操作步骤

1. 调用创建工作空间接口，创建一个工作空间，记录接口返回工作空间的ID。

   请求示例：

   POST https://{hostname}/v1/workspaces
   Body：

   {
     "name": "apieworkspace",
     "description": "apie test workspace"
   }
   响应示例：
   {   
       "id": "e935d0ef-f4eb-4b95-aff1-9d33ae9f57a6",   
       "name": "fabric",   
       "description": "fabric",   
       "create_time": "2023-05-30T12:24:30.401Z",   
       "create_domain_name": "admin",   
       "create_user_name": "user",   
       "metastore_id": "2180518f-42b8-4947-b20b-adfc53981a25",   
       "access_url": "https://:test.fabric.com/",   
       "enterprise_project_id": "01049549-82cd-4b2b-9733-ddb94350c125" 
   }
2. 调用创建端点接口，创建推理端点，记录接口返回的终端节点ID。

   请求示例：

   POST https://{hostname}/v1/workspaces/{workspace_id}/endpoints

   workspace_id：为步骤1中记录的工作空间ID。

   Body：

   {
     "name": "apie_test",
     "description": "apie test endpoint",
     "type": "inference",
     "reserved_resource": {
       "mu": {
         "spec_code": "mu.llama3.8b",
         "min": 0,
         "max": 1
       }
     }
   }
   响应示例：

   {
     "visibility": "PRIVATE",
     "id": "0b5633ba2b904511ad514346f4d23d4b",
     "name": "endpoint1",
     "type": "inference",
     "status": "CREATING",
     "description": "description",
     "create_time": "2023-05-30T12:24:30.401Z",
     "update_time": "2023-05-30T12:24:30.401Z",
     "owner": {
       "domain_name": "string",
       "domain_id": "xxx",
       "user_name": "string",
       "user_id": "xxx"
     }
     "reserved_resource": {
       "mu": {
         "spec_code": "mu.llama3.8b",
         "min": 0,
         "max": 1
       }
     }
   }
3. 调用创建模型接口，创建一个用户私有的模型，并记录接口返回模型ID。

   请求示例：

   POST https://{hostname}/v1/workspaces/{workspace_id}/models

   workspace_id：为步骤1中记录的工作空间ID。

   Body：

   {
     "name": "LLama3-8b",
     "description": "this is a apie test model",
     "type": "LLM_MODEL",
     "version": {
       "name": "v1",
       "description": "test description",
       "config": {
         "llm_model_config": {
           "base_model_type": "",
           "model_path": ""
         }
       }
     }
   }

   响应示例：

   {   
      "id": "ac8111bf-3601-4905-8ddd-b41d3e636a4e"}
   }
4. 调用创建推理服务接口，创建一个推理服务，记录接口返回推理服务的ID。

   请求示例：

   POST https://{hostname}/v1/workspaces/{workspace_id}/services/instances

   workspace_id：为步骤1中记录的工作空间ID。

   Body：

   {
     "source": {
       "id": ""
     },
     "name": "test_serviceInstanceName",
     "description": "description",
     "endpoint_id": ""}
   • id：为步骤3中接口返回记录的模型ID。
   • endpoint_id：为步骤2中接口返回记录的推理端点ID。

   响应示例：

   {   
      "id": "b935d0ef-f4eb-4b95-aff1-9d33ae9f57b6" 
   }
5. 调用推理请求接口，发起推理请求。

   请求示例：

   POST https://{hostname}/v1/workspaces/{workspace_id}/services/instances/{instance_id}/invocations
   • workspace_id：为步骤1中记录的工作空间ID。
   • instance_id：为步骤4中记录的推理服务的ID。

   Body：

   {
     "messages": [
       {
         "role": "user",
         "content": "hello"
       }
     ]
   }

   响应示例：推理请求接口为流式返回。

   {
     "id": "chatcmpl-62dda7304f53451c9477e0",
     "object": "chat.completion.chunk",
     "created": 1730120529,
     "model": "ada1d67d-f2a1-4e77-838f-0d8688d756f4",
     "choices": [
       {
         "index": 0,
         "delta": {
           "role": "assistant",
           "content": "\n\nHello! LLM stands for Large Language Model. It refers to artificial intelligence models, like myself,"
         },
         "finish_reason": null
       }
     ],
     "system_fingerprint": null,
     "usage": null
   }