Google Vertex AI component

The google-vertexai component calls Gemini and other models on Google Cloud Vertex AI: text and chat generation, streaming, multimodal prompts, embeddings, Imagen images, token counts and partner models such as Claude.

Scheme: google-vertexai: · Crate: cameleer-cloud · Cargo feature: cloud · Consumer: no · Producer: yes · Apache Camel: Google Vertex AI

When to use it

• Summarize, classify or extract data from messages with Gemini inside a route.
• Create embeddings for documents and store them for semantic search (RAG).
• Describe images or documents (generateMultimodal) from a folder or a gs:// bucket.
• Call Anthropic Claude or other partner models through Vertex AI (rawPredict) when your data must stay in your Google Cloud project.

URI format

google-vertexai:projectId:location:modelId[?options]

For example google-vertexai:my-project:us-central1:gemini-2.0-flash.

Example

Summarize every support ticket that lands in a folder and write the summary to another folder:

- route:
    id: summarize-tickets
    from:
      uri: "file:{{env:INBOX_DIR:tickets}}?include=.*\\.txt&move=.done"
      steps:
        - to: "google-vertexai:{{env:GCP_PROJECT:my-project}}:{{env:VERTEX_LOCATION:us-central1}}:{{env:VERTEX_MODEL:gemini-2.0-flash}}?temperature=0.2&maxOutputTokens=256&systemInstruction=Summarize the support ticket in one sentence."
        - log: "summary of ${file:name}: ${body} (${header.CamelGoogleVertexAITotalTokenCount} tokens)"
        - to: "file:{{env:OUT_DIR:summaries}}"

Create an embedding for each document and save the vector as JSON:

- route:
    id: embed-documents
    from:
      uri: "file:{{env:INBOX_DIR:docs}}?include=.*\\.txt&move=.done"
      steps:
        - convertBodyTo:
            type: String
        - to: "google-vertexai:{{env:GCP_PROJECT:my-project}}:{{env:VERTEX_LOCATION:us-central1}}:text-embedding-004?operation=generateEmbeddings&taskType=RETRIEVAL_DOCUMENT"
        - marshal:
            json: {}
        - setHeader:
            name: CamelFileName
            simple: "${file:onlyname.noext}.json"
        - to: "file:{{env:OUT_DIR:vectors}}"

Rust DSL

ctx.add_routes(|b| {
    b.from("file:tickets?include=.*\\.txt")
        .to("google-vertexai:my-project:us-central1:gemini-2.0-flash?systemInstruction=Summarize in one sentence.")
        .to("file:summaries");
})?;

Options

Credentials are shared by all Google components: see credentials (scope cloud-platform).

Operations

• generateText (default), generateChat, generateCode: a text body is one user turn; a list body is a conversation of {role, content} (or {role, parts}) maps, where role assistant becomes model; a map body is sent as the request as it is (use this for tools, responseSchema or safety settings). Body = the text of the first candidate.
• generateTextStreaming, generateChatStreaming, generateCodeStreaming: the same with streamGenerateContent (server-sent events, decoded as they arrive).
• generateMultimodal: the body bytes (MIME type CamelGoogleVertexAIMediaMimeType, default image/png) or a gs:// URI in CamelGoogleVertexAIMediaGcsUri, with the prompt in CamelGoogleVertexAIPrompt.
• countTokens: body = total token count.
• generateEmbeddings: a text body gives one vector, a list body a list of vectors.
• generateImage (Imagen): body = list of image bytes.
• rawPredict / streamRawPredict (partner models): a map body is sent as it is (for example an Anthropic Messages request); a text body is wrapped into an Anthropic Messages request (anthropic_version vertex-2023-10-16). The answer text is in CamelGoogleVertexAIResponseText (rawPredict) or the body (streamRawPredict).

What is not supported

• No automatic function-calling loop: you can send tools in a map body, but the body is only the text parts of the answer. Read functionCall parts from the raw response in the CamelGoogleVertexAIResponse header and call the tool yourself.
• No Live API (bidirectional audio / video), no context caching, no batch prediction jobs.

Message headers

Run it with Docker

docker run --rm -v "$PWD:/routes:ro" -v "$PWD/tickets:/in" -v "$PWD/summaries:/out" \
  -v "$HOME/keys/sa.json:/keys/sa.json:ro" -e GOOGLE_APPLICATION_CREDENTIALS=/keys/sa.json \
  -e GCP_PROJECT=my-project -e INBOX_DIR=/in -e OUT_DIR=/out \
  pmsudhi/cameleer run /routes/summarize-tickets.camel.yaml

Common questions

How do I send a chat history to Gemini?

Send a list of {role, content} maps with operation=generateChat; use roles user and assistant (or model).

How do I get JSON output from Gemini?

Send a map body with contents and generationConfig.responseMimeType=application/json (and a responseSchema), then unmarshal: json the body.

How do I call Claude on Vertex AI?

Use the Claude model id with publisher=anthropic&operation=rawPredict and a text body, or a map body with an Anthropic Messages request.

How does this compare to the openai component?

openai talks to any OpenAI-compatible server (OpenAI, Ollama, vLLM). See the AI components guide for a side-by-side view.

Related

• OpenAI, AWS Bedrock, Azure OpenAI
• AI components guide
• Apache Camel Google Vertex AI

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.