AI-Powered RAG Chatbot for Document Management
AI-Powered RAG Chatbot for Your Docs enables seamless interaction with documents stored in Google Drive. It automatically retrieves, processes, and stores document data in a Qdrant vector store, enhancing search capabilities with AI-extracted metadata. Users can engage in intelligent conversations, receiving context-aware responses while maintaining chat history in Google Docs. This workflow streamlines document management and improves access to information, making it ideal for organizations looking to leverage AI for efficient data retrieval and user engagement.
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Workflow Overview
AI-Powered RAG Chatbot for Your Docs enables seamless interaction with documents stored in Google Drive. It automatically retrieves, processes, and stores document data in a Qdrant vector store, enhancing search capabilities with AI-extracted metadata. Users can engage in intelligent conversations, receiving context-aware responses while maintaining chat history in Google Docs. This workflow streamlines document management and improves access to information, making it ideal for organizations looking to leverage AI for efficient data retrieval and user engagement.
Target Audience
- Businesses & Organizations: Those looking to enhance their document management and retrieval processes.
- Developers: Individuals interested in integrating AI-powered chatbots with document storage solutions.
- Data Analysts: Professionals needing to extract insights from large document repositories efficiently.
- Educators & Researchers: Users who require easy access to documents and the ability to interact with them conversationally.
- AI Enthusiasts: Individuals exploring the capabilities of AI in document processing and retrieval systems.
Problem Solved
This workflow addresses the challenges of document retrieval and interaction by providing an AI-powered chatbot that can:
- Efficiently retrieve documents from Google Drive.
- Process and extract metadata from documents for enhanced search capabilities.
- Enable users to interact with documents through a conversational interface, ensuring they receive accurate and context-aware responses.
- Maintain a chat history for reference, allowing users to track their interactions and insights.
Workflow Steps
- Trigger: The workflow begins when a user clicks the ‘Test workflow’ button or sends a chat message.
- Folder Setup: The Google Drive folder ID is set up to specify where documents are stored.
- File Retrieval: The workflow retrieves file IDs from the specified Google Drive folder.
- File Download: Each file is downloaded, and its contents are extracted.
- Metadata Extraction: The extracted text is processed to obtain relevant metadata such as themes, keywords, and pain points.
- Vector Storage: The processed data is split into chunks and stored in the Qdrant vector store for efficient retrieval.
- Chat Interface: Users can interact with the chatbot powered by Google Gemini, which retrieves relevant information based on user queries.
- Chat History Maintenance: All interactions are logged in Google Docs for future reference.
- Notifications: Telegram notifications are sent for important operations, such as deletions or completions.