回退旧版本
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9 changed files with 1582 additions and 1216 deletions
193
routes/common.py
193
routes/common.py
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@ -12,6 +12,7 @@ import logging
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from typing import Any
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import settings
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from utils.http import build_anthropic_headers, build_gemini_headers, build_openai_headers
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logger = logging.getLogger(__name__)
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@ -54,6 +55,42 @@ def build_route_context(client_model: str, is_stream: bool) -> RouteContext:
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)
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def build_openai_target(ctx: RouteContext) -> tuple[str, dict[str, str]]:
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"""根据路由上下文生成 OpenAI 兼容后端的地址和请求头。"""
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url = f'{ctx.target_url.rstrip("/")}/v1/chat/completions'
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headers = build_openai_headers(ctx.api_key)
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return url, headers
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def build_responses_target(ctx: RouteContext) -> tuple[str, dict[str, str]]:
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"""根据路由上下文生成 OpenAI Responses 后端的地址和请求头。"""
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url = f'{ctx.target_url.rstrip("/")}/v1/responses'
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headers = build_openai_headers(ctx.api_key)
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return url, headers
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def build_anthropic_target(ctx: RouteContext) -> tuple[str, dict[str, str]]:
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"""根据路由上下文生成 Anthropic 后端的地址和请求头。"""
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url = f'{ctx.target_url.rstrip("/")}/v1/messages'
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headers = build_anthropic_headers(ctx.api_key)
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return url, headers
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def build_gemini_target(ctx: RouteContext, stream: bool = False) -> tuple[str, dict[str, str]]:
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"""根据路由上下文生成 Gemini 后端的地址和请求头。
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Gemini URL 格式: {base}/v1/models/{model}:generateContent
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流式: {base}/v1/models/{model}:streamGenerateContent?alt=sse
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"""
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base = ctx.target_url.rstrip('/')
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model = ctx.upstream_model
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if stream:
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url = f'{base}/v1/models/{model}:streamGenerateContent?alt=sse'
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else:
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url = f'{base}/v1/models/{model}:generateContent'
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headers = build_gemini_headers(ctx.api_key)
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return url, headers
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def log_route_context(route_name: str, ctx: RouteContext, *, extra: str = '') -> None:
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"""统一输出路由级日志，避免不同入口的日志格式逐渐漂移。"""
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@ -100,6 +137,11 @@ def sse_event_message(event_type: str, data: Any) -> str:
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return f'event: {event_type}\ndata: {payload}\n\n'
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def chat_error_chunk(message: str, error_type: str = 'upstream_error') -> str:
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"""构造聊天补全流式接口使用的错误消息。"""
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return sse_data_message({'error': {'message': message, 'type': error_type}})
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def responses_error_event(message: str) -> str:
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"""构造 Responses 流式接口使用的错误事件。"""
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return sse_event_message('error', {'error': message})
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@ -173,20 +215,6 @@ def inject_instructions_anthropic(payload: dict[str, Any], instructions: str, po
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return payload
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def should_inject_thinking(backend: str) -> bool:
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"""判断当前后端是否需要注入历史 thinking。
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仅对明确能消费历史 reasoning/thinking 的后端启用：
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- anthropic
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- gemini
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- responses
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OpenAI Chat 兼容后端通常不接受 `reasoning_content` 历史字段，
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若注入会导致上游报错，因此显式排除。
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"""
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return backend in ('anthropic', 'gemini', 'responses')
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# ─── Body / Header 修改 ──────────────────────────
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@ -220,140 +248,3 @@ def apply_header_modifications(headers: dict[str, str], modifications: dict[str,
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headers[key] = str(value)
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logger.info('已应用 header_modifications: %s', list(modifications.keys()))
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return headers
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# ═══════════════════════════════════════════════════════════
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# 后端注册表 + ClientFormatter 实现
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# ═══════════════════════════════════════════════════════════
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def get_outbound(backend: str):
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"""根据后端类型获取对应的 OutboundTransformer 实例。"""
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from adapters.cc_anthropic_adapter import AnthropicOutbound
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from adapters.cc_gemini_adapter import GeminiOutbound
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from adapters.openai_compat_fixer import OpenAIChatOutbound
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from adapters.responses_cc_adapter import ResponsesOutbound
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registry = {
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'openai': OpenAIChatOutbound,
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'anthropic': AnthropicOutbound,
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'gemini': GeminiOutbound,
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'responses': ResponsesOutbound,
