Build a document-based AI chatbot with Google Drive, Llama 3, and Qdrant RAG

Overview This template allows users to set up an AI-powered chatbot that retrieves and processes knowledge from Google Drive documents using Retrieval-Augmented Generation (RAG). By leveraging Llama 3 for natural language responses and Qdrant vector storage for document embeddings, this chatbot provides accurate, context-aware answers based on stored files. Problem It Solves Standard AI chatbots often rely on predefined models with limited real-time knowledge access. This workflow overcomes that limitation by: Automatically fetching new documents from Google Drive. Embedding knowledge for fast retrieval using Qdrant. Generating human-like responses with Llama 3 AI. Providing accurate, source-backed answers in conversations. Use Cases ✔️ Customer Support – Retrieve and summarize FAQs stored in Google Drive. ✔️ Internal Knowledge Base – Automate document-based query responses. ✔️ AI-powered Research Assistant – Search and generate insights from uploaded files. ✔️ Business Automation – Enhance workflows with document-aware chat interactions. Setup Instructions 1️⃣ Google Drive Trigger: Detect & Fetch New Documents Watches for new files added to a specific Google Drive folder. Retrieves the latest file metadata and passes it into the workflow. 2️⃣ Processing & Embedding the Document The document is downloaded via the Google Drive node. Text data is split into smaller, retrievable chunks using Recursive Text Splitter. Embeddings are created using Ollama’s Nomic-Embed Model. Knowledge is stored in Qdrant Vector Database for fast AI-powered lookup. 3️⃣ AI Chatbot & Query Handling The Chat Trigger node listens for user queries. The AI Agent retrieves context-aware answers by searching Qdrant’s vectorized documents. The Llama 3 Model generates human-like responses based on stored knowledge. Detailed Workflow Explanation 🔹 Google Drive Trigger ✅ Monitors a specific folder for new documents. ✅ Automatically fetches document metadata when a file is uploaded. 🔹 Qdrant Vector Store ✅ Stores embedded document text, making retrieval instant & accurate. ✅ Allows the chatbot to reference stored knowledge dynamically. 🔹 Recursive Text Splitter ✅ Splits long documents into manageable chunks for efficient embedding. ✅ Improves chatbot response accuracy by organizing document data. 🔹 Llama 3 Chat Model ✅ Generates natural, human-like replies using AI. ✅ Uses retrieved document data for context-aware responses. Customization Options 🔹 Adjust polling frequency for document updates. 🔹 Expand knowledge base by adding more storage sources. 🔹 Refine chatbot responses with prompt tuning in Llama 3.