​
HybridLocalIndex Objects

class HybridLocalIndex(LocalIndex)

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add

def add(embeddings: List[List[float]],
        routes: List[str],
        utterances: List[str],
        function_schemas: Optional[List[Dict[str, Any]]] = None,
        metadata_list: List[Dict[str, Any]] = [],
        sparse_embeddings: Optional[List[SparseEmbedding]] = None,
        **kwargs)

Add embeddings to the index.

Arguments:

  • embeddings (List[List[float]]): List of embeddings to add to the index.
  • routes (List[str]): List of routes to add to the index.
  • utterances (List[str]): List of utterances to add to the index.
  • function_schemas (Optional[List[Dict[str, Any]]]): List of function schemas to add to the index.
  • metadata_list (List[Dict[str, Any]]): List of metadata to add to the index.
  • List[List[float]]0 (List[List[float]]1): List of sparse embeddings to add to the index.

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get_utterances

def get_utterances(include_metadata: bool = False) -> List[Utterance]

Gets a list of route and utterance objects currently stored in the index.

Arguments:

  • include_metadata (bool): Whether to include function schemas and metadata in the returned Utterance objects - HybridLocalIndex doesn’t include metadata so this parameter is ignored.

Returns:

List[Utterance]: A list of Utterance objects.

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query

def query(
    vector: np.ndarray,
    top_k: int = 5,
    route_filter: Optional[List[str]] = None,
    sparse_vector: dict[int, float] | SparseEmbedding | None = None
) -> Tuple[np.ndarray, List[str]]

Search the index for the query and return top_k results.

Arguments:

  • vector (np.ndarray): The query vector to search for.
  • top_k (int, optional): The number of top results to return, defaults to 5.
  • route_filter (Optional[List[str]], optional): A list of route names to filter the search results, defaults to None.
  • sparse_vector (dict[int, float]): The sparse vector to search for, must be provided.

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aquery

async def aquery(
    vector: np.ndarray,
    top_k: int = 5,
    route_filter: Optional[List[str]] = None,
    sparse_vector: dict[int, float] | SparseEmbedding | None = None
) -> Tuple[np.ndarray, List[str]]

Search the index for the query and return top_k results. This method calls the

sync query method as everything uses numpy computations which is CPU-bound and so no benefit can be gained from making this async.

Arguments:

  • vector (np.ndarray): The query vector to search for.
  • top_k (int, optional): The number of top results to return, defaults to 5.
  • route_filter (Optional[List[str]], optional): A list of route names to filter the search results, defaults to None.
  • sparse_vector (dict[int, float]): The sparse vector to search for, must be provided.

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aget_routes

def aget_routes()

Get all routes from the index.

Returns:

List[str]: A list of routes.

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delete

def delete(route_name: str)

Delete all records of a specific route from the index.

Arguments:

  • route_name (str): The name of the route to delete.

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delete_index

def delete_index()

Deletes the index, effectively clearing it and setting it to None.

Returns:

None: None

baselocal