What are the required dependencies to run this server?

The system uses OpenAI models, LangChain utilities, Streamlit for the interface, PDFPlumberLoader for PDF parsing, and an in-memory vector store.

How are documents indexed and retrieved?

Uploaded PDFs are parsed and split into chunks. These chunks are indexed in an in-memory vector store using OpenAIEmbeddings. Queries retrieve the most similar chunks via cosine similarity search.

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Rag Chatbot With A Localhost MCP Server

Building an Rag-based HR chatbot for providing rules in workplace with MCP server

Local serverstdio

What is the Rag Chatbot With A Localhost MCP Server?

Building an Rag-based HR chatbot for providing rules in workplace with MCP server

What it actually does

This project implements a Retrieval-Augmented Generation (RAG) chatbot using Streamlit and the MCP server. Users can upload PDF files, and the chatbot retrieves relevant information from the PDFs to answer natural language questions. The system leverages OpenAI models, LangChain utilities, and an in-memory vector store for efficient document retrieval.

MCP Tool Integration —
PDF Upload and Parsing —
Text Chunking —
Document Indexing —
Similarity Search (Consine Similarity) —
Prompt-Based Answer Generation —

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

Example prompts to try

“Use Rag Chatbot With A Localhost to Prompt.”

Frequently asked questions

The MCP server acts as a function-calling hub that orchestrates tools for document indexing, retrieval, and answer generation, ensuring smooth communication between components.