f9f1b332bd
qdrant-client 1.15.1 does not support datetime_range in FieldCondition. Use models.Range with string comparison (same as Lotus reference). Also wrap date filter in try-except to prevent crash on bad date format. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
119 lines
3.2 KiB
Python
119 lines
3.2 KiB
Python
from typing import Any
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from qdrant_client import AsyncQdrantClient, models
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from config import (
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DENSE_PREFETCH_K,
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QDRANT_COLLECTION_NAME,
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QDRANT_DENSE_VECTOR_NAME,
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QDRANT_SPARSE_VECTOR_NAME,
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RETRIEVE_K,
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SPARSE_PREFETCH_K,
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logger,
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)
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from schemas import Question, SparseVector
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def _build_filter(question: Question) -> models.Filter | None:
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must_conditions: list[models.Condition] = []
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if question.date_range:
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try:
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must_conditions.append(
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models.FieldCondition(
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key="metadata.end",
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range=models.Range(gte=question.date_range.from_),
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)
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)
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must_conditions.append(
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models.FieldCondition(
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key="metadata.start",
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range=models.Range(lte=question.date_range.to),
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)
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)
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except Exception as e:
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logger.warning("Date filter failed: %s", e)
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if question.asker:
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must_conditions.append(
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models.FieldCondition(
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key="metadata.participants",
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match=models.MatchValue(value=question.asker),
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)
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)
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return models.Filter(must=must_conditions) if must_conditions else None
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async def qdrant_search(
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client: AsyncQdrantClient,
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primary_dense: list[float],
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extra_dense: list[list[float]],
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sparse_vector: SparseVector,
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question: Question,
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) -> list[Any]:
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search_filter = _build_filter(question)
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prefetch: list[models.Prefetch] = []
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# Primary dense
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prefetch.append(
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models.Prefetch(
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query=primary_dense,
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using=QDRANT_DENSE_VECTOR_NAME,
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limit=DENSE_PREFETCH_K,
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filter=search_filter,
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)
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)
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# Extra dense (variants / hyde) - smaller budget per query
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extra_k = max(10, DENSE_PREFETCH_K // max(1, len(extra_dense)))
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for vec in extra_dense:
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prefetch.append(
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models.Prefetch(
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query=vec,
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using=QDRANT_DENSE_VECTOR_NAME,
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limit=extra_k,
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filter=search_filter,
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)
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)
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# Sparse
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prefetch.append(
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models.Prefetch(
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query=models.SparseVector(
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indices=sparse_vector.indices,
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values=sparse_vector.values,
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),
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using=QDRANT_SPARSE_VECTOR_NAME,
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limit=SPARSE_PREFETCH_K,
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filter=search_filter,
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)
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)
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response = await client.query_points(
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collection_name=QDRANT_COLLECTION_NAME,
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prefetch=prefetch,
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query=models.FusionQuery(fusion=models.Fusion.RRF),
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limit=RETRIEVE_K,
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with_payload=True,
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)
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if not response.points:
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logger.debug("Qdrant returned 0 points")
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return []
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logger.debug("Qdrant returned %d points", len(response.points))
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return list(response.points)
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def extract_message_ids(point: Any) -> list[str]:
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payload = point.payload or {}
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metadata = payload.get("metadata") or {}
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message_ids = metadata.get("message_ids") or []
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return [str(mid) for mid in message_ids]
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def extract_page_content(point: Any) -> str:
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payload = point.payload or {}
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return payload.get("page_content") or ""
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