345 lines
12 KiB
Python
345 lines
12 KiB
Python
from __future__ import annotations
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import base64
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import binascii
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from dataclasses import dataclass
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from time import perf_counter
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from typing import Any, Mapping
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import requests
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from rest_framework import status
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from apps.ai.aliases import (
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AliasResolutionError,
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ModelCapabilityError,
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REQUIRED_CAPABILITIES,
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resolve_model_alias,
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)
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from apps.ai.providers import AiCapabilityError, AiProviderError, get_provider
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from apps.billing.models import CallRecord, normalize_resolution
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from apps.billing.pricing import NoPricingRuleError, calculate_points_cost
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from apps.billing.services import (
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InsufficientPointsError,
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precharge_call,
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refund_call_points,
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mark_call_success,
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)
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from .errors import api_error
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from .storage import save_generated_image
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class ApiRequestError(ValueError):
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def __init__(self, code: str, message: str, http_status: int):
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self.code = code
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self.message = message
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self.http_status = http_status
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super().__init__(message)
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def as_response_data(self) -> dict:
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return api_error(self.code, self.message)
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@dataclass(frozen=True)
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class ImageInput:
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data: bytes
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mime_type: str = "image/png"
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filename: str = "image.png"
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def generate_title_response(*, user, api_key, request_data: Mapping[str, Any]) -> dict:
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prompt = request_data["prompt"]
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alias = request_data.get("model") or None
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resolution = normalize_resolution(request_data.get("resolution") or "1K") or "1K"
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parameters = dict(request_data.get("parameters") or {})
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image_input = load_image_input(request_data)
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model_alias = resolve_model_alias_or_raise(CallRecord.OperationType.TITLE, alias)
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resolved_model = resolved_model_or_raise(model_alias)
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provider = provider_or_raise(resolved_model)
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ensure_provider_supports(provider, CallRecord.OperationType.TITLE)
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points_cost = calculate_points_cost_or_raise(
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CallRecord.OperationType.TITLE,
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model_alias.alias,
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resolution,
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)
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charge = precharge_or_raise(
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user=user,
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api_key=api_key,
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operation_type=CallRecord.OperationType.TITLE,
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alias=model_alias.alias,
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model_used=resolved_model.model,
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resolution=resolution,
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prompt=prompt,
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points_cost=points_cost,
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)
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try:
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started = perf_counter()
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generation = provider.generate_text(
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prompt,
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resolved_model,
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image=image_input.data if image_input else None,
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image_mime_type=image_input.mime_type if image_input else "image/png",
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resolution=resolution,
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parameters=parameters,
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)
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latency_ms = elapsed_ms(started)
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titles = list(generation.titles or ())
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if not titles and generation.text:
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titles = [generation.text]
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result_summary = summarize_titles(titles, generation.text)
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call_record = mark_call_success(
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charge.call_record,
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result_summary=result_summary,
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upstream_latency_ms=latency_ms,
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)
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except AiCapabilityError as exc:
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refund_call_points(
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charge.call_record,
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error_message=str(exc),
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reason="Provider rejected the title request.",
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)
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raise ApiRequestError("bad_request", "请求参数不支持当前模型", status.HTTP_400_BAD_REQUEST) from exc
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except Exception as exc:
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refund_call_points(
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charge.call_record,
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error_message=str(exc),
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reason="Upstream title generation failed.",
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)
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raise upstream_error(exc) from exc
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return {
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"titles": titles,
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"alias": model_alias.alias,
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"model_used": generation.model_used,
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"points_cost": points_cost,
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"points_balance": charge.balance_after,
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"call_id": call_record.id,
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}
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def generate_image_response(*, user, api_key, request, request_data: Mapping[str, Any]) -> dict:
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prompt = request_data["prompt"]
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alias = request_data.get("model") or None
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resolution = normalize_resolution(request_data.get("resolution") or "1K") or "1K"
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aspect_ratio = request_data.get("aspect_ratio") or "1:1"
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parameters = dict(request_data.get("parameters") or {})
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image_input = load_image_input(request_data)
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model_alias = resolve_model_alias_or_raise(CallRecord.OperationType.IMAGE, alias)
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resolved_model = resolved_model_or_raise(model_alias)
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provider = provider_or_raise(resolved_model)
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ensure_provider_supports(provider, CallRecord.OperationType.IMAGE)
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points_cost = calculate_points_cost_or_raise(
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CallRecord.OperationType.IMAGE,
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model_alias.alias,
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resolution,
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)
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charge = precharge_or_raise(
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user=user,
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api_key=api_key,
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operation_type=CallRecord.OperationType.IMAGE,
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alias=model_alias.alias,
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model_used=resolved_model.model,
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resolution=resolution,
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prompt=prompt,
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points_cost=points_cost,
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)
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try:
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started = perf_counter()
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generation = provider.generate_image(
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prompt,
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resolved_model,
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image=image_input.data if image_input else None,
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image_mime_type=image_input.mime_type if image_input else "image/png",
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image_filename=image_input.filename if image_input else "image.png",
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resolution=resolution,
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aspect_ratio=aspect_ratio,
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parameters=parameters,
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)
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latency_ms = elapsed_ms(started)
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image_url = save_generated_image(generation.image, request=request)
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call_record = mark_call_success(
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charge.call_record,
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result_ref=image_url,
