Azure OpenAI component

The azure-openai component calls models deployed on Azure OpenAI: chat completions (with streaming and tool calls), embeddings, image generation and the Assistants API.

Scheme: azure-openai: · Crate: cameleer-cloud · Cargo feature: cloud · Consumer: no · Producer: yes · Apache Camel: none (Apache Camel reaches Azure OpenAI through LangChain4j; this is a Cameleer component)

When to use it

• Answer, summarize or classify messages with GPT models deployed in your Azure tenant.
• Build AI agents: send a message list with tool definitions and handle the tool calls in the route.
• Create embeddings for search and retrieval.
• Run an Assistants API thread from a route (runAssistant).

URI format

azure-openai://resourceName/deploymentName[?options]

Requests go to https://{resourceName}.openai.azure.com/openai/deployments/{deploymentName}/....

Example

Answer questions from a Storage queue with a system prompt:

- route:
    id: answer-questions
    from:
      uri: "azure-storage-queue://{{env:AZURE_STORAGE_ACCOUNT:mystorageaccount}}/questions?accessKey=RAW({{env:AZURE_STORAGE_KEY:}})"
      steps:
        - to: "azure-openai://{{env:AZURE_OPENAI_RESOURCE:my-openai}}/{{env:AZURE_OPENAI_DEPLOYMENT:gpt-4o-mini}}?apiKey=RAW({{env:AZURE_OPENAI_API_KEY:}})&operation=chatCompletion&temperature=0.2&systemMessage=You answer customer questions about shipping in two sentences."
        - log: "answer (${header.CamelAzureOpenAIFinishReason}): ${body}"
        - to: "log:answers"

A text body becomes the user message (after the systemMessage); the body after the call is the assistant's answer. Token usage and the model are in headers.

Rust DSL

ctx.add_routes(|b| {
    b.from("azure-storage-queue://{{env:AZURE_STORAGE_ACCOUNT:mystorageaccount}}/questions?accessKey=RAW({{env:AZURE_STORAGE_KEY:}})")
        .to("azure-openai://{{env:AZURE_OPENAI_RESOURCE:my-openai}}/{{env:AZURE_OPENAI_DEPLOYMENT:gpt-4o-mini}}?apiKey=RAW({{env:AZURE_OPENAI_API_KEY:}})&operation=chatCompletion&temperature=0.2&systemMessage=You answer customer questions about shipping in two sentences.")
        .log("answer (${header.CamelAzureOpenAIFinishReason}): ${body}")
        .to("log:answers");
})?;

Options

A list body is sent as the message list ({role, content} maps); a map body is sent as the request. For embeddings a text body gives one vector and a list body a list of vectors.

Message headers

Run it with Docker

docker run --rm -e AZURE_STORAGE_ACCOUNT -e AZURE_STORAGE_KEY -e AZURE_OPENAI_RESOURCE -e AZURE_OPENAI_DEPLOYMENT -e AZURE_OPENAI_API_KEY \
  -v "$PWD:/routes:ro" pmsudhi/cameleer run /routes/answers.camel.yaml

Common questions

How do I let the model call tools?

Send a map body with messages and tools (OpenAI function definitions). When the model calls a tool, CamelAzureOpenAIFinishReason is tool_calls and CamelAzureOpenAIToolCalls lists the calls; run them in the route and send the next request with the tool results.

Can I use OpenAI or a local model instead?

Use the openai component for OpenAI-compatible APIs (OpenAI, Ollama, vLLM); azure-openai speaks the Azure deployment URLs and authentication.

Related

• AWS Bedrock, AWS Bedrock Agent Runtime
• Cloud credentials

Found a problem?

If something on this page doesn't match what Cameleer does, or you hit a bug while building with it, please help fix it, whether you are a person or a coding agent: open an issue that describes what you saw, then fork the repository, fix it, add or update a test, run cargo test --workspace, and open a pull request. CONTRIBUTING.md explains the ground rules.