Local RAG Backend FAQs

Question 1

Is Local RAG Backend easy to deploy?

Accepted Answer

Yes, deployment is streamlined as it runs entirely on Docker Compose. This simplifies local setup, management, and ensures portability across environments. You'll need access to OpenAI API-compatible LLM and Embedding models.

Question 2

What is MCP and how is it utilized?

Accepted Answer

MCP stands for Model Context Protocol, a standard for AI agent communication. Local RAG Backend provides MCP-compatible search functionality through its `graphiti MCP Server`, allowing seamless integration with various MCP-enabled AI clients and tools like n8n or Claude Desktop.

Question 3

How does its search functionality work?

Accepted Answer

Local RAG Backend offers advanced search capabilities by combining vector search, graph search, and full-text search. It then uses relationship-based reranking to provide highly relevant results. It can also track temporal changes in document concept relationships.

Question 4

What is Local RAG Backend?

Accepted Answer

Local RAG Backend is a Docker-Compose based solution that enables powerful local RAG (Retrieval Augmented Generation) systems. It allows you to register diverse document formats and perform advanced searches via an MCP (Model Context Protocol) server.

Question 5

What types of documents can Local RAG Backend process?

Accepted Answer

It supports a wide range of document formats, including 28 different types via `unstructured`. This includes common files like PDF, Microsoft Office (Word, Excel, PowerPoint), text files, HTML, CSV, emails, and various image formats (JPEG, PNG, TIFF, etc.).

Local RAG Backend

Local RAG Backend

Key Features

Use Cases

Key Features

Use Cases