id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
be89f6ba731b-3 | 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... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.history.RunnableWithMessageHistory.html |
be89f6ba731b-4 | Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.history.RunnableWithMessageHistory.html |
be89f6ba731b-5 | 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/schema.runnable/langchain.schema.runnable.history.RunnableWithMessageHistory.html |
be89f6ba731b-6 | 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: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Output¶
Transform a single input... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.history.RunnableWithMessageHistory.html |
be89f6ba731b-7 | 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¶
cl... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.history.RunnableWithMessageHistory.html |
be89f6ba731b-8 | 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 se... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.history.RunnableWithMessageHistory.html |
be89f6ba731b-9 | 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... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.history.RunnableWithMessageHistory.html |
36314980203a-0 | langchain.schema.runnable.config.merge_configs¶
langchain.schema.runnable.config.merge_configs(*configs: Optional[RunnableConfig]) → RunnableConfig[source]¶
Merge multiple configs into one.
Parameters
*configs (Optional[RunnableConfig]) – The configs to merge.
Returns
The merged config.
Return type
RunnableConfig | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.config.merge_configs.html |
d57d4f5266e1-0 | langchain.schema.runnable.base.RunnableSerializable¶
class langchain.schema.runnable.base.RunnableSerializable[source]¶
Bases: Serializable, Runnable[Input, Output]
A Runnable that can be serialized to JSON.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the in... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
d57d4f5266e1-1 | 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/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
d57d4f5266e1-2 | 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/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
d57d4f5266e1-3 | 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 co... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
d57d4f5266e1-4 | 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 co... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
d57d4f5266e1-5 | A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
classmethod parse_file... | lang/api.python.langchain.com/en/latest/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
d57d4f5266e1-6 | 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/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
d57d4f5266e1-7 | 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/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
d57d4f5266e1-8 | 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/schema.runnable/langchain.schema.runnable.base.RunnableSerializable.html |
13208d0e0461-0 | langchain.evaluation.regex_match.base.RegexMatchStringEvaluator¶
class langchain.evaluation.regex_match.base.RegexMatchStringEvaluator(*, flags: int = 0, **kwargs: Any)[source]¶
Compute a regex match between the prediction and the reference.
Examples
>>> evaluator = RegexMatchStringEvaluator(flags=re.IGNORECASE)
>>> ev... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.regex_match.base.RegexMatchStringEvaluator.html |
13208d0e0461-1 | Asynchronously evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Addi... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.regex_match.base.RegexMatchStringEvaluator.html |
ddf5c79089a9-0 | langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEval¶
class langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEval[source]¶
A named tuple containing the score and reasoning for a trajectory.
score: float¶
The score for the trajectory, normalized from 0 to 1.
reasoning: str¶
The reasoning for the s... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEval.html |
c674d62d6b9e-0 | langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser¶
class langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser[source]¶
Bases: BaseOutputParser[dict]
A parser for the output of the CriteriaEvalChain.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableCo... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
c674d62d6b9e-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/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
c674d62d6b9e-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/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
c674d62d6b9e-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/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
c674d62d6b9e-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/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
c674d62d6b9e-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.
parse(text: str) → Dict[str, Any][source]¶
Parse the output text.
Parameters
text (st... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
c674d62d6b9e-6 | prompt – Input PromptValue.
Returns
Structured output
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: O... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
c674d62d6b9e-7 | 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... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
c674d62d6b9e-8 | The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.output_parser.T]¶
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 ... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaResultOutputParser.html |
a5aa86d5bf55-0 | langchain.evaluation.exact_match.base.ExactMatchStringEvaluator¶
class langchain.evaluation.exact_match.base.ExactMatchStringEvaluator(*, ignore_case: bool = False, ignore_punctuation: bool = False, ignore_numbers: bool = False, **kwargs: Any)[source]¶
Compute an exact match between the prediction and the reference.
Ex... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.exact_match.base.ExactMatchStringEvaluator.html |
a5aa86d5bf55-1 | Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Additional keyword arguments, including callbacks, tags, etc.
Returns
The evaluation... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.exact_match.base.ExactMatchStringEvaluator.html |
d87ea9743bb8-0 | langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain¶
class langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain[source]¶
Bases: CriteriaEvalChain
Criteria evaluation chain that requires references.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-1 | 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 wit... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-2 | 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/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-3 | 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/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-4 | Asynchronously evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Addi... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-5 | **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 th... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-6 | 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' s... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-7 | The jsonpatch ops can be applied in order to construct state.
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 th... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-8 | 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... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-9 | Evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Additional keyword ... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-10 | " not present in the input or reference?"
