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Satisfy github format check

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This commit is contained in:
Marko Henning 2025-03-25 19:09:17 +01:00
parent 94d9d3d590
commit 7531b7dcaa
5 changed files with 5 additions and 7 deletions
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4
backend/open_webui/retrieval/utils.py
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@ -174,9 +174,7 @@ def merge_get_results(get_results: list[dict]) -> dict:
return result
def merge_and_sort_query_results(
query_results: list[dict], k: int
) -> dict:
def merge_and_sort_query_results(query_results: list[dict], k: int) -> dict:
# Initialize lists to store combined data
combined = dict() # To store documents with unique document hashes

2
backend/open_webui/retrieval/vector/dbs/chroma.py
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@ -79,7 +79,7 @@ class ChromaClient:
# 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]]
distances = [[dist / 2 for dist in distances]]
return SearchResult(
**{

2
backend/open_webui/retrieval/vector/dbs/milvus.py
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@ -66,7 +66,7 @@ class MilvusClient:
_ids.append(item.get("id"))
# 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
_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"))

2
backend/open_webui/retrieval/vector/dbs/pgvector.py
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@ -280,7 +280,7 @@ class PgvectorClient:
ids[qid].append(row.id)
# 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)
distances[qid].append((2.0 - row.distance) / 2.0)
documents[qid].append(row.text)
metadatas[qid].append(row.vmetadata)

2
backend/open_webui/retrieval/vector/dbs/qdrant.py
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@ -100,7 +100,7 @@ class QdrantClient:
documents=get_result.documents,
metadatas=get_result.metadatas,
# qdrant distance is [-1, 1], normalize to [0, 1]
distances=[[(point.score + 1.0)/2.0 for point in query_response.points]],
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):