qdrant_client.async_qdrant_fastembed module
- class AsyncQdrantFastembedMixin(**kwargs: Any)[source]
Bases:
AsyncQdrantBase- async add(collection_name: str, documents: Iterable[str], metadata: Optional[Iterable[Dict[str, Any]]] = None, ids: Optional[Iterable[Union[int[int], str[str]]]] = None, batch_size: int = 32, parallel: Optional[int] = None, **kwargs: Any) List[Union[str, int]][source]
Adds text documents into qdrant collection. If collection does not exist, it will be created with default parameters. Metadata in combination with documents will be added as payload. Documents will be embedded using the specified embedding model.
If you want to use your own vectors, use upsert method instead.
- Parameters
collection_name (str) – Name of the collection to add documents to.
documents (Iterable[str]) – List of documents to embed and add to the collection.
metadata (Iterable[Dict[str, Any]], optional) – List of metadata dicts. Defaults to None.
ids (Iterable[models.ExtendedPointId], optional) – List of ids to assign to documents. If not specified, UUIDs will be generated. Defaults to None.
batch_size (int, optional) – How many documents to embed and upload in single request. Defaults to 32.
parallel (Optional[int], optional) – How many parallel workers to use for embedding. Defaults to None. If number is specified, data-parallel process will be used.
- Raises
ImportError – If fastembed is not installed.
- Returns
List of IDs of added documents. If no ids provided, UUIDs will be randomly generated on client side.
- get_fastembed_vector_params(on_disk: Optional[bool] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, hnsw_config: Optional[HnswConfigDiff] = None) Dict[str, VectorParams][source]
Generates vector configuration, compatible with fastembed models.
- Parameters
on_disk – if True, vectors will be stored on disk. If None, default value will be used.
quantization_config – Quantization configuration. If None, quantization will be disabled.
hnsw_config – HNSW configuration. If None, default configuration will be used.
- Returns
Configuration for vectors_config argument in create_collection method.
- get_vector_field_name() str[source]
Returns name of the vector field in qdrant collection, used by current fastembed model. :returns: Name of the vector field.
- async query(collection_name: str, query_text: str, query_filter: Optional[Filter] = None, limit: int = 10, **kwargs: Any) List[QueryResponse][source]
Search for documents in a collection. This method automatically embeds the query text using the specified embedding model. If you want to use your own query vector, use search method instead.
- Parameters
collection_name – Collection to search in
query_text – Text to search for. This text will be embedded using the specified embedding model. And then used as a query vector.
query_filter –
Exclude vectors which doesn’t fit given conditions.
If None - search among all vectors
limit – How many results return
**kwargs – Additional search parameters. See qdrant_client.models.SearchRequest for details.
- Returns
List[types.ScoredPoint] – List of scored points.
- async query_batch(collection_name: str, query_texts: List[str], query_filter: Optional[Filter] = None, limit: int = 10, **kwargs: Any) List[List[QueryResponse]][source]
Search for documents in a collection with batched query. This method automatically embeds the query text using the specified embedding model.
- Parameters
collection_name – Collection to search in
query_texts – A list of texts to search for. Each text will be embedded using the specified embedding model. And then used as a query vector for a separate search requests.
query_filter –
Exclude vectors which doesn’t fit given conditions.
If None - search among all vectors
This filter will be applied to all search requests.
limit – How many results return
**kwargs – Additional search parameters. See qdrant_client.models.SearchRequest for details.
- Returns
List[List[QueryResponse]] – List of lists of responses for each query text.
- set_model(embedding_model_name: str) None[source]
Set embedding model to use for encoding documents and queries. :param embedding_model_name: One of the supported embedding models. See SUPPORTED_EMBEDDING_MODELS for details.
- Raises
ValueError – If embedding model is not supported.
ImportError – If fastembed is not installed.
- Returns
None
