AI SqlDatabaseRetriever

AI SqlDatabaseRetriever
Certified
Retrieve context from SQL (experimental)

Uses LangChain4j’s experimental SqlDatabaseContentRetriever to translate questions into SQL and return rows as context. Requires a read-only JDBC user; supports PostgreSQL/MySQL/H2 with auto driver selection. Connection pooling defaults to size 2.

yaml
type: io.kestra.plugin.ai.retriever.SqlDatabaseRetriever

Examples

RAG chat with a SQL Database content retriever (answers grounded in database data)

yaml
id: rag
namespace: company.ai

tasks:
  - id: chat_with_rag_and_sql_retriever
    type: io.kestra.plugin.ai.rag.ChatCompletion
    chatProvider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-2.5-flash
      apiKey: "{{ secret('GOOGLE_API_KEY') }}"
    contentRetrievers:
      - type: io.kestra.plugin.ai.retriever.SqlDatabaseRetriever
        databaseType: POSTGRESQL
        jdbcUrl: "jdbc:postgresql://localhost:5432/mydb"
        username: "{{ secret('DB_USER') }}"
        password: "{{ secret('DB_PASSWORD') }}"
    prompt: "What are the top 5 customers by revenue?"

Properties

databaseType *string

Possible Values

POSTGRESQLMYSQLH2

Type of database to connect to (PostgreSQL, MySQL, or H2)

Determines the default JDBC driver and connection format.

password *string

Database password

provider *

Language model provider

Definitions

Use Amazon Bedrock models

Invokes Bedrock-hosted chat/embedding models with AWS credentials. Supports standard AWS region/auth settings; ensure model IDs match your Bedrock region and account access.

Example

Chat completion with OpenAI

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.AmazonBedrock
      accessKeyId: "{{ secret('AWS_ACCESS_KEY') }}"
      secretAccessKey: "{{ secret('AWS_SECRET_KEY') }}"
      modelName: anthropic.claude-3-sonnet-20240229-v1:0
      thinkingBudgetTokens: 1024
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

accessKeyId*string

AWS Access Key ID

modelName*string

Model name

secretAccessKey*string

AWS Secret Access Key

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

modelTypestring

DefaultCOHERE

Possible Values

COHERETITAN

Amazon Bedrock Embedding Model Type

typeobject

Possible Values

io.kestra.plugin.ai.provider.AmazonBedrockio.kestra.plugin.langchain4j.provider.AmazonBedrock

Use Anthropic Claude models

Provides Claude chat models only; embeddings and images are unsupported. Enforces Anthropic rules (no seed/responseFormat). Thinking mode requires max_tokens > thinking.budget_tokens; set API key and optional base URL.

Example

Chat completion with Anthropic

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.Anthropic
      apiKey: "{{ secret('ANTHROPIC_API_KEY') }}"
      modelName: claude-3-haiku-20240307
    configuration:
      thinkingEnabled: true
      thinkingBudgetTokens: 1024
      returnThinking: false
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

maxTokensintegerstring

Maximum Tokens

Specifies the maximum number of tokens that the model is allowed to generate in its response.

typeobject

Possible Values

io.kestra.plugin.ai.provider.Anthropicio.kestra.plugin.langchain4j.provider.Anthropic

Use Azure OpenAI deployments

Targets Azure-hosted OpenAI models via the resource endpoint and deployment name. Supports API key or AAD client credentials; set apiVersion when required by the deployment.

Example

Chat completion with Azure OpenAI

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.AzureOpenAI
      apiKey: "{{ secret('AZURE_API_KEY') }}"
      endpoint: https://your-resource.openai.azure.com/
      modelName: anthropic.claude-3-sonnet-20240229-v1:0
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

endpoint*string

API endpoint

The Azure OpenAI endpoint in the format: https://{resource}.openai.azure.com/

modelName*string

Model name

apiKeystring

API Key

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientIdstring

Client ID

clientPemstring

Client PEM certificate content

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

clientSecretstring

Client secret

serviceVersionstring

API version

tenantIdstring

Tenant ID

typeobject

Possible Values

io.kestra.plugin.ai.provider.AzureOpenAIio.kestra.plugin.langchain4j.provider.AzureOpenAI

Use DashScope (Qwen) models

Calls Alibaba Cloud DashScope for Qwen chat/embeddings/images with API key. Some params (timeouts, retries, stop, maxTokens) map directly to DashScope limits.

Example

Chat completion with DashScope (Qwen)

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.DashScope
      apiKey: "{{ secret('DASHSCOPE_API_KEY') }}"
      modelName: qwen-plus
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Defaulthttps://dashscope-intl.aliyuncs.com/api/v1

API base URL

If you use a model in the China (Beijing) region, you need to replace the URL with: https://dashscope.aliyuncs.com/api/v1,
otherwise use the Singapore region of: "https://dashscope-intl.aliyuncs.com/api/v1.
The default value is computed based on the system timezone.