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result_summary=f"image_bytes={len(generation.image)}",
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upstream_latency_ms=latency_ms,
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)
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except AiCapabilityError as exc:
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refund_call_points(
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charge.call_record,
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error_message=str(exc),
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reason="Provider rejected the image request.",
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)
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raise ApiRequestError("bad_request", "请求参数不支持当前模型", status.HTTP_400_BAD_REQUEST) from exc
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except Exception as exc:
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refund_call_points(
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charge.call_record,
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error_message=str(exc),
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reason="Upstream image generation failed.",
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)
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raise upstream_error(exc) from exc
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return {
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"image_url": image_url,
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"alias": model_alias.alias,
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"model_used": generation.model_used,
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"points_cost": points_cost,
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"points_balance": charge.balance_after,
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"call_id": call_record.id,
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}
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def resolve_model_alias_or_raise(operation_type: str, alias: str | None):
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try:
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return resolve_model_alias(operation_type, alias)
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except ModelCapabilityError as exc:
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raise ApiRequestError(
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"model_not_allowed",
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"该模型不支持此操作",
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status.HTTP_400_BAD_REQUEST,
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) from exc
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except AliasResolutionError as exc:
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raise ApiRequestError(
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"model_not_allowed",
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"模型别名不可用或不支持此操作",
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status.HTTP_400_BAD_REQUEST,
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) from exc
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def ensure_provider_supports(provider, operation_type: str) -> None:
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required_capability = REQUIRED_CAPABILITIES[operation_type]
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if required_capability not in provider.capabilities():
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raise ApiRequestError(
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"model_not_allowed",
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"该模型不支持此操作",
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status.HTTP_400_BAD_REQUEST,
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)
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def resolved_model_or_raise(model_alias):
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try:
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return model_alias.ai_model.to_resolved_model()
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except Exception as exc:
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raise ApiRequestError(
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"upstream_error",
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"上游模型配置不可用",
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status.HTTP_502_BAD_GATEWAY,
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) from exc
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def provider_or_raise(resolved_model):
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try:
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return get_provider(resolved_model.api_type, resolved_model.url)
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except AiProviderError as exc:
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raise ApiRequestError(
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"upstream_error",
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"上游模型配置不可用",
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status.HTTP_502_BAD_GATEWAY,
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) from exc
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def calculate_points_cost_or_raise(
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operation_type: str,
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alias: str,
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resolution: str,
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) -> int:
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try:
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return calculate_points_cost(operation_type, alias, resolution)
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except NoPricingRuleError as exc:
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raise ApiRequestError(
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"no_pricing_rule",
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"未配置对应计费规则",
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status.HTTP_400_BAD_REQUEST,
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) from exc
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def precharge_or_raise(**kwargs):
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try:
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return precharge_call(**kwargs)
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except InsufficientPointsError as exc:
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raise ApiRequestError(
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"insufficient_points",
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"点数不足，请先充值",
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status.HTTP_402_PAYMENT_REQUIRED,
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) from exc
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def upstream_error(exc: Exception) -> ApiRequestError:
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if isinstance(exc, ApiRequestError):
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return exc
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if isinstance(exc, AiProviderError | requests.RequestException | OSError):
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return ApiRequestError("upstream_error", "上游 AI 调用失败，已退回点数", status.HTTP_502_BAD_GATEWAY)
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return ApiRequestError("upstream_error", "生成失败，已退回点数", status.HTTP_502_BAD_GATEWAY)
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def load_image_input(data: Mapping[str, Any]) -> ImageInput | None:
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raw_base64 = str(data.get("image_base64") or "").strip()
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if raw_base64:
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return decode_image_input(raw_base64)
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image_url = str(data.get("image_url") or "").strip()
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if image_url:
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return download_image_input(image_url)
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return None
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def decode_image_input(value: str) -> ImageInput:
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mime_type = "image/png"
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encoded = value
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if value.startswith("data:"):
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if ";base64," not in value:
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raise ApiRequestError("bad_request", "image_base64 格式无效", status.HTTP_400_BAD_REQUEST)
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prefix, encoded = value.split(",", 1)
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mime_type = prefix[len("data:") :].split(";", 1)[0] or mime_type
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try:
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image = base64.b64decode(encoded, validate=True)
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except (binascii.Error, ValueError) as exc:
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raise ApiRequestError("bad_request", "image_base64 格式无效", status.HTTP_400_BAD_REQUEST) from exc
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if not image:
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raise ApiRequestError("bad_request", "image_base64 不能为空", status.HTTP_400_BAD_REQUEST)
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return ImageInput(data=image, mime_type=mime_type, filename=filename_for_mime(mime_type))
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def download_image_input(url: str) -> ImageInput:
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session = requests.Session()
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session.trust_env = False
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try:
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response = session.get(url, timeout=(10, 60))
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response.raise_for_status()
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except requests.RequestException as exc:
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raise ApiRequestError("bad_request", "无法读取 image_url", status.HTTP_400_BAD_REQUEST) from exc
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content_type = response.headers.get("Content-Type", "image/png").split(";", 1)[0]
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if not content_type.startswith("image/"):
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raise ApiRequestError("bad_request", "image_url 不是图片资源", status.HTTP_400_BAD_REQUEST)
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if not response.content:
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raise ApiRequestError("bad_request", "image_url 图片内容为空", status.HTTP_400_BAD_REQUEST)
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return ImageInput(
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data=response.content,
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mime_type=content_type,
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filename=filename_for_mime(content_type),
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)
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def filename_for_mime(mime_type: str) -> str:
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extension = {
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"image/jpeg": "jpg",
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"image/png": "png",
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"image/webp": "webp",
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}.get(mime_type, "png")
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return f"input.{extension}"
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def summarize_titles(titles: list[str], text: str) -> str:
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summary = " | ".join(titles[:3]) or str(text or "")
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return summary[:500]
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def elapsed_ms(started: float) -> int:
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return int((perf_counter() - started) * 1000)
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