LLM

LLM transform plugin

Description

Leverage the power of a large language model (LLM) to process data by sending it to the LLM and receiving the generated results. Utilize the LLM's capabilities to label, clean, enrich data, perform data inference, and more.

Options

name	type	required	default value
model_provider	enum	yes
output_data_type	enum	no	String
prompt	string	yes
model	string	yes
api_key	string	yes
openai.api_path	string	no	https://api.openai.com/v1/chat/completions

model_provider

The model provider to use. The available options are: OPENAI

output_data_type

The data type of the output data. The available options are: STRING,INT,BIGINT,DOUBLE,BOOLEAN. Default value is STRING.

prompt

The prompt to send to the LLM. This parameter defines how LLM will process and return data, eg:

The data read from source is a table like this:

name	age
Jia Fan	20
Hailin Wang	20
Eric	20
Guangdong Liu	20

The prompt can be:

Determine whether someone is Chinese or American by their name

The result will be:

name	age	llm_output
Jia Fan	20	Chinese
Hailin Wang	20	Chinese
Eric	20	American
Guangdong Liu	20	Chinese

model

The model to use. Different model providers have different models. For example, the OpenAI model can be gpt-4o-mini. If you use OpenAI model, please refer https://platform.openai.com/docs/models/model-endpoint-compatibility of /v1/chat/completions endpoint.

api_key

The API key to use for the model provider. If you use OpenAI model, please refer https://platform.openai.com/docs/api-reference/api-keys of how to get the API key.

openai.api_path

The API path to use for the OpenAI model provider. In most cases, you do not need to change this configuration. If you are using an API agent's service, you may need to configure it to the agent's API address.

common options [string]

Transform plugin common parameters, please refer to Transform Plugin for details

Example

Determine the user's country through a LLM.

env {
  parallelism = 1
  job.mode = "BATCH"
}

source {
  FakeSource {
    row.num = 5
    schema = {
      fields {
        id = "int"
        name = "string"
      }
    }
    rows = [
      {fields = [1, "Jia Fan"], kind = INSERT}
      {fields = [2, "Hailin Wang"], kind = INSERT}
      {fields = [3, "Tomas"], kind = INSERT}
      {fields = [4, "Eric"], kind = INSERT}
      {fields = [5, "Guangdong Liu"], kind = INSERT}
    ]
  }
}

transform {
  LLM {
    model_provider = OPENAI
    model = gpt-4o-mini
    api_key = sk-xxx
    prompt = "Determine whether someone is Chinese or American by their name"
  }
}

sink {
  console {
  }
}