Fix: Normalze all database distances to score in [0, 1]

backend/open_webui/retrieval/utils.py

@@ -175,7 +175,7 @@ def merge_get_results(get_results: list[dict]) -> dict:
 
 
 def merge_and_sort_query_results(
-    query_results: list[dict], k: int, reverse: bool = False
+    query_results: list[dict], k: int
 ) -> dict:
     # Initialize lists to store combined data
     combined = dict()  # To store documents with unique document hashes
@@ -196,28 +196,18 @@ def merge_and_sort_query_results(
                     continue  # if doc is new, no further comparison is needed
 
                 # if doc is alredy in, but new distance is better, update
-                if not reverse and distance < combined[doc_hash][0]:
-                    # Chroma uses unconventional cosine similarity, so we don't need to reverse the results
-                    # https://docs.trychroma.com/docs/collections/configure#configuring-chroma-collections
-                    combined[doc_hash] = (distance, document, metadata)
-                if reverse and distance > combined[doc_hash][0]:
+                if distance > combined[doc_hash][0]:
                     combined[doc_hash] = (distance, document, metadata)
 
     combined = list(combined.values())
     # Sort the list based on distances
-    combined.sort(key=lambda x: x[0], reverse=reverse)
+    combined.sort(key=lambda x: x[0], reverse=True)
 
     # Slice to keep only the top k elements
     sorted_distances, sorted_documents, sorted_metadatas = (
         zip(*combined[:k]) if combined else ([], [], [])
     )
 
-    # if chromaDB, the distance is 0 (best) to 2 (worse)
-    # re-order to -1 (worst) to 1 (best) for relevance score
-    if not reverse:
-        sorted_distances = tuple(-dist for dist in sorted_distances)
-        sorted_distances = tuple(dist + 1 for dist in sorted_distances)
-
     # Create and return the output dictionary
     return {
         "distances": [list(sorted_distances)],
@@ -267,12 +257,7 @@ def query_collection(
             else:
                 pass
 
-    if VECTOR_DB == "chroma":
-        # Chroma uses unconventional cosine similarity, so we don't need to reverse the results
-        # https://docs.trychroma.com/docs/collections/configure#configuring-chroma-collections
-        return merge_and_sort_query_results(results, k=k, reverse=False)
-    else:
-        return merge_and_sort_query_results(results, k=k, reverse=True)
+    return merge_and_sort_query_results(results, k=k)
 
 
 def query_collection_with_hybrid_search(
@@ -308,7 +293,7 @@ def query_collection_with_hybrid_search(
             "Hybrid search failed for all collections. Using Non hybrid search as fallback."
         )
 
-    return merge_and_sort_query_results(results, k=k, reverse=True)
+    return merge_and_sort_query_results(results, k=k)
 
 
 def get_embedding_function(

backend/open_webui/retrieval/vector/dbs/chroma.py

@@ -75,10 +75,16 @@ class ChromaClient:
                     n_results=limit,
                 )
 
+                # chromadb has cosine distance, 2 (worst) -> 0 (best). Re-odering to 0 -> 1
+                # https://docs.trychroma.com/docs/collections/configure cosine equation
+                distances: list = result["distances"][0]
+                distances = [2 - dist for dist in distances]
+                distances = [[dist/2 for dist in distances]]
+
                 return SearchResult(
                     **{
                         "ids": result["ids"],
-                        "distances": result["distances"],
+                        "distances": distances,
                         "documents": result["documents"],
                         "metadatas": result["metadatas"],
                     }

backend/open_webui/retrieval/vector/dbs/milvus.py

@@ -64,7 +64,10 @@ class MilvusClient:
 
             for item in match:
                 _ids.append(item.get("id"))
-                _distances.append(item.get("distance"))
+                # normalize milvus score from [-1, 1] to [0, 1] range
+                # https://milvus.io/docs/de/metric.md
+                _dist = (item.get("distance") + 1.0)/2.0
+                _distances.append(_dist)
                 _documents.append(item.get("entity", {}).get("data", {}).get("text"))
                 _metadatas.append(item.get("entity", {}).get("metadata"))
 

backend/open_webui/retrieval/vector/dbs/opensearch.py

@@ -120,7 +120,7 @@ class OpenSearchClient:
                     "script_score": {
                         "query": {"match_all": {}},
                         "script": {
-                            "source": "cosineSimilarity(params.query_value, doc[params.field]) + 1.0",
+                            "source": "(cosineSimilarity(params.query_value, doc[params.field]) + 1.0) / 2.0",
                             "params": {
                                 "field": "vector",
                                 "query_value": vectors[0],

backend/open_webui/retrieval/vector/dbs/pgvector.py

@@ -278,7 +278,9 @@ class PgvectorClient:
             for row in results:
                 qid = int(row.qid)
                 ids[qid].append(row.id)
-                distances[qid].append(row.distance)
+                # normalize and re-orders pgvec distance from [2, 0] to [0, 1] score range
+                # https://github.com/pgvector/pgvector?tab=readme-ov-file#querying
+                distances[qid].append((2.0 - row.distance)/2.0)
                 documents[qid].append(row.text)
                 metadatas[qid].append(row.vmetadata)
 

backend/open_webui/retrieval/vector/dbs/qdrant.py

@@ -99,7 +99,8 @@ class QdrantClient:
             ids=get_result.ids,
             documents=get_result.documents,
             metadatas=get_result.metadatas,
-            distances=[[point.score for point in query_response.points]],
+            # qdrant distance is [-1, 1], normalize to [0, 1]
+            distances=[[(point.score + 1.0)/2.0 for point in query_response.points]],
         )
 
     def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):