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chore: format

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This commit is contained in:
Timothy Jaeryang Baek
2025-02-05 00:07:45 -08:00
parent f6f8c08cb0
commit e41a2682f5
56 changed files with 355 additions and 29 deletions
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33
backend/open_webui/retrieval/utils.py
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@@ -17,7 +17,11 @@ from open_webui.retrieval.vector.connector import VECTOR_DB_CLIENT
from open_webui.utils.misc import get_last_user_message
from open_webui.models.users import UserModel
from open_webui.env import SRC_LOG_LEVELS, OFFLINE_MODE, ENABLE_FORWARD_USER_INFO_HEADERS
from open_webui.env import (
SRC_LOG_LEVELS,
OFFLINE_MODE,
ENABLE_FORWARD_USER_INFO_HEADERS,
)
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])
@@ -62,10 +66,7 @@ class VectorSearchRetriever(BaseRetriever):
def query_doc(
collection_name: str,
query_embedding: list[float],
k: int,
user: UserModel=None
collection_name: str, query_embedding: list[float], k: int, user: UserModel = None
):
try:
result = VECTOR_DB_CLIENT.search(
@@ -258,7 +259,7 @@ def get_embedding_function(
embedding_function,
url,
key,
embedding_batch_size
embedding_batch_size,
):
if embedding_engine == "":
return lambda query, user=None: embedding_function.encode(query).tolist()
@@ -269,14 +270,16 @@ def get_embedding_function(
text=query,
url=url,
key=key,
user=user
user=user,
)
def generate_multiple(query, user, func):
if isinstance(query, list):
embeddings = []
for i in range(0, len(query), embedding_batch_size):
embeddings.extend(func(query[i : i + embedding_batch_size], user=user))
embeddings.extend(
func(query[i : i + embedding_batch_size], user=user)
)
return embeddings
else:
return func(query, user)
@@ -428,7 +431,11 @@ def get_model_path(model: str, update_model: bool = False):
def generate_openai_batch_embeddings(
model: str, texts: list[str], url: str = "https://api.openai.com/v1", key: str = "", user: UserModel = None
model: str,
texts: list[str],
url: str = "https://api.openai.com/v1",
key: str = "",
user: UserModel = None,
) -> Optional[list[list[float]]]:
try:
r = requests.post(
@@ -506,7 +513,13 @@ def generate_embeddings(engine: str, model: str, text: Union[str, list[str]], **
)
else:
embeddings = generate_ollama_batch_embeddings(
**{"model": model, "texts": [text], "url": url, "key": key, "user": user}
**{
"model": model,
"texts": [text],
"url": url,
"key": key,
"user": user,
}
)
return embeddings[0] if isinstance(text, str) else embeddings
elif engine == "openai":