139 lines
4.8 KiB
Python
139 lines
4.8 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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根据模型映射的后端类型，通过统一的出站转换器转发到不同后端。
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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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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 responses_to_cc
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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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CCClientFormatter,
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build_route_context,
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get_outbound,
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inject_instructions_cc,
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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('chat', __name__)
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@bp.route('/v1/chat/completions', methods=['POST'])
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def chat_completions():
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"""处理聊天补全请求并按模型映射分发到不同后端。"""
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original_payload = request.get_json(force=True)
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payload, message_count = _normalize_chat_payload(
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json.loads(json.dumps(original_payload, ensure_ascii=False, default=str))
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)
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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='chat',
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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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metadata={'message_count': message_count},
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)
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log_route_context('聊天补全', ctx, extra=f'消息数={message_count}')
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_log_messages(payload)
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payload['model'] = ctx.upstream_model
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payload = normalize_request(payload)
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if should_inject_thinking(ctx.backend):
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payload['messages'] = thinking_cache.inject(payload.get('messages', []))
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payload = inject_instructions_cc(payload, ctx.custom_instructions, ctx.instructions_position)
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outbound = get_outbound(ctx.backend)
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client_fmt = CCClientFormatter()
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if ctx.is_stream:
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result = handle_stream(ctx, outbound, client_fmt, payload, turn)
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else:
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result = handle_non_stream(ctx, outbound, client_fmt, payload, turn)
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if not ctx.is_stream and isinstance(result, tuple):
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response_data = result
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elif hasattr(result, 'json'):
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try:
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response_data = result.get_json(silent=True) or {}
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except Exception:
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response_data = {}
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else:
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response_data = {}
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_try_cache_thinking(response_data)
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return result
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def _normalize_chat_payload(payload: dict[str, Any]) -> tuple[dict[str, Any], int]:
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"""整理聊天补全入口的请求体。
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当 Cursor 或调用方把 Responses 格式误发到 `/v1/chat/completions` 时，
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先降级转换成 Chat Completions，再进入统一主流程。
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"""
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message_count = len(payload.get('messages', []))
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if message_count == 0 and 'input' in payload:
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logger.info('检测到 Responses 格式误入聊天补全接口，已自动转换为 Chat Completions 格式')
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payload = responses_to_cc(payload)
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message_count = len(payload.get('messages', []))
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elif message_count == 0:
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logger.warning('消息列表为空，请求字段=%s', list(payload.keys()))
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return payload, message_count
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def _try_cache_thinking(response_data: dict[str, Any]) -> None:
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"""尝试从非流式响应中缓存思维链内容。"""
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if not isinstance(response_data, dict):
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return
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for choice in response_data.get('choices', []):
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msg = choice.get('message', {})
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if msg.get('reasoning_content'):
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thinking_cache.store_from_response(
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request.get_json(silent=True, force=True).get('messages', []),
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msg['reasoning_content'],
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)
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break
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def _log_messages(payload: dict[str, Any]) -> None:
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"""记录消息摘要，方便排查请求形态是否符合预期。"""
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for index, message in enumerate(payload.get('messages', [])):
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role = message.get('role', '?')
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content = message.get('content')
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extra = ''
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if 'tool_calls' in message:
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extra += f' 工具调用数={len(message["tool_calls"])}'
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if message.get('tool_call_id'):
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extra += f' 工具调用ID={message["tool_call_id"]}'
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if isinstance(content, list):
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content_info = f'列表[{len(content)}]'
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elif isinstance(content, str):
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content_info = f'文本[{len(content)}]'
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else:
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content_info = type(content).__name__
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logger.info(' 消息[%s] 角色=%s 内容=%s%s', index, role, content_info, extra)
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