Fixes scope of query Session in PGVector (#5194)

`vectorstore.PGVector`: The transactional boundary should be increased
to cover the query itself

Currently, within the `similarity_search_with_score_by_vector` the
transactional boundary (created via the `Session` call) does not include
the select query being made.

This can result in un-intended consequences when interacting with the
PGVector instance methods directly


---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>

langchain/vectorstores/pgvector.py

@@ -291,40 +291,43 @@ class PGVector(VectorStore):
             if not collection:
                 raise ValueError("Collection not found")
 
-        filter_by = EmbeddingStore.collection_id == collection.uuid
+            filter_by = EmbeddingStore.collection_id == collection.uuid
-
+
-        if filter is not None:
+            if filter is not None:
-            filter_clauses = []
+                filter_clauses = []
-            for key, value in filter.items():
+                for key, value in filter.items():
-                IN = "in"
+                    IN = "in"
-                if isinstance(value, dict) and IN in map(str.lower, value):
+                    if isinstance(value, dict) and IN in map(str.lower, value):
-                    value_case_insensitive = {k.lower(): v for k, v in value.items()}
+                        value_case_insensitive = {
-                    filter_by_metadata = EmbeddingStore.cmetadata[key].astext.in_(
+                            k.lower(): v for k, v in value.items()
-                        value_case_insensitive[IN]
+                        }
-                    )
+                        filter_by_metadata = EmbeddingStore.cmetadata[key].astext.in_(
-                    filter_clauses.append(filter_by_metadata)
+                            value_case_insensitive[IN]
-                else:
+                        )
-                    filter_by_metadata = EmbeddingStore.cmetadata[key].astext == str(
+                        filter_clauses.append(filter_by_metadata)
-                        value
+                    else:
-                    )
+                        filter_by_metadata = EmbeddingStore.cmetadata[
-                    filter_clauses.append(filter_by_metadata)
+                            key
-
+                        ].astext == str(value)
-            filter_by = sqlalchemy.and_(filter_by, *filter_clauses)
+                        filter_clauses.append(filter_by_metadata)
-
+
-        results: List[QueryResult] = (
+                filter_by = sqlalchemy.and_(filter_by, *filter_clauses)
-            session.query(
+
-                EmbeddingStore,
+            results: List[QueryResult] = (
-                self.distance_strategy(embedding).label("distance"),  # type: ignore
+                session.query(
-            )
+                    EmbeddingStore,
-            .filter(filter_by)
+                    self.distance_strategy(embedding).label("distance"),  # type: ignore
-            .order_by(sqlalchemy.asc("distance"))
+                )
-            .join(
+                .filter(filter_by)
-                CollectionStore,
+                .order_by(sqlalchemy.asc("distance"))
-                EmbeddingStore.collection_id == CollectionStore.uuid,
+                .join(
+                    CollectionStore,
+                    EmbeddingStore.collection_id == CollectionStore.uuid,
+                )
+                .limit(k)
+                .all()
             )
-            .limit(k)
+
-            .all()
-        )
         docs = [
             (
                 Document(