diff --git a/backend/open_webui/retrieval/utils.py b/backend/open_webui/retrieval/utils.py
index b05057b28..a3974f15b 100644
--- a/backend/open_webui/retrieval/utils.py
+++ b/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(
diff --git a/backend/open_webui/retrieval/vector/dbs/chroma.py b/backend/open_webui/retrieval/vector/dbs/chroma.py
index 006ee2076..3543cd545 100755
--- a/backend/open_webui/retrieval/vector/dbs/chroma.py
+++ b/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"],
}
diff --git a/backend/open_webui/retrieval/vector/dbs/milvus.py b/backend/open_webui/retrieval/vector/dbs/milvus.py
index ad05f9422..4d0da57ac 100644
--- a/backend/open_webui/retrieval/vector/dbs/milvus.py
+++ b/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"))
diff --git a/backend/open_webui/retrieval/vector/dbs/opensearch.py b/backend/open_webui/retrieval/vector/dbs/opensearch.py
index 99567c84e..432bcef41 100644
--- a/backend/open_webui/retrieval/vector/dbs/opensearch.py
+++ b/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],
diff --git a/backend/open_webui/retrieval/vector/dbs/pgvector.py b/backend/open_webui/retrieval/vector/dbs/pgvector.py
index eab02232f..0ddf48d1e 100644
--- a/backend/open_webui/retrieval/vector/dbs/pgvector.py
+++ b/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)
diff --git a/backend/open_webui/retrieval/vector/dbs/qdrant.py b/backend/open_webui/retrieval/vector/dbs/qdrant.py
index 28f0b3779..070bf3de5 100644
--- a/backend/open_webui/retrieval/vector/dbs/qdrant.py
+++ b/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):