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https://github.com/fawney19/Aether.git
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refactor: 移除 Python 后端源码，全面迁移至 Rust gateway 架构
- 删除全部 Python 源码 (src/) 及 Alembic 迁移脚本，归档至 _deprecated_py_src/ - 重构 Rust gateway ai_pipeline: 拆分 planner/finalize 模块，新增 contracts/adaptation 层 - 重组 handlers 模块为 admin/public/proxy/internal/shared 子模块结构 - 新增 executor 模块，引入 Rust 原生数据库迁移 (aether-data/migrations) - 简化 CI/Docker 构建流程，移除 base image 二级构建，统一为单一 app image - 移除 Python 相关基础设施文件 (entrypoint.sh, gunicorn_conf.py, Dockerfile.base)
This commit is contained in:
fawney19
226
_deprecated_py_src/api/handlers/openai_cli/handler.py
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226
_deprecated_py_src/api/handlers/openai_cli/handler.py
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"""
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OpenAI CLI Message Handler - 基于通用 CLI Handler 基类的简化实现
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继承 CliMessageHandlerBase，只需覆盖格式特定的配置和事件处理逻辑。
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代码量从原来的 900+ 行减少到 ~100 行。
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"""
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from typing import Any
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from src.api.handlers.base.cli_handler_base import (
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CliMessageHandlerBase,
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StreamContext,
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)
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from src.core.api_format import ApiFamily, EndpointKind
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class OpenAICliMessageHandler(CliMessageHandlerBase):
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"""
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OpenAI CLI Message Handler - 处理 OpenAI CLI Responses API 格式
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使用新三层架构 (Provider -> ProviderEndpoint -> ProviderAPIKey)
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通过 TaskService/FailoverEngine 实现自动故障转移、健康监控和并发控制
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响应格式特点：
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- 使用 output[] 数组而非 content[]
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- 使用 output_text 类型而非普通 text
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- 流式事件：response.output_text.delta, response.output_text.done
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模型字段：请求体顶级 model 字段
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"""
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FORMAT_ID = "openai:cli"
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API_FAMILY = ApiFamily.OPENAI
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ENDPOINT_KIND = EndpointKind.CLI
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def extract_model_from_request(
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self,
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request_body: dict[str, Any],
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path_params: dict[str, Any] | None = None, # noqa: ARG002
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) -> str:
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"""
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从请求中提取模型名 - OpenAI 格式实现
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OpenAI API 的 model 在请求体顶级字段。
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Args:
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request_body: 请求体
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path_params: URL 路径参数（OpenAI 不使用）
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Returns:
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模型名
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"""
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model = request_body.get("model")
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return str(model) if model else "unknown"
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def apply_mapped_model(
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self,
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request_body: dict[str, Any],
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mapped_model: str,
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) -> dict[str, Any]:
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"""
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OpenAI CLI (Responses API) 的 model 在请求体顶级字段。
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Args:
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request_body: 原始请求体
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mapped_model: 映射后的模型名
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Returns:
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更新了 model 字段的请求体
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"""
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result = dict(request_body)
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result["model"] = mapped_model
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return result
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def _process_event_data(
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self,
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ctx: StreamContext,
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event_type: str,
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data: dict[str, Any],
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) -> None:
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"""
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处理 OpenAI CLI 格式的 SSE 事件
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事件类型：
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- response.output_text.delta: 文本增量
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- response.completed: 响应完成（包含 usage）
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跨格式转换时（如 provider=claude:chat），原始事件数据是 Provider 格式而非 OpenAI CLI 格式。
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此时先调用基类方法通过 Provider 格式解析器提取 usage，再执行 OpenAI CLI 特定的处理逻辑。
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"""
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# 跨格式转换时：原始事件是 Provider 格式（如 Claude），
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# 基类 _process_event_data 会自动选择正确的 Provider 解析器提取 usage/text
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if ctx.provider_api_format and ctx.provider_api_format != ctx.client_api_format:
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super()._process_event_data(ctx, event_type, data)
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return
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# 以下是同格式（openai:cli）的处理逻辑
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# 提取 response_id
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if not ctx.response_id:
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response_obj = data.get("response")
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if isinstance(response_obj, dict) and response_obj.get("id"):
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ctx.response_id = response_obj["id"]
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elif "id" in data:
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ctx.response_id = data["id"]
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# 处理文本增量
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if event_type in ["response.output_text.delta", "response.outtext.delta"]:
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delta = data.get("delta")
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if isinstance(delta, str):
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ctx.append_text(delta)
