Rag chatbot with a localhost MCP server

What is Rag chatbot with a localhost MCP server?

A RAG (Retrieval-Augmented Generation) chatbot that uses the localhost MCP server as a function-calling hub to orchestrate document indexing, retrieval, and answer generation. The system allows users to upload PDF files and ask natural language questions about workplace rules, retrieving relevant answers via OpenAI models and an in-memory vector store.

How to use Rag chatbot with a localhost MCP server?

The server is integrated into a Streamlit application. Users upload a PDF file, then ask questions in a chat interface. The MCP server coordinates tools for parsing the PDF, chunking text, indexing embeddings, performing similarity search, and generating answers with a GPT-4 powered LLM.

Key features of Rag chatbot with a localhost MCP server

• MCP tool orchestration for document indexing, retrieval, and answer generation.
• PDF upload and parsing with PDFPlumberLoader.
• Text chunking using RecursiveCharacterTextSplitter.
• In-memory vector store with OpenAIEmbeddings for indexing.
• Cosine similarity search for relevant document retrieval.
• Prompt-based answer generation via ChatOpenAI (GPT-4).
• Interactive Streamlit chat interface.

Use cases of Rag chatbot with a localhost MCP server

• HR professionals uploading company policy PDFs to answer employee questions.
• Employees querying workplace rules through a natural language chat.
• Rapidly prototyping a RAG system with modular tool orchestration.
• Experimenting with MCP as a function-calling hub for LLM applications.