refac: embeddings endpoint

This commit is contained in:
Timothy Jaeryang Baek 2025-06-05 00:37:31 +04:00
parent b02a3da4da
commit ab36b8aeae
2 changed files with 38 additions and 75 deletions

View File

@ -1208,6 +1208,37 @@ async def get_base_models(request: Request, user=Depends(get_admin_user)):
return {"data": models}
##################################
# Embeddings
##################################
@app.post("/api/embeddings")
async def embeddings(
request: Request, form_data: dict, user=Depends(get_verified_user)
):
"""
OpenAI-compatible embeddings endpoint.
This handler:
- Performs user/model checks and dispatches to the correct backend.
- Supports OpenAI, Ollama, arena models, pipelines, and any compatible provider.
Args:
request (Request): Request context.
form_data (dict): OpenAI-like payload (e.g., {"model": "...", "input": [...]})
user (UserModel): Authenticated user.
Returns:
dict: OpenAI-compatible embeddings response.
"""
# Make sure models are loaded in app state
if not request.app.state.MODELS:
await get_all_models(request, user=user)
# Use generic dispatcher in utils.embeddings
return await generate_embeddings(request, form_data, user)
@app.post("/api/chat/completions")
async def chat_completion(
request: Request,
@ -1550,37 +1581,6 @@ async def get_app_latest_release_version(user=Depends(get_verified_user)):
async def get_app_changelog():
return {key: CHANGELOG[key] for idx, key in enumerate(CHANGELOG) if idx < 5}
##################################
# Embeddings
##################################
@app.post("/api/embeddings")
async def embeddings_endpoint(
request: Request,
form_data: dict,
user=Depends(get_verified_user)
):
"""
OpenAI-compatible embeddings endpoint.
This handler:
- Performs user/model checks and dispatches to the correct backend.
- Supports OpenAI, Ollama, arena models, pipelines, and any compatible provider.
Args:
request (Request): Request context.
form_data (dict): OpenAI-like payload (e.g., {"model": "...", "input": [...]})
user (UserModel): Authenticated user.
Returns:
dict: OpenAI-compatible embeddings response.
"""
# Make sure models are loaded in app state
if not request.app.state.MODELS:
await get_all_models(request, user=user)
# Use generic dispatcher in utils.embeddings
return await generate_embeddings(request, form_data, user)
############################
# OAuth Login & Callback

View File

@ -9,9 +9,10 @@ from open_webui.utils.models import check_model_access
from open_webui.env import SRC_LOG_LEVELS, GLOBAL_LOG_LEVEL, BYPASS_MODEL_ACCESS_CONTROL
from open_webui.routers.openai import embeddings as openai_embeddings
from open_webui.routers.ollama import embeddings as ollama_embeddings
from open_webui.routers.ollama import GenerateEmbeddingsForm
from open_webui.routers.pipelines import process_pipeline_inlet_filter
from open_webui.routers.ollama import (
embeddings as ollama_embeddings,
GenerateEmbeddingsForm,
)
from open_webui.utils.payload import convert_embedding_payload_openai_to_ollama
@ -29,7 +30,7 @@ async def generate_embeddings(
bypass_filter: bool = False,
):
"""
Dispatch and handle embeddings generation based on the model type (OpenAI, Ollama, Arena, pipeline, etc).
Dispatch and handle embeddings generation based on the model type (OpenAI, Ollama).
Args:
request (Request): The FastAPI request context.
@ -71,50 +72,12 @@ async def generate_embeddings(
if not bypass_filter and user.role == "user":
check_model_access(user, model)
# Arena "meta-model": select a submodel at random
if model.get("owned_by") == "arena":
model_ids = model.get("info", {}).get("meta", {}).get("model_ids")
filter_mode = model.get("info", {}).get("meta", {}).get("filter_mode")
if model_ids and filter_mode == "exclude":
model_ids = [
m["id"]
for m in list(models.values())
if m.get("owned_by") != "arena" and m["id"] not in model_ids
]
if isinstance(model_ids, list) and model_ids:
selected_model_id = random.choice(model_ids)
else:
model_ids = [
m["id"]
for m in list(models.values())
if m.get("owned_by") != "arena"
]
selected_model_id = random.choice(model_ids)
inner_form = dict(form_data)
inner_form["model"] = selected_model_id
response = await generate_embeddings(
request, inner_form, user, bypass_filter=True
)
# Tag which concreted model was chosen
if isinstance(response, dict):
response = {
**response,
"selected_model_id": selected_model_id,
}
return response
# Pipeline/Function models
if model.get("pipe"):
# The pipeline handler should provide OpenAI-compatible schema
return await process_pipeline_inlet_filter(request, form_data, user, models)
# Ollama backend
if model.get("owned_by") == "ollama":
ollama_payload = convert_embedding_payload_openai_to_ollama(form_data)
form_obj = GenerateEmbeddingsForm(**ollama_payload)
response = await ollama_embeddings(
request=request,
form_data=form_obj,
form_data=GenerateEmbeddingsForm(**ollama_payload),
user=user,
)
return convert_embedding_response_ollama_to_openai(response)
@ -124,4 +87,4 @@ async def generate_embeddings(
request=request,
form_data=form_data,
user=user,
)
)