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