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}
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cls = registry.get(backend, OpenAIChatOutbound)
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return cls()
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class CCClientFormatter:
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"""Chat Completions 客户端格式化器。
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将通用处理结果格式化为 OpenAI Chat Completions 格式，
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供 /v1/chat/completions 端点使用。
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"""
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def format_response(self, cc_response: dict[str, Any], model: str) -> dict[str, Any]:
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cc_response['model'] = model
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return cc_response
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def wrap_stream_item(self, item: Any) -> str:
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payload = item if isinstance(item, str) else json.dumps(item, ensure_ascii=False)
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return f'data: {payload}\n\n'
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def format_error(self, message: str) -> str:
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return sse_data_message({'error': {'message': message, 'type': 'upstream_error'}})
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def format_done(self) -> str | None:
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return sse_data_message('[DONE]')
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def start_events(self) -> list[str]:
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return []
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@property
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def usage_input_key(self) -> str:
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return 'prompt_tokens'
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@property
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def usage_output_key(self) -> str:
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return 'completion_tokens'
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class ResponsesClientFormatter:
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"""Responses API 客户端格式化器。
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将通用处理结果格式化为 OpenAI Responses 格式，
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供 /v1/responses 端点使用。
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流式场景使用 ResponsesStreamConverter 做 CC chunk → Responses SSE 转换。
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"""
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def __init__(self, model: str = ''):
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from adapters.responses_cc_adapter import ResponsesStreamConverter, cc_to_responses
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self._model = model
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self._converter = ResponsesStreamConverter(model=model)
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self._cc_to_responses = cc_to_responses
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def format_response(self, cc_response: dict[str, Any], model: str) -> dict[str, Any]:
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return self._cc_to_responses(cc_response, model)
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def wrap_stream_item(self, item: Any) -> str:
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if isinstance(item, str):
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return item
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events = self._converter.process_cc_chunk(item)
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return ''.join(events)
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def format_error(self, message: str) -> str:
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return responses_error_event(message)
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def format_done(self) -> str | None:
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events = self._converter.finalize()
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return ''.join(events) if events else None
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def start_events(self) -> list[str]:
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return self._converter.start_events()
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@property
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def usage_input_key(self) -> str:
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return 'input_tokens'
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@property
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def usage_output_key(self) -> str:
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return 'output_tokens'
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class ResponsesPassthroughFormatter:
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"""Responses 透传格式化器。
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当后端本身就是 Responses 格式时使用，做轻量模型名改写。
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"""
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def __init__(self, model: str = ''):
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self._model = model
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def format_response(self, response_data: dict[str, Any], model: str) -> dict[str, Any]:
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response_data['model'] = model
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return response_data
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def wrap_stream_item(self, item: Any) -> str:
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if isinstance(item, str):
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return item
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event_type = item.pop('_sse_event_type', None)
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if event_type:
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return f'event: {event_type}\ndata: {json.dumps(item, ensure_ascii=False)}\n\n'
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return f'data: {json.dumps(item, ensure_ascii=False)}\n\n'
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def format_error(self, message: str) -> str:
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return responses_error_event(message)
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def format_done(self) -> str | None:
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return None
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def start_events(self) -> list[str]:
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return []
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@property
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def usage_input_key(self) -> str:
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return 'input_tokens'
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@property
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def usage_output_key(self) -> str:
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return 'output_tokens'
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