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1e8bb26 0151e19 1e8bb26 0151e19 1e8bb26 0151e19 1e8bb26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | """Qdrant vector store: collection management, upsert, and search."""
from __future__ import annotations
import uuid
from functools import lru_cache
from typing import List, Optional
from qdrant_client import QdrantClient
from qdrant_client.http import models as qm
from .config import get_settings
@lru_cache
def get_client() -> QdrantClient:
settings = get_settings()
return QdrantClient(url=settings.qdrant_url, api_key=settings.qdrant_api_key, timeout=60)
def ensure_collection() -> None:
"""Create the collection if it does not exist."""
settings = get_settings()
client = get_client()
existing = {c.name for c in client.get_collections().collections}
if settings.qdrant_collection not in existing:
client.create_collection(
collection_name=settings.qdrant_collection,
vectors_config=qm.VectorParams(
size=settings.embedding_dim, distance=qm.Distance.COSINE
),
)
# Index on document_id so we can filter / delete by document.
client.create_payload_index(
collection_name=settings.qdrant_collection,
field_name="document_id",
field_schema=qm.PayloadSchemaType.KEYWORD,
)
def upsert_chunks(
document_id: str,
filename: str,
chunks: List[str],
vectors: List[List[float]],
) -> None:
settings = get_settings()
client = get_client()
points = [
qm.PointStruct(
id=str(uuid.uuid4()),
vector=vector,
payload={
"document_id": document_id,
"filename": filename,
"chunk_index": idx,
"text": chunk,
},
)
for idx, (chunk, vector) in enumerate(zip(chunks, vectors))
]
client.upsert(collection_name=settings.qdrant_collection, points=points)
def search(
query_vector: List[float],
top_k: int,
document_ids: Optional[List[str]] = None,
):
settings = get_settings()
client = get_client()
query_filter = None
if document_ids:
query_filter = qm.Filter(
must=[qm.FieldCondition(key="document_id", match=qm.MatchAny(any=document_ids))]
)
# query_points is the current API (works on qdrant-client >=1.10; the old
# .search() was removed in 1.18). Returns a QueryResponse; .points is the
# list of ScoredPoint (each has .payload and .score), matching prior usage.
return client.query_points(
collection_name=settings.qdrant_collection,
query=query_vector,
limit=top_k,
query_filter=query_filter,
with_payload=True,
).points
def delete_document(document_id: str) -> None:
settings = get_settings()
client = get_client()
client.delete(
collection_name=settings.qdrant_collection,
points_selector=qm.FilterSelector(
filter=qm.Filter(
must=[
qm.FieldCondition(
key="document_id", match=qm.MatchValue(value=document_id)
)
]
)
),
)
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