AI GoogleGemini

AI GoogleGemini
Certified
Use Google Gemini models

Supports Gemini chat and embeddings (image generation is not currently supported). 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, on Gemini 2.x models thinking is disabled by default (thinkingBudget = 0) to reduce token usage, unless thinkingEnabled: true or thinkingBudgetTokens > 0 is explicitly set. Gemini 3 models cannot have thinking turned off, so no thinking budget is sent for them by default and the model applies its own; set thinkingBudgetTokens to cap it.

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

Examples

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-3.5-flash-lite
    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-3.5-flash-lite
      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

apiKey string

API Key

Google AI Studio API key used to authenticate requests. Store it as a Kestra secret rather than inline. Required unless certificate-based authentication is configured through clientPem, optionally with caPem.

baseUrl string

Base URL

Custom base URL overriding the provider's default endpoint. Useful for enterprise gateways, proxies, self-hosted deployments, or test doubles such as WireMock. Defaults to the provider's public endpoint.

caPem string

CA PEM certificate content

PEM-encoded certificate authority chain, as text, used to verify the TLS certificate presented by a custom endpoint. Not set by default, in which case the JVM's default trust store is used.

clientPem string

Client PEM certificate content

PEM-encoded client certificate and private key, as text, used for mutual-TLS authentication against enterprise AI endpoints. Not set by default, in which case the default HTTP client is used.

embeddingModelConfiguration

Embedding model configuration

Settings applied when this provider is used to generate embeddings rather than chat completions. Not set by default, in which case the Gemini client defaults apply.

Definitions

io.kestra.plugin.ai.provider.GoogleGemini-EmbeddingModelConfiguration

maxRetriesintegerstring

Maximum retries

Number of times a failed embedding request is retried before the task fails. Not set by default, in which case the Gemini client default applies.

outputDimensionalityintegerstring

Output embedding size

Length the embedding vectors are truncated to, which trades a little accuracy for smaller storage. It must match the dimensionality already used in the embedding store. Not set by default (the model's full dimensionality).

taskTypestring

Possible Values

RETRIEVAL_QUERYRETRIEVAL_DOCUMENTSEMANTIC_SIMILARITYCLASSIFICATIONCLUSTERINGQUESTION_ANSWERINGFACT_VERIFICATION

Embedding task type

Downstream use the embeddings are optimized for, such as RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, SEMANTIC_SIMILARITY or CLASSIFICATION. Use the matching pair at ingestion and query time. Not set by default, in which case the Gemini default applies.

timeoutstring

Request timeout

Maximum time to wait for each embedding request. Not set by default, in which case the Gemini client default applies.

titleMetadataKeystring

Document title metadata key

Metadata key whose value is passed to the model as the document's title, which improves retrieval quality by giving the document context. Not set by default.

modelName string

Model name