prompt_ai

Use any of OpenAI’s models on a row-by-row basis. This step doesn’t feed the whole dataset into the model, so you won’t be able to perform operations that require more than one row at a time. It can be used to perform a variety of tasks. Keep in mind that OpenAI’s models are generative AI technologies, and thus can give incorrect responses. It comes with a predefined budget of 5 $USD, which will prevent the step from executing if it will cost over that budget. It is advised that you use a filter step first to test the prompt out on a few rows, then launch it on the whole dataset.

Keep in mind our budget is a rough estimate, if you’re concerned about cost you should set limits on OpenAI’s side. Your prompt will be configured by using two parameters: ‘prompt’ and ‘response_format’. prompt is a text field while response_format allows you to specify a JSON format for the model’s response, in the format of {[expected_column]: "description"}.

Both in the prompt and the response format descriptions you may refer to the row’s attributes by using ${attribute_name}. Check the examples and parameter documentation below for more information.

???+ info “API integration” To use this step your team needs to have the OpenAI integration configured in Graphext. The corresponding credentials are required to connect to a third-party API. You can configure API integrations following the INTEGRATIONS or ADD INTEGRATION link in the top-left corner of your Team’s page, selecting API keys, and then the name of the desired third-party service.

First, create an OpenAI account or sign in. Next, navigate to the API key page and “Create new secret key”, optionally naming the key. Make sure to save this somewhere safe and do not share it with anyone. Optionally, you can specify the organization the key belongs to. On OpenAI’s’ page, you can set general budgets for your api key and other settings that may interest you.

Usage

The following examples show how the step can be used in a recipe.

Examples

Specify model

prompt_ai(ds[["Local Address"]], { # contains column 'Local Address'
    "integration": "MY_INTEGRATION_ID",
    "model": {
      "id": "gpt-4o",
      "temperature": 0.2
    },
    "prompt": "What is the country for ${Local Address}"
}) -> (ds.country)

Specify model

prompt_ai(ds[["Local Address"]], { # contains column 'Local Address'
    "integration": "MY_INTEGRATION_ID",
    "model": {
      "id": "gpt-4o",
      "temperature": 0.2
    },
    "prompt": "What is the country for ${Local Address}"
}) -> (ds.country)

Get attributes from disneyland reviews

prompt_ai(ds[["Review_Text"]],
      {
          "integration": "open-ai-1-70",
          "prompt": "The following is a review from Disneyland. I want you to extract the topics mentioned, the Names of the Disney Characters mentioned and the Names of rides mentioned in this paragraph: '${Review_Text}'. If you do not find or recognize any name of people, company, or rides, simply do not answer anything. NEVER ANSWER WITH 'NULL' VALUE. IMPORTANT: DO NOT ANSWER ANYTHING ELSE IN ANY OTHER CIRCUMSTANCE. DO NOT ANSWER ANYTHING ELSE APART FROM THE JSON",
          "model": {
              "id": "gpt-4o-mini"
          },
          "response_format": {
              "topics": "topics mentioned",
              "names_of_characters": "names of Disney Characters",
              "names_of_rides": "names of rides"
          },
          "force_format": {
            "topics": ["fun", "children", "ride", "food"]
          },
          "out_types": {
            "topics": "list[category]"
          }
      }) -> (ds.topics,
            ds.names_of_characters,
            ds.names_of_rides)

Classify Tweets

prompt_ai(ds[["authorName", "text"]],
          {
              "integration": "victoriano-apikey",
              "budget": 15,
              "model": {
                  "id": "gpt-4o",
                  "temperature": 0.2
              },
              "prompt": "Classify the following tweet text if it implicitly: criticizes, benefits, is neutral, or is unrelated to each of the main political parties in Spain or any of their members and leaders: ${text} considering the bias of the medium that wrote it with the medium's name: ${authorName}",
              "response_format": {
                  "Clasificacion_PP": "classify the tweet text into only one of these 4 categories related to the Partido Popular (PP), its leader (Álberto Nuñez Feijoo), or any of its members: criticizes PP, benefits PP, neutral for PP, does not mention PP",
                  "Clasificacion_PSOE": "classify the tweet text into only one of these 4 categories related to the Spanish Socialist Workers' Party (PSOE), its leader (Pedro Sánchez), or any of its members: criticizes PSOE, benefits PSOE, neutral for PSOE, does not mention PSOE",
                  "Clasificacion_VOX": "classify the tweet text into only one of these 4 categories related to the VOX party, its leader (Santiago Abascal), or any of its members: criticizes VOX, benefits VOX, neutral for VOX, does not mention VOX",
                  "Clasificacion_SUMAR": "classify the tweet text into only one of these 4 categories related to the SUMAR party, its leader (Yolanda Díez), or any of its members: criticizes SUMAR, benefits SUMAR, neutral for SUMAR, does not mention SUMAR",
                  "media_bias": "classify the bias of the medium as: right, center, left"
              },
              "out_types": {
                  "Clasificacion_PP": "category",
                  "Clasificacion_PSOE": "category",
                  "Clasificacion_VOX": "category",
                  "Clasificacion_SUMAR": "category",
                  "media_bias": "category"
              }
          }) -> (ds.Clasificacion_PP,
                ds.Clasificacion_PSOE,
                ds.Clasificacion_VOX,
                ds.Clasificacion_SUMAR,
                ds.media_bias)

General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.

prompt_ai(ds: dataset, {
    "param": value,
    ...
}) -> (*outputs: column)

Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").

Inputs

Outputs

Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

prompt

string

Main prompt for the API call. The main body of instructions you wish to perform.

integration

string

Associated integration.

response_format

object

Prompt instructions for each output column. Further prompt instructions for each output column.

Item properties

force_format

object

Values allowed in each output column. If provided, values in each column will be restricted.

Item properties

out_types

object

Types for the output column(s). Desired types for each output column. By default, they will all be categories.

Item properties

model

object

Model Configuration. Configuration for OpenAI’s model.

Properties

string

default:"gpt-4o-mini"

OpenAI model to choose.

Values must be one of the following:

gpt-4o gpt-4o-mini o3-mini gpt-4.1 gpt-4.1-mini gpt-4.1-nano

temperature

number

default:"0.7"

Temperature. Higher means more creativity, but also makes the model more likely to hallucinate. Lower temperature yields more deterministic results.

Values must be in the following range:

0 ≤ temperature ≤ 1

budget

number

default:"5"

Budget. If present, the step will not execute if estimated input token cost exceeds this amount in USD. If max_out_tokens is not set, we will minimum of the cost. If it is set, we will give a ceiling. Actual cost may vary depending on a number of factors like your OpenAI plan. Check your plan before executing.

max_out_tokens

number

Maximum output tokens. If set, each individual response will add to at most this amount. Allows for a budget theorical ceiling to be calculated before executing.

batch_size

integer

default:"100"

Size of concurrent request at a time. Lowering this if you have very low rate limits in your plan might prevent empty responses.

Values must be in the following range:

1 ≤ batch_size ≤ 1000

timeout

integer

default:"60"

Timeout for requests to OpenAI.

Values must be in the following range:

1 ≤ timeout < inf

Prepare

Analyse

Report

Usage

Inputs & Outputs

Configuration

Prepare

Analyse

Report

​Usage

​Inputs & Outputs

​Configuration

Usage

Inputs & Outputs

Configuration