from poma.integrations.qdrant import PomaQdrant

PomaQdrant subclasses QdrantClient and adds POMA-specific convenience methods.

Constructor highlights

Use the normal QdrantClient connection arguments plus the POMA-specific settings:

Argument	Purpose
collection_name	Default collection name
dense_model	Dense embedding model name
sparse_model	Sparse model name, or None
dense_name	Dense vector field name
sparse_name	Sparse vector field name
dense_size	Dense vector size
distance	Vector distance metric
store_chunk_details	Store chunk details in payload
auto_create_collection	Create the collection automatically

If auto_create_collection=True, set dense_size.

Methods

upsert_poma_points

upsert_poma_points(
    chunk_data,
    *,
    collection_name: str | None = None,
    dense_model: str | None = None,
    sparse_model = _UNSET,
    dense_name: str | None = None,
    sparse_name: str | None = None,
    dense_options: dict | None = None,
    sparse_options: dict | None = None,
    store_chunk_details: bool | None = None,
    poma_batch_size: int | None = 100,
    batch_size: int | None = 100,
    **kwargs,
)

Accepts typed PomaResult, legacy chunk-data dictionaries, or a .poma archive path.

get_cheatsheets

get_cheatsheets(
    *,
    query: str | None = None,
    results = None,
    query_obj = None,
    prefetch = None,
    using: str | None = None,
    collection_name: str | None = None,
    chunk_data = None,
    limit: int = 5,
    query_filter = None,
    with_vectors: bool | list[str] = False,
    dense_model: str | None = None,
    sparse_model = _UNSET,
    dense_name: str | None = None,
    sparse_name: str | None = None,
    dense_options: dict | None = None,
    sparse_options: dict | None = None,
    prefetch_limit: int | None = None,
    **kwargs,
) -> list[dict]

Use query for the convenience path. Use query_obj and prefetch when you want raw Qdrant query control.