RAG Knowledge Base FAQs

Question 1

Is RAG Knowledge Base compatible with OpenAI models?

Accepted Answer

Yes, RAG Knowledge Base integrates seamlessly with OpenAI-compatible embedding APIs. Users can configure their preferred OpenAI API key and endpoint for generating text embeddings.

Question 2

How does RAG Knowledge Base manage data and embeddings?

Accepted Answer

The system utilizes PostgreSQL with the pgvector extension for robust and efficient storage and search of text documents and their corresponding vector embeddings, leveraging HNSW indexes for fast retrieval.

Question 3

What is RAG Knowledge Base?

Accepted Answer

RAG Knowledge Base is a Model Context Protocol (MCP) server designed to facilitate retrieval-augmented generation (RAG) queries against a text-based knowledge base, providing relevant context to AI models.

Question 4

What search modalities does RAG Knowledge Base support?

Accepted Answer

It supports multiple search modalities including semantic search for conceptual similarity, question/answer search for direct query responses, and style-based search for finding text with a similar tone or feeling.

Question 5

Can I add custom search functionalities to RAG Knowledge Base?

Accepted Answer

Absolutely. The framework is designed to be highly extensible, allowing developers to easily add new search modalities, tasks, and tool functions to tailor the system to unique requirements.

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