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.
