初始化提交

routes/chat.py

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+"""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}')