RAG Chat Assistant FAQs

Question 1

What document formats does it support for ingestion?

Accepted Answer

It offers comprehensive multi-format document ingestion, supporting files like PDF, DOCX, TXT, HTML, PPTX, XLSX, ODT, MD, CSV, JSON, and XML, allowing you to build rich knowledge bases.

Question 2

What is the RAG Chat Assistant?

Accepted Answer

The RAG Chat Assistant is a production-ready Retrieval-Augmented Generation (RAG) application built with Streamlit, LangChain, and ChromaDB. It creates intelligent AI assistants capable of conversing with your multi-format documents.

Question 3

Is the RAG Chat Assistant suitable for production environments?

Accepted Answer

Yes, it's designed for production readiness, featuring concurrent document processing, robust error handling, session-based chat history, and a scalable architecture built on battle-tested frameworks like LangChain and Streamlit.

Question 4

What is the role of MCP integration in the RAG Chat Assistant?

Accepted Answer

MCP (Model Context Protocol) integration enables a modular, tool-based architecture. This allows the LangGraph-powered agent to perform advanced actions and interact with external systems through intelligent tool calling, enhancing its capabilities.

Question 5

Which Large Language Models (LLMs) can be used with this assistant?

Accepted Answer

The RAG Chat Assistant provides flexible LLM integration, supporting major providers like OpenAI (e.g., GPT-4o, GPT-3.5-turbo) and local models via Ollama (e.g., Qwen2.5, Llama3.2, Mistral).

RAG Chat Assistant

소개

주요 기능

사용 사례

RAG Chat Assistant

소개

주요 기능

사용 사례