(1)Invalid operation: The `response.parts` quick accessor requires a singlecandidate, but but `response.candidates` is empty. 解决方案:设置safety_settings(上面那种配置的写法貌似还是会遇到这种报错,改用https://ai.google.dev/gemini-api/docs/safety-settings?hl=en的写法就不会) from google.generative...
generation_config=generation_config, safety_settings=safety_settings) convo = model.start_chat(history=[ { "role": "user", "parts": "Apple" }, { "role": "model", "parts": "Fruit" }, { "role": "user", "parts": "Banana" }, { "role": "model", "parts": "Fruit" }, { "r...
此选项能够针对单个提示生成多个响应,有助于快速测试提示。 Safety settings- 调整用于管理模型响应的安全设置。如需详细了解这些控制措施,请参阅安全设置。 第4 步 - 后续步骤 现在,您已经为生成式 AI 应用设计了原型,接下来可以保存您的工作或生成代码,以便在您自己的开发环境中使用此提示。 如需保存您创建的提示...
Represents a list of safety settings. C# [System.Text.Json.Serialization.JsonPropertyName("safety_settings")]publicSystem.Collections.Generic.IList<Microsoft.SemanticKernel.Connectors.Google.GeminiSafetySetting>? SafetySettings {get;set; } Property Value ...
],"safetySettings": [ {"category":"HARM_CATEGORY_SEXUALLY_EXPLICIT","threshold":"BLOCK_NONE"}, {"category":"HARM_CATEGORY_HATE_SPEECH","threshold":"BLOCK_NONE"}, {"category":"HARM_CATEGORY_HARASSMENT","threshold":"BLOCK_NONE"},
API功能特点安全性设置(Safety settings)JSON模式(JSON mode)可重复输出(Reproducible outputs)Assistants ...
Short description of the problem this fixes or functionality that this introduces. This may be used for the CHANGELOG LLM class: addsafety_settingsfor Gemini models (Google only). Improvemax_tokensdefaulting. Give a summary of what the PR does, explaining any non-trivial design decisions ...
max_output_tokens":8192,"temperature":1,"top_p":0.95,}safety_settings={generative_models.Harm...
safety_settings = { HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
response = model.generate_content(messages, safety_settings=safety_settings) try: return response.text except ValueError: print(response) return "" with ThreadPoolExecutor(max_workers=4) as executor: predictions = list( executor.map( map_zero_shot, ...