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