sanitize_model_kwargs
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朱潮
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@ -18,7 +18,7 @@ from langchain.agents.middleware import SummarizationMiddleware as LangchainSumm
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from .summarization_middleware import SummarizationMiddleware
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from sympy.printing.cxx import none
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from utils.fastapi_utils import detect_provider
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from utils.fastapi_utils import detect_provider, sanitize_model_kwargs
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from .guideline_middleware import GuidelineMiddleware
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from .tool_output_length_middleware import ToolOutputLengthMiddleware
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from .tool_use_cleanup_middleware import ToolUseCleanupMiddleware
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@ -200,47 +200,22 @@ async def init_agent(config: AgentConfig):
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# 检测或使用指定的提供商
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model_provider, base_url = detect_provider(config.model_name, config.model_server)
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# 构建模型参数
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model_kwargs = {
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"model": config.model_name,
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"model_provider": model_provider,
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"temperature": 0.8,
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"base_url": base_url,
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"api_key": config.api_key
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}
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if config.generate_cfg:
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# 内部使用的参数，不应传给任何 LLM
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internal_params = {
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'tool_output_max_length',
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'tool_output_truncation_strategy',
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'tool_output_filters',
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'tool_output_exclude',
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'preserve_code_blocks',
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'preserve_json',
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}
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# Anthropic 不支持的 OpenAI 特有参数
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openai_only_params = {
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'n', # 生成多少个响应
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'presence_penalty',
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'frequency_penalty',
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'logprobs',
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'top_logprobs',
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'logit_bias',
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'seed',
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'suffix',
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'best_of',
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'echo',
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'user',
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}
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# 根据提供商决定需要过滤的参数
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params_to_filter = internal_params.copy()
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if model_provider == 'anthropic':
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params_to_filter.update(openai_only_params)
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filtered_cfg = {k: v for k, v in config.generate_cfg.items() if k not in params_to_filter}
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model_kwargs.update(filtered_cfg)
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model_kwargs, dropped_params, default_temperature_applied = sanitize_model_kwargs(
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model_name=config.model_name,
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model_provider=model_provider,
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base_url=base_url,
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api_key=config.api_key,
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generate_cfg=config.generate_cfg,
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source="init_agent"
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)
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if dropped_params:
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logger.info(
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"init_agent dropped_params=%s model=%s provider=%s default_temperature_applied=%s",
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dropped_params,
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config.model_name,
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model_provider,
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default_temperature_applied
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)
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llm_instance = init_chat_model(**model_kwargs)
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# 创建新的 agent（不再缓存）
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@ -514,6 +514,7 @@ async def chat_completions(request: ChatRequest, authorization: Optional[str] =
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# 收集额外参数作为 generate_cfg
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exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'n', 'shell_env', 'max_tokens'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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logger.info("chat_completions generate_cfg_keys=%s model=%s", list(generate_cfg.keys()), request.model)
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# 处理消息
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messages = process_messages(request.messages, request.language)
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# 创建 AgentConfig 对象
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@ -665,9 +666,8 @@ async def chat_warmup_v2(request: ChatRequestV2, authorization: Optional[str] =
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# 处理消息
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messages = process_messages(empty_messages, request.language or "ja")
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# 收集额外参数作为 generate_cfg
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exclude_fields = {'messages', 'stream', 'tool_response', 'bot_id', 'language', 'user_identifier', 'session_id', 'n', 'model', 'model_server', 'api_key', 'shell_env', 'max_tokens'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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logger.info("chat_warmup_v2 generate_cfg_keys=%s requested_model=%s", list(generate_cfg.keys()), request.model)
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# 从请求中提取 model/model_server/api_key，优先级高于 bot_config（排除 "whatever" 和空值）
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req_data = request.model_dump()
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req_model = req_data.get("model") or ""
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@ -775,6 +775,7 @@ async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[st
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# 收集额外参数作为 generate_cfg
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exclude_fields = {'messages', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings', 'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'n', 'model', 'model_server', 'api_key', 'shell_env', 'max_tokens'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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logger.info("chat_completions_v2 generate_cfg_keys=%s requested_model=%s", list(generate_cfg.keys()), request.model)