),
}
>>> chain = LabeledCriteriaEvalChain.from_llm(
llm=llm,
criteria=criteria,
)
classmethod from_orm(obj: Any) → Model¶
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
generate(i... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-11 | 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 singl... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-12 | to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool =... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-13 | 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, ... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-14 | 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/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-15 | 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: Optiona... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-16 | 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... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
d87ea9743bb8-17 | 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 specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable field... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
7200e6c87d13-0 | langchain.evaluation.schema.LLMEvalChain¶
class langchain.evaluation.schema.LLMEvalChain[source]¶
Bases: Chain
A base class for evaluators that use an LLM.
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.
par... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-1 | 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 the global verbose value,
accessible via langchain.globals.get_verbose().
__call__(in... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-2 | Returns
A dict of named outputs. Should contain all outputs specified inChain.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 ... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-3 | 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. Sho... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-4 | 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, ... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-5 | 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... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-6 | Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-7 | method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
abstract classmethod from_llm(llm: BaseLanguageModel, **kwargs: Any) → LLMEvalChain[source]¶
Create a new evaluator from an LLM.
classmethod from_orm(obj: Any) → Model¶
get_input... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-8 | 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/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-9 | 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/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-10 | 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... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-11 | 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/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-12 | 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/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7200e6c87d13-13 | 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.
abstract property input_keys: List[str]¶
Keys expected to be in the chain input.
property input_schema: Type[pydantic.... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.LLMEvalChain.html |
7090f9c69c8a-0 | langchain.evaluation.qa.eval_chain.ContextQAEvalChain¶
class langchain.evaluation.qa.eval_chain.ContextQAEvalChain[source]¶
Bases: LLMChain, StringEvaluator, LLMEvalChain
LLM Chain for evaluating QA w/o GT based on context
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-1 | 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 gene... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-2 | 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/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-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/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-4 | Asynchronously evaluate Chain or LLM output, based on optional input and label.
Parameters
prediction (str) – The LLM or chain prediction to evaluate.
reference (Optional[str], optional) – The reference label to evaluate against.
input (Optional[str], optional) – The input to consider during evaluation.
**kwargs – Addi... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-5 | **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 th... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-6 | 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' s... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-7 | The jsonpatch ops can be applied in order to construct state.
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 th... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-8 | 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... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-9 | # -> {"_type": "foo", "verbose": False, ...}
evaluate(examples: List[dict], predictions: List[dict], question_key: str = 'query', context_key: str = 'context', prediction_key: str = 'result', *, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[dict][source]¶
Evaluate question an... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-10 | 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.
get_input_schema(config: Optional[RunnableConfig] = None) →... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-11 | 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 ... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-12 | classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-13 | 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.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = N... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-14 | 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 memor... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-15 | 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 se... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
7090f9c69c8a-16 | 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... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
bbacb63116c2-0 | langchain.evaluation.qa.generate_chain.QAGenerateChain¶
class langchain.evaluation.qa.generate_chain.QAGenerateChain[source]¶
Bases: LLMChain
LLM Chain for generating examples for question answering.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-1 | param output_key: str = 'qa_pairs'¶
param output_parser: BaseLLMOutputParser = RegexParser(regex='QUESTION: (.*?)\\n+ANSWER: (.*)', output_keys=['query', 'answer'])¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Pro... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-2 | 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
addi... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-3 | 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, *, ta... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-4 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-5 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-6 | # -> "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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-7 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-8 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-9 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-10 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-11 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-12 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-13 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-14 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
bbacb63116c2-15 | 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/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
5a6e6d8dece0-0 | langchain.evaluation.criteria.eval_chain.resolve_criteria¶
langchain.evaluation.criteria.eval_chain.resolve_criteria(criteria: Optional[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple, str]]) → Dict[str, str][source]¶
Resolve the criteria to evaluate.
Parameters
criteria (CRITERIA_TYPE) –
The criteria to ev... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.resolve_criteria.html |
5fb8425d8d9b-0 | langchain.evaluation.string_distance.base.StringDistance¶
class langchain.evaluation.string_distance.base.StringDistance(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Distance metric to use.
DAMERAU_LEVENSHTEIN¶
The Damerau-Levenshtein distance.
LEVENSHTEIN¶
The Levenshte... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistance.html |
d5dc544b6b17-0 | langchain.evaluation.qa.eval_chain.CotQAEvalChain¶
class langchain.evaluation.qa.eval_chain.CotQAEvalChain[source]¶
Bases: ContextQAEvalChain
LLM Chain for evaluating QA using chain of thought reasoning.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Ca... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
d5dc544b6b17-1 | 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 gene... | lang/api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
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