Collaborative RAG FAQs

Question 1

How does Collaborative RAG perform document search?

Accepted Answer

It utilizes OpenAI for generating document embeddings and FAISS for efficient in-memory vector similarity search. This ensures fast and accurate retrieval of relevant information from your documents.

Question 2

What is Collaborative RAG?

Accepted Answer

Collaborative RAG is a multi-tenant Retrieval-Augmented Generation (RAG) server that allows you to query isolated document collections securely, integrating with Model Context Protocol (MCP) clients.

Question 3

What document formats can I upload?

Accepted Answer

You can upload documents in PDF, DOCX, and plain text formats. Documents can be provided via file path or base64 encoding for flexible integration.

Question 4

How does it integrate with other AI tools?

Accepted Answer

Built as a Model Context Protocol (MCP) server, Collaborative RAG seamlessly integrates with MCP-compatible clients like Claude Desktop and Cline, allowing you to incorporate its RAG capabilities into your existing AI workflows.

Question 5

What is the benefit of multi-tenancy?

Accepted Answer

Multi-tenancy provides complete isolation for each knowledge base, meaning different users or projects can maintain their own secure and private document collections without cross-contamination or data leakage.

Collaborative RAG

主な機能

ユースケース

Collaborative RAG

主な機能

ユースケース