|
|
|
@@ -1,320 +0,0 @@ |
|
|
|
from __future__ import annotations |
|
|
|
|
|
|
|
import asyncio |
|
|
|
import base64 |
|
|
|
import logging |
|
|
|
import os |
|
|
|
import shutil |
|
|
|
from contextlib import asynccontextmanager |
|
|
|
from datetime import datetime |
|
|
|
from pathlib import Path |
|
|
|
from typing import AsyncGenerator, Dict, Iterable, Tuple |
|
|
|
|
|
|
|
from fastapi import APIRouter, Request |
|
|
|
from fastapi.responses import FileResponse |
|
|
|
from openai import AsyncClient |
|
|
|
from openai.types.file_object import FileObject |
|
|
|
from sse_starlette.sse import EventSourceResponse |
|
|
|
|
|
|
|
# from chatchat.configs import BASE_TEMP_DIR, log_verbose |
|
|
|
|
|
|
|
|
|
|
|
from .api_schemas import * |
|
|
|
|
|
|
|
logger = logging.getLogger() |
|
|
|
|
|
|
|
|
|
|
|
DEFAULT_API_CONCURRENCIES = 5 # 默认单个模型最大并发数 |
|
|
|
model_semaphores: Dict[ |
|
|
|
Tuple[str, str], asyncio.Semaphore |
|
|
|
] = {} # key: (model_name, platform) |
|
|
|
openai_router = APIRouter(prefix="/v1", tags=["OpenAI 兼容平台整合接口"]) |
|
|
|
|
|
|
|
|
|
|
|
# @asynccontextmanager |
|
|
|
# async def get_model_client(model_name: str) -> AsyncGenerator[AsyncClient]: |
|
|
|
# """ |
|
|
|
# 对重名模型进行调度，依次选择：空闲的模型 -> 当前访问数最少的模型 |
|
|
|
# """ |
|
|
|
# max_semaphore = 0 |
|
|
|
# selected_platform = "" |
|
|
|
# model_infos = get_model_info(model_name=model_name, multiple=True) |
|
|
|
# for m, c in model_infos.items(): |
|
|
|
# key = (m, c["platform_name"]) |
|
|
|
# api_concurrencies = c.get("api_concurrencies", DEFAULT_API_CONCURRENCIES) |
|
|
|
# if key not in model_semaphores: |
|
|
|
# model_semaphores[key] = asyncio.Semaphore(api_concurrencies) |
|
|
|
# semaphore = model_semaphores[key] |
|
|
|
# if semaphore._value >= api_concurrencies: |
|
|
|
# selected_platform = c["platform_name"] |
|
|
|
# break |
|
|
|
# elif semaphore._value > max_semaphore: |
|
|
|
# selected_platform = c["platform_name"] |
|
|
|
# |
|
|
|
# key = (m, selected_platform) |
|
|
|
# semaphore = model_semaphores[key] |
|
|
|
# try: |
|
|
|
# await semaphore.acquire() |
|
|
|
# yield get_OpenAIClient(platform_name=selected_platform, is_async=True) |
|
|
|
# except Exception: |
|
|
|
# logger.error(f"failed when request to {key}", exc_info=True) |
|
|
|
# finally: |
|
|
|
# semaphore.release() |
|
|
|
|
|
|
|
|
|
|
|
async def openai_request( |
|
|
|
method, body, extra_json: Dict = {}, header: Iterable = [], tail: Iterable = [] |
|
|
|
): |
|
|
|
""" |
|
|
|
helper function to make openai request with extra fields |
|
|
|
""" |
|
|
|
|
|
|
|
async def generator(): |
|
|
|
for x in header: |
|
|
|
if isinstance(x, str): |
|
|
|
x = OpenAIChatOutput(content=x, object="chat.completion.chunk") |
|
|
|
elif isinstance(x, dict): |
|
|
|
x = OpenAIChatOutput.model_validate(x) |
|
|
|
else: |
|
|
|
raise RuntimeError(f"unsupported value: {header}") |
|
|
|
for k, v in extra_json.items(): |
|
|
|
setattr(x, k, v) |
|
|
|
yield x.model_dump_json() |
|
|
|
|
|
|
|
async for chunk in await method(**params): |
|
|
|
for k, v in extra_json.items(): |
|
|
|
setattr(chunk, k, v) |
|
|
|
yield chunk.model_dump_json() |
|
|
|
|
|
|
|
for x in tail: |
|
|
|
if isinstance(x, str): |
|
|
|
x = OpenAIChatOutput(content=x, object="chat.completion.chunk") |
|
|
|
elif isinstance(x, dict): |
|
|
|
x = OpenAIChatOutput.model_validate(x) |
|
|
|
else: |
|
|
|
raise RuntimeError(f"unsupported value: {tail}") |
|
|
|
for k, v in extra_json.items(): |
|
|
|
setattr(x, k, v) |
|
|
|
yield x.model_dump_json() |
|
|
|
|
|
|
|
params = body.model_dump(exclude_unset=True) |
|
|
|
|
|
|
|
if hasattr(body, "stream") and body.stream: |
|
|
|
return EventSourceResponse(generator()) |
|
|
|
else: |
|
|
|
result = await method(**params) |
|
|
|
for k, v in extra_json.items(): |
