langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector

class langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector[source]

Bases: _VectorStoreExampleSelector

Select examples based on semantic similarity.

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.

async aadd_example(example: Dict[str, str]) → str

Add new example to vectorstore.

Parameters

example (Dict[str, str]) –

Return type

str

add_example(example: Dict[str, str]) → str

Add new example to vectorstore.

Parameters

example (Dict[str, str]) –

Return type

str

async classmethod afrom_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[dict]) – List of examples to use in the prompt.

  • embeddings (Embeddings) – An initialized embedding API interface, e.g. OpenAIEmbeddings().

  • vectorstore_cls (Type[VectorStore]) – A vector store DB interface class, e.g. FAISS.

  • k (int) – Number of examples to select

  • input_keys (Optional[List[str]]) – If provided, the search is based on the input variables instead of all variables.

  • example_keys (Optional[List[str]]) – If provided, keys to filter examples to.

  • vectorstore_kwargs (Optional[dict]) – Extra arguments passed to similarity_search function of the vectorstore.

  • vectorstore_cls_kwargs (Any) – optional kwargs containing url for vector store

Returns

The ExampleSelector instantiated, backed by a vector store.

Return type

SemanticSimilarityExampleSelector

async aselect_examples(input_variables: Dict[str, str]) → List[dict][source]

Asynchronously select examples based on semantic similarity.

Parameters

input_variables (Dict[str, str]) –

Return type

List[dict]

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

Parameters

  • _fields_set (Optional[SetStr]) –

  • values (Any) –

Return type

Model

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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

  • update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model

  • self (Model) –

Returns

new model instance

Return type

Model

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.

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

Return type

DictStrAny

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[dict]) – List of examples to use in the prompt.

  • embeddings (Embeddings) – An initialized embedding API interface, e.g. OpenAIEmbeddings().

  • vectorstore_cls (Type[VectorStore]) – A vector store DB interface class, e.g. FAISS.

  • k (int) – Number of examples to select

  • input_keys (Optional[List[str]]) – If provided, the search is based on the input variables instead of all variables.

  • example_keys (Optional[List[str]]) – If provided, keys to filter examples to.

  • vectorstore_kwargs (Optional[dict]) – Extra arguments passed to similarity_search function of the vectorstore.

  • vectorstore_cls_kwargs (Any) – optional kwargs containing url for vector store

Returns

The ExampleSelector instantiated, backed by a vector store.

Return type

SemanticSimilarityExampleSelector

classmethod from_orm(obj: Any) → Model
Parameters

obj (Any) –

Return type

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().

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

  • encoder (Optional[Callable[[Any], Any]]) –

  • models_as_dict (bool) –

  • dumps_kwargs (Any) –

Return type

unicode

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
Parameters

  • path (Union[str, Path]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model
Parameters

obj (Any) –

Return type

Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
Parameters

  • b (Union[str, bytes]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny
Parameters

  • by_alias (bool) –

  • ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode
Parameters

  • by_alias (bool) –

  • ref_template (unicode) –

  • dumps_kwargs (Any) –

Return type

unicode

select_examples(input_variables: Dict[str, str]) → List[dict][source]

Select examples based on semantic similarity.

Parameters

input_variables (Dict[str, str]) –

Return type

List[dict]

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) → Model
Parameters

value (Any) –

Return type

Model

Examples using SemanticSimilarityExampleSelector