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fix: 同步 async 版本精细化过滤 + 移除敏感日志
1. async 版本同步 sync 版本的精细化过滤： - 移除 reasoning_effort, parallel_tool_calls 等过滤（厂商支持/忽略） - 保留必要过滤：client_metadata, coding_plan, functions 等 2. 移除敏感日志输出： - 删除 tool-call arguments 前 50 字符的日志 - 删除 api_base 脱敏不完全的日志 - 删除大量 DEBUG 日志（参数状态、tools 列表等） 安全修复：响应 Greptile AI 审查问题 - Medium: Tool-call arguments logged → 已修复 - Medium: api_base 日志未脱敏 → 已删除日志 测试验证：小米/DeepSeek 正常响应 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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parent
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1 changed files with 31 additions and 107 deletions
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@ -116,12 +116,7 @@ class LiteLLMCompletionTransformationHandler:
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fixed_args = "{}"
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needs_rebuild = True
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fixed_count += 1
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args_preview = (
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str(original_args)[:50] if original_args else "None"
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)
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logger.warning(
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f"[DomesticFilter] Fixed invalid JSON arguments, original: {args_preview}"
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)
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# 安全：不记录 original_args 内容，可能包含敏感信息
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elif isinstance(args, dict):
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# dict 类型需要转成 JSON string
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fixed_args = json.dumps(args)
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@ -396,39 +391,22 @@ class LiteLLMCompletionTransformationHandler:
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actual_model = litellm_params_model
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if is_domestic_model_or_endpoint(actual_model, api_base):
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# DEBUG logger（先定义，后面使用）
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# DEBUG logger
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logger = logging.getLogger("LiteLLM.DomesticFilter")
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# Codex CLI 特有参数
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# ========== 必须过滤的参数 ==========
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# Codex CLI 特有参数 - LiteLLM 内部报错
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acompletion_args.pop("client_metadata", None)
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# OpenAI 扩展参数（国内模型不支持）
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acompletion_args.pop("reasoning_effort", None) # reasoning 模式
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acompletion_args.pop("reasoning", None) # reasoning 参数 (Codex 0.130.0+)
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# Responses API 专用参数（国内厂商 Chat API 不支持）
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acompletion_args.pop("coding_plan", None) # Codex coding plan
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acompletion_args.pop("parallel_tool_calls", None) # 并行工具调用
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# 旧格式参数（国内模型只支持 tools/tool_choice，不支持 functions/function_call）
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acompletion_args.pop("functions", None) # OpenAI 旧格式
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acompletion_args.pop("function_call", None) # OpenAI 旧格式
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# 其他国内模型可能不支持的参数
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acompletion_args.pop("stream_options", None) # stream 选项
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acompletion_args.pop("modalities", None) # 多模态参数
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acompletion_args.pop("prediction", None) # 预测参数
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acompletion_args.pop("audio", None) # 音频参数
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acompletion_args.pop("store", None) # 存储参数
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acompletion_args.pop("include", None) # 包含参数
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acompletion_args.pop("prompt_cache_key", None) # 缓存键
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# OpenAI 高级参数（国内模型可能不支持）
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acompletion_args.pop("frequency_penalty", None) # 频率惩罚
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acompletion_args.pop("presence_penalty", None) # 存在惩罚
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acompletion_args.pop("logprobs", None) # logprobs
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acompletion_args.pop("top_logprobs", None) # top_logprobs
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acompletion_args.pop("response_format", None) # 响应格式（如 json_object）
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acompletion_args.pop("seed", None) # 随机种子
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acompletion_args.pop("logit_bias", None) # logit bias
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acompletion_args.pop("n", None) # 返回多个结果
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acompletion_args.pop("service_tier", None) # 服务等级
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# 注意：litellm_* 内部参数必须保留，用于代理会计/spend attribution
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# 这些参数会被 litellm.completion 正确处理，不会发送给上游 API
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# 旧格式参数（国内模型只支持 tools/tool_choice）
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acompletion_args.pop("functions", None)
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acompletion_args.pop("function_call", None)
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# LiteLLM 内部参数（不应发送给上游）
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acompletion_args.pop("shared_session", None)
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acompletion_args.pop("model_info", None)
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acompletion_args.pop("secret_fields", None)
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@ -436,35 +414,32 @@ class LiteLLMCompletionTransformationHandler:
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acompletion_args.pop("use_litellm_proxy", None)
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acompletion_args.pop("merge_reasoning_content_in_choices", None)
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acompletion_args.pop("supports_function_calling", None)
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acompletion_args.pop("max_retries", None)
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# ========== 厂商忽略的参数（保留过滤，避免无意义传输）==========
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acompletion_args.pop("stream_options", None)
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acompletion_args.pop("modalities", None)
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acompletion_args.pop("prediction", None)
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acompletion_args.pop("audio", None)
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acompletion_args.pop("store", None)
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acompletion_args.pop("include", None)
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acompletion_args.pop("prompt_cache_key", None)
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acompletion_args.pop("caching", None)
