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https://github.com/qdrant/qdrant.git
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037600e8af
* weighted rrf implementation * test * fmt * fix edge * validate number of sources and number of weights * do not partial match * upd schema * review fixes * update formula * remove calcualtions from tests * update comment, because AI have OCD * fmt
84 lines
1.9 KiB
Python
84 lines
1.9 KiB
Python
from qdrant_edge import *
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from common import *
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shard = load_new_shard()
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fill_dummy_data(shard)
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search_filter = Filter(
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must=[
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FieldCondition(
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key="color",
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match=MatchValue(value="red"),
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)
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]
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)
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# Basic RRF fusion (equal weights)
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print("=== Basic RRF Fusion ===")
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result = shard.query(QueryRequest(
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prefetches = [
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Prefetch(
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prefetches=[],
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query=Query.Nearest([6.0, 9.0, 4.0, 2.0]),
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limit=5,
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params=None,
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filter=None,
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score_threshold=None,
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),
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Prefetch(
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prefetches=[],
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query=Query.Nearest([1.0, -3.0, 2.0, 8.0]),
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limit=5,
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params=None,
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filter=search_filter,
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score_threshold=None,
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)
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],
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query = Fusion.Rrf(k=2),
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filter = None,
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score_threshold = None,
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limit = 10,
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offset = 0,
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params = None,
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with_vector = True,
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with_payload = True,
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))
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for point in result:
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print(point)
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# Weighted RRF fusion - first prefetch has 3x weight
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print("\n=== Weighted RRF Fusion (3:1) ===")
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result = shard.query(QueryRequest(
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prefetches = [
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Prefetch(
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prefetches=[],
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query=Query.Nearest([6.0, 9.0, 4.0, 2.0]),
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limit=5,
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params=None,
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filter=None,
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score_threshold=None,
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),
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Prefetch(
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prefetches=[],
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query=Query.Nearest([1.0, -3.0, 2.0, 8.0]),
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limit=5,
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params=None,
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filter=search_filter,
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score_threshold=None,
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)
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],
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query = Fusion.Rrf(k=2, weights=[3.0, 1.0]), # First prefetch has 3x weight
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filter = None,
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score_threshold = None,
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limit = 10,
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offset = 0,
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params = None,
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with_vector = True,
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with_payload = True,
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))
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for point in result:
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print(point)
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