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https://github.com/open-webui/open-webui
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@ -15,6 +15,8 @@ from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
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from fastapi.middleware.cors import CORSMiddleware
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from faster_whisper import WhisperModel
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from pydantic import BaseModel
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import requests
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import hashlib
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@ -67,6 +69,36 @@ SPEECH_CACHE_DIR = Path(CACHE_DIR).joinpath("./audio/speech/")
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SPEECH_CACHE_DIR.mkdir(parents=True, exist_ok=True)
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class OpenAIConfigUpdateForm(BaseModel):
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url: str
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key: str
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@app.get("/config")
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async def get_openai_config(user=Depends(get_admin_user)):
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return {
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"OPENAI_API_BASE_URL": app.state.OPENAI_API_BASE_URL,
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"OPENAI_API_KEY": app.state.OPENAI_API_KEY,
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}
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@app.post("/config/update")
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async def update_openai_config(
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form_data: OpenAIConfigUpdateForm, user=Depends(get_admin_user)
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):
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if form_data.key == "":
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raise HTTPException(status_code=400, detail=ERROR_MESSAGES.API_KEY_NOT_FOUND)
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app.state.OPENAI_API_BASE_URL = form_data.url
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app.state.OPENAI_API_KEY = form_data.key
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return {
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"status": True,
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"OPENAI_API_BASE_URL": app.state.OPENAI_API_BASE_URL,
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"OPENAI_API_KEY": app.state.OPENAI_API_KEY,
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}
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@app.post("/speech")
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async def speech(request: Request, user=Depends(get_verified_user)):
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idx = None
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@ -96,8 +96,8 @@ app.state.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
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app.state.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
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app.state.RAG_TEMPLATE = RAG_TEMPLATE
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app.state.RAG_OPENAI_API_BASE_URL = RAG_OPENAI_API_BASE_URL
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app.state.RAG_OPENAI_API_KEY = RAG_OPENAI_API_KEY
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app.state.OPENAI_API_BASE_URL = RAG_OPENAI_API_BASE_URL
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app.state.OPENAI_API_KEY = RAG_OPENAI_API_KEY
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app.state.PDF_EXTRACT_IMAGES = False
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@ -150,8 +150,8 @@ async def get_embedding_config(user=Depends(get_admin_user)):
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"embedding_engine": app.state.RAG_EMBEDDING_ENGINE,
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"embedding_model": app.state.RAG_EMBEDDING_MODEL,
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"openai_config": {
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"url": app.state.RAG_OPENAI_API_BASE_URL,
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"key": app.state.RAG_OPENAI_API_KEY,
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"url": app.state.OPENAI_API_BASE_URL,
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"key": app.state.OPENAI_API_KEY,
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},
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}
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@ -182,8 +182,8 @@ async def update_embedding_config(
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app.state.sentence_transformer_ef = None
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if form_data.openai_config != None:
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app.state.RAG_OPENAI_API_BASE_URL = form_data.openai_config.url
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app.state.RAG_OPENAI_API_KEY = form_data.openai_config.key
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app.state.OPENAI_API_BASE_URL = form_data.openai_config.url
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app.state.OPENAI_API_KEY = form_data.openai_config.key
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else:
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sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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@ -201,8 +201,8 @@ async def update_embedding_config(
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"embedding_engine": app.state.RAG_EMBEDDING_ENGINE,
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"embedding_model": app.state.RAG_EMBEDDING_MODEL,
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"openai_config": {
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"url": app.state.RAG_OPENAI_API_BASE_URL,
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"key": app.state.RAG_OPENAI_API_KEY,
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"url": app.state.OPENAI_API_BASE_URL,
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"key": app.state.OPENAI_API_KEY,
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},
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}
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@ -317,8 +317,8 @@ def query_doc_handler(
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query_embeddings = generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=form_data.query,
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key=app.state.RAG_OPENAI_API_KEY,
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url=app.state.RAG_OPENAI_API_BASE_URL,
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key=app.state.OPENAI_API_KEY,
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url=app.state.OPENAI_API_BASE_URL,
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)
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return query_embeddings_doc(
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@ -369,8 +369,8 @@ def query_collection_handler(
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query_embeddings = generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=form_data.query,
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key=app.state.RAG_OPENAI_API_KEY,
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url=app.state.RAG_OPENAI_API_BASE_URL,
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key=app.state.OPENAI_API_KEY,
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url=app.state.OPENAI_API_BASE_URL,
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)
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return query_embeddings_collection(
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@ -486,8 +486,8 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
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generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=text,
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key=app.state.RAG_OPENAI_API_KEY,
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url=app.state.RAG_OPENAI_API_BASE_URL,
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key=app.state.OPENAI_API_KEY,
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url=app.state.OPENAI_API_BASE_URL,
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)
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for text in texts
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]
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