Azure OpenAI

Generate text, structured outputs, and analyze files using Azure OpenAI Service deployments

Overview

Azure OpenAI tools let you run prompts against your own Azure OpenAI Service deployments. They support plain text generation, structured output validated by a JSON schema, and document/image analysis, all against the deployment, endpoint, and API key you configure.

Key Features

• AZURE_OPENAI_INVOKE
  • Send a prompt (prompt) to an Azure OpenAI deployment for text generation
  • Optional structured output with a JSON schema (json_schema) — the schema must have "type": "object" at the top level
• AZURE_OPENAI_INVOKE_WITH_FILE
  • Send files along with a prompt for document and image analysis
  • file_link accepts up to 30 file URLs; supported types are PDF, PNG, JPEG/JPG, WEBP, and GIF, up to 100MB per file
  • Also supports structured output via json_schema (object-type schemas only)

Authentication

Azure OpenAI tools are configured per step (or fall back to environment variables):

• deployment — the Azure OpenAI deployment name (not the model name). Falls back to AZURE_OPENAI_MODEL_DEPLOYMENT_NAME / AZURE_OPENAI_DEPLOYMENT_NAME.
• endpoint — the resource endpoint, e.g. https://my-resource.openai.azure.com/. Falls back to AZURE_OPENAI_ENDPOINT. Alternatively, set resource_name (the resource subdomain, e.g. my-resource); it is derived from the endpoint when omitted.
• api_key — the Azure OpenAI API key (stored as a secret). Falls back to AZURE_OPENAI_API_KEY.
• api_version — the API version. Falls back to AZURE_OPENAI_API_VERSION, then preview. Use preview for the rolling v1 surface, or a dated value such as 2025-04-01-preview.

Optional settings: temperature (0–2, default 0.7), is_reasoning_model (see Notes), and timeout_seconds (default 300).

Note: Treat API keys as sensitive information and never commit them to public repositories.

Usage Examples

Example: Structured Output with JSON Schema

- id: extract_entities
  name: extract_entities
  tool: AZURE_OPENAI_INVOKE
  config:
    - name: deployment
      value: gpt-4o
    - name: endpoint
      value: https://my-resource.openai.azure.com/
    - name: api_key
      value: "{{secrets.AZURE_OPENAI_API_KEY}}"
    - name: temperature
      value: 0.1
  input:
    - name: prompt
      value: |
        Extract the names of people and organizations from the text:

        {{steps.input.result.content}}
    - name: json_schema
      value: |
        {
          "type": "object",
          "properties": {
            "people": {
              "type": "array",
              "items": {"type": "string"}
            },
            "organizations": {
              "type": "array",
              "items": {"type": "string"}
            }
          }
        }

Example: Document Analysis

- id: analyze_document
  name: analyze_document
  tool: AZURE_OPENAI_INVOKE_WITH_FILE
  config:
    - name: deployment
      value: gpt-4o
    - name: endpoint
      value: https://my-resource.openai.azure.com/
    - name: api_key
      value: "{{secrets.AZURE_OPENAI_API_KEY}}"
  input:
    - name: prompt
      value: |
        Please analyze this document and provide:
        1. A brief summary
        2. Key topics discussed
        3. Any action items or recommendations
    - name: file_link
      value: "{{steps.upload_file.result.url}}"

Notes

• The json_schema must have "type": "object" at the top level. Wrap arrays in an object property, e.g. { "type": "object", "properties": { "items": { "type": "array", "items": { "type": "string" } } } }. Without json_schema, the result is returned as a string.
• Reasoning models (o-series / GPT-5) reject the temperature parameter. Since Azure deployment names are user-chosen, this is inferred from the deployment name; set is_reasoning_model explicitly to override the heuristic.
• Requests time out after timeout_seconds (default 300 seconds / 5 minutes).
• AZURE_OPENAI_INVOKE_WITH_FILE accepts at most 30 files per call, 100MB per file. Unsupported file types cause the step to fail with an error.