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CrateDB: Vector Store -- make _euclidean_relevance_score_fn identity f.

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We don't need anything on top of it, ie we don't need this function and
instead should use value from CrateDB as is.

Similarity is already in the (0,1] interval and dividing by math.sqrt(2)
won't normalize it but return wrong result, for example 1 will become
0.714.
This commit is contained in:
Andreas Motl 2024-11-11 05:49:07 +01:00
parent 0561dcc754
commit 1ee02dd76c
2 changed files with 5 additions and 6 deletions
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5
libs/community/langchain_community/vectorstores/cratedb/base.py
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@ -1,7 +1,6 @@
from __future__ import annotations
import enum
import math
from typing import (
Any,
Callable,
@ -466,10 +465,10 @@ class CrateDBVectorStore(PGVector):
# others are not!)
# - embedding dimensionality
# - etc.
# This function converts the euclidean norm of normalized embeddings
# This function converts the Euclidean norm of normalized embeddings
# (0 is most similar, sqrt(2) most dissimilar)
# to a similarity function (0 to 1)
# Original:
# return 1.0 - distance / math.sqrt(2)
return similarity / math.sqrt(2)
return similarity

6
libs/community/tests/integration_tests/vectorstores/test_cratedb.py
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@ -470,9 +470,9 @@ def test_cratedb_relevance_score() -> None:
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
# Original score values: 1.0, 0.9996744261675065, 0.9986996093328621
assert output == [
(Document(page_content="foo", metadata={"page": "0"}), 0.7071067811865475),
(Document(page_content="bar", metadata={"page": "1"}), 0.35355339059327373),
(Document(page_content="baz", metadata={"page": "2"}), 0.1414213562373095),
(Document(page_content="foo", metadata={"page": "0"}), 1.0),
(Document(page_content="bar", metadata={"page": "1"}), 0.5),
(Document(page_content="baz", metadata={"page": "2"}), 0.2),
]