viruPVS/langchain-docs huggingface dataset:
param prompt: BasePromptTemplate [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to the global verbose value,
accessible via langchain.globals.get_verbose().
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
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 aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Utilize the LLM generate method for speed gains.
async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async 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.
create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]¶
Create outputs from response.
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
evaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
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 arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict
classmethod from_llm(llm: BaseLanguageModel, criteria: Optional[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple]] = None, *, prompt: Optional[BasePromptTemplate] = None, **kwargs: Any) → CriteriaEvalChain[source]¶
Create a LabeledCriteriaEvalChain instance from an llm and criteria.
Parameters
llm (BaseLanguageModel) – The language model to use for evaluation.
criteria (CRITERIA_TYPE - default=None for "helpfulness") – 
The criteria to evaluate the runs against. It can be:
a mapping of a criterion name to its description
a single criterion name present in one of the default criteria
a single ConstitutionalPrinciple instance
a single criterion name present in one of the default criteria
a single ConstitutionalPrinciple instance
prompt (Optional[BasePromptTemplate], default=None) – The prompt template to use for generating prompts. If not provided,
a default prompt will be used.
**kwargs (Any) – Additional keyword arguments to pass to the LLMChain
constructor.
Returns
An instance of the LabeledCriteriaEvalChain class.
Return type
LabeledCriteriaEvalChain
Examples
>>> from langchain.llms import OpenAI
>>> from langchain.evaluation.criteria import LabeledCriteriaEvalChain
>>> llm = OpenAI()
>>> criteria = {
        "hallucination": (
            "Does this submission contain information"
            " 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(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
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.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool[source]¶
Is this class serializable?
classmethod is_lc_serializable() → bool[source]¶
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.
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¶
predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶
Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, Any]]¶
Call predict and then parse the results.
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the final outputs.
Returns
A dict of the final chain outputs.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
classmethod resolve_criteria(criteria: Optional[Union[Mapping[str, str], Criteria, ConstitutionalPrinciple, str]]) → Dict[str, str]¶
Resolve the criteria to evaluate.
Parameters
Resolve the criteria to evaluate.
Parameters
criteria (CRITERIA_TYPE) – 
The criteria to evaluate the runs against. It can be:
a mapping of a criterion name to its description
a single criterion name present in one of the default criteria
a single ConstitutionalPrinciple instance
Returns
A dictionary mapping criterion names to descriptions.
Return type
Dict[str, str]
Examples
>>> criterion = "relevance"
>>> CriteriaEvalChain.resolve_criteria(criteria)
{'relevance': 'Is the submission referring to a real quote from the text?'}
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain_core.runnables.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain_core.runnables.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property evaluation_name: str¶
Get the name of the evaluation.
Returns
The name of the evaluation.
Return type
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.
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¶
Whether this evaluator requires an input string.
property requires_reference: bool¶
Whether the evaluation requires a reference text.
langchain.evaluation.embedding_distance.base.EmbeddingDistance¶
class langchain.evaluation.embedding_distance.base.EmbeddingDistance(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Embedding Distance Metric.
COSINE¶
Cosine distance metric.
EUCLIDEAN¶
Euclidean distance metric.
MANHATTAN¶
Manhattan distance metric.
CHEBYSHEV¶
Chebyshev distance metric.
HAMMING¶
Hamming distance metric.
COSINE = 'cosine'¶
EUCLIDEAN = 'euclidean'¶
MANHATTAN = 'manhattan'¶
CHEBYSHEV = 'chebyshev'¶
HAMMING = 'hamming'¶
Examples using EmbeddingDistance¶
Pairwise Embedding Distance 
Embedding Distance
langchain.evaluation.loading.load_evaluator¶
langchain.evaluation.loading.load_evaluator(evaluator: EvaluatorType, *, llm: Optional[BaseLanguageModel] = None, **kwargs: Any) → Union[Chain, StringEvaluator][source]¶
Load the requested evaluation chain specified by a string.
Parameters
evaluator (EvaluatorType) – The type of evaluator to load.
llm (BaseLanguageModel, optional) – The language model to use for evaluation, by default None
**kwargs (Any) – Additional keyword arguments to pass to the evaluator.
Returns
The loaded evaluation chain.
