Emil_Shanaty/open-webui-ai4me
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

chore: format

Browse Source
This commit is contained in:
Timothy Jaeryang Baek
2026-03-08 18:14:09 -05:00
parent 2cb28369b7
commit 352391fa76
69 changed files with 124 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backend/open_webui/config.py
+10 -3
View File
@@ -850,7 +850,11 @@ def load_oauth_providers():
if FEISHU_CLIENT_ID.value:
configured_providers.append("Feishu")
if configured_providers and not OPENID_PROVIDER_URL.value and not OPENID_END_SESSION_ENDPOINT.value:
if (
configured_providers
and not OPENID_PROVIDER_URL.value
and not OPENID_END_SESSION_ENDPOINT.value
):
provider_list = ", ".join(configured_providers)
log.warning(
f"⚠️ OAuth providers configured ({provider_list}) but OPENID_PROVIDER_URL not set - logout will not work!"
@@ -2371,7 +2375,8 @@ if VECTOR_DB == "chroma":
MARIADB_VECTOR_DB_URL = os.environ.get("MARIADB_VECTOR_DB_URL", "").strip()
MARIADB_VECTOR_INITIALIZE_MAX_VECTOR_LENGTH = int(
os.environ.get("MARIADB_VECTOR_INITIALIZE_MAX_VECTOR_LENGTH", "1536").strip() or "1536"
os.environ.get("MARIADB_VECTOR_INITIALIZE_MAX_VECTOR_LENGTH", "1536").strip()
or "1536"
)
# Distance strategy:
@@ -2382,7 +2387,9 @@ MARIADB_VECTOR_DISTANCE_STRATEGY = (
)
# HNSW M parameter (MariaDB VECTOR INDEX ... M=<int>)
MARIADB_VECTOR_INDEX_M = int(os.environ.get("MARIADB_VECTOR_INDEX_M", "8").strip() or "8")
MARIADB_VECTOR_INDEX_M = int(
os.environ.get("MARIADB_VECTOR_INDEX_M", "8").strip() or "8"
)
# Pooling (MariaDB-Vector)
MARIADB_VECTOR_POOL_SIZE = os.environ.get("MARIADB_VECTOR_POOL_SIZE", None)
backend/open_webui/retrieval/utils.py
+6 -2
View File
@@ -590,7 +590,9 @@ def generate_openai_batch_embeddings(
if "data" in data:
return [elem["embedding"] for elem in data["data"]]
else:
raise ValueError("Unexpected OpenAI embeddings response: missing 'data' key")
raise ValueError(
"Unexpected OpenAI embeddings response: missing 'data' key"
)
except Exception as e:
log.exception(f"Error generating openai batch embeddings: {e}")
return None
@@ -767,7 +769,9 @@ def generate_ollama_batch_embeddings(
if "embeddings" in data:
return data["embeddings"]
else:
raise ValueError("Unexpected Ollama embeddings response: missing 'embeddings' key")
raise ValueError(
"Unexpected Ollama embeddings response: missing 'embeddings' key"
)
except Exception as e:
log.exception(f"Error generating ollama batch embeddings: {e}")
return None
backend/open_webui/retrieval/vector/dbs/mariadb_vector.py
+29 -10
View File
@@ -22,7 +22,12 @@ from open_webui.config import (
MARIADB_VECTOR_POOL_TIMEOUT,
MARIADB_VECTOR_POOL_RECYCLE,
)
from open_webui.retrieval.vector.main import GetResult, SearchResult, VectorDBBase, VectorItem
from open_webui.retrieval.vector.main import (
GetResult,
SearchResult,
VectorDBBase,
VectorItem,
)
from open_webui.retrieval.vector.utils import process_metadata
log = logging.getLogger(__name__)
@@ -157,8 +162,7 @@ class MariaDBVectorClient(VectorDBBase):
with conn.cursor() as cur:
try:
dist = self.distance_strategy
cur.execute(
f"""
cur.execute(f"""
CREATE TABLE IF NOT EXISTS document_chunk (
-- MariaDB Vector requires the table PRIMARY KEY used with a VECTOR INDEX to be <= 256 bytes.
-- VARCHAR has internal length/metadata overhead, so VARCHAR(255) can exceed the 256-byte limit.
@@ -173,8 +177,7 @@ class MariaDBVectorClient(VectorDBBase):
VECTOR INDEX (embedding) M={self.index_m} DISTANCE={dist},
INDEX idx_document_chunk_collection_name (collection_name)
) ENGINE=InnoDB;
"""
)
""")
conn.commit()
except Exception as e:
conn.rollback()
@@ -220,7 +223,11 @@ class MariaDBVectorClient(VectorDBBase):
"""
Return the MariaDB Vector distance function name for the configured strategy.
