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"""Qdrant reader."""
from typing import List, Optional, cast
from gpt_index.readers.base import BaseReader
from gpt_index.readers.schema.base import Document
class QdrantReader(BaseReader):
"""Qdrant reader.
Retrieve documents from existing Qdrant collections.
Args:
host: Host name of Qdrant service.
port: Port of the REST API interface. Default: 6333
grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc: If `true` - use gPRC interface whenever possible in custom methods.
https: If `true` - use HTTPS(SSL) protocol. Default: `false`
api_key: API key for authentication in Qdrant Cloud. Default: `None`
prefix:
If not `None` - add `prefix` to the REST URL path.
Example: `service/v1` will result in
`http://localhost:6333/service/v1/{qdrant-endpoint}` for REST API.
Default: `None`
timeout:
Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
"""
def __init__(
self,
host: str,
port: int = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
):
"""Initialize with parameters."""
import_err_msg = (
"`qdrant-client` package not found, please run `pip install qdrant-client`"
)
try:
import qdrant_client # noqa: F401
except ImportError:
raise ImportError(import_err_msg)
self._client = qdrant_client.QdrantClient(
url=host,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
)
def load_data(
self,
collection_name: str,
query_vector: List[float],
limit: int = 10,
) -> List[Document]:
"""Load data from Qdrant.
Args:
collection_name (str): Name of the Qdrant collection.
query_vector (List[float]): Query vector.
limit (int): Number of results to return.
Returns:
List[Document]: A list of documents.
"""
from qdrant_client.http.models.models import Payload
response = self._client.search(
collection_name=collection_name,
query_vector=query_vector,
with_vectors=True,
with_payload=True,
limit=limit,
)
documents = []
for point in response:
payload = cast(Payload, point.payload)
try:
vector = cast(List[float], point.vector)
except ValueError as e:
raise ValueError("Could not cast vector to List[float].") from e
document = Document(
doc_id=payload.get("doc_id"),
text=payload.get("text"),
embedding=vector,
)
documents.append(document)
return documents
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