Merge branch 'refs/heads/dev' into feat/sqlalchemy-instead-of-peewee

# Conflicts:
#	backend/requirements.txt

backend/apps/audio/main.py

@@ -14,7 +14,6 @@ from fastapi import (
 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,

backend/apps/images/main.py

@@ -12,7 +12,6 @@ from fastapi import (
     Form,
 )
 from fastapi.middleware.cors import CORSMiddleware
-from faster_whisper import WhisperModel
 
 from constants import ERROR_MESSAGES
 from utils.utils import (

backend/apps/ollama/main.py

@@ -153,7 +153,7 @@ async def cleanup_response(
         await session.close()
 
 
-async def post_streaming_url(url: str, payload: str):
+async def post_streaming_url(url: str, payload: str, stream: bool = True):
     r = None
     try:
         session = aiohttp.ClientSession(
@@ -162,12 +162,20 @@ async def post_streaming_url(url: str, payload: str):
         r = await session.post(url, data=payload)
         r.raise_for_status()
 
-        return StreamingResponse(
-            r.content,
-            status_code=r.status,
-            headers=dict(r.headers),
-            background=BackgroundTask(cleanup_response, response=r, session=session),
-        )
+        if stream:
+            return StreamingResponse(
+                r.content,
+                status_code=r.status,
+                headers=dict(r.headers),
+                background=BackgroundTask(
+                    cleanup_response, response=r, session=session
+                ),
+            )
+        else:
+            res = await r.json()
+            await cleanup_response(r, session)
+            return res
+
     except Exception as e:
         error_detail = "Open WebUI: Server Connection Error"
         if r is not None:
@@ -963,7 +971,11 @@ async def generate_openai_chat_completion(
     url = app.state.config.OLLAMA_BASE_URLS[url_idx]
     log.info(f"url: {url}")
 
-    return await post_streaming_url(f"{url}/v1/chat/completions", json.dumps(payload))
+    return await post_streaming_url(
+        f"{url}/v1/chat/completions",
+        json.dumps(payload),
+        stream=payload.get("stream", False),
+    )
 
 
 @app.get("/v1/models")

backend/apps/rag/main.py

@@ -48,8 +48,6 @@ import mimetypes
 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,

backend/apps/rag/utils.py

@@ -442,8 +442,6 @@ from langchain_core.documents import BaseDocumentCompressor, Document
 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 @@ class RerankCompressor(BaseDocumentCompressor):
                 [(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]

backend/apps/webui/main.py

@@ -259,6 +259,9 @@ async def generate_function_chat_completion(form_data, user):
                         if isinstance(line, BaseModel):
                             line = line.model_dump_json()
                             line = f"data: {line}"
+                        if isinstance(line, dict):
+                            line = f"data: {json.dumps(line)}"
+
                         try:
                             line = line.decode("utf-8")
                         except: