Can HTTP Request connect with Text Classifier?

Yes, HTTP Request can connect with Text Classifier using n8n.io. With n8n, you can create workflows that automate tasks and transfer data between HTTP Request and Text Classifier. Configure nodes for HTTP Request and Text Classifier in the n8n interface, specifying actions and triggers to set up their connection.

Can I use HTTP Request’s API with n8n?

Yes, with n8n, you can programmatically interact with HTTP Request’s API via pre-defined supported actions or raw HTTP requests. With the HTTP Request node, you create a REST API call. You need to understand basic API terminology and concepts.

Can I use Text Classifier’s API with n8n?

Yes, with n8n, you can programmatically interact with Text Classifier’s API via pre-defined supported actions or raw HTTP requests. With the HTTP Request node, you create a REST API call. You need to understand basic API terminology and concepts.

Is n8n secure for integrating HTTP Request and Text Classifier?

Yes, it is safe to use n8n to integrate HTTP Request and Text Classifier. n8n offers various features to ensure the safe handling of your data. These include encrypted data transfers, secure credential storage, RBAC functionality, and compliance with industry-standard security practices (SOC2 compliant). For hosted plans, data is stored within the EU on servers located in Frankfurt, Germany. You can also host it on your own infrastructure for added control. Learn more about n8n’s security practices here.

How to get started with HTTP Request and Text Classifier integration in n8n.io?

To start integrating HTTP Request and Text Classifier in n8n, you have different options depending on how you intend to use it: n8n Cloud: hosted solution, no need to install anything. Self-host: recommended method for full-control or customized use cases. Unlike other platforms that charge per operation, step, or task, n8n charges only for full workflow executions. This approach guarantees predictable costs and scalability, no matter the complexity or volume of your workflows.

HTTP Request and Text Classifier: Automate Workflows with n8n

HTTP Request and Text Classifier integration

Save yourself the work of writing custom integrations for HTTP Request and Text Classifier and use n8n instead. Build adaptable and scalable Development, Core Nodes, AI, and Langchain workflows that work with your technology stack. All within a building experience you will love.

How to connect HTTP Request and Text Classifier

Step 1: Create a new workflow
Step 2: Add and configure nodes
Step 3: Connect
Step 4: Customize and extend your integration
Step 5: Test and activate your workflow

Step 1: Create a new workflow and add the first step

In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.

HTTP Request and Text Classifier integration: Create a new workflow and add the first step

Step 2: Add and configure HTTP Request and Text Classifier nodes

You can find HTTP Request and Text Classifier in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure HTTP Request and Text Classifier nodes one by one: input data on the left, parameters in the middle, and output data on the right.

HTTP Request and Text Classifier integration: Add and configure HTTP Request and Text Classifier nodes

Step 3: Connect HTTP Request and Text Classifier

A connection establishes a link between HTTP Request and Text Classifier (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.

HTTP Request and Text Classifier integration: Connect HTTP Request and Text Classifier

Step 4: Customize and extend your HTTP Request and Text Classifier integration

Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect HTTP Request and Text Classifier with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

HTTP Request and Text Classifier integration: Customize and extend your HTTP Request and Text Classifier integration

Step 5: Test and activate your HTTP Request and Text Classifier workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from HTTP Request to Text Classifier or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.

HTTP Request and Text Classifier integration: Test and activate your HTTP Request and Text Classifier workflow

🤖 Telegram Messaging Agent for Text/Audio/Images

Core Components

Message Reception and Validation 📥
🔄 Implements webhook-based message reception for real-time processing
🔐 Features a robust user validation system that verifies sender credentials
🔀 Supports both testing and production webhook endpoints for development flexibility

Message Processing Pipeline ⚡
🔄 Uses a smart router to detect and categorize incoming message types
📝 Processes three main message formats:
💬 Text messages
🎤 Voice recordings
📸 Images with captions

AI Integration 🧠
🤖 Leverages OpenAI's GPT-4 for message classification and processing
🗣️ Incorporates voice transcription capabilities for audio messages
👁️ Features image analysis using GPT-4 Vision API for processing visual content

Technical Architecture

Webhook Management 🔌
🌐 Maintains separate endpoints for testing and production environments
📊 Implements automatic webhook status monitoring
⚡ Provides real-time webhook configuration updates

