AI-Powered RAG Chatbot for Seamless Document Interaction
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Unlock the power of your documents with an AI-powered RAG chatbot. Seamlessly integrate with Google Drive, leverage Gemini's intelligence, and store knowledge in Qdrant for instant, accurate insights.
About This Workflow
This n8n workflow orchestrates a sophisticated RAG (Retrieval Augmented Generation) chatbot designed to understand and interact with your documents. It begins by intelligently extracting metadata and content from your files, whether they are stored in Google Drive or provided directly. Using Langchain's powerful tools, the workflow tokenizes this data for efficient processing. It then leverages Google Gemini to analyze and generate insights from the extracted information. Crucially, this knowledge is then vectorized and stored in a Qdrant vector database, enabling rapid retrieval and contextually relevant answers when you query the chatbot. This solution transforms your static documents into an interactive knowledge base.
Key Features
- Intelligent Document Ingestion: Automatically fetches and processes documents from Google Drive.
- Advanced Metadata Extraction: Captures key themes, topics, pain points, and analytical insights from your content.
- Tokenization and Chunking: Optimizes document data for efficient AI processing.
- Powerful AI Generation: Utilizes Google Gemini for sophisticated text generation and summarization.
- Vectorized Knowledge Base: Stores extracted information in Qdrant for fast and accurate retrieval.
How To Use
- Trigger: Configure the
When clicking ‘Test workflow’node for manual execution or integrate with an external trigger. - Google Drive Integration: Use the
Find File Ids in Google Drive Foldernode to specify the folder containing your documents and theDownload File From Google Drivenode to retrieve them, linking them via{{ $json.id }}. - Data Loading & Extraction: The
Get File Contentsnode reads the downloaded file, andExtract Meta Datauses system prompts to pull out structured information (theme, topics, etc.). - Token Splitting: The
Token Splitternode breaks down the document content into manageable chunks for the AI. - Vector Storage: Configure the
Qdrant Vector Storenode with your Qdrant collection name (nostr-damus-user-profilesin this example) and credentials to store vectorized document data. - AI Generation: The
Google Gemini Chat Modelnode is ready to process prompts and generate responses. - Workflow Orchestration: Use
Loop Over ItemsandMergenodes as needed to manage data flow and batch processing, withWaitnodes to control execution timing.
Apps Used
Workflow JSON
{
"id": "ceb77aa7-01a4-4203-87cf-d436d1ee4930",
"name": "AI-Powered RAG Chatbot for Seamless Document Interaction",
"nodes": 17,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: ceb77aa7-01a4...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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