126 lines
4.2 KiB
Python
126 lines
4.2 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 格式请求。
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转换为 CC 格式后分发到对应后端，响应再转回 Responses 格式。
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"""
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import json
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import logging
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from flask import Blueprint, request, jsonify
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import settings
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from adapters.responses_adapter import responses_to_cc, cc_to_responses, ResponsesStreamConverter
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from adapters.openai_fixer import normalize_request, fix_response, fix_stream_chunk
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from adapters.openai_anthropic import cc_to_messages_request, messages_to_cc_response
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from utils.http import (
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build_openai_headers, build_anthropic_headers,
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forward_request, sse_response,
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iter_openai_sse, iter_anthropic_sse,
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)
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from utils.think_tag import ThinkTagExtractor
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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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payload = request.get_json(force=True)
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model = payload.get('model', 'unknown')
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is_stream = payload.get('stream', False)
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mapping = settings.resolve_model(model)
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backend = mapping['backend']
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upstream = mapping['upstream_model']
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url_base = mapping['target_url']
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api_key = mapping['api_key']
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logger.info(f'[Responses] {model} → {upstream} 后端={backend} 流式={is_stream}')
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# Responses → CC
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cc_payload = responses_to_cc(payload)
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cc_payload['model'] = upstream
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if backend == 'openai':
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return _via_openai(cc_payload, url_base, api_key, is_stream, model)
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else:
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return _via_anthropic(cc_payload, url_base, api_key, is_stream, model)
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# ─── OpenAI 后端 ──────────────────────────────────
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def _via_openai(cc_payload, url_base, api_key, is_stream, display_model):
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"""通过 OpenAI 后端处理"""
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cc_payload = normalize_request(cc_payload)
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headers = build_openai_headers(api_key)
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url = f'{url_base.rstrip("/")}/v1/chat/completions'
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if not is_stream:
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cc_payload['stream'] = False
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resp, err = forward_request(url, headers, cc_payload)
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if err:
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return err
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return jsonify(cc_to_responses(fix_response(resp.json()), display_model))
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# 流式处理
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cc_payload['stream'] = True
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converter = ResponsesStreamConverter(model=display_model)
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def generate():
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yield from converter.start_events()
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resp, err = forward_request(url, headers, cc_payload, stream=True)
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if err:
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yield f'event: error\ndata: {json.dumps({"error": err})}\n\n'
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return
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think_ext = ThinkTagExtractor()
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for chunk in iter_openai_sse(resp):
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if chunk is None:
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yield from converter.finalize()
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return
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chunk = fix_stream_chunk(chunk)
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for out in think_ext.process_chunk(chunk):
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yield from converter.process_cc_chunk(out)
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return sse_response(generate())
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# ─── Anthropic 后端 ───────────────────────────────
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def _via_anthropic(cc_payload, url_base, api_key, is_stream, display_model):
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"""通过 Anthropic 后端处理"""
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anthropic_payload = cc_to_messages_request(cc_payload)
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headers = build_anthropic_headers(api_key)
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url = f'{url_base.rstrip("/")}/v1/messages'
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if not is_stream:
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anthropic_payload['stream'] = False
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resp, err = forward_request(url, headers, anthropic_payload)
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if err:
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return err
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cc_data = messages_to_cc_response(resp.json())
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return jsonify(cc_to_responses(cc_data, display_model))
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# 流式处理：Anthropic SSE → Responses SSE（跳过 CC 中间态）
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anthropic_payload['stream'] = True
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converter = ResponsesStreamConverter(model=display_model)
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def generate():
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yield from converter.start_events()
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resp, err = forward_request(url, headers, anthropic_payload, stream=True)
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if err:
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yield f'event: error\ndata: {json.dumps({"error": err})}\n\n'
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return
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for event_type, event_data in iter_anthropic_sse(resp):
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yield from converter.process_anthropic_event(event_type, event_data)
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yield from converter.finalize()
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return sse_response(generate())
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