|
| 1 | +{ |
| 2 | + "name": "伶鹊-对话分析-flash", |
| 3 | + "description": "伶鹊-对话分析-flash是专注于日常任务，如对话信息抽取、场景分类等分析类需求的模型，自定义分析标准遵循与对话语义理解能力显著提升，适用于低时延的离线在线分析任务。", |
| 4 | + "items": [ |
| 5 | + { |
| 6 | + "inferenceMetadata": { |
| 7 | + "response_modality": [ |
| 8 | + "Text" |
| 9 | + ], |
| 10 | + "request_modality": [ |
| 11 | + "Text" |
| 12 | + ] |
| 13 | + }, |
| 14 | + "description": "伶鹊-对话分析-flash是基于千问最新版本调优2026.09.20日更新、专注于日常任务的领域模型，如对话信息抽取、场景分类等分析类需求的模型，自定义分析标准遵循与对话语义理解能力显著提升，适用于低时延的离线在线分析任务。", |
| 15 | + "features": [], |
| 16 | + "provider": "qwen-domain-model", |
| 17 | + "model": "tongyi-xiaomi-analysis-flash", |
| 18 | + "prices": [ |
| 19 | + { |
| 20 | + "priceUnit": "每百万tokens", |
| 21 | + "price": "0.2", |
| 22 | + "timeBand": "standard", |
| 23 | + "ntmAmountType": "input_token", |
| 24 | + "type": "input_token", |
| 25 | + "priceName": "输入" |
| 26 | + }, |
| 27 | + { |
| 28 | + "priceUnit": "每百万tokens", |
| 29 | + "price": "0.4", |
| 30 | + "timeBand": "standard", |
| 31 | + "ntmAmountType": "output_token", |
| 32 | + "type": "output_token", |
| 33 | + "priceName": "输出" |
| 34 | + }, |
| 35 | + { |
| 36 | + "priceUnit": "每百万tokens", |
| 37 | + "price": "0.04", |
| 38 | + "timeBand": "standard", |
| 39 | + "ntmAmountType": "input_token_cache", |
| 40 | + "type": "input_token_cache", |
| 41 | + "priceName": "输入（缓存命中）" |
| 42 | + } |
| 43 | + ], |
| 44 | + "qpmInfo": { |
| 45 | + "model-default-actual": { |
| 46 | + "count_limit_period": 60, |
| 47 | + "usage_limit": 1000000, |
| 48 | + "usage_limit_field": "total_tokens", |
| 49 | + "count_limit": 600, |
| 50 | + "usage_limit_period": 60, |
| 51 | + "type": "model-default" |
| 52 | + }, |
| 53 | + "model-default": { |
| 54 | + "count_limit_period": 60, |
| 55 | + "usage_limit": 1000000, |
| 56 | + "usage_limit_field": "total_tokens", |
| 57 | + "count_limit": 600, |
| 58 | + "usage_limit_period": 60, |
| 59 | + "type": "model-default" |
| 60 | + } |
| 61 | + }, |
| 62 | + "priceTimeBands": [ |
| 63 | + "standard" |
| 64 | + ], |
| 65 | + "capabilities": [ |
| 66 | + "TG" |
| 67 | + ], |
| 68 | + "versionTag": "MAJOR", |
| 69 | + "maxOutputTokens": 4096, |
| 70 | + "latestOnlineAt": "2026-01-09T03:23:03.000+00:00", |
| 71 | + "contextWindow": 32768, |
| 72 | + "maxInputTokens": 28672, |
| 73 | + "inferenceProvider": "aliyun-bailian", |
| 74 | + "name": "伶鹊-对话分析-flash", |
| 75 | + "docUrl": "https://docs.bailian.console.aliyun.com/zh/model-studio/dialogue-analysis", |
| 76 | + "samples": { |
| 77 | + "openai": { |
| 78 | + "default": { |
| 79 | + "curl": "curl --location \"https://[workspace-id].cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions\" \\\n-H \"Authorization: Bearer $DASHSCOPE_API_KEY\" \\\n-H \"Content-Type: application/json\" \\\n-d '{\n \"model\": \"tongyi-xiaomi-analysis-flash\",\n \"messages\": [\n {\n \"role\": \"user\", \n \"content\": \"请帮我提取下面对话中的关键信息：[1] 客服：您好，欢迎致电AB电商平台，请问有什么可以帮您？[2] 客户：你好，我上周在你们店买了一台料理机，现在运行时有点异响。\"\n }\n ],\n \"temperature\": 0.0,\n \"top_k\": 1\n}'", |
| 80 | + "python": "from openai import OpenAI\nimport os\n\n\nanalysis_prompt = f\"\"\"\n请帮我提取下面对话中的关键信息：[1] 客服：您好，欢迎致电AB电商平台，请问有什么可以帮您？[2] 客户：你好，我上周在你们店买了一台料理机，现在运行时有点异响。\n\"\"\"\n\nclient = OpenAI(\n api_key=os.getenv(\"DASHSCOPE_API_KEY\"),\n base_url=\"https://[workspace-id].cn-beijing.maas.aliyuncs.com/compatible-mode/v1\",\n)\n\ncompletion = client.chat.completions.create(\n model=\"tongyi-xiaomi-analysis-flash\",\n messages=[\n {\n 'role': 'user',\n 'content': analysis_prompt\n }\n ],\n temperature=0,\n extra_body={\n \"top_k\": 1\n }\n)\n\nprint(completion.choices[0].message.content)", |
