400 lines
13 KiB
Python
400 lines
13 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，转发到 OpenAI 兼容接口、Anthropic Messages 接口，
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或原生 OpenAI Responses 接口。
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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.cc_anthropic_adapter import (
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AnthropicStreamConverter,
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cc_to_messages_request,
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messages_to_cc_response,
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)
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from adapters.openai_compat_fixer import fix_response, fix_stream_chunk, normalize_request
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from adapters.responses_cc_adapter import (
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ResponsesToCCStreamConverter,
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cc_to_responses_request,
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responses_to_cc,
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responses_to_cc_response,
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)
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from config import Config
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from routes.common import (
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RouteContext,
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build_anthropic_target,
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build_openai_target,
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build_responses_target,
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build_route_context,
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chat_error_chunk,
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inject_instructions_anthropic,
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inject_instructions_cc,
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inject_instructions_responses,
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log_route_context,
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log_usage,
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sse_data_message,
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)
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from utils.http import (
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forward_request,
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iter_anthropic_sse,
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iter_openai_sse,
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iter_responses_sse,
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sse_response,
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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('chat', __name__)
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def _dbg(message: str) -> None:
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"""仅在调试模式下输出详细日志。"""
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if Config.DEBUG:
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logger.info('[聊天补全调试] %s', message)
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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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payload = request.get_json(force=True)
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payload, message_count = _normalize_chat_payload(payload)
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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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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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if ctx.backend == 'openai':
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return _handle_openai_backend(ctx, payload)
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if ctx.backend == 'responses':
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return _handle_responses_backend(ctx, payload)
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return _handle_anthropic_backend(ctx, payload)
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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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这里保留了一层兼容逻辑：当 Cursor 或调用方把 Responses 格式误发到
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`/v1/chat/completions` 时，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 _handle_openai_backend(ctx: RouteContext, payload: dict[str, Any]):
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"""处理走 OpenAI 兼容后端的聊天补全请求。"""
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_dbg(
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'原始请求字段=' + str(list(payload.keys())) + ' '
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+ '附加字段='
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+ json.dumps(
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{k: v for k, v in payload.items() if k != 'messages'},
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ensure_ascii=False,
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default=str,
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)[:500]
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)
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payload = normalize_request(payload, ctx.upstream_model)
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payload = inject_instructions_cc(payload, ctx.custom_instructions, ctx.instructions_position)
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_dbg(
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f'标准化完成：模型={payload.get("model")} '
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f'工具数={len(payload.get("tools", []))}'
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)
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url, headers = build_openai_target(ctx)
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if ctx.is_stream:
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return _handle_openai_stream(ctx, payload, url, headers)
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return _handle_openai_non_stream(ctx, payload, url, headers)
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def _handle_openai_non_stream(
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ctx: RouteContext,
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payload: dict[str, Any],
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url: str,
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headers: dict[str, str],
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):
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"""处理 OpenAI 兼容后端的非流式返回。"""
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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('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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return _finalize_chat_response(ctx, data, debug_label='修复后响应')
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def _handle_openai_stream(
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ctx: RouteContext,
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payload: dict[str, Any],
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url: str,
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headers: dict[str, str],
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):
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"""处理 OpenAI 兼容后端的流式返回。"""
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payload['stream'] = True
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def generate():
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"""消费上游 OpenAI SSE，并逐段产出给 Cursor 的聊天补全流。"""
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resp, err = forward_request(url, headers, payload, stream=True)
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if err:
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yield chat_error_chunk(str(err))
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return
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think_extractor = ThinkTagExtractor()
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chunk_count = 0
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for chunk in iter_openai_sse(resp):
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if chunk is None:
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_dbg(f'流式响应结束，共 {chunk_count} 个数据片段')
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close_chunk = think_extractor.finalize()
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if close_chunk:
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yield sse_data_message(close_chunk)
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yield sse_data_message('[DONE]')
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return
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if chunk_count < 10:
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_dbg(
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f'上游原始片段#{chunk_count}='
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+ json.dumps(chunk, ensure_ascii=False, default=str)[:500]
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)
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chunk = fix_stream_chunk(chunk)
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chunk['model'] = ctx.client_model
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for out in think_extractor.process_chunk(chunk):
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if chunk_count < 10:
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_dbg(
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f'返回片段#{chunk_count}='
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+ json.dumps(out, ensure_ascii=False, default=str)[:500]
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)
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yield sse_data_message(out)
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chunk_count += 1
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return sse_response(generate())
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def _handle_responses_backend(ctx: RouteContext, payload: dict[str, Any]):
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"""处理走原生 Responses 后端的聊天补全请求。
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当上游只支持 `/v1/responses` 时，需要先把聊天补全请求转换为 Responses 请求，
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返回时再转换回聊天补全协议。
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"""
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responses_payload = cc_to_responses_request(payload)
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responses_payload['model'] = ctx.upstream_model
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responses_payload = inject_instructions_responses(responses_payload, ctx.custom_instructions, ctx.instructions_position)
