id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
a43807e0b3e8-9 | 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 LabeledCriteriaEv... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
a43807e0b3e8-10 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
a43807e0b3e8-11 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
a43807e0b3e8-12 | 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 ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
a43807e0b3e8-13 | Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
cont... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
a43807e0b3e8-14 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
a43807e0b3e8-15 | property requires_input: bool¶
Whether this evaluator requires an input string.
property requires_reference: bool¶
Whether the evaluation requires a reference text. | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html |
7996fb867bb2-0 | langchain.evaluation.qa.eval_chain.QAEvalChain¶
class langchain.evaluation.qa.eval_chain.QAEvalChain[source]¶
Bases: LLMChain, StringEvaluator, LLMEvalChain
LLM Chain for evaluating question answering.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Call... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-1 | 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 wil... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-2 | 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 ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-3 | Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, on... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-4 | Returns
The evaluation results containing the score or value.
Return type
dict
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kw... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-5 | Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCal... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-6 | question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-7 | 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 of batch, which calls invoke N times.
Subclasses should override this method if they can ba... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-8 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-9 | 'input' –
prompt –
evaluation. (for) –
PROMPT. (Defaults to) –
**kwargs – additional keyword arguments.
Returns
the loaded QA eval chain.
Return type
QAEvalChain
classmethod from_orm(obj: Any) → Model¶
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-10 | 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 e... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-11 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-12 | 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 c... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-13 | to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
7996fb867bb2-14 | 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]¶
property requires_input: bool¶
Whether this evaluator requires an input string.
property requires_reference: bool¶
Whether this ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html |
81c585114414-0 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.exact_match.base.ExactMatchStringEvaluator.html |
81c585114414-1 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.exact_match.base.ExactMatchStringEvaluator.html |
e71552fa17f8-0 | 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 ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.resolve_pairwise_criteria.html |
27db7e7359da-0 | 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
... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.AgentTrajectoryEvaluator.html |
af0738dd3bf7-0 | langchain.evaluation.loading.load_dataset¶
langchain.evaluation.loading.load_dataset(uri: str) → List[Dict][source]¶
Load a dataset from the LangChainDatasets on HuggingFace.
Parameters
uri – The uri of the dataset to load.
Returns
A list of dictionaries, each representing a row in the dataset.
Prerequisites
pip instal... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.loading.load_dataset.html |
89d10d7b3c4c-0 | 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 metr... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistance.html |
b9fda5d461cb-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.
... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-1 | 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 ass... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-2 | 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 ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-3 | Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, on... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-4 | Returns
The evaluation results containing the score or value.
Return type
dict
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kw... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-5 | Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCal... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-6 | question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-7 | 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 of batch, which calls invoke N times.
Subclasses should override this method if they can ba... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-8 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-9 | 'query' –
prompt –
evaluation. (for) –
PROMPT. (Defaults to) –
**kwargs – additional keyword arguments.
Returns
the loaded QA eval chain.
Return type
ContextQAEvalChain
classmethod from_orm(obj: Any) → Model¶
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and tem... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-10 | 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 e... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-11 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-12 | 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 c... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-13 | to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
b9fda5d461cb-14 | 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]¶
property requires_input: bool¶
Whether the chain requires an input string.
property requires_reference: bool¶
Whether the chain ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.ContextQAEvalChain.html |
14acebc3ee2b-0 | langchain.evaluation.schema.EvaluatorType¶
class langchain.evaluation.schema.EvaluatorType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
The types of the evaluators.
QA = 'qa'¶
Question answering evaluator, which grades answers to questions
directly using an LLM.
COT_QA =... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.EvaluatorType.html |
14acebc3ee2b-1 | EMBEDDING_DISTANCE = 'embedding_distance'¶
Compare a prediction to a reference label using embedding distance.
PAIRWISE_EMBEDDING_DISTANCE = 'pairwise_embedding_distance'¶
Compare two predictions using embedding distance.
JSON_VALIDITY = 'json_validity'¶
Check if a prediction is valid JSON.
