Classification

Classification

Classify text into provided classes

yaml
type: io.kestra.plugin.ai.completion.Classification

Examples

yaml
id: text_categorization
namespace: company.ai

tasks:
  - id: categorize
    type: io.kestra.plugin.ai.completion.Classification
    prompt: "Categorize the sentiment of: I love this product!"
    classes:
      - positive
      - negative
      - neutral
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
      modelName: gemini-2.5-flash

Properties

classes *array

SubTypestring

provider *

Definitions

Use Amazon Bedrock models

Example

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

modelName*string

secretAccessKey*string

type*object

Possible Values

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

baseUrlstring

caPemstring

clientPemstring

modelTypestring

DefaultCOHERE

Possible Values

COHERETITAN

Use Anthropic Claude models

Example

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
      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

modelName*string

type*object

Possible Values

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

baseUrlstring

caPemstring

clientPemstring

maxTokensintegerstring

Use Azure OpenAI deployments

Example

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

modelName*string

type*object

Possible Values

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

apiKeystring

baseUrlstring

caPemstring

clientIdstring

clientPemstring

clientSecretstring

serviceVersionstring

tenantIdstring

Use DashScope (Qwen) models

Example

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

modelName*string

type*object

baseUrlstring

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

caPemstring

clientPemstring

enableSearchbooleanstring

maxTokensintegerstring

repetitionPenaltynumberstring

Use DeepSeek models

Example

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

modelName*string

type*object

Possible Values

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

baseUrlstring

Defaulthttps://api.deepseek.com/v1

caPemstring

clientPemstring

Use GitHub Models via Azure AI Inference

Example

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

modelName*string

type*object

baseUrlstring

caPemstring

clientPemstring

Use Google Gemini models

Example

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
      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 }}"

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
      clientPem: "{{ secret('CLIENT_PEM') }}"
      caPem: "{{ secret('CA_PEM') }}"
      baseUrl: "https://internal.gemini.company.com/endpoint"
      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 }}"

apiKey*string

modelName*string

type*object

Possible Values

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

baseUrlstring

caPemstring

clientPemstring

embeddingModelConfiguration

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

maxRetriesintegerstring

outputDimensionalityintegerstring

taskTypestring

Possible Values

RETRIEVAL_QUERYRETRIEVAL_DOCUMENTSEMANTIC_SIMILARITYCLASSIFICATIONCLUSTERINGQUESTION_ANSWERINGFACT_VERIFICATION

timeoutstring

titleMetadataKeystring

Use Google Vertex AI models

Example

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

location*string

modelName*string

project*string

type*object

Possible Values

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

baseUrlstring

caPemstring

clientPemstring

Use Hugging Face Inference endpoints

Example

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

modelName*string

type*object

baseUrlstring

Defaulthttps://router.huggingface.co/v1

caPemstring

clientPemstring

Use LocalAI OpenAI-compatible server

Example

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

modelName*string

type*object

Possible Values

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

caPemstring

clientPemstring

Use Mistral models

Example

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

modelName*string

type*object

Possible Values

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

baseUrlstring

caPemstring

clientPemstring

Use OCI Generative AI models

Example

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

modelName*string

region*string

type*object

authProviderstring

baseUrlstring

caPemstring

clientPemstring

Use local Ollama models

Example

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
      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

modelName*string

type*object

Possible Values

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

baseUrlstring

caPemstring

clientPemstring

Use OpenAI models

Example

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

modelName*string

type*object

Possible Values

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

baseUrlstring

Defaulthttps://api.openai.com/v1

caPemstring

clientPemstring

Use OpenRouter models

Example

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

modelName*string

type*object

Possible Values

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

baseUrlstring

caPemstring

clientPemstring

Use IBM watsonx.ai models

Example

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

modelName*string

projectId*string

type*object

baseUrlstring

caPemstring

clientPemstring

Use Cloudflare Workers AI models

Example

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

apiKey*string

modelName*string

type*object

Possible Values

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

baseUrlstring

caPemstring

clientPemstring

Use ZhiPu AI models

Example

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

modelName*string

type*object

baseUrlstring

Defaulthttps://open.bigmodel.cn/

caPemstring

clientPemstring

maxRetriesintegerstring

maxTokenintegerstring

stopsarray

SubTypestring

configuration

Default{}

Definitions

io.kestra.plugin.ai.domain.ChatConfiguration

logRequestsbooleanstring

logResponsesbooleanstring

maxTokenintegerstring

promptCachingbooleanstring

responseFormat

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

jsonSchemaobject

jsonSchemaDescriptionstring

strictJsonbooleanstring

Defaultfalse

typestring

DefaultTEXT

Possible Values

TEXTJSON

returnThinkingbooleanstring

seedintegerstring

temperaturenumberstring

thinkingBudgetTokensintegerstring

thinkingEnabledbooleanstring

topKintegerstring

topPnumberstring

contentBlocks array

Definitions

io.kestra.plugin.ai.domain.ChatMessage-ContentBlock

textstring

typestring

Possible Values

TEXTIMAGEPDF

uristring

guardrails

Definitions

io.kestra.plugin.ai.domain.Guardrails

inputarray

io.kestra.plugin.ai.domain.GuardrailRule

expression*string

Min length1

message*string

Min length1

outputarray

io.kestra.plugin.ai.domain.GuardrailRule

expression*string

Min length1

message*string

Min length1

prompt string

systemMessage string

DefaultRespond by only one of the following classes by typing just the exact class name: {{ classes }}

Outputs

classification string

finishReason string

Possible Values

STOPLENGTHTOOL_EXECUTIONCONTENT_FILTEROTHER

guardrailViolated boolean

Defaultfalse

guardrailViolationMessage string

tokenUsage

Definitions

io.kestra.plugin.ai.domain.TokenUsage

inputTokenCountinteger

outputTokenCountinteger

totalTokenCountinteger

Metrics

input.token.count counter

Unittoken

output.token.count counter

Unittoken

total.token.count counter

Unittoken

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Gemini Plugin for Kestra

Classification Classify text into provided classes

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Classification

Classify text into provided classes