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.
Building a RAG-based HR chatbot for providing rules in the workplace with the localhost MCP server as a function-calling hub
Features
MCP Tool Integration: Tool orchestration with MCP ensures smooth communication between document indexing, retrieval, and answer generation. The backend tools can be extended or replaced easily as new…
PDF Upload and Parsing: Upload a PDF file which is then parsed using PDFPlumberLoader to extract text content.
Text Chunking: The extracted text is split into smaller chunks using RecursiveCharacterTextSplitter to facilitate efficient indexing and retrieval.
Document Indexing: Chunks are indexed in an in-memory vector store with embeddings generated via OpenAIEmbeddings.
Similarity Search (Consine Similarity): When a user submits a query, the chatbot performs a similarity search to retrieve the most relevant document chunks based on the query.
Prompt-Based Answer Generation: A custom prompt template combines the user question with retrieved context, and a GPT-4 powered LLM (using ChatOpenAI) generates the final answer.
Interactive Interface: The application uses Streamlit to provide an interactive, chat-like interface where user questions and bot responses are displayed.
Details on this page are taken from the project's README. Open README
MaxKB = Max Knowledge Brain, it is an open-source platform for building enterprise-grade agents. MaxKB integrates Retrieval-Augmented Generation (RAG) pipelines, supports robust workflows, and provides advanced MCP tool-use capabilities.
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[!WARNING] DevDocs Status: Not publicly maintained. Enhanced internal version at CyberAGI — public release coming soon. If you have any questions please reach out to info@cyberagi.ai.