LLM-RAG assistants

• About LLM-RAG As technologies around large language models (LLMs) evolve, several key challenges have emerged:
• Set up LLM-RAG Complete the following steps to set-up and begin using large language model retrieval-augmented generation (LLM-RAG).
• Set up additional LLM-RAG configurations DSDL version 5.2.1 introduced an optional setup page that allows you to configure additional LLM-RAG features. You can tailor your configurations for Large Language Models (LLM), embedding models, vector databases (VectorDB), and graph d…
• About the compute command The Splunk App for Data Science and Deep Learning (DSDL) version 5.2.0 introduced the `compute` command. This command provides an alternative to the `fit` command from the Splunk AI Toolkit, and accelerates the DSDL search.
• LLM-RAG use cases The large language model retrieval-augmented generation (LLM-RAG) functionalities with assistive guidance dashboards handles the following use cases:
• Use Standalone LLM You can use Standalone LLM through a set of dashboards. The following processes are covered:
• Use Standalone VectorDB Use Standalone VectorDB to run a vector search through a set of dashboards. The following processes are covered:
• Use Document-based LLM-RAG Use Document-based large language model retrieval-augmented generation (LLM-RAG) through a set of dashboards.
• Use Function Calling LLM-RAG You can use Function Calling-based LLM through a set of dashboards. The following processes are covered:
• Encode data into a vector database To encode data into a vector database, complete these tasks:
• Query LLM with vector data After pulling the LLM model to your local Docker container and encoding document or log data into the vector database, you can carry out inferences using the LLM. See, Encode data into a vector database.

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