JSON_EQUALITY = 'json_equali... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.EvaluatorType.html |
1e696fc11519-0 | langchain.evaluation.schema.StringEvaluator¶
class langchain.evaluation.schema.StringEvaluator[source]¶
Grade, tag, or otherwise evaluate predictions relative to their inputs
and/or reference labels.
Attributes
evaluation_name
The name of the evaluation.
requires_input
Whether this evaluator requires an input string.
r... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.StringEvaluator.html |
1e696fc11519-1 | **kwargs – Additional keyword arguments, including callbacks, tags, etc.
Returns
The evaluation results containing the score or value.
Return type
dict
Examples using StringEvaluator¶
Custom String Evaluator | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.StringEvaluator.html |
6eaa1124d477-0 | langchain.evaluation.parsing.base.JsonValidityEvaluator¶
class langchain.evaluation.parsing.base.JsonValidityEvaluator(**kwargs: Any)[source]¶
Evaluates whether the prediction is valid JSON.
This evaluator checks if the prediction is a valid JSON string. It does notrequire any input or reference.
requires_input¶
Whethe... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.parsing.base.JsonValidityEvaluator.html |
6eaa1124d477-1 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.parsing.base.JsonValidityEvaluator.html |
aa320c6aa2f6-0 | langchain.evaluation.criteria.eval_chain.CriteriaEvalChain¶
class langchain.evaluation.criteria.eval_chain.CriteriaEvalChain[source]¶
Bases: StringEvaluator, LLMEvalChain, LLMChain
LLM Chain for evaluating runs against criteria.
Parameters
llm (BaseLanguageModel) – The language model to use for evaluation.
criteria (Un... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-1 | {
'reasoning': 'Here is my step-by-step reasoning for the given criteria:\n\nThe criterion is: "Is the submission the most amazing ever?" This is a subjective criterion and open to interpretation. The submission suggests an aquamarine-colored ice cream flavor which is creative but may or may not be considered the m... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-2 | 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 criterion_name: str [Required]¶
The name of the criterion bei... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-3 | 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 langchain.verbose value... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-4 | 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(inp... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-5 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-6 | 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]]¶
Utilize the LLM generate method for speed gains.
apply_and_parse(input_list: List[Dict[str, Any]], cal... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-7 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-8 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-9 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-10 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-11 | 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 L... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-12 | 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, encod... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-13 | 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, ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-14 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-15 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
aa320c6aa2f6-16 | 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]¶
property InputType: Type[langchain.schema.runnable.utils.Input]¶
property OutputType: Type[langchain.schema.runna... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html |
54749449b015-0 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-1 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-2 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-3 | Subclasses should override this method if they can batch more efficiently.
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,... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-4 | Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
apply(input_list: List[Dict[str, ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-5 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-6 | # -> "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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-7 | Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ fr... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-8 | # -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, **kwargs: Any) → QAGenerateChain[source]¶
Load QA Generate Chain from LLM.
classmethod from_orm(obj: Any) → Model¶
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
g... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-9 | 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 e... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-10 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-11 | 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 c... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-12 | to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
54749449b015-13 | A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.generate_chain.QAGenerateChain.html |
3085dddfcb75-0 | langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser¶
class langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser[source]¶
Bases: BaseOutputParser[dict]
A parser for the output of the PairwiseStringEvalChain.
_type¶
The type of the output parser.
Type
str
Create a new model ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser.html |
3085dddfcb75-1 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser.html |
3085dddfcb75-2 | Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Mode... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser.html |
3085dddfcb75-3 | namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser.html |
3085dddfcb75-4 | 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¶
parse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser.html |
3085dddfcb75-5 | to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser.html |
3085dddfcb75-6 | For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringResultOutputParser.html |
1909f0dfdf4e-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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-1 | 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 ass... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-2 | 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 ... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-3 | Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, on... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-4 | Returns
The evaluation results containing the score or value.
Return type
dict
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kw... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-5 | Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCal... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-6 | question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-7 | 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 of batch, which calls invoke N times.
Subclasses should override this method if they can ba... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-8 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-9 | Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.ope... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-10 | 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... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-11 | 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 fi... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
1909f0dfdf4e-12 | Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
cont... | https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.CotQAEvalChain.html |
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