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

enableSearchbooleanstring

Whether the model uses Internet search results for reference when generating text or not

maxTokensintegerstring

The maximum number of tokens returned by this request

repetitionPenaltynumberstring

Repetition in a continuous sequence during model generation

Increasing repetition_penalty reduces the repetition in model generation,
1.0 means no penalty. Value range: (0, +inf)

typeobject

Use DeepSeek models

Connects to DeepSeek’s OpenAI-compatible endpoint with API key and model name for chat/embedding tasks.

Example

Chat completion with DeepSeek

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.DeepSeek
      apiKey: "{{ secret('DEEPSEEK_API_KEY') }}"
      modelName: deepseek-chat
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Defaulthttps://api.deepseek.com/v1

API base URL

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Possible Values

io.kestra.plugin.ai.provider.DeepSeekio.kestra.plugin.langchain4j.provider.DeepSeek

Use GitHub Models via Azure AI Inference

Calls GitHub Models through the Azure AI Inference API with a GitHub token. Supports chat and embeddings; response format options map to Azure AI Inference capabilities.

Example

Chat completion with GitHub Models

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.GitHubModels
      gitHubToken: "{{ secret('GITHUB_TOKEN') }}"
      modelName: gpt-4o-mini
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely.
      - type: USER
        content: "{{ inputs.prompt }}"

gitHubToken*string

GitHub Token

Personal Access Token (PAT) used to access GitHub Models.

modelName*string

Model name

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

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.

Example

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 }}"

modelName*string

Model name

apiKeystring

API Key

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

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

embeddingModelConfiguration

The configuration for embeddingModel

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

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.

typeobject

Possible Values

io.kestra.plugin.ai.provider.GoogleGeminiio.kestra.plugin.langchain4j.provider.GoogleGemini

Use Google Vertex AI models

Calls Vertex AI Gemini chat, embeddings, or images using project, location, and endpoint settings. Requires GCP credentials; ensure response formats are supported by the selected model/region.

Example

Chat completion with Google Vertex AI

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.GoogleVertexAI
      endpoint: your-vertex-ai-endpoint
      location: your-google-cloud-region
      project: your-google-cloud-project-id
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

endpoint*string

Endpoint URL

location*string

Project location

modelName*string

Model name

project*string

Project ID

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Possible Values

io.kestra.plugin.ai.provider.GoogleVertexAIio.kestra.plugin.langchain4j.provider.GoogleVertexAI

Use Hugging Face Inference endpoints

Routes requests to Hugging Face Inference Endpoints via the OpenAI-compatible gateway (default router.huggingface.co). Requires an API token and deployment model name.

Example

Chat completion with HuggingFace

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.HuggingFace
      apiKey: "{{ secret('HUGGING_FACE_API_KEY') }}"
      modelName: HuggingFaceTB/SmolLM3-3B:hf-inference
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Defaulthttps://router.huggingface.co/v1

API base URL

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Use LocalAI OpenAI-compatible server

Targets a self-hosted LocalAI instance via its OpenAI-compatible API for chat/embeddings/images. Set baseUrl if your server is not on the default.

Example

Chat completion with LocalAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.LocalAI
      modelName: gemma-3-1b-it
      baseUrl: http://localhost:8080/v1
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

baseUrl*string

API base URL

modelName*string

Model name

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Possible Values

io.kestra.plugin.ai.provider.LocalAIio.kestra.plugin.langchain4j.provider.LocalAI

Use Mistral models

Calls Mistral chat/embedding APIs with an API key. topK is not supported; chat configuration must respect model limits.

Example

Chat completion with Mistral AI

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.MistralAI
      apiKey: "{{ secret('MISTRAL_API_KEY') }}"
      modelName: mistral:7b
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Possible Values

io.kestra.plugin.ai.provider.MistralAIio.kestra.plugin.langchain4j.provider.MistralAI

Use OCI Generative AI models

Calls Oracle Cloud GenAI chat/embedding models with compartment OCID and region. Auth is handled via OCI SDK provider (config file or instance principals). Ensure the model is permitted in the chosen compartment.

Example

Chat completion with OciGenAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.OciGenAI
      region: "{{ secret('OCI_GENAI_MODEL_REGION_PROPERTY') }}"
      compartmentId: "{{ secret('OCI_GENAI_COMPARTMENT_ID_PROPERTY') }}"
      authProvider: "{{ secret('OCI_GENAI_CONFIG_PROFILE_PROPERTY') }}"
      modelName: oracle.chat.gpt-3.5
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

compartmentId*string

OCID of OCI Compartment with the model

modelName*string

Model name

region*string

OCI Region to connect the client to

authProviderstring

OCI SDK Authentication provider

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Use local Ollama models

Calls an Ollama server for chat/embeddings using the given endpoint and model name. Ideal for self-hosted/local models; ensure the Ollama daemon is reachable.

Example

Chat completion with Ollama

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.Ollama
      modelName: llama3
      endpoint: http://localhost:11434
    configuration:
      thinkingEnabled: true
      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 }}"

endpoint*string

Model endpoint

modelName*string

Model name

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Possible Values

io.kestra.plugin.ai.provider.Ollamaio.kestra.plugin.langchain4j.provider.Ollama

Use OpenAI models

Connects to OpenAI-compatible endpoints (defaults to api.openai.com) for chat, embeddings, or images. Requires API key; override baseUrl for Azure-compatible or proxy setups.

Example

Chat completion with OpenAI

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.OpenAI
      apiKey: "{{ secret('OPENAI_API_KEY') }}"
      modelName: gpt-5-mini
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Defaulthttps://api.openai.com/v1

API base URL

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Possible Values

io.kestra.plugin.ai.provider.OpenAIio.kestra.plugin.langchain4j.provider.OpenAI

Use OpenRouter models

Routes requests through OpenRouter’s multi-model API using your API key. Supports chat/embedding/image models exposed by OpenRouter; honor provider-specific safety/usage limits.

Example

Chat completion with OpenRouter

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.OpenRouter
      apiKey: "{{ secret('OPENROUTER_API_KEY') }}"
      baseUrl: https://openrouter.ai/api/v1
      modelName: x-ai/grok-beta
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Possible Values

io.kestra.plugin.ai.provider.OpenRouterio.kestra.plugin.langchain4j.provider.OpenRouter

Use IBM watsonx.ai models

Calls IBM watsonx.ai chat/embedding endpoints with API key and project ID. Ensure the selected model ID is available in the configured project.

Example

Chat completion with Watsonx AI

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.WatsonxAI
      apiKey: "{{ secret('WATSONX_API_KEY') }}"
      projectId: "{{ secret('WATSONX_PROJECT_ID') }}"
      modelName: ibm/granite-3-3-8b-instruct
      baseUrl : "https://api.eu-de.dataplatform.cloud.ibm.com/wx"
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

projectId*string

Project Id

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Use Cloudflare Workers AI models

Invokes Workers AI chat, embedding, and image models using account ID and API key. Ensure the selected model is available in your account/region.

Example

Chat completion with WorkersAI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.WorkersAI
      accountId: "{{ secret('WORKERS_AI_ACCOUNT_ID') }}"
      apiKey: "{{ secret('WORKERS_AI_API_KEY') }}"
      modelName: @cf/meta/llama-2-7b-chat-fp16
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

accountId*string

Account Identifier

Unique identifier assigned to an account

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Base URL

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

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

typeobject

Possible Values

io.kestra.plugin.ai.provider.WorkersAIio.kestra.plugin.langchain4j.provider.WorkersAI

Use ZhiPu AI models

Calls ZhiPu’s OpenAI-compatible chat/embedding/image APIs with API key and model name. Supports stop tokens, retry count, and max tokens per request.

Example

Chat completion with ZhiPu AI

yaml
id: chat_completion
namespace: company.ai

inputs:
  - id: prompt
    type: STRING

tasks:
  - id: chat_completion
    type: io.kestra.plugin.ai.ChatCompletion
    provider:
      type: io.kestra.plugin.ai.provider.ZhiPuAI
      apiKey: "{{ secret('ZHIPU_API_KEY') }}"
      modelName: glm-4.5-flash
    messages:
      - type: SYSTEM
        content: You are a helpful assistant, answer concisely, avoid overly casual language or unnecessary verbosity.
      - type: USER
        content: "{{ inputs.prompt }}"

apiKey*string

API Key

modelName*string

Model name

baseUrlstring

Defaulthttps://open.bigmodel.cn/

API base URL

The base URL for ZhiPu API (defaults to https://open.bigmodel.cn/)

caPemstring

CA PEM certificate content

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

clientPemstring

Client PEM certificate content

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

maxRetriesintegerstring

The maximum retry times to request

maxTokenintegerstring

The maximum number of tokens returned by this request

stopsarray

SubTypestring

With the stop parameter, the model will automatically stop generating text when it is about to contain the specified string or token_id

typeobject

username *string

Database username

configuration

Default{}

Language model configuration

Definitions

io.kestra.plugin.ai.domain.ChatConfiguration

logRequestsbooleanstring

Log LLM requests

If true, prompts and configuration sent to the LLM will be logged at INFO level.

logResponsesbooleanstring

Log LLM responses

If true, raw responses from the LLM will be logged at INFO level.