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elif isinstance(delta, dict) and "text" in delta:
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ctx.append_text(delta["text"])
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# 处理完成事件
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elif event_type == "response.completed":
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ctx.has_completion = True
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response_obj = data.get("response")
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if isinstance(response_obj, dict):
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ctx.final_response = response_obj
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usage_obj = response_obj.get("usage")
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if isinstance(usage_obj, dict):
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ctx.final_usage = usage_obj
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ctx.input_tokens = usage_obj.get("input_tokens", 0)
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ctx.output_tokens = usage_obj.get("output_tokens", 0)
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details = usage_obj.get("input_tokens_details")
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if isinstance(details, dict):
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ctx.cached_tokens = details.get("cached_tokens", 0)
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# 如果没有收集到文本，从 output 中提取
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if not ctx.collected_text and "output" in response_obj:
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for output_item in response_obj.get("output", []):
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if output_item.get("type") != "message":
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continue
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for content_item in output_item.get("content", []):
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if content_item.get("type") == "output_text":
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text = content_item.get("text", "")
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if text:
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ctx.append_text(text)
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# 备用：从顶层 usage 提取
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usage_obj = data.get("usage")
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if isinstance(usage_obj, dict) and not ctx.final_usage:
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ctx.final_usage = usage_obj
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ctx.input_tokens = usage_obj.get("input_tokens", 0)
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ctx.output_tokens = usage_obj.get("output_tokens", 0)
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details = usage_obj.get("input_tokens_details")
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if isinstance(details, dict):
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ctx.cached_tokens = details.get("cached_tokens", 0)
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# 备用：从 response 字段提取
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response_obj = data.get("response")
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if isinstance(response_obj, dict) and not ctx.final_response:
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ctx.final_response = response_obj
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def _extract_response_metadata(
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self,
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response: dict[str, Any],
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) -> dict[str, Any]:
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"""
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从 OpenAI 响应中提取元数据
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提取 model、status、response_id 等字段作为元数据。
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Args:
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response: OpenAI API 响应
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Returns:
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提取的元数据字典
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"""
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metadata: dict[str, Any] = {}
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# 提取模型名称（实际使用的模型）
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if "model" in response:
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metadata["model"] = response["model"]
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# 提取响应 ID
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if "id" in response:
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metadata["response_id"] = response["id"]
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# 提取状态
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if "status" in response:
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metadata["status"] = response["status"]
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# 提取对象类型
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if "object" in response:
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metadata["object"] = response["object"]
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# 提取系统指纹（如果存在）
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if "system_fingerprint" in response:
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metadata["system_fingerprint"] = response["system_fingerprint"]
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return metadata
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def _finalize_stream_metadata(self, ctx: StreamContext) -> None:
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"""
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从流上下文中提取最终元数据
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在流传输完成后调用，从收集的事件中提取元数据。
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Args:
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ctx: 流上下文
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"""
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# 从 response_id 提取响应 ID
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if ctx.response_id:
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ctx.response_metadata["response_id"] = ctx.response_id
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# 从 final_response 提取更多元数据
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if ctx.final_response and isinstance(ctx.final_response, dict):
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if "model" in ctx.final_response:
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ctx.response_metadata["model"] = ctx.final_response["model"]
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if "status" in ctx.final_response:
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ctx.response_metadata["status"] = ctx.final_response["status"]
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if "object" in ctx.final_response:
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ctx.response_metadata["object"] = ctx.final_response["object"]
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if "system_fingerprint" in ctx.final_response:
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ctx.response_metadata["system_fingerprint"] = ctx.final_response[
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"system_fingerprint"
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]
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# 如果没有从响应中获取到 model，使用上下文中的
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if "model" not in ctx.response_metadata and ctx.model:
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ctx.response_metadata["model"] = ctx.model
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