Rag FAQs

Question 1

How can I add my own data to Rag?

Accepted Answer

You can ingest data from files, URLs, or directly input text into Rag. Simply use the '#' prefix followed by the file path, URL, or your custom text.

Question 2

What configuration options are available in Rag?

Accepted Answer

Rag is highly configurable using environment variables. You can control various aspects like the LLM used, embeddings database, context size, and more. Use `:settings` in the app to view current settings.

Question 3

What types of RAG methodologies does Rag support?

Accepted Answer

Rag supports both Vector RAG, which uses vector search for context retrieval, and Graph RAG, which leverages knowledge graphs for path traversal and context generation.

Question 4

What is txtai and how is it used in Rag?

Accepted Answer

txtai is an all-in-one AI framework that provides the foundation for Rag's embeddings and LLM interactions. It enables efficient vector search and language generation.

Question 5

What is Rag and how does it work?

Accepted Answer

Rag combines search and Large Language Models (LLMs) using Retrieval Augmented Generation (RAG). It retrieves relevant context from your data and uses it to generate more accurate and insightful answers.

Rag

主な機能

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Rag

主な機能

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