|
|
|
setattr(result, k, v) |
|
|
|
return result.model_dump() |
|
|
|
|
|
|
|
|
|
|
|
# @openai_router.get("/models") |
|
|
|
# async def list_models() -> Dict: |
|
|
|
# """ |
|
|
|
# 整合所有平台的模型列表。 |
|
|
|
# """ |
|
|
|
# |
|
|
|
# async def task(name: str, config: Dict): |
|
|
|
# try: |
|
|
|
# client = get_OpenAIClient(name, is_async=True) |
|
|
|
# models = await client.models.list() |
|
|
|
# return [{**x.model_dump(), "platform_name": name} for x in models.data] |
|
|
|
# except Exception: |
|
|
|
# logger.error(f"failed request to platform: {name}", exc_info=True) |
|
|
|
# return [] |
|
|
|
# |
|
|
|
# result = [] |
|
|
|
# tasks = [ |
|
|
|
# asyncio.create_task(task(name, config)) |
|
|
|
# for name, config in get_config_platforms().items() |
|
|
|
# ] |
|
|
|
# for t in asyncio.as_completed(tasks): |
|
|
|
# result += await t |
|
|
|
# |
|
|
|
# return {"object": "list", "data": result} |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/chat/completions") |
|
|
|
# async def create_chat_completions( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIChatInput, |
|
|
|
# ): |
|
|
|
# if log_verbose: |
|
|
|
# print(body) |
|
|
|
# async with get_model_client(body.model) as client: |
|
|
|
# result = await openai_request(client.chat.completions.create, body) |
|
|
|
# return result |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/completions") |
|
|
|
# async def create_completions( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIChatInput, |
|
|
|
# ): |
|
|
|
# async with get_model_client(body.model) as client: |
|
|
|
# return await openai_request(client.completions.create, body) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/embeddings") |
|
|
|
# async def create_embeddings( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIEmbeddingsInput, |
|
|
|
# ): |
|
|
|
# params = body.model_dump(exclude_unset=True) |
|
|
|
# client = get_OpenAIClient(model_name=body.model) |
|
|
|
# return (await client.embeddings.create(**params)).model_dump() |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/images/generations") |
|
|
|
# async def create_image_generations( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIImageGenerationsInput, |
|
|
|
# ): |
|
|
|
# async with get_model_client(body.model) as client: |
|
|
|
# return await openai_request(client.images.generate, body) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/images/variations") |
|
|
|
# async def create_image_variations( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIImageVariationsInput, |
|
|
|
# ): |
|
|
|
# async with get_model_client(body.model) as client: |
|
|
|
# return await openai_request(client.images.create_variation, body) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/images/edit") |
|
|
|
# async def create_image_edit( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIImageEditsInput, |
|
|
|
# ): |
|
|
|
# async with get_model_client(body.model) as client: |
|
|
|
# return await openai_request(client.images.edit, body) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/audio/translations", deprecated="暂不支持") |
|
|
|
# async def create_audio_translations( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIAudioTranslationsInput, |
|
|
|
# ): |
|
|
|
# async with get_model_client(body.model) as client: |
|
|
|
# return await openai_request(client.audio.translations.create, body) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/audio/transcriptions", deprecated="暂不支持") |
|
|
|
# async def create_audio_transcriptions( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIAudioTranscriptionsInput, |
|
|
|
# ): |
|
|
|
# async with get_model_client(body.model) as client: |
|
|
|
# return await openai_request(client.audio.transcriptions.create, body) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/audio/speech", deprecated="暂不支持") |
|