Error Handling ⚠️
🔍 Features comprehensive error detection and reporting
🔄 Implements fallback mechanisms for unprocessable messages
💬 Provides user feedback for failed operations

Message Classification System 📋
🏷️ Categorizes incoming messages into tasks and general conversation
🔀 Implements separate processing paths for different message types
🧩 Maintains context awareness across message processing

Security Features

User Authentication 🔒
✅ Validates user credentials against predefined parameters
👤 Implements first name, last name, and user ID verification
🚫 Restricts access to authorized users only

Response System

Intelligent Responses 💡
🤖 Generates contextual responses based on message classification

Nodes used in this workflow

Popular HTTP Request and Text Classifier workflows

✨🔪 Advanced AI Powered Document Parsing & Text Extraction with Llama Parse

Description This workflow automates document processing using LlamaParse to extract and analyze text from various file formats. It intelligently processes documents, extracts structured data, and delivers actionable insights through multiple channels. How It Works Document Ingestion & Processing 📄 Monitors Gmail for incoming attachments or accepts documents via webhook Validates file formats against supported LlamaParse extensions Uploads documents to LlamaParse for advanced text extraction Stores original documents in Google Drive for reference Intelligent Document Analysis 🧠 Automatically classifies document types (invoices, reports, etc.) Extracts structured data using customized AI prompts Generates comprehensive document summaries with key insights Converts unstructured text into organized JSON data Invoice Processing Automation 💼 Extracts critical invoice details (dates, amounts, line items) Organizes financial data into structured formats Calculates tax breakdowns, subtotals, and payment information Maintains detailed records for accounting purposes Multi-Channel Delivery 📱 Saves extracted data to Google Sheets for tracking and analysis Sends concise summaries via Telegram for immediate review Creates searchable document archives in Google Drive Updates spreadsheets with structured financial information Setup Steps Configure API Credentials 🔑 Set up LlamaParse API connection Configure Gmail OAuth for email monitoring Set up Google Drive and Sheets integrations Add Telegram bot credentials for notifications Customize AI Processing ⚙️ Adjust document classification parameters Modify extraction templates for specific document types Fine-tune summary generation prompts Customize invoice data extraction schema Test and Deploy 🚀 Test with sample documents of various formats Verify data extraction accuracy Confirm notification delivery Monitor processing pipeline performance

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🤖 AI-Powered WhatsApp Assistant for Restaurants & Delivery Automation

