AI GoogleGemini

AI GoogleGemini

Certified

Use Google Gemini models

Supports Gemini chat, embeddings, and images. Tools do not support JSON Schema anyOf, and tools cannot be combined with responseFormat; configure either but not both.

Thinking models (e.g. gemini-3.5-flash) attach a thought_signature to every function-call part. This provider automatically captures those signatures (returnThinking defaults to true) and re-attaches them to the conversation history for every follow-up request (sendThinking is always enabled), preventing the 400 INVALID_ARGUMENT – Function call is missing a thought_signature error.

In addition, thinking is disabled by default (thinkingBudget = 0) to reduce token usage, unless thinkingEnabled: true or thinkingBudgetTokens > 0 is explicitly set.

yaml
type: io.kestra.plugin.ai.provider.GoogleGemini

Chat completion with Google Gemini

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ secret('GOOGLE_API_KEY') }}"
      modelName: gemini-2.5-flash
    configuration:
      thinkingEnabled: true
      thinkingBudgetTokens: 1024
      returnThinking: true
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

Chat completion with Google Gemini with a local base URL + PEM certificates

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.completion.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-2.5-flash
      clientPem: "{{ secret('CLIENT_PEM') }}"
      caPem: "{{ secret('CA_PEM') }}"
      baseUrl: "https://internal.gemini.company.com/endpoint"
    configuration:
      thinkingEnabled: true
      thinkingBudgetTokens: 1024
      returnThinking: true
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"
Properties

Model name

API Key

Required unless certificate-based authentication is configured with clientPem (optionally with caPem).

Base URL

Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).

CA PEM certificate content

CA certificate as text, used to verify SSL/TLS connections when using custom endpoints.

Client PEM certificate content

PEM client certificate as text, used to authenticate the connection to enterprise AI endpoints.

The configuration for embeddingModel

Definitions
maxRetriesintegerstring

Maximum number of retries for failed requests

outputDimensionalityintegerstring

Used to specify output embedding size

If set, output embeddings will be truncated to the size specified.

taskTypestring
Possible Values
RETRIEVAL_QUERYRETRIEVAL_DOCUMENTSEMANTIC_SIMILARITYCLASSIFICATIONCLUSTERINGQUESTION_ANSWERINGFACT_VERIFICATION

Used to convey intended downstream application to help the model produce better embeddings.

timeoutstring

Timeout in seconds for each request

titleMetadataKeystring

The headline or name of the document (passed to the model as metadata).

If set, this help improving retrieval quality by providing context for a document.

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