Classification
Categorize text into one of the provided classes using an LLM.
Categorize text into one of the provided classes using an LLM.
Text categorization with LLMs
Categorize text into one of the provided classes using an LLM.
type: "io.kestra.plugin.ai.completion.Classification"Examples
Perform sentiment analysis of product reviews
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: "{{ kv('GEMINI_API_KEY') }}"
modelName: gemini-2.5-flash
Properties
classes*Requiredarray
Classification Options
The list of possible classification categories
prompt*Requiredstring
Text prompt
The input text to classify
provider*RequiredNon-dynamic
Language Model Provider
Amazon Bedrock Model Provider
AWS Access Key ID
AWS Secret Access Key
COHERECOHERETITANAmazon Bedrock Embedding Model Type
Anthropic AI Model Provider
Maximum Tokens
Specifies the maximum number of tokens that the model is allowed to generate in its response.
Azure OpenAI Model Provider
API endpoint
The Azure OpenAI endpoint in the format: https://{resource}.openai.azure.com/
Client ID
Client secret
API version
Tenant ID
DashScope (Qwen) Model Provider from Alibaba Cloud
https://dashscope-intl.aliyuncs.com/api/v1If 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.
Whether the model uses Internet search results for reference when generating text or not
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)
Deepseek Model Provider
https://api.deepseek.com/v1GitHub Models AI Model Provider
GitHub Token
Personal Access Token (PAT) used to access GitHub Models.
Google Gemini Model Provider
Google VertexAI Model Provider
Endpoint URL
Project location
Project ID
HuggingFace Model Provider
https://router.huggingface.co/v1LocalAI Model Provider
Mistral AI Model Provider
OciGenAI Model Provider
OCID of OCI Compartment with the model
OCI Region to connect the client to
OCI SDK Authentication provider
Ollama Model Provider
Model endpoint
OpenAI Model Provider
https://api.openai.com/v1OpenRouter Model Provider
WorkersAI Model Provider
Account Identifier
Unique identifier assigned to an account
Base URL
Custom base URL to override the default endpoint (useful for local tests, WireMock, or enterprise gateways).
ZhiPu AI Model Provider
API Key
Model name
https://open.bigmodel.cn/API base URL
The base URL for ZhiPu API (defaults to https://open.bigmodel.cn/)
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 maximum retry times to request
The maximum number of tokens returned by this request
With the stop parameter, the model will automatically stop generating text when it is about to contain the specified string or token_id
configurationNon-dynamic
{}Chat configuration
io.kestra.plugin.ai.domain.ChatConfiguration
Log LLM requests
If true, prompts and configuration sent to the LLM will be logged at INFO level.
Log LLM responses
If true, raw responses from the LLM will be logged at INFO level.
Maximum number of tokens the model can generate in the completion (response). This limits the length of the output.
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
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.
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."
TEXTTEXTJSONResponse 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.
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. It Does not trigger the thinking process itself—only affects whether the output is parsed and returned.
Seed
Optional random seed for reproducibility. Provide a positive integer (e.g., 42, 1234). Using the same seed with identical settings produces repeatable outputs.
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.
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.
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.
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.
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.
systemMessagestring
Respond by only one of the following classes by typing just the exact class name: {{ classes }}Optional system message
Instruction message for the model. Defaults to a standard classification instruction using the provided classes.
Outputs
classificationstring
Classification Result
The classified category of the input text
finishReasonstring
STOPLENGTHTOOL_EXECUTIONCONTENT_FILTEROTHERFinish reason
tokenUsage
Token usage
io.kestra.plugin.ai.domain.TokenUsage
Metrics
input.token.countcounter
tokenLarge Language Model (LLM) input token count
output.token.countcounter
tokenLarge Language Model (LLM) output token count
total.token.countcounter
tokenLarge Language Model (LLM) total token count