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_dbg(
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'已转换为 Responses 请求：字段=' + str(list(responses_payload.keys()))
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+ f' 输入项数={len(responses_payload.get("input", []))}'
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)
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url, headers = build_responses_target(ctx)
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if ctx.is_stream:
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return _handle_responses_stream(ctx, responses_payload, url, headers)
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return _handle_responses_non_stream(ctx, responses_payload, url, headers)
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def _handle_responses_non_stream(
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ctx: RouteContext,
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payload: dict[str, Any],
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url: str,
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headers: dict[str, str],
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):
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"""处理原生 Responses 后端的非流式返回。"""
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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('上游原始响应=' + json.dumps(raw, ensure_ascii=False, default=str)[:1000])
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data = responses_to_cc_response(raw, ctx.client_model)
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return _finalize_chat_response(ctx, data, debug_label='Responses 转回聊天补全后')
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def _handle_responses_stream(
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ctx: RouteContext,
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payload: dict[str, Any],
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url: str,
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headers: dict[str, str],
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):
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"""处理原生 Responses 后端的流式返回。"""
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payload['stream'] = True
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converter = ResponsesToCCStreamConverter(model=ctx.client_model)
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def generate():
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"""消费上游 Responses 事件，并实时转换成聊天补全 chunk。"""
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resp, err = forward_request(url, headers, payload, stream=True)
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if err:
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yield chat_error_chunk(str(err))
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return
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event_count = 0
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for event_type, event_data in iter_responses_sse(resp):
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if event_count < 10:
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_dbg(
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f'上游事件#{event_count} 类型={event_type} 数据='
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+ json.dumps(event_data, ensure_ascii=False, default=str)[:500]
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)
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for chunk in converter.process_event(event_type, event_data):
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if event_count < 10:
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_dbg(
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f'返回片段#{event_count}='
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+ json.dumps(chunk, ensure_ascii=False, default=str)[:500]
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)
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yield sse_data_message(chunk)
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event_count += 1
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_dbg(f'流式响应结束，共 {event_count} 个事件')
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yield sse_data_message('[DONE]')
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return sse_response(generate())
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def _handle_anthropic_backend(ctx: RouteContext, payload: dict[str, Any]):
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"""处理走 Anthropic Messages 后端的聊天补全请求。"""
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payload['model'] = ctx.upstream_model
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anthropic_payload = cc_to_messages_request(payload)
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anthropic_payload = inject_instructions_anthropic(anthropic_payload, ctx.custom_instructions, ctx.instructions_position)
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_dbg(
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'已转换为 Messages 请求：字段=' + str(list(anthropic_payload.keys()))
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+ f' 消息数={len(anthropic_payload.get("messages", []))}'
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)
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url, headers = build_anthropic_target(ctx)
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if ctx.is_stream:
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return _handle_anthropic_stream(ctx, anthropic_payload, url, headers)
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return _handle_anthropic_non_stream(ctx, anthropic_payload, url, headers)
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def _handle_anthropic_non_stream(
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ctx: RouteContext,
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payload: dict[str, Any],
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url: str,
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headers: dict[str, str],
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):
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"""处理 Anthropic 后端的非流式返回。"""
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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('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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return _finalize_chat_response(ctx, data, debug_label='Messages 转回聊天补全后')
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def _handle_anthropic_stream(
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ctx: RouteContext,
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payload: dict[str, Any],
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url: str,
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headers: dict[str, str],
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):
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"""处理 Anthropic 后端的流式返回。
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这里仍然保留独立的事件级转换器，而不是先落成完整响应再回放，
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是为了尽量保持 Cursor 端的流式体验和工具调用时序。
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"""
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payload['stream'] = True
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converter = AnthropicStreamConverter()
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def generate():
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"""消费上游 Anthropic 事件流，并逐步映射为聊天补全 SSE。"""
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resp, err = forward_request(url, headers, payload, stream=True)
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if err:
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yield chat_error_chunk(str(err))
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return
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event_count = 0
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for event_type, event_data in iter_anthropic_sse(resp):
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if event_count < 10:
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_dbg(
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f'上游事件#{event_count} 类型={event_type} 数据='
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+ json.dumps(event_data, ensure_ascii=False, default=str)[:500]
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)
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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'] = ctx.client_model
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chunk_str = json.dumps(chunk_obj, ensure_ascii=False)
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except (json.JSONDecodeError, TypeError):
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pass
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if event_count < 10:
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_dbg(f'返回片段#{event_count}={chunk_str[:500]}')
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yield sse_data_message(chunk_str)
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event_count += 1
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_dbg(f'流式响应结束，共 {event_count} 个事件')
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yield sse_data_message('[DONE]')
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return sse_response(generate())
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def _finalize_chat_response(
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ctx: RouteContext,
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data: dict[str, Any],
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*,
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debug_label: str,
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):
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"""统一收尾非流式聊天补全响应。
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三条后端链路最终都会回到 Chat Completions 格式，因此这里集中做：
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- 回填给 Cursor 展示的模型名
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- 输出统一调试日志
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- 输出统一令牌统计日志
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"""
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data['model'] = ctx.client_model
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_dbg(debug_label + '=' + json.dumps(data, ensure_ascii=False, default=str)[:1000])
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log_usage('聊天补全', data.get('usage', {}), input_key='prompt_tokens', output_key='completion_tokens')
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return jsonify(data)
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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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