maxTokenintegerstring

Maximum number of tokens the model can generate in the completion (response). This limits the length of the output.

promptCachingbooleanstring

Enable Prompt Caching

When enabled, instructs the provider to cache system messages and tool definitions across requests. This can significantly reduce latency and cost for repeated calls with the same system prompt or tools. Currently supported by Anthropic only; other providers silently ignore this setting.

responseFormat

Response format

Defines the expected output format. Default is plain text. Some providers allow requesting JSON or schema-constrained outputs, but support varies and may be incompatible with tool use. When using a JSON schema, the output will be returned under the key jsonOutput.

io.kestra.plugin.ai.domain.ChatConfiguration-ResponseFormat

jsonSchemaobject

JSON Schema (used when type = JSON)

Provide a JSON Schema describing the expected structure of the response. In Kestra flows, define the schema in YAML (it is still a JSON Schema object). Example (YAML):

responseFormat: 
    type: JSON
    jsonSchema: 
      type: object
      required: ["category", "priority"]
      properties: 
        category: 
          type: string
          enum: ["ACCOUNT", "BILLING", "TECHNICAL", "GENERAL"]
        priority: 
          type: string
          enum: ["LOW", "MEDIUM", "HIGH"]

Note: Provider support for strict schema enforcement varies. If unsupported, guide the model about the expected output structure via the prompt and validate downstream.

jsonSchemaDescriptionstring

Schema description (optional)

Natural-language description of the schema to help the model produce the right fields. Example: "Classify a customer ticket into category and priority."

strictJsonbooleanstring

Defaultfalse

Enable strict JSON schema mode

When true, providers that support it enforce strict JSON schema output when type is JSON.

typestring

DefaultTEXT

Possible Values

TEXTJSON

Response format type

Specifies how the LLM should return output. Allowed values:

TEXT (default): free-form natural language.
JSON: structured output validated against a JSON Schema.

returnThinkingbooleanstring

Return Thinking

Controls whether to return the model's internal reasoning or 'thinking' text, if available. When enabled, the reasoning content is extracted from the response and made available in the AiMessage object. Does not trigger the thinking process itself—only affects whether the output is parsed and returned.

For Google Gemini: defaults to true so that thought_signature values on function-call parts are captured and automatically re-sent in subsequent requests, preventing tool-call failures on native thinking models (e.g. gemini-3.5-flash).

seedintegerstring

Seed

Optional random seed for reproducibility. Provide a positive integer (e.g., 42, 1234). Using the same seed with identical settings produces repeatable outputs.

temperaturenumberstring

Temperature

Controls randomness in generation. Typical range is 0.0–1.0. Lower values (e.g., 0.2) make outputs more focused and deterministic, while higher values (e.g., 0.7–1.0) increase creativity and variability.

thinkingBudgetTokensintegerstring

Thinking Token Budget

Specifies the maximum number of tokens allocated as a budget for internal reasoning processes, such as generating intermediate thoughts or chain-of-thought sequences, allowing the model to perform multi-step reasoning before producing the final output.

For Google Gemini: when neither this property nor thinkingEnabled is set, the budget defaults to 0 (thinking disabled) to prevent tool-call failures on native thinking models such as gemini-3.5-flash. Set this to a positive integer (e.g. 1024) to allow thinking.

thinkingEnabledbooleanstring

Enable Thinking

Enables internal reasoning ('thinking') in supported language models, allowing the model to perform intermediate reasoning steps before producing a final output; this is useful for complex tasks like multi-step problem solving or decision making, but may increase token usage and response time, and is only applicable to compatible models.

For Google Gemini: when neither this property nor thinkingBudgetTokens is set, thinking is explicitly disabled (thinkingBudget = 0) to prevent tool-call failures on native thinking models such as gemini-3.5-flash. Set thinkingEnabled: true or thinkingBudgetTokens > 0 to opt back in.

topKintegerstring

Top-K

Limits sampling to the top K most likely tokens at each step. Typical values are between 20 and 100. Smaller values reduce randomness; larger values allow more diverse outputs.

topPnumberstring

Top-P (nucleus sampling)

Selects from the smallest set of tokens whose cumulative probability is ≤ topP. Typical values are 0.8–0.95. Lower values make the output more focused, higher values increase diversity.

driver string

Optional JDBC driver class name – automatically resolved if not provided.

jdbcUrl string

JDBC connection URL to the target database

maxPoolSize integerstring

Default2

Maximum number of database connections in the pool

Metrics

ai.provider.calls counter

Unitcalls

Number of times a chat model is obtained from a provider, tagged by provider class name

Weaviate

Tasks that manage and query vectors in Weaviate.

DataAI

Mistral

Tasks and triggers for Mistral AI: chat completions and workflow orchestration.

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