viruPVS/langchain-docs huggingface dataset:
langchain.evaluation.comparison.eval_chain.resolve_pairwise_criteria¶
langchain.evaluation.comparison.eval_chain.resolve_pairwise_criteria(criteria: Optional[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple, str, List[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple]]]]) → dict[source]¶
Resolve the criteria for the pairwise evaluator.
Parameters
criteria (Union[CRITERIA_TYPE, str, List[CRITERIA_TYPE]], optional) – 
use. (The criteria to) – 
Returns
The resolved criteria.
Return type
dict
langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain¶
class langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain[source]¶
Bases: PairwiseStringEvaluator, LLMEvalChain, LLMChain
A chain for comparing two outputs, such as the outputsof two models, prompts, or outputs of a single model on similar inputs.
output_parser¶
The output parser for the chain.
Type
BaseOutputParser
Example
>>> from langchain.chat_models import ChatOpenAI
>>> from langchain.evaluation.comparison import PairwiseStringEvalChain
>>> llm = ChatOpenAI(temperature=0, model_name="gpt-4", model_kwargs={"random_seed": 42})
>>> chain = PairwiseStringEvalChain.from_llm(llm=llm)
>>> result = chain.evaluate_string_pairs(
...     input = "What is the chemical formula for water?",
...     prediction = "H2O",
...     prediction_b = (
...        "The chemical formula for water is H2O, which means"
...        " there are two hydrogen atoms and one oxygen atom."
...     reference = "The chemical formula for water is H2O.",
... )
>>> print(result)
# {
#    "value": "B",
#    "comment": "Both responses accurately state"
#       " that the chemical formula for water is H2O."
#       " However, Response B provides additional information"
# .     " by explaining what the formula means.\n[[B]]"
# }
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,
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param llm: Union[Runnable[LanguageModelInput, str], Runnable[LanguageModelInput, BaseMessage]] [Required]¶
Language model to call.
param llm_kwargs: dict [Optional]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param output_parser: BaseOutputParser [Optional]¶
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
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 inChain.output_keys.
async aevaluate_string_pairs(*, prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Asynchronously evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (Optional[str], optional) – The expected output / reference string.
input (Optional[str], optional) – The input string.
**kwargs – Additional keyword arguments, such as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/or other information.
Return type
dict
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
# -> {"_type": "foo", "verbose": False, ...}
evaluate_string_pairs(*, prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (Optional[str], optional) – The expected output / reference string.
input (Optional[str], optional) – The input string.
**kwargs – Additional keyword arguments, such as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/or other information.
Return type
dict
classmethod from_llm(llm: BaseLanguageModel, *, prompt: Optional[PromptTemplate] = None, criteria: Optional[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple, str]] = None, **kwargs: Any) → PairwiseStringEvalChain[source]¶
Initialize the PairwiseStringEvalChain from an LLM.
Parameters
llm (BaseChatModel) – The LLM to use (GPT-4 recommended).
prompt (PromptTemplate, optional) – The prompt to use.
**kwargs (Any) – Additional keyword arguments.
Returns
The initialized PairwiseStringEvalChain.
Return type
PairwiseStringEvalChain
Raises
ValueError – If the input variables are not as expected.
classmethod from_orm(obj: Any) → Model¶
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
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.
property requires_input: bool¶
Return whether the chain requires an input.
Returns
True if the chain requires an input, False otherwise.
Return type
bool
property requires_reference: bool¶
Return whether the chain requires a reference.
Returns
True if the chain requires a reference, False otherwise.
Return type
bool
langchain.evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser¶
class langchain.evaluation.agents.trajectory_eval_chain.TrajectoryOutputParser[source]¶
Bases: BaseOutputParser
Trajectory output parser.
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 ainvoke(input: str | langchain_core.messages.base.BaseMessage, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, whichis assumed to be the highest-likelihood Generation.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
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.
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) → TrajectoryEval[source]¶
Parse the output text and extract the score and reasoning.
Parameters
text (str) – The output text to parse.
Returns
A named tuple containing the normalized score and reasoning.
Return type
TrajectoryEval
Raises
OutputParserException – If the score is not found in the output text or
    if the LLM’s score is not a digit in the range 1-5.
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¶
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[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 ainvoke(input: str | langchain_core.messages.base.BaseMessage, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, whichis assumed to be the highest-likelihood Generation.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
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.
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 (str) – The output text to parse.
Returns
The parsed output.
Return type
Dict
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¶
langchain.evaluation.string_distance.base.PairwiseStringDistanceEvalChain¶
class langchain.evaluation.string_distance.base.PairwiseStringDistanceEvalChain[source]¶
Bases: PairwiseStringEvaluator, _RapidFuzzChainMixin
Compute string edit distances between two predictions.
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 distance: StringDistance = StringDistance.JARO_WINKLER¶
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 normalize_score: bool = True¶
Whether to normalize the score to a value between 0 and 1.
Applies only to the Levenshtein and Damerau-Levenshtein distances.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
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 inChain.output_keys.
async aevaluate_string_pairs(*, prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Asynchronously evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (Optional[str], optional) – The expected output / reference string.
input (Optional[str], optional) – The input string.
**kwargs – Additional keyword arguments, such as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/or other information.
Return type
dict
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.
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.
compute_metric(a: str, b: str) → float¶
Compute the distance between two strings.
Parameters
a (str) – The first string.
b (str) – The second string.
Returns
The distance between the two strings.
Return type
float
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.
**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, ...}
evaluate_string_pairs(*, prediction: str, prediction_b: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
Evaluate the output string pairs.
Parameters
prediction (str) – The output string from the first model.
prediction_b (str) – The output string from the second model.
reference (Optional[str], optional) – The expected output / reference string.
input (Optional[str], optional) – The input string.
**kwargs – Additional keyword arguments, such as callbacks and optional reference strings.
Returns
A dictionary containing the preference, scores, and/or other information.
Return type
dict
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”]
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 evaluation_name: str¶
Get the evaluation name.
Returns
The evaluation name.
Return type
str
property input_keys: List[str]¶
Get the input keys.
Returns
The input keys.
Return type
List[str]
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 metric: Callable¶
Get the distance metric function.
Returns
The distance metric function.
Return type
Callable
property output_keys: List[str]¶
Get the output keys.
Returns
The output keys.
Return type
List[str]
property output_schema: Type[pydantic.main.BaseModel]¶