|
|
# async def create_audio_speech( |
|
|
|
# request: Request, |
|
|
|
# body: OpenAIAudioSpeechInput, |
|
|
|
# ): |
|
|
|
# async with get_model_client(body.model) as client: |
|
|
|
# return await openai_request(client.audio.speech.create, body) |
|
|
|
# |
|
|
|
# |
|
|
|
# def _get_file_id( |
|
|
|
# purpose: str, |
|
|
|
# created_at: int, |
|
|
|
# filename: str, |
|
|
|
# ) -> str: |
|
|
|
# today = datetime.fromtimestamp(created_at).strftime("%Y-%m-%d") |
|
|
|
# return base64.urlsafe_b64encode(f"{purpose}/{today}/{filename}".encode()).decode() |
|
|
|
# |
|
|
|
# |
|
|
|
# def _get_file_info(file_id: str) -> Dict: |
|
|
|
# splits = base64.urlsafe_b64decode(file_id).decode().split("/") |
|
|
|
# created_at = -1 |
|
|
|
# size = -1 |
|
|
|
# file_path = _get_file_path(file_id) |
|
|
|
# if os.path.isfile(file_path): |
|
|
|
# created_at = int(os.path.getmtime(file_path)) |
|
|
|
# size = os.path.getsize(file_path) |
|
|
|
# |
|
|
|
# return { |
|
|
|
# "purpose": splits[0], |
|
|
|
# "created_at": created_at, |
|
|
|
# "filename": splits[2], |
|
|
|
# "bytes": size, |
|
|
|
# } |
|
|
|
# |
|
|
|
# |
|
|
|
# def _get_file_path(file_id: str) -> str: |
|
|
|
# file_id = base64.urlsafe_b64decode(file_id).decode() |
|
|
|
# return os.path.join(BASE_TEMP_DIR, "openai_files", file_id) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.post("/files") |
|
|
|
# async def files( |
|
|
|
# request: Request, |
|
|
|
# file: UploadFile, |
|
|
|
# purpose: str = "assistants", |
|
|
|
# ) -> Dict: |
|
|
|
# created_at = int(datetime.now().timestamp()) |
|
|
|
# file_id = _get_file_id( |
|
|
|
# purpose=purpose, created_at=created_at, filename=file.filename |
|
|
|
# ) |
|
|
|
# file_path = _get_file_path(file_id) |
|
|
|
# file_dir = os.path.dirname(file_path) |
|
|
|
# os.makedirs(file_dir, exist_ok=True) |
|
|
|
# with open(file_path, "wb") as fp: |
|
|
|
# shutil.copyfileobj(file.file, fp) |
|
|
|
# file.file.close() |
|
|
|
# |
|
|
|
# return dict( |
|
|
|
# id=file_id, |
|
|
|
# filename=file.filename, |
|
|
|
# bytes=file.size, |
|
|
|
# created_at=created_at, |
|
|
|
# object="file", |
|
|
|
# purpose=purpose, |
|
|
|
# ) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.get("/files") |
|
|
|
# def list_files(purpose: str) -> Dict[str, List[Dict]]: |
|
|
|
# file_ids = [] |
|
|
|
# root_path = Path(BASE_TEMP_DIR) / "openai_files" / purpose |
|
|
|
# for dir, sub_dirs, files in os.walk(root_path): |
|
|
|
# dir = Path(dir).relative_to(root_path).as_posix() |
|
|
|
# for file in files: |
|
|
|
# file_id = base64.urlsafe_b64encode( |
|
|
|
# f"{purpose}/{dir}/{file}".encode() |
|
|
|
# ).decode() |
|
|
|
# file_ids.append(file_id) |
|
|
|
# return { |
|
|
|
# "data": [{**_get_file_info(x), "id": x, "object": "file"} for x in file_ids] |
|
|
|
# } |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.get("/files/{file_id}") |
|
|
|
# def retrieve_file(file_id: str) -> Dict: |
|
|
|
# file_info = _get_file_info(file_id) |
|
|
|
# return {**file_info, "id": file_id, "object": "file"} |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.get("/files/{file_id}/content") |
|
|
|
# def retrieve_file_content(file_id: str) -> Dict: |
|
|
|
# file_path = _get_file_path(file_id) |
|
|
|
# return FileResponse(file_path) |
|
|
|
# |
|
|
|
# |
|
|
|
# @openai_router.delete("/files/{file_id}") |
|
|
|
# def delete_file(file_id: str) -> Dict: |
|
|
|
# file_path = _get_file_path(file_id) |
|
|
|
# deleted = False |
|
|
|
# |
|
|
|
# try: |
|
|
|
# if os.path.isfile(file_path): |
|
|
|
# os.remove(file_path) |
|
|
|
# deleted = True |
|
|
|
# except: |
|
|
|
# ... |
|
|
|
# |
|
|
|
# return {"id": file_id, "deleted": deleted, "object": "file"} |
gjl
1 year ago