AI-Powered WhatsApp Assistant for Restaurant & Delivery – Automate Orders, Calculate Delivery Fees & Enhance Customer Service with n8n 📌 Optimize your restaurant's delivery process with AI-powered automation! This n8n workflow enables an intelligent WhatsApp assistant for restaurants, burger joints, and delivery businesses. The AI chatbot efficiently handles orders, calculates delivery fees based on distance, manages customer interactions, and seamlessly integrates with your CRM and database for streamlined operations. ⚠️ Important Notice: This template is only compatible with self-hosted n8n instances as it uses community nodes. Ensure secure credential management when configuring APIs. 🔹 Key Features ✅ AI-Powered Order Processing – Automatically receive and confirm orders via WhatsApp. ✅ Real-Time Distance Calculation – Determines the delivery distance and calculates the fee dynamically. ✅ Automated Customer Interaction – Engages customers with intelligent conversation and order updates. ✅ WhatsApp Integration – Processes messages, saves customer details, and retrieves past interactions. ✅ Secure Customer Data Storage – Saves order history, customer preferences, and location details in a database. ✅ Seamless CRM & POS System Integration – Sync orders and customer data with your existing platforms. 📌 How It Works 1️⃣ A customer sends a message via WhatsApp to start an order. 2️⃣ The AI assistant guides them through the menu, recommends items, and confirms their choices. 3️⃣ The system retrieves and saves customer details (name, phone number, and address). 4️⃣ The AI calculates the delivery distance and applies the appropriate delivery fee. 5️⃣ The order details are saved and forwarded to the restaurant’s system for preparation. 6️⃣ The assistant keeps the customer updated about the order status. ⚙️ Setup & Customization 🔧 Webhook for WhatsApp – Capture and process customer messages automatically. 🔧 OpenAI-Powered AI Assistant – Configure conversation flows for natural and engaging interactions. 🔧 Delivery Fee Calculation – Set your delivery price per kilometer and adjust base rates. 🔧 Database & CRM Integration – Store customer details, order history, and location data. 🔧 Customizable Order Flow – Adapt the workflow for different restaurant models. 🔧 Secure Credential Management – Store API keys safely to prevent unauthorized access. 💡 Requirements: This template is recommended for users with basic knowledge of n8n. If you need custom development or setup assistance, contact us via WhatsApp: +55 17 99155-7874. 🚀 Automate order processing, reduce manual tasks, and improve customer satisfaction with AI-powered WhatsApp automation! Assistente de WhatsApp com IA para Restaurantes & Delivery – Automatize Pedidos, Calcule Taxas de Entrega e Melhore o Atendimento ao Cliente com n8n 📌 Otimize o processo de entrega do seu restaurante com automação baseada em IA! Este fluxo para n8n permite criar um atendente virtual inteligente no WhatsApp, ideal para hamburguerias, restaurantes e serviços de delivery. O chatbot baseado em IA recebe pedidos, calcula a taxa de entrega com base na distância, interage com os clientes e integra-se ao seu CRM para operações mais eficientes. ⚠️ Aviso Importante: Este template é compatível apenas com instâncias auto-hospedadas do n8n, pois utiliza nós da comunidade. Certifique-se de gerenciar credenciais de forma segura ao configurar APIs. 🔹 Principais Funcionalidades ✅ Processamento Inteligente de Pedidos – Recebe e confirma pedidos automaticamente via WhatsApp. ✅ Cálculo Dinâmico da Taxa de Entrega – Mede a distância e aplica a taxa correta automaticamente. ✅ Atendimento Automatizado ao Cliente – Interage com clientes de forma natural e profissional. ✅ Integração com WhatsApp – Captura mensagens, armazena dados e recupera histórico de pedidos. ✅ Armazenamento Seguro de Dados do Cliente – Salva pedidos, preferências e endereços de entrega. ✅ Sincronização com CRM e Sistema de PDV – Conecta-se ao seu sistema de gestão para um fluxo contínuo. 📌 Como Funciona 1️⃣ O cliente envia uma mensagem via WhatsApp para iniciar um pedido. 2️⃣ O assistente de IA apresenta o menu, sugere itens e confirma a escolha do cliente. 3️⃣ O sistema captura e salva os dados do cliente (nome, telefone e endereço). 4️⃣ A IA calcula a distância de entrega e aplica a taxa de acordo com a localização. 5️⃣ Os detalhes do pedido são registrados e enviados para a cozinha. 6️⃣ O assistente atualiza o cliente sobre o status do pedido. ⚙️ Configuração e Personalização 🔧 Webhook para WhatsApp – Configuração para capturar e processar pedidos automaticamente. 🔧 Assistente de IA com OpenAI – Configuração de fluxos conversacionais naturais e envolventes. 🔧 Cálculo de Taxa de Entrega – Definição de valores por quilômetro e ajustes de tarifas base. 🔧 Banco de Dados & CRM – Armazena dados de clientes, histórico de pedidos e localização. 🔧 Fluxo de Pedidos Personalizável – Adapte para diferentes tipos de restaurantes e serviços de delivery. 🔧 Gerenciamento Seguro de Credenciais – Proteja suas chaves de API contra acessos não autorizados. 💡 Requisitos: Este template é recomendado para usuários que já possuem conhecimentos básicos em n8n. Se precisar de personalização ou suporte para instalação, entre em contato pelo WhatsApp: +55 17 99155-7874. 🚀 Automatize o processamento de pedidos, reduza tarefas manuais e melhore a satisfação dos clientes com um WhatsApp automatizado e inteligente!

