f42fb978a8
- app/indexing/qdrant_client.py: remove the identity-only _qid()
helper and pass chunk_id straight to PointStruct (Qdrant accepts
the UUID string directly).
- services/types.ts: SearchHit gets an explicit, optional
ocr_confidence field so consumers can type the value instead of
casting through metadata.
- widgets/SearchResultCard.tsx: replaces the
(hit.metadata as { ocr_confidence? }) cast with the new field. No
behavior change when the backend omits it.
tsc --noEmit: clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
"""Qdrant client + collection bootstrap + chunk upsert."""
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from __future__ import annotations
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from functools import lru_cache
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from typing import Any, Sequence
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as qm
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from app.config import settings
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from app.logging_config import get_logger
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logger = get_logger(__name__)
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DENSE_VECTOR_NAME = "dense"
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@lru_cache(maxsize=1)
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def get_qdrant() -> QdrantClient:
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return QdrantClient(
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host=settings.qdrant_host,
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port=settings.qdrant_port,
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api_key=settings.qdrant_api_key or None,
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timeout=60,
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)
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def ensure_collection(collection: str | None = None, dim: int | None = None) -> None:
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name = collection or settings.qdrant_collection_chunks
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vector_size = dim or settings.embedding_dim
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client = get_qdrant()
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existing = {c.name for c in client.get_collections().collections}
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if name in existing:
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logger.debug("qdrant.collection.exists", collection=name)
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return
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logger.info("qdrant.collection.create", collection=name, dim=vector_size)
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client.create_collection(
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collection_name=name,
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vectors_config={
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DENSE_VECTOR_NAME: qm.VectorParams(
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size=vector_size,
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distance=qm.Distance.COSINE,
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)
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},
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optimizers_config=qm.OptimizersConfigDiff(default_segment_number=2),
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)
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# Payload indexes for filtering.
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for field in ("document_id", "source_path", "block_type"):
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client.create_payload_index(
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collection_name=name,
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field_name=field,
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field_schema=qm.PayloadSchemaType.KEYWORD,
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)
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client.create_payload_index(
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collection_name=name,
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field_name="page_number",
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field_schema=qm.PayloadSchemaType.INTEGER,
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)
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client.create_payload_index(
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collection_name=name,
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field_name="ocr_confidence",
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field_schema=qm.PayloadSchemaType.FLOAT,
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)
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def upsert_chunks(
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points: Sequence[tuple[str, list[float], dict[str, Any]]],
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collection: str | None = None,
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) -> int:
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"""Upsert (chunk_id, vector, payload) triples. Returns count upserted."""
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name = collection or settings.qdrant_collection_chunks
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if not points:
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return 0
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qpoints = [
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qm.PointStruct(
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id=chunk_id,
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vector={DENSE_VECTOR_NAME: vector},
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payload={**payload, "chunk_id": chunk_id},
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)
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for chunk_id, vector, payload in points
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]
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get_qdrant().upsert(collection_name=name, points=qpoints, wait=False)
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return len(qpoints)
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def delete_by_document(document_id: str, collection: str | None = None) -> int:
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name = collection or settings.qdrant_collection_chunks
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client = get_qdrant()
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client.delete(
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collection_name=name,
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points_selector=qm.FilterSelector(
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filter=qm.Filter(
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must=[qm.FieldCondition(key="document_id", match=qm.MatchValue(value=document_id))]
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)
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),
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)
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return 1
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