"""路由: /v1/chat/completions 处理 Cursor 发来的 OpenAI Chat Completions 格式请求。 根据模型映射的 backend 字段分发到 OpenAI 或 Anthropic 后端。 """ import json import logging from flask import Blueprint, request, jsonify import settings from config import Config from adapters.openai_fixer import normalize_request, fix_response, fix_stream_chunk from adapters.openai_anthropic import ( cc_to_messages_request, messages_to_cc_response, AnthropicStreamConverter, ) from adapters.responses_adapter import responses_to_cc from utils.http import ( build_openai_headers, build_anthropic_headers, forward_request, sse_response, iter_openai_sse, iter_anthropic_sse, ) from utils.think_tag import ThinkTagExtractor logger = logging.getLogger(__name__) def _dbg(msg): """DEBUG 模式下输出详细日志""" if Config.DEBUG: logger.info(f'[调试] {msg}') bp = Blueprint('chat', __name__) @bp.route('/v1/chat/completions', methods=['POST']) def chat_completions(): payload = request.get_json(force=True) is_stream = payload.get('stream', False) # 保留 Cursor 发送的原始模型名，响应时需要回填 cursor_model = payload.get('model', 'unknown') msg_count = len(payload.get('messages', [])) # 容错：Responses 格式误入 CC 端点 if msg_count == 0 and 'input' in payload: logger.info('检测到 Responses 格式（有 input 无 messages），自动转换') payload = responses_to_cc(payload) msg_count = len(payload.get('messages', [])) elif msg_count == 0: logger.warning(f'messages 为空, payload keys: {list(payload.keys())}') mapping = settings.resolve_model(cursor_model) backend = mapping['backend'] upstream = mapping['upstream_model'] url_base = mapping['target_url'] api_key = mapping['api_key'] logger.info( f'[CC] {cursor_model} → {upstream} ' f'后端={backend} 流式={is_stream} 消息数={msg_count}' ) _log_messages(payload) if backend == 'openai': return _via_openai(payload, upstream, url_base, api_key, is_stream, cursor_model) else: return _via_anthropic(payload, upstream, url_base, api_key, is_stream, cursor_model) # ─── OpenAI 后端 ────────────────────────────────── def _via_openai(payload, upstream, url_base, api_key, is_stream, cursor_model): """通过 OpenAI 兼容后端转发""" _dbg(f'Cursor 原始请求 keys={list(payload.keys())} ' f'其他字段={json.dumps({k: v for k, v in payload.items() if k != "messages"}, ensure_ascii=False, default=str)[:500]}') payload = normalize_request(payload, upstream) _dbg(f'normalize 后 model={payload.get("model")} tools数={len(payload.get("tools", []))}') headers = build_openai_headers(api_key) url = f'{url_base.rstrip("/")}/v1/chat/completions' if not is_stream: payload['stream'] = False resp, err = forward_request(url, headers, payload) if err: return err raw = resp.json() _dbg(f'上游原始响应={json.dumps(raw, ensure_ascii=False, default=str)[:1000]}') data = fix_response(raw) data['model'] = cursor_model _dbg(f'修复后响应={json.dumps(data, ensure_ascii=False, default=str)[:1000]}') usage = data.get('usage', {}) logger.info( f'[CC] 完成 prompt={usage.get("prompt_tokens", 0)} ' f'completion={usage.get("completion_tokens", 0)}' ) return jsonify(data) # 流式处理 payload['stream'] = True _n = [0] def generate(): resp, err = forward_request(url, headers, payload, stream=True) if err: yield f'data: {json.dumps({"error": {"message": err, "type": "upstream_error"}})}\n\n' return think_ext = ThinkTagExtractor() for chunk in iter_openai_sse(resp): if chunk is None: # [DONE] _dbg(f'流结束，共 {_n[0]} 个 chunk') yield 'data: [DONE]\n\n' return if _n[0] < 10: _dbg(f'上游原始 chunk#{_n[0]}={json.dumps(chunk, ensure_ascii=False, default=str)[:500]}') chunk = fix_stream_chunk(chunk) chunk['model'] = cursor_model for out in think_ext.process_chunk(chunk): if _n[0] < 10: _dbg(f'发给Cursor chunk#{_n[0]}={json.dumps(out, ensure_ascii=False, default=str)[:500]}') yield f'data: {json.dumps(out)}\n\n' _n[0] += 1 return sse_response(generate()) # ─── Anthropic 后端 ─────────────────────────────── def _via_anthropic(payload, upstream, url_base, api_key, is_stream, cursor_model): """通过 Anthropic 后端转发（CC → Messages → CC）""" payload['model'] = upstream anthropic_payload = cc_to_messages_request(payload) _dbg(f'CC→Messages 转换后 keys={list(anthropic_payload.keys())} ' f'messages数={len(anthropic_payload.get("messages", []))}') headers = build_anthropic_headers(api_key) url = f'{url_base.rstrip("/")}/v1/messages' if not is_stream: anthropic_payload['stream'] = False resp, err = forward_request(url, headers, anthropic_payload) if err: return err raw = resp.json() _dbg(f'上游原始响应={json.dumps(raw, ensure_ascii=False, default=str)[:1000]}') data = messages_to_cc_response(raw) data['model'] = cursor_model _dbg(f'Messages→CC 转换后={json.dumps(data, ensure_ascii=False, default=str)[:1000]}') usage = data.get('usage', {}) logger.info( f'[CC] 完成 prompt={usage.get("prompt_tokens", 0)} ' f'completion={usage.get("completion_tokens", 0)}' ) return jsonify(data) # 流式处理 anthropic_payload['stream'] = True converter = AnthropicStreamConverter() _n = [0] def generate(): resp, err = forward_request(url, headers, anthropic_payload, stream=True) if err: yield f'data: {json.dumps({"error": {"message": err, "type": "upstream_error"}})}\n\n' return for event_type, event_data in iter_anthropic_sse(resp): if _n[0] < 10: _dbg(f'上游事件#{_n[0]} {event_type}={json.dumps(event_data, ensure_ascii=False, default=str)[:500]}') for chunk_str in converter.process_event(event_type, event_data): try: chunk_obj = json.loads(chunk_str) chunk_obj['model'] = cursor_model chunk_str = json.dumps(chunk_obj) except (json.JSONDecodeError, TypeError): pass if _n[0] < 10: _dbg(f'发给Cursor chunk#{_n[0]}={chunk_str[:500]}') yield f'data: {chunk_str}\n\n' _n[0] += 1 _dbg(f'流结束，共 {_n[0]} 个事件') yield 'data: [DONE]\n\n' return sse_response(generate()) def _log_messages(payload): """记录请求中的消息摘要""" for i, msg in enumerate(payload.get('messages', [])): role = msg.get('role', '?') content = msg.get('content') extra = '' if 'tool_calls' in msg: extra += f' tool_calls={len(msg["tool_calls"])}' if msg.get('tool_call_id'): extra += f' tool_call_id={msg["tool_call_id"]}' if isinstance(content, list): info = f'list[{len(content)}]' elif isinstance(content, str): info = f'str[{len(content)}]' else: info = type(content).__name__ logger.info(f' 消息[{i}] {role} {info}{extra}')