is this class serializable?
Return type
bool
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – 
by_alias (bool) – 
skip_defaults (Optional[bool]) – 
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
encoder (Optional[Callable[[Any], Any]]) – 
models_as_dict (bool) – 
dumps_kwargs (Any) – 
Return type
unicode
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.
Return type
List[str]
map(run: Run) → Dict[str, str][source]¶
Maps the Run to a dictionary.
Parameters
run (Run) – 
Return type
Dict[str, str]
Parameters
run (Run) – 
Return type
Dict[str, str]
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
path (Union[str, Path]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod parse_obj(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
serialize_chat_messages(messages: List[Dict]) → str[source]¶
Extract the input messages from the run.
Parameters
messages (List[Dict]) – 
Return type
str
serialize_inputs(inputs: Dict) → str[source]¶
Parameters
Return type
str
serialize_inputs(inputs: Dict) → str[source]¶
Parameters
inputs (Dict) – 
Return type
str
serialize_outputs(outputs: Dict) → str[source]¶
Parameters
outputs (Dict) – 
Return type
str
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
Return type
Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented¶
Return type
SerializedNotImplemented
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
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_keys: List[str]¶
The keys to extract from the run.