Building a RAG Architecture: Local Files and Web Data Extraction - Pinecone, Mongo and ChatGPT

Welcome to our comprehensive guide on RAG (Retrieval-Augmented Generation) architecture! In this video, we'll take you step-by-step through the entire process, from extracting data from your local computer, file servers, and company websites to embedding this information into a Pinecone vector database and storing original contents in MongoDB. We'll also build an application that leverages this architecture for powerful AI capabilities.

Key Highlights:
- Data Extraction Nodes: Learn how our TEXTEN and WEBTEXTEN nodes extract data from various sources. The TEXTEN node can be configured to run manually or on a schedule, ensuring you capture all relevant data without including excluded files or directories. The WEBTEXTEN node crawls your specified domain, pulling in text and PDF files while ignoring external links.
- Handling PII and Exclusions: Understand the importance of managing Personally Identifiable Information (PII). Our system checks for PII patterns and allows you to approve or reject flagged content, ensuring your data remains secure and compliant.
- Edge Server Processing: Discover how our edge server processes and stores data without bogging down your local system. The server handles the heavy lifting, ensuring efficient data processing and storage.
- Data Cleanup and Storage: See how we clean up data by removing stop words and unnecessary numbers before storing it in Pinecone and MongoDB. This step is crucial for optimizing the vector database and ensuring accurate and efficient data retrieval.
- Building the Application: Watch as we build an application that utilizes the RAG architecture to provide insightful responses using OpenAI's ChatGPT. We'll walk through the code, showing you how to integrate embeddings and query the vector database to get the most relevant information.

GitHub:
https://github.com/msuliot/texten.git
https://github.com/msuliot/webtexten.git
https://github.com/msuliot/chunken.git
https://github.com/msuliot/datamyn.git

Chapters:
0:00 Introduction to RAG Architecture
1:20 Overview of TEXTEN Node
3:45 Configuring Exclusions and Handling PII
5:30 Detailed Walkthrough of WEBTEXTEN Node
7:15 Data Cleanup and Vector Database Integration
10:00 Storing Original Content in MongoDB
12:45 Setting Up Pinecone for Vector Storage
15:30 Integrating OpenAI for Embeddings
18:00 Live Demo: From Data Extraction to Application
20:00 Analyzing and Verifying Results for Accuracy

By the end of this video, you'll have a comprehensive understanding of how to set up and utilize RAG architecture for efficient and secure data management and AI applications. Whether you're dealing with local files, web data, or a combination of both, our detailed walkthrough will equip you with the knowledge to build a robust RAG system.

Key Features:
- TEXTEN Node: Extracts data from local systems and file servers.
- WEBTEXTEN Node: Crawls web domains to gather text and PDF files.
- Edge Server: Processes and stores data efficiently with customizable data loaders.
- PII Handling: Ensures compliance by flagging and managing sensitive information.
- Data Cleanup: Optimizes data by removing unnecessary elements.
- Vector Database Integration: Uses Pinecone for efficient data retrieval.
- Application Development: Builds an AI-driven application with OpenAI's ChatGPT.

Keywords:
RAG architecture, data extraction, TEXTEN, WEBTEXTEN, Pinecone vector database, MongoDB, edge server, PII handling, data cleanup, OpenAI, ChatGPT, embeddings, AI applications, tech tutorial, data management, AI integration, vector database, file servers, local systems, web crawler, AI development, machine learning, data processing, information retrieval, secure data storage, compliance, tech walkthrough, artificial intelligence, data science, prompt engineering.

Join us as we explore the exciting world of RAG architecture and learn how to build powerful AI applications from the ground up. Don't forget to like, share, and subscribe for more AI and tech tutorials!

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