101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
"""路由: /v1/responses
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处理 Cursor 对 GPT、Claude-Opus 等模型发出的 Responses API 请求。
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请求先转换为 Chat Completions 中间表示，再通过统一出站转换器分发。
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"""
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from __future__ import annotations
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import json
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import logging
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from typing import Any
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import settings
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from flask import Blueprint, jsonify, request
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from adapters.openai_compat_fixer import normalize_request
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from adapters.responses_cc_adapter import (
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AnthropicOutboundForResponses,
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ResponsesNativeOutbound,
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responses_to_cc,
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)
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from adapters.unified import handle_non_stream, handle_stream
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from routes.common import (
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ResponsesClientFormatter,
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ResponsesPassthroughFormatter,
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build_route_context,
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get_outbound,
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inject_instructions_cc,
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inject_instructions_responses,
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log_route_context,
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should_inject_thinking,
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)
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from utils.request_logger import start_turn
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from utils.thinking_cache import thinking_cache
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logger = logging.getLogger(__name__)
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bp = Blueprint('responses', __name__)
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@bp.route('/v1/responses', methods=['POST'])
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def responses_endpoint():
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"""处理 Responses 请求并按模型映射分发。"""
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original_payload = request.get_json(force=True)
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payload = json.loads(json.dumps(original_payload, ensure_ascii=False, default=str))
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client_model = payload.get('model', 'unknown')
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is_stream = payload.get('stream', False)
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ctx = build_route_context(client_model, is_stream)
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turn = start_turn(
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route='responses',
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client_model=client_model,
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backend=ctx.backend,
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stream=is_stream,
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client_request=original_payload,
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request_headers=dict(request.headers),
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target_url=ctx.target_url,
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upstream_model=ctx.upstream_model,
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)
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log_route_context('响应生成', ctx)
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if ctx.backend == 'responses':
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return _handle_native_responses(ctx, payload, turn)
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cc_payload = _build_cc_payload(payload, ctx)
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if ctx.backend == 'anthropic':
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outbound = AnthropicOutboundForResponses()
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else:
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outbound = get_outbound(ctx.backend)
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client_fmt = ResponsesClientFormatter(model=ctx.client_model)
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if ctx.is_stream:
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return handle_stream(ctx, outbound, client_fmt, cc_payload, turn)
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return handle_non_stream(ctx, outbound, client_fmt, cc_payload, turn)
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def _handle_native_responses(ctx, payload: dict[str, Any], turn: dict[str, Any]):
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"""处理走原生 Responses 后端的请求（直接透传）。"""
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payload = dict(payload)
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payload['model'] = ctx.upstream_model
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payload = inject_instructions_responses(payload, ctx.custom_instructions, ctx.instructions_position)
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outbound = ResponsesNativeOutbound()
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client_fmt = ResponsesPassthroughFormatter(model=ctx.client_model)
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if ctx.is_stream:
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return handle_stream(ctx, outbound, client_fmt, payload, turn)
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return handle_non_stream(ctx, outbound, client_fmt, payload, turn)
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def _build_cc_payload(payload: dict[str, Any], ctx) -> dict[str, Any]:
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"""将 Responses 请求统一降级为 Chat Completions 中间表示。"""
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cc_payload = responses_to_cc(payload)
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cc_payload['model'] = ctx.upstream_model
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cc_payload = normalize_request(cc_payload)
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if should_inject_thinking(ctx.backend):
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cc_payload['messages'] = thinking_cache.inject(cc_payload.get('messages', []))
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cc_payload = inject_instructions_cc(cc_payload, ctx.custom_instructions, ctx.instructions_position)
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return cc_payload
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