Openmanus调用claude3.7报错

调用claude3.7报错

问题1:启动时提示Input should be a valid list

app.llm:ask_tool:763

2025-04-29 10:54:26.351 | ERROR    | app.llm:ask_tool:763 - OpenAI API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: b2bc5516-8a47-43a2-8e16-c4c22a9e471c, ValidationException: messages: Input should be a valid list (request id: 202504290254262291875375937263) (request id: 2025042910542591952335055389127)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:26.352 | ERROR    | app.llm:ask_tool:769 - API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: b2bc5516-8a47-43a2-8e16-c4c22a9e471c, ValidationException: messages: Input should be a valid list (request id: 202504290254262291875375937263) (request id: 2025042910542591952335055389127)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:30.038 | ERROR    | app.llm:ask_tool:763 - OpenAI API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: c666975b-9655-41ac-b8b3-06820190e996, ValidationException: messages: Input should be a valid list (request id: 2025042902542972768780449736700) (request id: 2025042910542962454758402865614)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:30.038 | ERROR    | app.llm:ask_tool:769 - API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: c666975b-9655-41ac-b8b3-06820190e996, ValidationException: messages: Input should be a valid list (request id: 2025042902542972768780449736700) (request id: 2025042910542962454758402865614)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:33.707 | ERROR    | app.llm:ask_tool:763 - OpenAI API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: 1a137fc4-ad0f-49f3-b02e-4ebad4d3d6a4, ValidationException: messages: Input should be a valid list (request id: 2025042902543326672631284143208) (request id: 2025042910543316394898536002475)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:33.708 | ERROR    | app.llm:ask_tool:769 - API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: 1a137fc4-ad0f-49f3-b02e-4ebad4d3d6a4, ValidationException: messages: Input should be a valid list (request id: 2025042902543326672631284143208) (request id: 2025042910543316394898536002475)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:39.309 | ERROR    | app.llm:ask_tool:763 - OpenAI API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: 15a29111-67fe-43b1-b2cb-30eff55adaca, ValidationException: messages: Input should be a valid list (request id: 2025042902543899527405591667911) (request id: 2025042910543889391969012954096)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:39.309 | ERROR    | app.llm:ask_tool:769 - API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: 15a29111-67fe-43b1-b2cb-30eff55adaca, ValidationException: messages: Input should be a valid list (request id: 2025042902543899527405591667911) (request id: 2025042910543889391969012954096)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:48.260 | ERROR    | app.llm:ask_tool:763 - OpenAI API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: 3b5fb41f-8710-4f35-a8bd-ec5e5a8c36b4, ValidationException: messages: Input should be a valid list (request id: 202504290254488899699225193586) (request id: 2025042910544798702295458742426)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:54:48.261 | ERROR    | app.llm:ask_tool:769 - API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: 3b5fb41f-8710-4f35-a8bd-ec5e5a8c36b4, ValidationException: messages: Input should be a valid list (request id: 202504290254488899699225193586) (request id: 2025042910544798702295458742426)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:55:06.027 | ERROR    | app.llm:ask_tool:763 - OpenAI API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: 8b9b63dc-02ac-4653-a15d-310385cd35f4, ValidationException: messages: Input should be a valid list (request id: 2025042902550585903706503939768) (request id: 2025042910550575697018148808461)', 'type': '', 'param': '', 'code': None}}
2025-04-29 10:55:06.027 | ERROR    | app.llm:ask_tool:769 - API error: Error code: 500 - {'error': {'message': 'InvokeModel: operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: 8b9b63dc-02ac-4653-a15d-310385cd35f4, ValidationException: messages: Input should be a valid list (request id: 2025042902550585903706503939768) (request id: 2025042910550575697018148808461)', 'type': '', 'param': '', 'code': None}}

需要做anthropic的数据格式适配改造:

自己扩展的适配接口:
正常调用的入参:

响应成功的返回信息:

报错时的入参:

所以真正的报错在这一行:

response = await self.client.client.messages.create(**params)

没有正常返回content,所以导致了下一行的报错;

"operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: fc8e87f7-c7db-4a14-a44e-defa3e3bdf3d, ValidationException: messages.3.content.0.tool_result.tool_use_id: String should match pattern '^[a-zA-Z0-9_-] $'"

工具tool_use_id为“”

问题2:browser_use 返回:No content was extracted from the page.

调试关键字:

1、判断anthropic接口调用的返回响应中,是否存在error信息:

response = await self.client.client.messages.create(**params)
if 'error' in response.model_extra:
    print(response.model_extra['error'],f'param:{params}')

{'type': 'invalid_request_error', 'message': 'operation error Bedrock Runtime: InvokeModel, https response error StatusCode: 400, RequestID: b35431c9-9868-4476-9c87-3390645736d4, ValidationException: messages: final assistant content cannot end with trailing whitespace'} 

根因是网页提取的信息被截断,

要解决"final assistant content cannot end with trailing whitespace"错误,需要对消息内容进行以下优化处理:

根据错误提示和请求参数分析,主要问题出现在[系统提示词]和[网页内容]部分的末尾存在非法空白符:

