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][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.
- param example_keys: list[str] | None = None#
Optional keys to filter examples to.
- param input_keys: list[str] | None = 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 that contains information about examples.
- param vectorstore_kwargs: dict[str, Any] | None = None#
Extra arguments passed to similarity_search function of the vectorstore.
- async aadd_example(example: dict[str, str]) str #
Async add new example to vectorstore.
- Parameters:
example (dict[str, str]) – A dictionary with keys as input variables and values as their values.
- Returns:
The ID of the added example.
- Return type:
str
- add_example(example: dict[str, str]) str #
Add a new example to vectorstore.
- Parameters:
example (dict[str, str]) – A dictionary with keys as input variables and values as their values.
- Returns:
The ID of the added example.
- Return type:
str
- async classmethod afrom_examples(examples: list[dict], embeddings: Embeddings, vectorstore_cls: type[VectorStore], k: int = 4, input_keys: list[str] | None = None, *, example_keys: list[str] | None = None, vectorstore_kwargs: dict | None = None, **vectorstore_cls_kwargs: Any) SemanticSimilarityExampleSelector [source]#
Async 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. Default is 4.
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:
- async aselect_examples(input_variables: dict[str, str]) list[dict] [source]#
Asynchronously select examples based on semantic similarity.
- Parameters:
input_variables (dict[str, str]) – The input variables to use for search.
- Returns:
The selected examples.
- Return type:
list[dict]
- classmethod from_examples(examples: list[dict], embeddings: Embeddings, vectorstore_cls: type[VectorStore], k: int = 4, input_keys: list[str] | None = None, *, example_keys: list[str] | None = None, vectorstore_kwargs: dict | None = 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. Default is 4.
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:
Examples using SemanticSimilarityExampleSelector