Multi-Scenario Intelligent Automation Showcase
This workflow integrates various intelligent automation features, enabling smart email categorization, semantic question answering for PDF documents, and intelligent appointment management. Through AI models and vector databases, users can efficiently process email and document information, quickly retrieving key content. Additionally, the built-in calendar interface can automatically schedule meetings, avoiding appointment conflicts and enhancing work efficiency. It is suitable for business users who need to manage information and schedules effectively, optimizing customer experience and team collaboration.
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Workflow Name
Multi-Scenario Intelligent Automation Showcase
Key Features and Highlights
- Leverages the n8n low-code automation platform to integrate AI models and various services, enabling intelligent email classification, semantic Q&A for PDF documents, and calendar-based smart scheduling.
- Combines large language models such as OpenAI and Anthropic to support diverse AI capabilities including text classification, question-answering chains, and chatbot interactions.
- Utilizes Pinecone vector database for vectorized storage and retrieval of documents, enhancing the accuracy and efficiency of content-based queries.
- Built-in Google Calendar API integration for real-time schedule queries and automatic appointment creation, minimizing scheduling conflicts.
- Supports Slack message notifications, webhook-triggered data inputs, customizable JavaScript code nodes, and user-friendly annotations to facilitate workflow understanding and maintenance.
Core Problems Addressed
- Automatically filters and tags large volumes of emails, saving manual classification time and improving work efficiency.
- Converts complex document contents such as PDFs into queryable vector data, enabling intelligent content-based Q&A to help users quickly obtain key information.
- Provides intelligent scheduling and appointment management by automatically detecting free time slots, avoiding conflicts, and enhancing meeting arrangement efficiency.
Application Scenarios
- Automated email classification and label management for corporate mailboxes, suitable for customer service, sales, and administrative staff.
- Knowledge base construction and intelligent Q&A for documents in legal, financial, research, and other fields, accelerating information retrieval.
- Intelligent scheduling assistant for sales, consultants, technical support, and other roles requiring frequent client or colleague meetings.
- Internal enterprise automation for notifications and task triggers, improving team communication and collaboration efficiency.
Main Workflow Steps
- Intelligent Email Classification: Listens for new emails via Gmail trigger, invokes AI text classification models to analyze email content, automatically tags emails with labels such as “automation” or “music,” and sends notifications through Slack.
- PDF Document Processing and Q&A: Automatically downloads specified PDF documents, uses PDF loaders and recursive character splitters to segment text, generates text embeddings via OpenAI, and inserts them into the Pinecone vector database. Users submit queries through a chat interface; the system retrieves relevant vectors and combines them with GPT-4o to generate precise answers based on document content.
- Smart Scheduling Assistant: Receives user appointment requests via chat triggers, employs multi-turn conversations and contextual memory, queries Google Calendar API for availability, and upon confirming no conflicts, automatically creates 30-minute meeting appointments to complete intelligent schedule management.
Involved Systems and Services
- Gmail: Email reception and label management
- Slack: Message notifications
- Webhook: External data input triggers
- OpenAI GPT-4o, Anthropic models: Text classification, Q&A, and dialogue generation
- Pinecone Vector Database: Document vector storage and retrieval
- Google Calendar API: Schedule availability queries and appointment creation
- JavaScript Code Nodes: Custom data processing logic
Target Users and Value Proposition
- Technical teams and automation enthusiasts who want to rapidly build complex intelligent workflows using a low-code platform.
- Enterprise users needing efficient management of emails, documents, and schedules to significantly boost productivity and collaboration experience.
- Businesses and developers aiming to enhance process intelligence through AI, reduce repetitive tasks, and focus on core operations.
- Functional roles such as customer service, sales, and executive assistants seeking intelligent information sorting and automated scheduling to optimize customer experience.
This workflow demonstrates the powerful capabilities of the n8n platform in integrating AI, document processing, and schedule management across multiple scenarios, empowering users to create an intelligent and efficient automated office environment.
AI Voice Chat using Webhook, Memory Manager, OpenAI, Google Gemini & ElevenLabs
This workflow builds a complete AI voice chat system that can transcribe user speech into text in real time and achieve understanding and generation of multi-turn conversations through context memory management. By combining advanced language models with high-quality text-to-speech technology, the system can provide natural and smooth voice responses, making it suitable for scenarios such as intelligent customer service and voice assistants, thereby enhancing user interaction experience and efficiency.
🐋🤖 DeepSeek AI Agent + Telegram + LONG TERM Memory 🧠
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WhatsApp Multimedia Intelligent Interaction Assistant
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Insert and Retrieve Documents
This workflow is designed to automatically scrape the latest articles from the Paul Graham website, extract and clean their main content, generate vectors, and store them in the Milvus database. Users can query through a chat interface, and the system will retrieve relevant text based on vector searches, utilizing the GPT-4 model for intelligent Q&A, ensuring that the answers are accurate and traceable. It is suitable for knowledge base construction, intelligent customer service, content aggregation, and research assistance, enhancing the management and utilization efficiency of text data.
Multimodal Video Analysis and AI Voiceover Generation Workflow
This workflow implements automated video analysis and voiceover generation. By extracting key frames from the video, it utilizes a multimodal large language model to generate narration scripts, and combines text-to-speech technology to synthesize high-quality voiceovers, ultimately uploading the audio files to the cloud. This process significantly reduces the difficulty and time costs associated with video commentary production, making it suitable for various fields such as education, marketing, and media. It helps users quickly generate vivid narration content, enhancing video production efficiency.
OpenAI-model-examples
This workflow integrates various OpenAI models, providing functionalities such as text generation, summarization, translation, audio transcription, and image generation. Users can automate the processing of text and multimodal content by calling interfaces like Davinci, ChatGPT, Whisper, and DALLE-2, catering to different business needs. The system helps content creators quickly extract information, supports multilingual translation, converts speech to text, and generates creative images for design teams, enhancing work efficiency and automation levels.
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NeurochainAI Basic API Integration
This workflow achieves deep integration with the NeurochainAI platform, allowing users to send text commands via a Telegram bot to automatically invoke AI interfaces for natural language processing and image generation. The system intelligently handles input validation and error prompts, providing real-time feedback to users in the form of text or images, enhancing the interaction experience and stability. It is suitable for AI chatbots, customer service assistants, and creative support tools, effectively improving response efficiency and saving time on manual processing.