open-webui · tjbck · Jul 9, 2024 · Jun 18, 2024 · Jun 21, 2024 · Jun 24, 2024
diff --git a/backend/apps/audio/main.py b/backend/apps/audio/main.py
@@ -14,7 14,6 @@
 from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
 
 from fastapi.middleware.cors import CORSMiddleware
-from faster_whisper import WhisperModel
 from pydantic import BaseModel
 
 import uuid
@@ -277,6 276,8 @@ def transcribe(
  f.close()
 
  if app.state.config.STT_ENGINE == "":
  from faster_whisper import WhisperModel
 
  whisper_kwargs = {
  "model_size_or_path": WHISPER_MODEL,
  "device": whisper_device_type,

diff --git a/backend/apps/images/main.py b/backend/apps/images/main.py
@@ -12,7 12,6 @@
  Form,
 )
 from fastapi.middleware.cors import CORSMiddleware
-from faster_whisper import WhisperModel
 
 from constants import ERROR_MESSAGES
 from utils.utils import (

diff --git a/backend/apps/rag/main.py b/backend/apps/rag/main.py
@@ -48,8 48,6 @@
 import uuid
 import json
 
-import sentence_transformers
-
 from apps.webui.models.documents import (
  Documents,
  DocumentForm,
@@ -190,6 188,8 @@ def update_embedding_model(
  update_model: bool = False,
 ):
  if embedding_model and app.state.config.RAG_EMBEDDING_ENGINE == "":
  import sentence_transformers
 
  app.state.sentence_transformer_ef = sentence_transformers.SentenceTransformer(
  get_model_path(embedding_model, update_model),
  device=DEVICE_TYPE,
@@ -204,6 204,8 @@ def update_reranking_model(
  update_model: bool = False,
 ):
  if reranking_model:
  import sentence_transformers
 
  app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder(
  get_model_path(reranking_model, update_model),
  device=DEVICE_TYPE,

diff --git a/backend/apps/rag/utils.py b/backend/apps/rag/utils.py
@@ -442,8 442,6 @@ def _get_relevant_documents(
 from langchain_core.callbacks import Callbacks
 from langchain_core.pydantic_v1 import Extra
 
-from sentence_transformers import util
-
 
 class RerankCompressor(BaseDocumentCompressor):
  embedding_function: Any
@@ -468,6 466,8 @@ def compress_documents(
  [(query, doc.page_content) for doc in documents]
  )
  else:
  from sentence_transformers import util
 
  query_embedding = self.embedding_function(query)
  document_embedding = self.embedding_function(
  [doc.page_content for doc in documents]