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"""Qdrant vector store index.
An index that is built on top of an existing Qdrant collection.
"""
import logging
from typing import Any, Dict, List, Optional, cast
from gpt_index.data_structs.node_v2 import DocumentRelationship, Node
from gpt_index.utils import iter_batch
from gpt_index.vector_stores.types import (
NodeEmbeddingResult,
VectorStore,
VectorStoreQueryResult,
VectorStoreQuery,
)
logger = logging.getLogger(__name__)
class QdrantVectorStore(VectorStore):
"""Qdrant Vector Store.
In this vector store, embeddings and docs are stored within a
Qdrant collection.
During query time, the index uses Qdrant to query for the top
k most similar nodes.
Args:
collection_name: (str): name of the Qdrant collection
client (Optional[Any]): QdrantClient instance from `qdrant-client` package
"""
stores_text: bool = True
def __init__(
self, collection_name: str, client: Optional[Any] = None, **kwargs: Any
) -> None:
"""Init params."""
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)
if client is None:
raise ValueError("Missing Qdrant client!")
self._client = cast(qdrant_client.QdrantClient, client)
self._collection_name = collection_name
self._collection_initialized = self._collection_exists(collection_name)
self._batch_size = kwargs.get("batch_size", 100)
@classmethod
def from_dict(cls, config_dict: Dict[str, Any]) -> "VectorStore":
if "client" not in config_dict:
raise ValueError("Missing Qdrant client!")
return cls(**config_dict)
@property
def config_dict(self) -> dict:
"""Return config dict."""
return {
"collection_name": self._collection_name,
}
def add(self, embedding_results: List[NodeEmbeddingResult]) -> List[str]:
"""Add embedding results to index.
Args
embedding_results: List[NodeEmbeddingResult]: list of embedding results
"""
from qdrant_client.http import models as rest
if len(embedding_results) > 0 and not self._collection_initialized:
self._create_collection(
collection_name=self._collection_name,
vector_size=len(embedding_results[0].embedding),
)
ids = []
for result_batch in iter_batch(embedding_results, self._batch_size):
new_ids = []
vectors = []
payloads = []
for result in result_batch:
new_ids.append(result.id)
vectors.append(result.embedding)
node = result.node
payloads.append(
{
"doc_id": result.doc_id,
"text": node.get_text(),
"extra_info": node.extra_info,
}
)
self._client.upsert(
collection_name=self._collection_name,
points=rest.Batch(
ids=new_ids,
vectors=vectors,
payloads=payloads,
),
)
ids.extend(new_ids)
return ids
def delete(self, doc_id: str, **delete_kwargs: Any) -> None:
"""Delete a document.
Args:
doc_id: (str): document id
"""
from qdrant_client.http import models as rest
self._client.delete(
collection_name=self._collection_name,
points_selector=rest.Filter(
must=[
rest.FieldCondition(
key="doc_id", match=rest.MatchValue(value=doc_id)
)
]
),
)
@property
def client(self) -> Any:
"""Return the Qdrant client."""
return self._client
def _create_collection(self, collection_name: str, vector_size: int) -> None:
"""Create a Qdrant collection."""
from qdrant_client.http import models as rest
self._client.recreate_collection(
collection_name=collection_name,
vectors_config=rest.VectorParams(
size=vector_size,
distance=rest.Distance.COSINE,
),
)
self._collection_initialized = True
def _collection_exists(self, collection_name: str) -> bool:
"""Check if a collection exists."""
from grpc import RpcError
from qdrant_client.http.exceptions import UnexpectedResponse
try:
self._client.get_collection(collection_name)
except (RpcError, UnexpectedResponse):
return False
return True
def query(
self,
query: VectorStoreQuery,
) -> VectorStoreQueryResult:
"""Query index for top k most similar nodes.
Args:
query_embedding (List[float]): query embedding
similarity_top_k (int): top k most similar nodes
doc_ids (Optional[List[str]]): list of doc_ids to filter by
"""
from qdrant_client.http.models import (
FieldCondition,
Filter,
MatchValue,
Payload,
)
query_embedding = cast(List[float], query.query_embedding)
response = self._client.search(
collection_name=self._collection_name,
query_vector=query_embedding,
limit=cast(int, query.similarity_top_k),
query_filter=None
if not query.doc_ids
else Filter(
must=[
Filter(
should=[
FieldCondition(key="doc_id", match=MatchValue(value=doc_id))
for doc_id in query.doc_ids
],
)
]
),
)
logger.debug(f"> Top {len(response)} nodes:")
nodes = []
similarities = []
ids = []
for point in response:
payload = cast(Payload, point.payload)
node = Node(
doc_id=str(point.id),
text=payload.get("text"),
extra_info=payload.get("extra_info"),
relationships={
DocumentRelationship.SOURCE: payload.get("doc_id", "None"),
},
)
nodes.append(node)
similarities.append(point.score)
ids.append(str(point.id))
return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)
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