Ragchatbot MCP Server

The Ragchatbot Mcpserver tackles a common challenge in building AI‑powered help desks: delivering accurate, up‑to‑date answers from private documents without exposing raw data to external APIs. By running an MCP server locally, the system keeps all document processing and retrieval within a controlled environment while still letting Claude or other AI assistants invoke sophisticated tooling through function calls. This eliminates latency and privacy concerns that arise when cloud‑based services must be queried for every user request.

At its core, the server orchestrates a Retrieval‑Augmented Generation pipeline. Users upload PDFs through a Streamlit interface; the server parses these files, splits the text into manageable chunks, and stores them in an in‑memory vector index. When a user asks a question, the MCP server performs a cosine‑similarity search to pull the most relevant chunks and hands them to a GPT‑4 LLM via a custom prompt template. The assistant then crafts a context‑aware response that references the exact sections of the original documents, ensuring answers are both precise and traceable.

Key capabilities include:

• Modular MCP Tooling – The server exposes tools for indexing, searching, and generating answers, allowing developers to swap in alternative embeddings or LLMs without touching the client code.
• PDF‑to‑Vector Pipeline – Automatic extraction, chunking, and embedding generation streamline onboarding of new policy documents or knowledge bases.
• Real‑time Chat Interface – Streamlit provides a lightweight chat UI that displays both user queries and assistant replies, making the experience intuitive for HR staff or other domain experts.
• Local Execution – All heavy lifting happens on the host machine, preserving sensitive corporate data and reducing reliance on external APIs.

Typical use cases include internal HR chatbots that answer policy questions, compliance assistants that pull from legal documents, or any scenario where a domain expert needs quick access to proprietary text. By integrating seamlessly with existing AI workflows, the Ragchatbot Mcpserver lets teams prototype and deploy knowledge‑base chatbots rapidly while maintaining control over data flow, privacy, and performance.