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refactor: drop the unused synchronous batch embedding helpers

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Every batch embedding provider in the retrieval layer exists twice, once synchronous and once asynchronous. Only the asynchronous versions are reachable: the single entry point that all embedding paths go through awaits those. The synchronous copies have no caller anywhere in the tree and were kept looking alive only by their own log lines.

This removes around 110 lines of duplicated request handling for OpenAI, Azure OpenAI and Ollama, so there is one implementation per provider instead of two that can silently drift apart. Nothing called the removed code, so behaviour is unchanged. Two log lines in the Ollama router named functions that no longer exist and now name the endpoints they sit in.

One thing worth knowing before this lands: the removed Azure helper was the only code in the tree that backed off on a 429 with Retry-After. The live asynchronous path raises instead. That gap is not new, but the deletion takes away the last worked example of it.
This commit is contained in:
Classic298 2026-08-20 22:06:20 +02:00
parent 8a42aa53e8
commit 9a638b3ad5
2 changed files with 2 additions and 114 deletions
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112
backend/open_webui/retrieval/utils.py
View file

@ -5,7 +5,6 @@ import hashlib
import logging
import os
import re
import time
from typing import Awaitable, Optional, Union
from urllib.parse import quote
@ -856,39 +855,6 @@ async def query_collection_with_hybrid_search(
return merge_and_sort_query_results(results, k=k)
def generate_openai_batch_embeddings(
model: str,
texts: list[str],
url: str = 'https://api.openai.com/v1',
key: str = '',
prefix: str = None,
user: UserModel = None,
) -> list[list[float]]:
log.debug('generate_openai_batch_embeddings:model %s batch size: %s', model, len(texts))
json_data = {'input': texts, 'model': model}
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
json_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {key}',
}
if ENABLE_FORWARD_USER_INFO_HEADERS and user:
headers = include_user_info_headers(headers, user)
r = requests.post(
f'{url}/embeddings',
headers=headers,
json=json_data,
)
r.raise_for_status()
data = r.json()
if 'data' in data:
return [elem['embedding'] for elem in data['data']]
else:
raise ValueError("Unexpected OpenAI embeddings response: missing 'data' key")
async def agenerate_openai_batch_embeddings(
model: str,
texts: list[str],
@ -926,48 +892,6 @@ async def agenerate_openai_batch_embeddings(
raise ValueError("Unexpected OpenAI embeddings response: missing 'data' key")
def generate_azure_openai_batch_embeddings(
model: str,
texts: list[str],
url: str,
key: str = '',
version: str = '',
prefix: str = None,
user: UserModel = None,
) -> list[list[float]]:
log.debug('generate_azure_openai_batch_embeddings:deployment %s batch size: %s', model, len(texts))
json_data = {'input': texts}
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
json_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
url = f'{url}/openai/deployments/{model}/embeddings?api-version={version}'
for _ in range(5):
headers = {
'Content-Type': 'application/json',
'api-key': key,
}
if ENABLE_FORWARD_USER_INFO_HEADERS and user:
headers = include_user_info_headers(headers, user)
r = requests.post(
url,
headers=headers,
json=json_data,
)
if r.status_code == 429:
retry = float(r.headers.get('Retry-After', '1'))
time.sleep(retry)
continue
r.raise_for_status()
data = r.json()
if 'data' in data:
return [elem['embedding'] for elem in data['data']]
else:
raise ValueError("Unexpected Azure OpenAI embeddings response: missing 'data' key")
raise Exception('Azure OpenAI embedding request failed: max retries (429) exceeded')
async def agenerate_azure_openai_batch_embeddings(
model: str,
texts: list[str],
@ -1008,42 +932,6 @@ async def agenerate_azure_openai_batch_embeddings(
raise ValueError("Unexpected Azure OpenAI embeddings response: missing 'data' key")
def generate_ollama_batch_embeddings(
model: str,
texts: list[str],
url: str,
key: str = '',
prefix: str = None,
user: UserModel = None,
) -> list[list[float]]:
log.debug('generate_ollama_batch_embeddings:model %s batch size: %s', model, len(texts))
json_data = {'input': texts, 'model': model, 'truncate': True}
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
json_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {key}',
}
if ENABLE_FORWARD_USER_INFO_HEADERS and user:
headers = include_user_info_headers(headers, user)
r = requests.post(
f'{url}/api/embed',
headers=headers,
json=json_data,
)
if r.status_code != 200:
error_detail = r.json().get('error', r.text)
raise Exception(f'Ollama embed error ({r.status_code}): {error_detail}')
data = r.json()
if 'embeddings' in data:
return data['embeddings']
else:
raise ValueError("Unexpected Ollama embeddings response: missing 'embeddings' key")
async def agenerate_ollama_batch_embeddings(
model: str,
texts: list[str],

4
backend/open_webui/routers/ollama.py
View file

@ -881,7 +881,7 @@ async def embed(
if not await Config.get('ollama.enable'):
raise HTTPException(status_code=503, detail=ERROR_MESSAGES.OLLAMA_API_DISABLED)
log.info('generate_ollama_batch_embeddings %s', form_data)
log.info('embed %s', form_data)
await check_model_access(user, await Models.get_model_by_id(form_data.model), BYPASS_MODEL_ACCESS_CONTROL)
await validate_ollama_backend_idx(request, form_data.model, url_idx, user)
@ -932,7 +932,7 @@ async def embeddings(
if not await Config.get('ollama.enable'):
raise HTTPException(status_code=503, detail=ERROR_MESSAGES.OLLAMA_API_DISABLED)
log.info('generate_ollama_embeddings %s', form_data)
log.info('embeddings %s', form_data)
await check_model_access(user, await Models.get_model_by_id(form_data.model), BYPASS_MODEL_ACCESS_CONTROL)
await validate_ollama_backend_idx(request, form_data.model, url_idx, user)