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https://github.com/open-webui/open-webui
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refac: embeddings endpoint
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@ -1208,6 +1208,37 @@ async def get_base_models(request: Request, user=Depends(get_admin_user)):
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return {"data": models}
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##################################
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# Embeddings
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##################################
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@app.post("/api/embeddings")
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async def embeddings(
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request: Request, form_data: dict, user=Depends(get_verified_user)
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):
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"""
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OpenAI-compatible embeddings endpoint.
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This handler:
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- Performs user/model checks and dispatches to the correct backend.
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- Supports OpenAI, Ollama, arena models, pipelines, and any compatible provider.
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Args:
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request (Request): Request context.
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form_data (dict): OpenAI-like payload (e.g., {"model": "...", "input": [...]})
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user (UserModel): Authenticated user.
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Returns:
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dict: OpenAI-compatible embeddings response.
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"""
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# Make sure models are loaded in app state
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if not request.app.state.MODELS:
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await get_all_models(request, user=user)
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# Use generic dispatcher in utils.embeddings
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return await generate_embeddings(request, form_data, user)
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@app.post("/api/chat/completions")
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async def chat_completion(
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request: Request,
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@ -1550,37 +1581,6 @@ async def get_app_latest_release_version(user=Depends(get_verified_user)):
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async def get_app_changelog():
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return {key: CHANGELOG[key] for idx, key in enumerate(CHANGELOG) if idx < 5}
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##################################
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# Embeddings
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##################################
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@app.post("/api/embeddings")
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async def embeddings_endpoint(
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request: Request,
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form_data: dict,
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user=Depends(get_verified_user)
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):
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"""
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OpenAI-compatible embeddings endpoint.
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This handler:
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- Performs user/model checks and dispatches to the correct backend.
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- Supports OpenAI, Ollama, arena models, pipelines, and any compatible provider.
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Args:
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request (Request): Request context.
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form_data (dict): OpenAI-like payload (e.g., {"model": "...", "input": [...]})
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user (UserModel): Authenticated user.
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Returns:
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dict: OpenAI-compatible embeddings response.
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"""
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# Make sure models are loaded in app state
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if not request.app.state.MODELS:
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await get_all_models(request, user=user)
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# Use generic dispatcher in utils.embeddings
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return await generate_embeddings(request, form_data, user)
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############################
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# OAuth Login & Callback
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@ -9,9 +9,10 @@ from open_webui.utils.models import check_model_access
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from open_webui.env import SRC_LOG_LEVELS, GLOBAL_LOG_LEVEL, BYPASS_MODEL_ACCESS_CONTROL
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from open_webui.routers.openai import embeddings as openai_embeddings
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from open_webui.routers.ollama import embeddings as ollama_embeddings
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from open_webui.routers.ollama import GenerateEmbeddingsForm
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from open_webui.routers.pipelines import process_pipeline_inlet_filter
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from open_webui.routers.ollama import (
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embeddings as ollama_embeddings,
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GenerateEmbeddingsForm,
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)
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from open_webui.utils.payload import convert_embedding_payload_openai_to_ollama
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@ -29,7 +30,7 @@ async def generate_embeddings(
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bypass_filter: bool = False,
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):
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"""
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Dispatch and handle embeddings generation based on the model type (OpenAI, Ollama, Arena, pipeline, etc).
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Dispatch and handle embeddings generation based on the model type (OpenAI, Ollama).
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Args:
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request (Request): The FastAPI request context.
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@ -71,50 +72,12 @@ async def generate_embeddings(
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if not bypass_filter and user.role == "user":
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check_model_access(user, model)
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# Arena "meta-model": select a submodel at random
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if model.get("owned_by") == "arena":
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model_ids = model.get("info", {}).get("meta", {}).get("model_ids")
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filter_mode = model.get("info", {}).get("meta", {}).get("filter_mode")
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if model_ids and filter_mode == "exclude":
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model_ids = [
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m["id"]
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for m in list(models.values())
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if m.get("owned_by") != "arena" and m["id"] not in model_ids
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]
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if isinstance(model_ids, list) and model_ids:
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selected_model_id = random.choice(model_ids)
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else:
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model_ids = [
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m["id"]
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for m in list(models.values())
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if m.get("owned_by") != "arena"
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]
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selected_model_id = random.choice(model_ids)
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inner_form = dict(form_data)
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inner_form["model"] = selected_model_id
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response = await generate_embeddings(
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request, inner_form, user, bypass_filter=True
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)
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# Tag which concreted model was chosen
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if isinstance(response, dict):
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response = {
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**response,
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"selected_model_id": selected_model_id,
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}
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return response
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# Pipeline/Function models
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if model.get("pipe"):
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# The pipeline handler should provide OpenAI-compatible schema
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return await process_pipeline_inlet_filter(request, form_data, user, models)
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# Ollama backend
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if model.get("owned_by") == "ollama":
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ollama_payload = convert_embedding_payload_openai_to_ollama(form_data)
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form_obj = GenerateEmbeddingsForm(**ollama_payload)
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response = await ollama_embeddings(
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request=request,
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form_data=form_obj,
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form_data=GenerateEmbeddingsForm(**ollama_payload),
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user=user,
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)
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return convert_embedding_response_ollama_to_openai(response)
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@ -124,4 +87,4 @@ async def generate_embeddings(
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request=request,
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form_data=form_data,
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user=user,
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)
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)
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