  1. 系统提示部分存在多个连续换行符\n
  2. 页面内容截断导致</solu\n非法结尾
  3. 文本块拼接时无意添加的换行符

相关技术原理

  1. Claude 3.7 Sonnet的输入验证机制要求文本块必须以非空白字符结束,这是为了防止模型生成未闭合的JSON结构
  2. AWS Bedrock会对每个消息块执行正则校验:/\s+$/匹配结尾空白符
  3. 混合推理架构对输入格式更敏感,深度思考模式会严格检查语法结构

网页截断问题,可以重试解决,暂不影响,待后续有需要再处理;

调用gpt-4o报错

2025-04-29 11:08:59.827 | ERROR    | app.llm:ask_tool:763 - OpenAI API error: Error code: 400 - {'error': {'message': "This model's maximum context length is 128000 tokens. However, you requested 129267 tokens (149 in the messages, 1118 in the functions, and 128000 in the completion). Please reduce the length of the messages, functions, or completion. (request id: 2025042903085950224491761877647) (request id: 2025042911085941036376554350856)", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}
2025-04-29 11:08:59.830 | ERROR    | app.llm:ask_tool:769 - API error: Error code: 400 - {'error': {'message': "This model's maximum context length is 128000 tokens. However, you requested 129267 tokens (149 in the messages, 1118 in the functions, and 128000 in the completion). Please reduce the length of the messages, functions, or completion. (request id: 2025042903085950224491761877647) (request id: 2025042911085941036376554350856)", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}

解决办法:

注释掉max_token,不指定,使用默认;

<think>好的,用户想了解如何通过API调用Claude 3.7模型进行商用Sonnet生成。首先,我需要回忆一下Claude的API调用流程,特别是最新的3.7版本有没有什么变化。根据提供的引用,Claude 3.7 Sonnet在编码方面表现优异,所以应该支持文本生成任务,包括诗歌类。 接下来,用户需要的是商用环境下的示例,这意味着可能需要关注授权、计费、以及API请求中的参数设置是否符合商业使用的要求。比如API密钥的安全性,请求的速率限制,以及是否需要在请求头中添加特定信息。 然后,Sonnet是一种十四行诗,有特定的格式和押韵要求。虽然模型本身可能已经了解这些结构,但在prompt中明确要求生成Sonnet会更准确。需要构造一个合适的系统提示和用户提示,确保生成的文本符合要求。 还要考虑API的端点是否正确。Anthropic的API通常使用messages端点,发送用户和系统的消息。需要检查最新的文档确认URL是否有变化,但通常应该是类似/v1/messages的路径。 另外,返回的响应处理也很重要。需要解析JSON响应,提取出生成的文本内容。同时,处理可能的错误,比如认证失败、额度不足等,确保商用应用的稳定性。 最后,需要提醒用户注意商业使用的条款,比如是否允许生成内容的商用,是否需要注明来源,以及费用方面的考虑。比如Claude的API定价模型,按token计费,商用情况下可能需要预估成本。 综合这些点,应该提供一个完整的代码示例,包含必要的请求头、JSON数据格式,以及错误处理的基本方法。同时,给出优化建议,比如调整temperature参数控制生成多样性,使用max_tokens限制长度,确保生成的Sonnet符合格式要求。</think>我将为您分步骤说明如何通过API调用Claude 3.7模型进行商用Sonnet生成: 1. **API准备工作** - 注册Anthropic开发者账号并获取API密钥[^2] - 确认已开通商用权限和配额 - 安装最新版Anthropic Python SDK:`pip install anthropic` 2. **基础调用代码** ```python import anthropic client = anthropic.Anthropic( api_key="your_api_key_here", base_url="https://api.anthropic.com/v1/" # 正式环境地址 ) response = client.messages.create( model="claude-3-7-sonnet-20240607", system="你是一位专业诗人,请用英文创作符合莎士比亚风格的商用工整Sonnet", messages=[ {"role": "user", "content": "生成一首关于人工智能革命的十四行诗,要求:ABAB CDCD EFEF GG押韵格式,包含商业应用元素"} ], temperature=0.7, max_tokens=500 ) print(response.content[0].text) ``` 3. **商用优化参数建议** ```python # 专业商用场景推荐配置 response = client.messages.create( model="claude-3-7-sonnet-20240607", system="商业诗歌生成器:创作适合品牌宣传、产品发布的专业Sonnet,确保:1.符合iambic pentameter 2.包含3个商业隐喻 3.结尾双行点睛", messages=[ {"role": "user", "content": "为新型AI芯片发布会创作一首Sonnet,强调算力突破和能效提升"} ], temperature=0.5, # 平衡创意与稳定性 max_tokens=300, top_p=0.9, stop_sequences=["\n\n"], # 防止过度生成 metadata={"project_id": "商业文案2024"} # 追踪使用情况 ) ``` 4. **响应处理示例** ```python try: poem = response.content[0].text # 商用前建议进行内容审核 if validate_commercial_content(poem): save_to_database(poem) print("生成成功:", poem) else: trigger_human_review(poem) except anthropic.APIConnectionError: # 处理API连接错误 implement_fallback_strategy() except anthropic.RateLimitError: # 处理速率限制 schedule_retry_after_delay() ``` **注意事项**: - 商用需遵守[Anthropic商业条款](https://www.anthropic.com/terms) - 推荐启用[内容审核层](https://docs.anthropic.com/en/docs/moderation) - 批量生成建议使用异步接口`AsyncAnthropic` - 最新计费标准为每百万tokens输入$3,输出$15
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