| 81 | + "java": "import com.openai.client.OpenAIClient;\nimport com.openai.client.okhttp.OpenAIOkHttpClient;\nimport com.openai.core.JsonValue;\nimport com.openai.models.chat.completions.ChatCompletion;\nimport com.openai.models.chat.completions.ChatCompletionCreateParams;\n\npublic class Main {\n public static void main(String[] args) {\n try {\n OpenAIClient client = OpenAIOkHttpClient.builder()\n .apiKey(System.getenv(\"DASHSCOPE_API_KEY\"))\n .baseUrl(\"https://[workspace-id].cn-beijing.maas.aliyuncs.com/compatible-mode/v1\")\n .build();\n \n ChatCompletionCreateParams params = ChatCompletionCreateParams.builder()\n .model(\"tongyi-xiaomi-analysis-flash\")\n .addUserMessage(getUserMessage())\n .temperature(0.0)\n .putAdditionalBodyProperty(\"top_k\", JsonValue.from(1))\n .build();\n \n ChatCompletion chatCompletion = client.chat().completions().create(params);\n String content = chatCompletion.choices().get(0).message().content().orElse(\"未返回有效内容\");\n System.out.println(content);\n\n } catch (Exception e) {\n System.err.println(\"Error Message: \" + e.getMessage());\n }\n }\n\n private static String getUserMessage() {\n String analysisPrompt = \"请帮我提取下面对话中的关键信息：[1] 客服：您好，欢迎致电AB电商平台，请问有什么可以帮您？[2] 客户：你好，我上周在你们店买了一台料理机，现在运行时有点异响。\";\n\n return analysisPrompt;\n }\n}" |
| 82 | + } |
| 83 | + }, |
| 84 | + "dashscope": { |
| 85 | + "default": { |
| 86 | + "curl": "curl --location \"https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation\" \\\n--header \"Authorization: Bearer $DASHSCOPE_API_KEY\" \\\n--header \"Content-Type: application/json\" \\\n--data '{\n \"model\": \"tongyi-xiaomi-analysis-flash\",\n \"input\":{\n \"messages\":[\n {\n \"role\": \"user\",\n \"content\": \"请帮我提取下面对话中的关键信息：[1] 客服：您好，欢迎致电AB电商平台，请问有什么可以帮您？[2] 客户：你好，我上周在你们店买了一台料理机，现在运行时有点异响。\"\n }\n ],\n \"temperature\": 0.0,\n \"top_k\": 1\n },\n \"parameters\": {\n \"result_format\": \"message\"\n }\n}'", |
| 87 | + "python": "from http import HTTPStatus\nimport dashscope\nimport os\n\ndashscope.base_http_api_url = \"https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1\"\n\nanalysis_prompt = f\"\"\"\n请帮我提取下面对话中的关键信息：[1] 客服：您好，欢迎致电AB电商平台，请问有什么可以帮您？[2] 客户：你好，我上周在你们店买了一台料理机，现在运行时有点异响。\n\"\"\"\n\nresponse = dashscope.Generation.call(\n api_key=os.getenv('DASHSCOPE_API_KEY'),\n model=\"tongyi-xiaomi-analysis-flash\",\n messages=[\n {\n 'role': 'user',\n 'content': analysis_prompt\n }\n ],\n temperature=0.0,\n top_k=1,\n result_format=\"message\"\n)\n\nif response.status_code == HTTPStatus.OK:\n print(response.output.choices[0].message.content)\nelse:\n print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (\n response.request_id, response.status_code,\n response.code, response.message\n ))", |
| 88 | + "java": "import java.util.Arrays;\nimport java.lang.System;\nimport com.alibaba.dashscope.aigc.generation.Generation;\nimport com.alibaba.dashscope.aigc.generation.GenerationParam;\nimport com.alibaba.dashscope.aigc.generation.GenerationResult;\nimport com.alibaba.dashscope.common.Message;\nimport com.alibaba.dashscope.common.Role;\nimport com.alibaba.dashscope.exception.ApiException;\nimport com.alibaba.dashscope.exception.InputRequiredException;\nimport com.alibaba.dashscope.exception.NoApiKeyException;\nimport com.alibaba.dashscope.protocol.Protocol;\n\npublic class Main {\n public static GenerationResult callWithMessage() throws ApiException, NoApiKeyException, InputRequiredException {\n Generation gen = new Generation(Protocol.HTTP.getValue(), \"https://[workspace-id].cn-beijing.maas.aliyuncs.com/api/v1\");\n\n Message userMsg = Message.builder()\n .role(Role.USER.getValue())\n .content(getContent())\n .build();\n\n GenerationParam param = GenerationParam.builder()\n .apiKey(System.getenv(\"DASHSCOPE_API_KEY\"))\n .model(\"tongyi-xiaomi-analysis-flash\")\n .messages(Arrays.asList(userMsg))\n .temperature(0.0f)\n .topK(1)\n .resultFormat(GenerationParam.ResultFormat.MESSAGE)\n .build();\n return gen.call(param);\n }\n\n public static void main(String[] args) {\n try {\n GenerationResult result = callWithMessage();\n System.out.println(result.getOutput().getChoices().get(0).getMessage().getContent());\n } catch (ApiException | NoApiKeyException | InputRequiredException e) {\n System.err.println(\"Error message: \"+e.getMessage());\n }\n }\n\n private static String getContent() {\n\n String analysisPrompt = \"请帮我提取下面对话中的关键信息：[1] 客服：您好，欢迎致电AB电商平台，请问有什么可以帮您？[2] 客户：你好，我上周在你们店买了一台料理机，现在运行时有点异响。\";\n\n return analysisPrompt;\n }\n}" |
| 89 | + } |
| 90 | + } |
| 91 | + } |
| 92 | + } |
| 93 | + ] |
| 94 | +} |
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