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 iniialized 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.
select_examples(input_variables: Dict[str, str]) → List[dict][source]#
Select which examples to use based on semantic similarity.
pydantic model langchain.prompts.example_selector.SemanticSimilarityExampleSelector[source]#
Example selector that selects examples based on SemanticSimilarity.
field example_keys: Optional[List[str]] = None#
Optional keys to filter examples to.
field 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.
field k: int = 4#
Number of examples to select.
field vectorstore: langchain.vectorstores.base.VectorStore [Required]#
VectorStore than contains information about examples.
add_example(example: Dict[str, str]) → str[source]#
Add new example to vectorstore.
classmethod from_examples(examples: List[dict], embeddings: langchain.embeddings.base.Embeddings, vectorstore_cls: Type[langchain.vectorstores.base.VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, **vectorstore_cls_kwargs: Any) → langchain.prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector[source]#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/example_selector.html