LLM availability¶

The following sections describe support for the various elements that are part of GenAI model creation:

• LLM availability, including deprecation and retirement information.
• Embeddings availability, including multilingual language support.
• Sharing and permissions.
• Supported dataset types.

See also, for reference, brief descriptions of each available LLM.

Note the following when working with LLMs and the LLM gateway:

LLM availability by provider¶

The tables below lists LLM availability by provider as well as the self-managed version in which the LLM was introduced. Managed SaaS users have access to all listed models unless limited by their administrator.

• Amazon Bedrock
• Azure OpenAI
• Google Gemini Enterprise Agent Platform (formerly Vertex AI)
• Anthropic
• Cerebras

• TogetherAI

The tables below lists LLM availability by provider as well as the self-managed version in which the LLM was introduced. Managed AI Platform users have access to all listed models unless limited by their administrator. Note the following:

Amazon Bedrock¶

Azure OpenAI¶

Google Gemini Enterprise Agent Platform (formerly Vertex AI)¶

Anthropic¶

Cerebras¶

TogetherAI¶

GPAI information by LLM provider¶

Amazon Bedrock¶

Azure OpenAI¶

Google Gemini Enterprise Agent Platform (formerly Vertex AI)¶

Anthropic¶

Cerebras¶

TogetherAI¶

LLM retirement announcements¶

In the quickly advancing agentic AI landscape, LLMs are constantly improving, with new versions replacing older models. To address this, DataRobot's LLM deprecation process marks LLMs and LLM blueprints with a badge to indicate upcoming changes. Note that retirement dates are set by the provider and are subject to change.

The following LLMs are currently, or will soon be, deprecated and removed:

To help protect experiments and deployments from unexpected removal of provider support, badges for deprecated LLMs are shown in the LLM blueprint creation panel:

Or if built, affected LLM blueprints are marked with a warning or notice, with dates provided on hover:

• When an LLM is in the deprecation process, support for the LLM will be removed in 90 days. Badges and warnings are present, but functionality is not restricted.

• When retired, assets created from the retired model are still viewable, but the creation of new assets is prevented. Retired LLMs cannot be used in single or comparison prompts.

Some evaluation metrics, for example faithfulness and correctness, use an LLM in their configuration. For those, messages are displayed when viewing or configuring the metrics, as well as in the prompt response.

If an LLM has been deployed, because DataRobot does not have control over the credentials used for the underlying LLM, the deployment will fail to return predictions. If this happens, replace the deployed LLM with a new model.

Embeddings availability¶

DataRobot supports the following types of embeddings for encoding data; all are transformer models trained on a mixture of supervised and unsupervised data.

* Input Dimension = max_sequence_length

 Supported languages:

      "Afrikaans",
        "Amharic",
        "Arabic",
        "Assamese",
        "Azerbaijani",
        "Belarusian",
        "Bulgarian",
        "Bengali",
        "Breton",
        "Bosnian",
        "Catalan",
        "Czech",
        "Welsh",
        "Danish",
        "German",
        "Greek",
        "English",
        "Esperanto",
        "Spanish",
        "Estonian",
        "Basque",
        "Persian",
        "Finnish",
        "French",
        "Western Frisian",
        "Irish",
        "Scottish Gaelic",
        "Galician",
        "Gujarati",
        "Hausa",
        "Hebrew",
        "Hindi",
        "Croatian",
        "Hungarian",
        "Armenian",
        "Indonesian",
        "Icelandic",
        "Italian",
        "Japanese",
        "Javanese",
        "Georgian",
        "Kazakh",
        "Khmer",
        "Kannada",
        "Korean",
        "Kurdish",
        "Kyrgyz",
        "Latin",
        "Lao",
        "Lithuanian",
        "Latvian",
        "Malagasy",
        "Macedonian",
        "Malayalam",
        "Mongolian",
        "Marathi",
        "Malay",
        "Burmese",
        "Nepali",
        "Dutch",
        "Norwegian",
        "Oromo",
        "Oriya",
        "Panjabi",
        "Polish",
        "Pashto",
        "Portuguese",
        "Romanian",
        "Russian",
        "Sanskrit",
        "Sindhi",
        "Sinhala",
        "Slovak",
        "Slovenian",
        "Somali",
        "Albanian",
        "Serbian",
        "Sundanese",
        "Swedish",
        "Swahili",
        "Tamil",
        "Telugu",
        "Thai",
        "Tagalog",
        "Turkish",
        "Uyghur",
        "Ukrainian",
        "Urdu",
        "Uzbek",
        "Vietnamese",
        "Xhosa",
        "Yiddish",
        "Chinese"

Sharing and permissions¶

The following table describes GenAI component-related user permissions. All roles (Consumer, Editor, Owner) refer to the user's role in the Use Case; access to various function are based on the Use Case roles. For example, because sharing is handled on the Use Case level, you cannot share only a vector database (vector databases do not define any sharing rules).

Supported dataset types¶

The following describes requirements for vector databases and evaluation datasets.

Vector database formats¶

When uploading datasets for use in creating a vector database, the supported formats are either .zip or .csv. Two columns are mandatory for the files—document and document_file_path. Additional metadata columns, up to 50, can be added for use in filtering during prompt queries. Note that for purposes of metadata filtering, document_file_path is displayed as source.

For .zip files, DataRobot processes the file to create a .csv version that contains text columns (document) with an associated reference ID (document_file_path) column. All content in the text column is treated as strings. The reference ID column is created automatically when the .zip is uploaded. All files should be either in the root of the archive or in a single folder inside an archive. Using a folder tree hierarchy is not supported.

Regarding file types, DataRobot provides the following support:

• .txt documents

• PDF documents

  • Text-based PDFs are supported.
  • To extract text from image-based PDFs, you must use the Python API client. Extracting text from image-based PDFs via the GUI is not fully supported.
  • Documents with mixed image and text content are supported; only the text is parsed.
  • Single documents consisting only of images result in empty documents and are ignored.
  • Datasets consisting of image-only documents (no text) are not processable.

• .docx documents are supported but older .doc format is not supported.

• .md documents, and the .markdown variant, are supported.

• A mix of all supported document types in a single dataset is allowed.

Evaluation datasets¶

Evaluation datasets serve as reference data for evaluation metrics and aggregated metrics. The evaluation dataset must be:

• A CSV file.
• In the Data Registry.
• Have at least one text or categorical column.