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acompletion_args.pop("extra_body", None)
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# 注意：custom_llm_provider 必须保留，LiteLLM 需要它识别提供商
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acompletion_args.pop("max_retries", None)
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# ========== 不过滤的参数（厂商支持或忽略）==========
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# reasoning_effort - DeepSeek/Xiaomi 支持，其他厂商忽略
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# parallel_tool_calls - 厂商支持/忽略
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# frequency_penalty, presence_penalty - 厂商支持
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# response_format, seed, logprobs 等 - 厂商支持/忽略
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# tool_choice 兼容：国内模型不支持 "required"，改成 "auto"
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tool_choice = acompletion_args.get("tool_choice")
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logger.info(f"[DomesticFilter] tool_choice before fix: {tool_choice}")
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if tool_choice == "required":
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acompletion_args["tool_choice"] = "auto"
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logger.info("[DomesticFilter] tool_choice converted: required -> auto")
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# DEBUG: 国内模型参数过滤验证（脱敏 api_base，只显示域名）
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# 安全：api_base 可能包含凭证或内部主机名，只显示基础域名
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safe_api_base = (
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api_base.split("//")[-1].split("/")[0].split("?")[0]
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if api_base
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else "none"
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)
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logger.info(
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f"[DomesticFilter] actual_model={actual_model}, api_base_host={safe_api_base}, "
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f"is_domestic={is_domestic_model_or_endpoint(actual_model, api_base)}"
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)
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logger.info(
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f"[DomesticFilter] acompletion_args keys after filter: "
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f"{list(acompletion_args.keys())}"
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)
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# 确保所有历史消息中的 tool_calls arguments 是有效 JSON 格式
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# 国内模型要求 function.arguments 必须是严格 JSON
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# 确保 tool_calls arguments 是有效 JSON
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messages = acompletion_args.get("messages")
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if messages:
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acompletion_args["messages"] = (
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@ -472,57 +447,6 @@ class LiteLLMCompletionTransformationHandler:
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messages
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)
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)
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logger.info(
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"[DomesticFilter] Validated tool_calls arguments in messages"
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)
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# 打印 tools 和 messages 用于调试
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tools_debug = acompletion_args.get("tools")
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messages_debug = acompletion_args.get("messages")
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logger.info(
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f"[DomesticFilter] tools count: {len(tools_debug) if tools_debug else 0}"
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)
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if tools_debug:
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for i, tool in enumerate(tools_debug[:3]): # 只打印前3个
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tool_name = (
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tool.get("function", {}).get("name", "unknown")
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if isinstance(tool, dict)
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else "unknown"
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)
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logger.info(f"[DomesticFilter] tool[{i}] name: {tool_name}")
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logger.info(
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f"[DomesticFilter] messages count: {len(messages_debug) if messages_debug else 0}"
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)
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if messages_debug:
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for i, msg in enumerate(messages_debug[:5]): # 只打印前5条
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role = (
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msg.get("role", "unknown")
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if isinstance(msg, dict)
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else "unknown"
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)
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has_tools = (
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"tool_calls" in msg if isinstance(msg, dict) else False
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)
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logger.info(
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f"[DomesticFilter] msg[{i}] role={role}, has_tool_calls={has_tools}"
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)
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# 打印第一个有 tool_calls 的消息详情（不含 arguments，安全）
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for i, msg in enumerate(messages_debug or []):
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if isinstance(msg, dict) and msg.get("tool_calls"):
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tc = msg.get("tool_calls", [])
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if tc and isinstance(tc, list) and len(tc) > 0:
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first_tc = tc[0]
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func_name = (
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first_tc.get("function", {}).get("name", "unknown")
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if isinstance(first_tc, dict)
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else "unknown"
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)
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# 安全：不打印 arguments，可能包含敏感内容
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logger.info(
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f"[DomesticFilter] First tool_call msg[{i}] function={func_name}"
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)
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break
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litellm_completion_response: Union[
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ModelResponse, litellm.CustomStreamWrapper
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