GET
/
v2
/
deployments
# Replace with your values
API_KEY="your_workspace_api_key"
PROMPT_ID="my_prompt_id"
DEPLOYMENT_ID="my_deployment_id"

BASE_URL="https://api.adaline.ai/v2/deployments"
URL="${BASE_URL}?promptId=${PROMPT_ID}&deploymentId=${DEPLOYMENT_ID}"

curl -X GET \
  "${URL}" \
  -H "Authorization: Bearer ${API_KEY}" \
  -H "Content-Type: application/json"

{
  "id": "deployment_id",
  "createdAt": 1704067200000,
  "updatedAt": 1704067200000,
  "createdByUserId": "user_id",
  "updatedByUserId": "user_id",
  "projectId": "project_id",
  "promptId": "prompt_id",
  "deploymentEnvironmentId": "env_id",
  "prompt": {
    "config": {
      "model": "gpt-4o-mini",
      "providerName": "openai",
      "providerId": "provider_id",
      "settings": {
        "temperature": 0.7,
        "maxTokens": 1000,
        "topP": 1,
        "frequencyPenalty": 0,
        "presencePenalty": 0
      }
    },
    "messages": [
      {
          "role": "system",
          "content": [
              {
                  "modality": "text",
                  "value": "You are a helpful assistant. You answer questions based on a reference image and user input."
              }
          ]
      },
      {
          "role": "user",
          "content": [
              {
                  "modality": "text",
                  "value": "This is the user input\n\n###\n{{user_input}}\n###"
              },
              {
                  "modality": "image",
                  "detail": "auto",
                  "value": {
                      "type": "url",
                      "url": "{{reference_image}}"
                  }
              }
          ]
      }
    ],
    "tools": [],
    "variables": [
      {
        "name": "user_input",
        "modality": "text"
      },
      {
        "name": "reference_image",
        "modality": "image"
      }
    ]
  }
}

​
Request

​
Authorization
string
required

Bearer Token Authentication

All API requests must include a valid workspace API key in the Authorization header using the Bearer token format.

Authorization: Bearer YOUR_WORKSPACE_API_KEY

You can create your workspace API key in Adaline by visiting Settings > API Keys.

​
Query Parameters

​
promptId
string
required

The unique identifier of the prompt.

​
deploymentId
string
required

The unique identifier of the deployment.

Find your promptId and deploymentId by navigating to the specific deployment in platform.

​
Response

​
id
string

The unique identifier of the deployment.

​
createdAt
number

Unix timestamp when the deployment was created.

​
updatedAt
number

Unix timestamp when the deployment was last updated.

​
createdByUserId
string

Unique identifier of the user who created the deployment.

​
updatedByUserId
string

Unique identifier of the user who last updated the deployment.

​
projectId
string

The unique identifier of the project.

​
promptId
string

The unique identifier of the prompt.

​
deploymentEnvironmentId
string

The unique identifier of the deployment environment.

​
prompt.config
ConfigType

The configuration and settings for the prompt, see ConfigType for more details.

​
prompt.messages
MessageType[]

The messages in the prompt, see MessageType for more details.

​
prompt.tools
ToolType[]

The tools in the prompt, see ToolType for more details.

​
prompt.variables
{ name: string; modality: 'text' | 'image' }[]

An array of variables with their modality (text or image) used in the prompt. Helpful in detecting and replacing variables in the prompt with end-user inputs, context, etc.

# Replace with your values
API_KEY="your_workspace_api_key"
PROMPT_ID="my_prompt_id"
DEPLOYMENT_ID="my_deployment_id"

BASE_URL="https://api.adaline.ai/v2/deployments"
URL="${BASE_URL}?promptId=${PROMPT_ID}&deploymentId=${DEPLOYMENT_ID}"

curl -X GET \
  "${URL}" \
  -H "Authorization: Bearer ${API_KEY}" \
  -H "Content-Type: application/json"

{
  "id": "deployment_id",
  "createdAt": 1704067200000,
  "updatedAt": 1704067200000,
  "createdByUserId": "user_id",
  "updatedByUserId": "user_id",
  "projectId": "project_id",
  "promptId": "prompt_id",
  "deploymentEnvironmentId": "env_id",
  "prompt": {
    "config": {
      "model": "gpt-4o-mini",
      "providerName": "openai",
      "providerId": "provider_id",
      "settings": {
        "temperature": 0.7,
        "maxTokens": 1000,
        "topP": 1,
        "frequencyPenalty": 0,
        "presencePenalty": 0
      }
    },
    "messages": [
      {
          "role": "system",
          "content": [
              {
                  "modality": "text",
                  "value": "You are a helpful assistant. You answer questions based on a reference image and user input."
              }
          ]
      },
      {
          "role": "user",
          "content": [
              {
                  "modality": "text",
                  "value": "This is the user input\n\n###\n{{user_input}}\n###"
              },
              {
                  "modality": "image",
                  "detail": "auto",
                  "value": {
                      "type": "url",
                      "url": "{{reference_image}}"
                  }
              }
          ]
      }
    ],
    "tools": [],
    "variables": [
      {
        "name": "user_input",
        "modality": "text"
      },
      {
        "name": "reference_image",
        "modality": "image"
      }
    ]
  }
}

​
Best Practices

  1. Cache Deployments: Cache deployment responses when possible to reduce API calls and improve performance.

  2. Environment Management: Use different deployment environments (production, staging, development) to manage your prompt lifecycle.

  3. Rate Limit Awareness: Monitor your usage to avoid hitting rate limits, especially on free plans.