"""
return "vec_distance_cosine" if self.distance_strategy == "cosine" else "vec_distance_euclidean"
return (
"vec_distance_cosine"
if self.distance_strategy == "cosine"
else "vec_distance_euclidean"
)
def _score_from_dist(self, dist: float) -> float:
"""
@@ -442,12 +449,19 @@ class MariaDBVectorClient(VectorDBBase):
documents[q_idx].append(rtext)
metadatas[q_idx].append(_safe_json(rmeta))
return SearchResult(ids=ids, distances=distances, documents=documents, metadatas=metadatas)
return SearchResult(
ids=ids,
distances=distances,
documents=documents,
metadatas=metadatas,
)
except Exception as e:
log.exception(f"[MARIADB_VECTOR] search() failed: {e}")
return None
def query(self, collection_name: str, filter: Dict[str, Any], limit: Optional[int] = None) -> Optional[GetResult]:
def query(
self, collection_name: str, filter: Dict[str, Any], limit: Optional[int] = None
) -> Optional[GetResult]:
"""
Retrieve documents by metadata filter (non-vector query).
"""
@@ -472,7 +486,9 @@ class MariaDBVectorClient(VectorDBBase):
metadatas = [[_safe_json(r[2]) for r in rows]]
return GetResult(ids=ids, documents=documents, metadatas=metadatas)
def get(self, collection_name: str, limit: Optional[int] = None) -> Optional[GetResult]:
def get(
self, collection_name: str, limit: Optional[int] = None
) -> Optional[GetResult]:
"""
Retrieve documents in a collection without filtering (optionally limited).
"""
@@ -549,7 +565,10 @@ class MariaDBVectorClient(VectorDBBase):
try:
with self._connect() as conn:
with conn.cursor() as cur:
cur.execute("SELECT 1 FROM document_chunk WHERE collection_name = ? LIMIT 1", (collection_name,))
cur.execute(
"SELECT 1 FROM document_chunk WHERE collection_name = ? LIMIT 1",
(collection_name,),
)
return cur.fetchone() is not None
except Exception:
return False
backend/open_webui/retrieval/vector/factory.py
+3 -1
View File
@@ -58,7 +58,9 @@ class Vector:
return OpenGaussClient()
case VectorType.MARIADB_VECTOR:
from open_webui.retrieval.vector.dbs.mariadb_vector import MariaDBVectorClient
from open_webui.retrieval.vector.dbs.mariadb_vector import (
MariaDBVectorClient,
)
return MariaDBVectorClient()
case VectorType.ELASTICSEARCH:
backend/open_webui/routers/files.py
+1
View File
@@ -583,6 +583,7 @@ def update_file_data_content_by_id(
request,
ProcessFileForm(file_id=id, content=form_data.content),
user=user,
db=db,
)
file = Files.get_file_by_id(id=id, db=db)
except Exception as e:
backend/open_webui/routers/ollama.py
+3 -1
View File
@@ -1747,7 +1747,9 @@ async def download_file_stream(
yield f"data: {json.dumps(res)}\n\n"
else:
raise RuntimeError("Ollama: Could not create blob, Please try again.")
raise RuntimeError(
"Ollama: Could not create blob, Please try again."
)
# url = "https://huggingface.co/TheBloke/stablelm-zephyr-3b-GGUF/resolve/main/stablelm-zephyr-3b.Q2_K.gguf"
backend/open_webui/utils/oauth.py
+3 -1
View File
@@ -1706,7 +1706,9 @@ class OAuthManager:
redirect_url = f"{redirect_base_url}/auth"
if error_message:
redirect_url = f"{redirect_url}?error={urllib.parse.quote_plus(error_message)}"
redirect_url = (
f"{redirect_url}?error={urllib.parse.quote_plus(error_message)}"
)
return RedirectResponse(url=redirect_url, headers=response.headers)
response = RedirectResponse(url=redirect_url, headers=response.headers)
backend/open_webui/utils/task.py
+2 -6
View File
@@ -162,9 +162,7 @@ def truncate_content(content: str, max_chars: int, mode: str = "middletruncate")
return f"{content[:half]}...{content[-(max_chars - half):]}"
def apply_content_filter(
messages: list[dict], filter_str: str
) -> list[dict]:
def apply_content_filter(messages: list[dict], filter_str: str) -> list[dict]:
"""Apply a content filter to each message's content.
filter_str is like 'middletruncate:500', 'start:200', or 'end:200'.
@@ -238,9 +236,7 @@ def replace_messages_variable(
else:
half = mid // 2
start_msgs = messages[:half]
end_msgs = (
messages[-half:] if mid % 2 == 0 else messages[-(half + 1) :]
)
end_msgs = messages[-half:] if mid % 2 == 0 else messages[-(half + 1) :]
selected = start_msgs + end_msgs
content_filter = middle_filter
else:
backend/open_webui/utils/webhook.py
+1 -2
View File
@@ -32,8 +32,7 @@ async def post_webhook(name: str, url: str, message: str, event_data: dict) -> b
else:
user_dict = json.loads(user_data)
facts = [
{"name": name, "value": value}
for name, value in user_dict.items()
{"name": name, "value": value} for name, value in user_dict.items()
]
payload = {
"@type": "MessageCard",