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QdrantIndex Objects

class QdrantIndex(BaseIndex)

The name of the collection to use

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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]] = [],
        batch_size: int = DEFAULT_UPLOAD_BATCH_SIZE,
        **kwargs)

Add records to the index.

Arguments:

  • embeddings (List[List[float]]): The embeddings to add.
  • routes (List[str]): The routes to add.
  • utterances (List[str]): The utterances to add.
  • function_schemas (Optional[List[Dict[str, Any]]]): The function schemas to add.
  • metadata_list (List[Dict[str, Any]]): The metadata to add.
  • List[List[float]]0 (List[List[float]]1): The batch size to use for the upload.

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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 - QdrantIndex does not currently support this parameter so it is ignored. If required for your use-case please reach out to semantic-router maintainers on GitHub via an issue or PR.

Returns:

List[Utterance]: A list of Utterance objects.

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delete

def delete(route_name: str)

Delete records from the index.

Arguments:

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

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describe

def describe() -> IndexConfig

Describe the index.

Returns:

IndexConfig: The index configuration.

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is_ready

def is_ready() -> bool

Checks if the index is ready to be used.

Returns:

bool: True if the index is ready, False otherwise.

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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]]

Query the index.

Arguments:

  • vector (np.ndarray): The vector to query.
  • top_k (int): The number of results to return.
  • route_filter (Optional[List[str]]): The route filter to apply.
  • sparse_vector (dict[int, float] | SparseEmbedding | None): The sparse vector to query.

Returns:

Tuple[np.ndarray, List[str]]: A tuple of the scores and route names.

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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]]

Asynchronously query the index.

Arguments:

  • vector (np.ndarray): The vector to query.
  • top_k (int): The number of results to return.
  • route_filter (Optional[List[str]]): The route filter to apply.
  • sparse_vector (dict[int, float] | SparseEmbedding | None): The sparse vector to query.

Returns:

Tuple[np.ndarray, List[str]]: A tuple of the scores and route names.

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aget_routes

def aget_routes()

Asynchronously get all routes from the index.

Returns:

List[str]: A list of routes.

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delete_index

def delete_index()

Delete the index.

Returns:

None: None

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convert_metric

def convert_metric(metric: Metric)

Convert the metric to a Qdrant distance metric.

Arguments:

  • metric (Metric): The metric to convert.

Returns:

Distance: The converted metric.

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