Example Selector#

Logic for selecting examples to include in prompts.

pydantic model langchain.prompts.example_selector.LengthBasedExampleSelector[source]#

Select examples based on length.

Validators
  • calculate_example_text_lengths » example_text_lengths

field example_prompt: langchain.prompts.prompt.PromptTemplate [Required]#

Prompt template used to format the examples.

field examples: List[dict] [Required]#

A list of the examples that the prompt template expects.

field get_text_length: Callable[[str], int] = <function _get_length_based>#

Function to measure prompt length. Defaults to word count.

field max_length: int = 2048#

Max length for the prompt, beyond which examples are cut.

add_example(example: Dict[str, str]) None[source]#

Add new example to list.

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

Select which examples to use based on the input lengths.

pydantic model langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector[source]#

ExampleSelector that selects examples based on Max Marginal Relevance.

This was shown to improve performance in this paper: https://arxiv.org/pdf/2211.13892.pdf

field fetch_k: int = 20#

Number of examples to fetch to rerank.

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, fetch_k: int = 20, **vectorstore_cls_kwargs: Any) langchain.prompts.example_selector.semantic_similarity.MaxMarginalRelevanceExampleSelector[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 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]#

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.

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

Select which examples to use based on semantic similarity.