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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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__len__

def __len__()
Returns the total number of vectors in the index. If the index is not initialized returns 0. Returns: int: The total number of vectors.

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adelete

async def adelete(route_name: str) -> list[str]
Asynchronously delete records from the index by route name. Arguments:
  • route_name (str): The name of the route to delete.
Returns: list[str]: List of IDs of the vectors deleted (empty list, as Qdrant does not return IDs).

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adelete_index

async def adelete_index()
Asynchronously delete the index (collection) from Qdrant. Returns: None: None

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ais_ready

async def ais_ready() -> bool
Checks if the index is ready to be used asynchronously.

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aadd

async def aadd(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)
Asynchronously add records to the index, including metadata in the payload.

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aget_utterances

async def aget_utterances(include_metadata: bool = False) -> List[Utterance]
Asynchronously gets a list of route and utterance objects currently stored in the index, including metadata.