Answer Server 26.3
Answer Server Help

# Type

The type of model to use for your passage extractor LLM or RAG system.

For a RAG system, you can use the following options:

- GenerativeLua. Use a Lua script that performs generative question answering. For this type, you must set Script to the name and path for the script to use.

- GenerativePython. Use a Python script that performs generative question answering. For this type, you must set Script to the name and path for the script to use.

For a passage extractor LLM system, you can use GenerativeLua and GenerativePython, or one of the following options:

- ExtractiveQuestionAnsweringLLM. Use an extractive question answering LLM from Hugging Face. For this type, you must set ModelName to the model to use.

- GenerativeLLM. Use an generative LLM from Hugging Face. For this type, you must set ModelName to set the model to use.

- ExtractiveQuestionAnsweringLua. Use a Lua script that performs extractive question answering. For this type, you must set Script to the name and path for the script to use.

Type: | String
Default: |
Required: | Yes
Configuration Section: | MyLLMModule
Example: | `Type=GenerativeLLM`
See Also: | ModelName Script

Copyright 2016-2026 Open Text
Version 26.3 | Last updated July 2026

Send documentation feedback

Close

Send documentation feedback

To send feedback using your default email client, open an email window.

Otherwise, send your feedback and the information below to MFI-swpdl.idoldocsfeedback@opentext.com.

We appreciate your feedback!

Product: Knowledge Discovery Answer Server 26.3

Topic Title: Type