Fixed similarity fetching for Weaviate hybrid searches (run-llama#9033)

llama_index/vector_stores/weaviate.py

@@ -249,9 +249,12 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul
             }
             query_builder = query_builder.with_where(filter_with_node_ids)
 
-        query_builder = query_builder.with_additional(["id", "vector", "distance"])
+        query_builder = query_builder.with_additional(
+            ["id", "vector", "distance", "score"]
+        )
 
         vector = query.query_embedding
+        similarity_key = "distance"
         if query.mode == VectorStoreQueryMode.DEFAULT:
             logger.debug("Using vector search")
             if vector is not None:
@@ -262,6 +265,7 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul
                 )
         elif query.mode == VectorStoreQueryMode.HYBRID:
             logger.debug(f"Using hybrid search with alpha {query.alpha}")
+            similarity_key = "score"
             query_builder = query_builder.with_hybrid(
                 query=query.query_str,
                 alpha=query.alpha,
@@ -284,7 +288,7 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul
         parsed_result = parse_get_response(query_result)
         entries = parsed_result[self.index_name]
 
-        similarities = [get_node_similarity(entry) for entry in entries]
+        similarities = [get_node_similarity(entry, similarity_key) for entry in entries]
         nodes = [to_node(entry, text_key=self.text_key) for entry in entries]
 
         nodes = nodes[: query.similarity_top_k]