Effortless Email Management with AI-Powered Summarization & Review

How it Works This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or suggestions before sending replies. Below is an explanation of its functionality divided into two main sections: Email Handling and Summarization: The process begins with the Email Trigger (IMAP) node which listens for new emails in a specified inbox. Once an email is received, the Markdown node converts its HTML content into plain text if necessary, followed by the Email Summarization Chain that uses AI to create a concise summary of up to 100 words. Response Generation and Approval: A Write email node generates a professional response based on the summarized content, ensuring brevity and professionalism while keeping within the word limit. Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through the Gmail node configured with free-text response options. If approved, the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention. Additionally, there's a Text Classifier node designed to categorize feedback from humans as either "Approved" or "Declined", guiding whether the email should proceed directly to being sent or require additional editing. Set Up Steps To replicate this workflow within your own n8n environment, follow these essential configuration steps: Configuration: Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site. Import the provided JSON configuration file into your workspace, making sure all required credentials such as IMAP, SMTP, OpenAI API keys, etc., are properly set up under Credentials since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals. Customization: Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?. Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications. Ensure correct mappings between fields when appending data to external systems where records might need tracking post-interaction completion, such as Google Sheets or similar platforms.

🤖 Telegram Messaging Agent for Text/Audio/Images

🤖 This n8n workflow creates an intelligent Telegram bot that processes multiple types of messages and provides automated responses using AI capabilities. The bot serves as a personal assistant that can handle text, voice messages, and images through a sophisticated processing pipeline. Core Components Message Reception and Validation 📥 🔄 Implements webhook-based message reception for real-time processing 🔐 Features a robust user validation system that verifies sender credentials 🔀 Supports both testing and production webhook endpoints for development flexibility Message Processing Pipeline ⚡ 🔄 Uses a smart router to detect and categorize incoming message types 📝 Processes three main message formats: 💬 Text messages 🎤 Voice recordings 📸 Images with captions AI Integration 🧠 🤖 Leverages OpenAI's GPT-4 for message classification and processing 🗣️ Incorporates voice transcription capabilities for audio messages 👁️ Features image analysis using GPT-4 Vision API for processing visual content Technical Architecture Webhook Management 🔌 🌐 Maintains separate endpoints for testing and production environments 📊 Implements automatic webhook status monitoring ⚡ Provides real-time webhook configuration updates Error Handling ⚠️ 🔍 Features comprehensive error detection and reporting 🔄 Implements fallback mechanisms for unprocessable messages 💬 Provides user feedback for failed operations Message Classification System 📋 🏷️ Categorizes incoming messages into tasks and general conversation 🔀 Implements separate processing paths for different message types 🧩 Maintains context awareness across message processing Security Features User Authentication 🔒 ✅ Validates user credentials against predefined parameters 👤 Implements first name, last name, and user ID verification 🚫 Restricts access to authorized users only Response System Intelligent Responses 💡 🤖 Generates contextual responses based on message classification

Turn BBC News Articles into Podcasts using Hugging Face and Google Gemini

Turn BBC News Articles into Podcasts using Hugging Face and Google Gemini Effortlessly transform BBC news articles into engaging podcasts with this automated n8n workflow. Who is this for? This template is perfect for: Content creators** who want to quickly produce podcasts from current events. Students** looking for an efficient way to create audio content for projects or assignments. Individuals** interested in generating their own podcasts without technical expertise. Setup Information Install n8n: If you haven't already, download and install n8n from n8n.io. Import the Workflow: Copy the JSON code for this workflow and import it into your n8n instance. Configure Credentials: Gemini API: Set up your Gemini API credentials in the workflow's LLM nodes. Hugging Face Token: Obtain an access token from Hugging Face and add it to the HTTP Request node for the text-to-speech model. Customize (Optional): Filtering Criteria: Adjust the News Classifier node to fine-tune the selection of news articles based on your preferences. Output Options: Modify the workflow to save the generated audio file to a cloud storage service or publish it to a podcast hosting platform. Prerequisites An active n8n instance. Basic understanding of n8n workflows (no coding required). API credentials for Gemini and a Hugging Face account with an access token. What problem does it solve? This workflow eliminates the manual effort involved in creating podcasts from news articles. It automates the entire process, from fetching and filtering news to generating the final audio file. What are the benefits? Time-saving:** Create podcasts in minutes, not hours. Easy to use:** No coding or technical skills required. Customizable:** Adapt the workflow to your specific needs and preferences. Cost-effective:** Leverage free or low-cost services like Gemini and Hugging Face. How does it work? The workflow fetches news articles from the BBC website. It filters articles based on their suitability for a podcast. It extracts the full content of the selected articles. It uses Gemini LLM to create a podcast script. It converts the script to speech using Hugging Face's text-to-speech model. The final podcast audio is ready for use. Nodes in the Workflow Fetch BBC News Page: Retrieves the main BBC News page. News Classifier: Categorizes news articles using Gemini LLM. Fetch BBC News Detail: Extracts detailed content from suitable articles. Basic Podcast LLM Chain: Generates a podcast script using Gemini LLM. HTTP Request: Converts the script to speech using Hugging Face. Add Story I'm excited to share this workflow with the n8n community and help content creators and students easily produce engaging podcasts! Additional Tips Explore the n8n documentation and community resources for more advanced customization options. Experiment with different filtering criteria and LLM prompts to achieve your desired podcast style.

