fix: pgvector search_type of similarity_score_threshold not working (#7771)

- Description: VectorStoreRetriever->similarity_score_threshold with
search_type of "similarity_score_threshold" not working with the
following two minor issues,
- Issue: 1. In line 237 of `vectorstores/base.py`, "score_threshold" is
passed to `_similarity_search_with_relevance_scores` as in the kwargs,
while score_threshold is not a valid argument of this method. As a fix,
before calling `_similarity_search_with_relevance_scores`,
score_threshold is popped from kwargs. 2. In line 596 to 607 of
`vectorstores/pgvector.py`, it's checking the distance_strategy against
the string in Enum. However, self.distance_strategy will get the
property of distance_strategy from line 316, where the callable function
is passed. To solve this issue, self.distance_strategy is changed to
self._distance_strategy to avoid calling the property method.,
  - Dependencies: No,
  - Tag maintainer: @rlancemartin, @eyurtsev,
  - Twitter handle: No

---------

Co-authored-by: Bin Wang <bin@arcanum.ai>

langchain/vectorstores/base.py

@@ -234,6 +234,8 @@ class VectorStore(ABC):
         Returns:
             List of Tuples of (doc, similarity_score)
         """
+        score_threshold = kwargs.pop("score_threshold", None)
+
         docs_and_similarities = self._similarity_search_with_relevance_scores(
             query, k=k, **kwargs
         )
@@ -246,7 +248,6 @@ class VectorStore(ABC):
                 f" 0 and 1, got {docs_and_similarities}"
             )
 
-        score_threshold = kwargs.get("score_threshold")
         if score_threshold is not None:
             docs_and_similarities = [
                 (doc, similarity)

langchain/vectorstores/pgvector.py

@@ -593,15 +593,15 @@ class PGVector(VectorStore):
 
         # Default strategy is to rely on distance strategy provided
         # in vectorstore constructor
-        if self.distance_strategy == DistanceStrategy.COSINE:
+        if self._distance_strategy == DistanceStrategy.COSINE:
             return self._cosine_relevance_score_fn
-        elif self.distance_strategy == DistanceStrategy.EUCLIDEAN:
+        elif self._distance_strategy == DistanceStrategy.EUCLIDEAN:
             return self._euclidean_relevance_score_fn
-        elif self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
+        elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
             return self._max_inner_product_relevance_score_fn
         else:
             raise ValueError(
                 "No supported normalization function"
-                f" for distance_strategy of {self.distance_strategy}."
+                f" for distance_strategy of {self._distance_strategy}."
                 "Consider providing relevance_score_fn to PGVector constructor."
             )