LLM Dataset Processor

Pricing

Pay per usage

LLM Dataset Processor

Allows you to process output of other actors or stored dataset with single LLM prompt. It's useful if you need to enrich data, summarize content, extract specific information, or manipulate data in a structured way using AI.

Pricing

Pay per usage

Rating

0.0

(0)

Developer

Dušan Vystrčil

Maintained by Community

Actor stats

Bookmarked

108

Total users

Monthly active users

3 days ago

Last modified

Categories

Open source

You can access the LLM Dataset Processor programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

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  "components": {
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        "required": [
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        ],
        "properties": {
          "inputDatasetId": {
            "title": "Input Dataset ID",
            "type": "string",
            "description": "The ID of the dataset to process."
          },
          "model": {
            "title": "Large Language Model",
            "type": "string",
            "description": "The LLM to use for processing. Each model has different capabilities and pricing. GPT-4o-mini and Claude 3.5 Haiku are recommended for cost-effective processing, while models like Claude 3 Opus or GPT-4o offer higher quality but at a higher cost."
          },
          "llmProviderApiKey": {
            "title": "LLM Provider API Key",
            "type": "string",
            "description": "Your API key for the LLM Provider (e.g., OpenAI)."
          },
          "temperature": {
            "title": "Temperature",
            "type": "string",
            "description": "Sampling temperature for the LLM API (controls randomness). We recommend using a value closer to 0 for exact results. In case of more 'creative' results, we recommend to use a value closer to 1.",
            "default": "0.1"
          },
          "multipleColumns": {
            "title": "Multiple columns in output",
            "type": "boolean",
            "description": "When enabled, instructs the LLM to return responses as JSON objects, creating multiple columns in the output dataset. The columns need to be named and described in the prompt. If disabled, responses are stored in a single `llmresponse` column.",
            "default": false
          },
          "prompt": {
            "title": "Prompt Template",
            "minLength": 1,
            "type": "string",
            "description": "The prompt template to use for processing. You can use ${fieldName} placeholders to reference fields from the input dataset."
          },
          "skipItemIfEmpty": {
            "title": "Skip item if one or more ${fields} are empty",
            "type": "boolean",
            "description": "When enabled, items will be skipped if any ${field} referenced in the prompt is empty, null, undefined, or contains only whitespace. This helps prevent processing incomplete data.",
            "default": true
          },
          "maxTokens": {
            "title": "Max Tokens",
            "type": "integer",
            "description": "Maximum number of tokens in the LLM API response for each item.",
            "default": 300
          },
          "testPrompt": {
            "title": "Test Prompt Mode",
            "type": "boolean",
            "description": "Test mode that processes only a limited number of items (defined by `testItemsCount`). Use this to validate your prompt and configuration before running on the full dataset. We highly recommend enabling this option first to validate your prompt because of ambiguity of the LLM responses.",
            "default": true
          },
          "testItemsCount": {
            "title": "Test Items Count",
            "minimum": 1,
            "type": "integer",
            "description": "Number of items to process when `Test Prompt Mode` is enabled.",
            "default": 3
          },
          "preprocessingFunction": {
            "title": "Preprocessing function",
            "type": "string",
            "description": "Function to transform item before they are put into the promp."
          }
        }
      },
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              "userId": {
                "type": "string"
              },
              "startedAt": {
                "type": "string",
                "format": "date-time",
                "example": "2025-01-08T00:00:00.000Z"
              },
              "finishedAt": {
                "type": "string",
                "format": "date-time",
                "example": "2025-01-08T00:00:00.000Z"
              },
              "status": {
                "type": "string",
                "example": "READY"
              },
              "meta": {
                "type": "object",
                "properties": {
                  "origin": {
                    "type": "string",
                    "example": "API"
                  },
                  "userAgent": {
                    "type": "string"
                  }
                }
              },
              "stats": {
                "type": "object",
                "properties": {
                  "inputBodyLen": {
                    "type": "integer",
                    "example": 2000
                  },
                  "rebootCount": {
                    "type": "integer",
                    "example": 0
                  },
                  "restartCount": {
                    "type": "integer",
                    "example": 0
                  },
                  "resurrectCount": {
                    "type": "integer",
                    "example": 0
                  },
                  "computeUnits": {
                    "type": "integer",
                    "example": 0
                  }
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              },
              "options": {
                "type": "object",
                "properties": {
                  "build": {
                    "type": "string",
                    "example": "latest"
                  },
                  "timeoutSecs": {
                    "type": "integer",
                    "example": 300
                  },
                  "memoryMbytes": {
                    "type": "integer",
                    "example": 1024
                  },
                  "diskMbytes": {
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              "defaultKeyValueStoreId": {
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                  },
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                  },
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                  },
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                    "example": 0
                  },
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                    "example": 0
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              },
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                    "example": 0
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                    "example": 0
                  },
                  "DATASET_WRITES": {
                    "type": "integer",
                    "example": 0
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                    "example": 0
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LLM Dataset Processor OpenAPI definition

OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.

OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.

By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.

You can download the OpenAPI definitions for LLM Dataset Processor from the options below:

OpenAPI.json

If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.

You can also check out our other API clients:

LLM Dataset Processor API in Python

LLM Dataset Processor API in JavaScript

LLM Dataset Processor API through CLI

LLM Dataset Processor API

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