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🐶 AI Agent for PetShop Appointments (Agente de IA para agendamentos de PetShop)

🐶🤖 AI Agent for Pet Shops – Automate Customer Service & Bookings! 🐾💡 Transform Your Pet Shop with AI-Powered Automation! 🚀 Enhance customer experience and optimize operations with this n8n AI Agent designed for pet shops. 📲🐾 Automate client interactions, appointment scheduling, and service recommendations—saving time and increasing revenue! 🔹 Key Features: ✅ Instant WhatsApp responses – AI-powered chatbot handles customer inquiries. 💬 ✅ Automated appointment scheduling – Clients can book services hassle-free. 📅✂️ ✅ Personalized reminders – Reduce no-shows with automated notifications. 📢🐾 ✅ Customer data & service history management – Track interactions effortlessly. 📊📁 ✅ Product & service recommendations – Improve sales with smart suggestions. 🎁🐶 📌 How It Works 1️⃣ The workflow captures customer inquiries via WhatsApp. 2️⃣ AI processes requests, provides information, and offers booking options. 3️⃣ Clients can schedule grooming, vet visits, or other services in seconds. 4️⃣ Automated reminders ensure appointments are remembered. 5️⃣ Customer data is stored for better service personalization. ⚙️ Setup & Customization 🔧 Connect your WhatsApp API (evolution) for instant messaging. 🔧 Integrate with Google Calendar for appointment booking. 🔧 Customize reminders, services, and pricing rules to fit your business. 💡 Reduce manual work, improve customer satisfaction, and scale your pet shop with AI automation! 🐶🤖 [PT-BR] Agente de IA para Pet Shops – Atendimento e Agendamentos Automatizados! 🐾💡 Transforme Seu Pet Shop com Automação Inteligente! 🚀 Otimize o atendimento ao cliente e agilize processos com este Agente de IA para n8n. 📲🐾 Automatize interações, agendamentos e recomendações de serviços—economizando tempo e aumentando as vendas! 🔹 Principais Funcionalidades: ✅ Atendimento automático no WhatsApp – IA responde clientes instantaneamente. 💬 ✅ Agendamento de serviços automatizado – Clientes marcam banho, tosa ou consultas facilmente. 📅✂️ ✅ Lembretes personalizados – Reduza faltas com notificações automáticas. 📢🐾 ✅ Gestão de clientes e histórico de serviços – Controle dados de forma eficiente. 📊📁 ✅ Sugestão de produtos e serviços – Venda mais com recomendações inteligentes. 🎁🐶 📌 Como Funciona 1️⃣ O fluxo recebe perguntas dos clientes via WhatsApp. 2️⃣ A IA processa os pedidos e fornece opções de agendamento. 3️⃣ O cliente escolhe o serviço desejado e agenda em segundos. 4️⃣ Lembretes automáticos garantem que os clientes não esqueçam os horários. 5️⃣ O histórico do cliente é salvo para oferecer um atendimento mais personalizado. ⚙️ Configuração e Personalização 🔧 Conecte sua API do WhatsApp (evolution) para interação automática. 🔧 Integre ao Google Calendar para gerenciar agendamentos. 🔧 Personalize valores, serviços e regras de envio de lembretes conforme sua necessidade. 💡 Automatize processos, melhore a experiência do cliente e escale seu pet shop com IA! 🚀