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# 从请求中提取 model/model_server/api_key，优先级高于 bot_config（排除 "whatever" 和空值）
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req_data = request.model_dump()
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req_model = req_data.get("model") or ""
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@ -36,6 +36,83 @@ def detect_provider(model_name,model_server):
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# 默认使用 openai 兼容格式
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return "openai",model_server
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def is_anthropic_opus_model(model_name: Optional[str]) -> bool:
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"""判断是否为 Anthropic Opus 模型"""
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return bool(model_name and "opus" in model_name.lower())
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def sanitize_model_kwargs(
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model_name: str,
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model_provider: str,
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base_url: Optional[str],
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api_key: Optional[str],
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generate_cfg: Optional[Dict[str, Any]] = None,
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source: str = "agent"
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) -> tuple[Dict[str, Any], List[str], bool]:
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"""清洗模型参数，过滤不兼容参数并返回日志所需信息"""
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model_kwargs = {
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"model": model_name,
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"model_provider": model_provider,
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"base_url": base_url,
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"api_key": api_key
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}
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internal_params = {
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'tool_output_max_length',
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'tool_output_truncation_strategy',
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'tool_output_filters',
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'tool_output_exclude',
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'preserve_code_blocks',
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'preserve_json',
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}
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openai_only_params = {
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'n',
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'presence_penalty',
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'frequency_penalty',
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'logprobs',
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'top_logprobs',
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'logit_bias',
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'seed',
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'suffix',
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'best_of',
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'echo',
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'user',
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}
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params_to_filter = set(internal_params)
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is_opus_model = model_provider == 'anthropic' and is_anthropic_opus_model(model_name)
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if model_provider == 'anthropic':
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params_to_filter.update(openai_only_params)
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if is_opus_model:
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params_to_filter.add('temperature')
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original_keys = list((generate_cfg or {}).keys())
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filtered_cfg = {k: v for k, v in (generate_cfg or {}).items() if k not in params_to_filter}
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dropped_params = [k for k in original_keys if k in params_to_filter]
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default_temperature_applied = False
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if not is_opus_model:
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model_kwargs["temperature"] = 0.8
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default_temperature_applied = True
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model_kwargs.update(filtered_cfg)
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logger.info(
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"sanitize_model_kwargs source=%s provider=%s model=%s original_keys=%s dropped_keys=%s default_temperature_applied=%s",
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source,
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model_provider,
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model_name,
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original_keys,
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dropped_params,
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default_temperature_applied
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)
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return model_kwargs, dropped_params, default_temperature_applied
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def get_versioned_filename(upload_dir: str, name_without_ext: str, file_extension: str) -> tuple[str, int]:
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"""
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获取带版本号的文件名，自动处理文件删除和版本递增
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@ -451,15 +528,22 @@ async def _sync_call_llm(llm_config, messages) -> str:
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api_key = llm_config.get('api_key')
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# 检测或使用指定的提供商
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model_provider,base_url = detect_provider(model_name,model_server)
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# 构建模型参数
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model_kwargs = {
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"model": model_name,
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"model_provider": model_provider,
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"temperature": 0.8,
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"base_url":base_url,
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"api_key":api_key
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}
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model_kwargs, dropped_params, default_temperature_applied = sanitize_model_kwargs(
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model_name=model_name,
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model_provider=model_provider,
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base_url=base_url,
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api_key=api_key,
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source="_sync_call_llm"
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)
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if dropped_params:
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logger.info(
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"_sync_call_llm dropped_params=%s model=%s provider=%s default_temperature_applied=%s",
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dropped_params,
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model_name,
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model_provider,
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default_temperature_applied
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)
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llm_instance = init_chat_model(**model_kwargs)
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# 转换消息格式为LangChain格式
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