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4 | 4 | import httpx
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5 | 5 | from qdrant_client import QdrantClient
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6 | 6 | from qdrant_client._pydantic_compat import construct
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7 |
| -from qdrant_client.http import models as rest |
| 7 | +from qdrant_client import models |
8 | 8 |
|
9 | 9 | from dataset_reader.base_reader import Query
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10 | 10 | from engine.base_client.search import BaseSearcher
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@@ -37,24 +37,35 @@ def init_client(cls, host, distance, connection_params: dict, search_params: dic
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37 | 37 |
|
38 | 38 | @classmethod
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39 | 39 | def search_one(cls, query: Query, top: int) -> List[Tuple[int, float]]:
|
| 40 | + |
40 | 41 | # Can query only one till we introduce re-ranking in the benchmarks
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41 | 42 | if query.sparse_vector is None:
|
42 | 43 | query_vector = query.vector
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43 | 44 | else:
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44 | 45 | query_vector = construct(
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45 |
| - rest.SparseVector, |
| 46 | + models.SparseVector, |
46 | 47 | indices=query.sparse_vector.indices,
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47 | 48 | values=query.sparse_vector.values,
|
48 | 49 | )
|
49 | 50 |
|
| 51 | + |
| 52 | + prefetch = cls.search_params.get("prefetch") |
| 53 | + |
| 54 | + if prefetch: |
| 55 | + prefetch = models.Prefetch( |
| 56 | + **prefetch, |
| 57 | + query=query_vector, |
| 58 | + ) |
| 59 | + |
50 | 60 | try:
|
51 | 61 | res = cls.client.query_points(
|
52 | 62 | using="sparse" if query.sparse_vector else None,
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53 | 63 | collection_name=QDRANT_COLLECTION_NAME,
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| 64 | + prefetch=prefetch, |
54 | 65 | query=query_vector,
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55 | 66 | query_filter=cls.parser.parse(query.meta_conditions),
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56 | 67 | limit=top,
|
57 |
| - search_params=rest.SearchParams(**cls.search_params.get("config", {})), |
| 68 | + search_params=models.SearchParams(**cls.search_params.get("config", {})), |
58 | 69 | with_payload=cls.search_params.get("with_payload", False),
|
59 | 70 | )
|
60 | 71 | except Exception as ex:
|
|
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