langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector¶
- class langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector[source]¶
Bases:
BaseExampleSelector,BaseModelExample selector that selects examples based on SemanticSimilarity.
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 example_keys: Optional[List[str]] = None¶
Optional keys to filter examples to.
- param input_keys: Optional[List[str]] = None¶
Optional keys to filter input to. If provided, the search is based on the input variables instead of all variables.
- param k: int = 4¶
Number of examples to select.
- param vectorstore: VectorStore [Required]¶
VectorStore than contains information about examples.
- param vectorstore_kwargs: Optional[Dict[str, Any]] = None¶
Extra arguments passed to similarity_search function of the vectorstore.
- 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
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include – 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 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, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- classmethod from_examples(examples: List[dict], embeddings: Embeddings, vectorstore_cls: Type[VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, *, example_keys: Optional[List[str]] = None, vectorstore_kwargs: Optional[dict] = None, **vectorstore_cls_kwargs: Any) SemanticSimilarityExampleSelector[source]¶
Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
- Parameters
examples – List of examples to use in the prompt.
embeddings – An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g. FAISS.
k – Number of examples to select
input_keys – If provided, the search is based on the input variables instead of all variables.
vectorstore_cls_kwargs – optional kwargs containing url for vector store
- Returns
The ExampleSelector instantiated, backed by a vector store.
- classmethod from_orm(obj: Any) Model¶
- 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 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¶
- 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¶
- select_examples(input_variables: Dict[str, str]) List[dict][source]¶
Select which examples to use based on semantic similarity.
- 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¶