Return type
Chain
Examples
>>> from langchain.evaluation import load_evaluator, EvaluatorType
>>> evaluator = load_evaluator(EvaluatorType.QA)
Examples using load_evaluator¶
Comparing Chain Outputs
Agent Trajectory
Pairwise Embedding Distance 
Pairwise String Comparison
Criteria Evaluation
String Distance
Embedding Distance
langchain.evaluation.parsing.base.JsonEqualityEvaluator¶
class langchain.evaluation.parsing.base.JsonEqualityEvaluator(operator: Optional[Callable] = None, **kwargs: Any)[source]¶
Evaluates whether the prediction is equal to the reference afterparsing both as JSON.
This evaluator checks if the prediction, after parsing as JSON, is equalto the reference,
which is also parsed as JSON. It does not require an input string.
requires_input¶
Whether this evaluator requires an
input string. Always False.
Type
bool
requires_reference¶
Whether this evaluator requires
a reference string. Always True.
Type
bool
evaluation_name¶
The name of the evaluation metric.
Always “parsed_equality”.
Type
str
Examples
>>> evaluator = JsonEqualityEvaluator()
>>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 1}')
{'score': True}
>>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 2}')
{'score': False}
>>> evaluator = JsonEqualityEvaluator(operator=lambda x, y: x['a'] == y['a'])
>>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 1}')
{'score': True}
>>> evaluator.evaluate_strings('{"a": 1}', reference='{"a": 2}')
{'score': False}
Attributes
evaluation_name
The name of the evaluation.
requires_input
Whether this evaluator requires an input string.
requires_reference
Whether this evaluator requires a reference label.
Methods
__init__([operator])
aevaluate_strings(*, prediction[, ...])
Asynchronously evaluate Chain or LLM output, based on optional input and label.
evaluate_strings(*, prediction[, reference, ...])
Evaluate Chain or LLM output, based on optional input and label.
Evaluate Chain or LLM output, based on optional input and label.
__init__(operator: Optional[Callable] = None, **kwargs: Any) → None[source]¶
async aevaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
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 – Additional keyword arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict
evaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = None, **kwargs: Any) → dict¶
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 arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict
langchain.evaluation.schema.AgentTrajectoryEvaluator¶
class langchain.evaluation.schema.AgentTrajectoryEvaluator[source]¶
Interface for evaluating agent trajectories.
Attributes
requires_input
Whether this evaluator requires an input string.
requires_reference
Whether this evaluator requires a reference label.
Methods
__init__()
aevaluate_agent_trajectory(*, prediction, ...)
Asynchronously evaluate a trajectory.
evaluate_agent_trajectory(*, prediction, ...)
Evaluate a trajectory.
__init__()¶
async aevaluate_agent_trajectory(*, prediction: str, agent_trajectory: Sequence[Tuple[AgentAction, str]], input: str, reference: Optional[str] = None, **kwargs: Any) → dict[source]¶
Asynchronously evaluate a trajectory.
Parameters
prediction (str) – The final predicted response.
agent_trajectory (List[Tuple[AgentAction, str]]) – The intermediate steps forming the agent trajectory.
input (str) – The input to the agent.
reference (Optional[str]) – The reference answer.
Returns
The evaluation result.
Return type
dict
evaluate_agent_trajectory(*, prediction: str, agent_trajectory: Sequence[Tuple[AgentAction, str]], input: str, reference: Optional[str] = None, **kwargs: Any) → dict[source]¶
Evaluate a trajectory.
Parameters
prediction (str) – The final predicted response.
agent_trajectory (List[Tuple[AgentAction, str]]) – The intermediate steps forming the agent trajectory.
input (str) – The input to the agent.
reference (Optional[str]) – The reference answer.
Returns
The evaluation result.
Return type
dict
Examples using AgentTrajectoryEvaluator¶
Custom Trajectory Evaluator
langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain¶
class langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain[source]¶
Bases: _EmbeddingDistanceChainMixin, PairwiseStringEvaluator
Use embedding distances to score semantic difference between two predictions.
Examples:
>>> chain = PairwiseEmbeddingDistanceEvalChain()
>>> result = chain.evaluate_string_pairs(prediction=”Hello”, prediction_b=”Hi”)
>>> print(result)
{‘score’: 0.5}
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param distance_metric: EmbeddingDistance = EmbeddingDistance.COSINE¶
param embeddings: Embeddings [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.