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.

FAQ from Rag chatbot with a localhost MCP server

What is the role of the MCP server in this chatbot?

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

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.

What LLM model is used for answer generation?

The answer generation uses GPT-4 via ChatOpenAI, with a custom prompt template that combines the user question and retrieved context.

Are there any known limits of this system?

The vector store is in-memory, so indexed data is not persisted across sessions. The system currently supports only PDF files as input.