AI-Powered Email Automation for Business: Summarize & Respond with RAG

This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional communication. How It Works Email Trigger: The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow. Email Preprocessing: The Markdown node converts the email's HTML content into plain text for easier processing by the AI models. Email Summarization: The Email Summarization Chain node uses an AI model (DeepSeek R1) to generate a concise summary of the email. The summary is limited to 100 words and is written in Italian. Email Classification: The Email Classifier node categorizes the email into predefined categories (e.g., "Company info request"). If the email does not fit any category, it is classified as "other". Email Response Generation: The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words. The Review email node uses another AI model (DeepSeek) to review and format the drafted response. It ensures the response is professional and formatted in HTML (e.g., using , , , tags where necessary). Email Sending: The Send Email node sends the reviewed and formatted response back to the original sender. Vector Database Integration: The Qdrant Vector Store node retrieves relevant information from a vector database (Qdrant) to assist in generating accurate responses. This is particularly useful for emails classified as "Company info request". The Embeddings OpenAI node generates embeddings for the email content, which are used to query the vector database. Document Vectorization: The workflow includes steps to create and refresh a Qdrant collection (Create collection and Refresh collection nodes). Documents from Google Drive are downloaded (Get folder and Download Files nodes), processed into embeddings (Embeddings OpenAI1 node), and stored in the Qdrant vector store (Qdrant Vector Store1 node). Set Up Steps Configure Email Trigger: Set up the Email Trigger (IMAP) node with the appropriate IMAP credentials to monitor the email inbox. Set Up AI Models: Configure the DeepSeek R1, OpenAI, and DeepSeek nodes with the appropriate API credentials for text summarization, response generation, and review. Set Up Email Classification: Define the categories in the Email Classifier node (e.g., "Company info request", "Other"). Ensure the OpenAI 4-o-mini node is configured to assist in classification. Set Up Vector Database: Configure the Qdrant Vector Store and Qdrant Vector Store1 nodes with the appropriate Qdrant API credentials and collection details. Set up the Embeddings OpenAI and Embeddings OpenAI1 nodes to generate embeddings for the email content and documents. Set Up Document Processing: Configure the Get folder and Download Files nodes to access and download documents from Google Drive. Use the Token Splitter and Default Data Loader nodes to process and split the documents into manageable chunks for vectorization. Set Up Email Sending: Configure the Send Email node with the appropriate SMTP credentials to send responses. Test the Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all steps execute correctly. Verify that emails are summarized, classified, and responded to accurately. Activate the Workflow: Once tested, activate the workflow to automate the process of handling incoming emails. Key Features Automated Email Handling**: Automatically processes incoming emails, summarizes them, and generates professional responses. AI-Powered Classification**: Uses AI to classify emails into relevant categories for targeted responses. Vector Database Integration**: Retrieves relevant information from a vector database to enhance response accuracy. Document Vectorization**: Processes and stores documents from Google Drive in a vector database for quick retrieval. Professional Email Formatting**: Ensures responses are professionally formatted and concise.

AI-Powered Information Monitoring with OpenAI, Google Sheets, Jina AI and Slack

Check Legal Regulations: This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry! 📌 Purpose This workflow enables automated and AI-driven topic monitoring, delivering concise article summaries directly to a Slack channel in a structured and easy-to-read format. It allows users to stay informed on specific topics of interest effortlessly, without manually checking multiple sources, ensuring a time-efficient and focused monitoring experience. To get started, copy the Google Sheets template required for this workflow from here. 🎯 Target Audience This workflow is designed for: Industry professionals** looking to track key developments in their field. Research teams** who need up-to-date insights on specific topics. Companies** aiming to keep their teams informed with relevant content. ⚙️ How It Works Trigger: A Scheduler initiates the workflow at regular intervals (default: every hour). Data Retrieval: RSS feeds are fetched using the RSS Read node. Previously monitored articles are checked in Google Sheets to avoid duplicates. Content Processing: The article relevance is assessed using OpenAI (GPT-4o-mini). Relevant articles are scraped using Jina AI to extract content. Summaries are generated and formatted for Slack. Output: Summaries are posted to the specified Slack channel. Article metadata is stored in Google Sheets for tracking. 🛠️ Key APIs and Nodes Used Scheduler Node:** Triggers the workflow periodically. RSS Read:** Fetches the latest articles from defined RSS feeds. Google Sheets:** Stores monitored articles and manages feed URLs. OpenAI API (GPT-4o-mini):** Classifies article relevance and generates summaries. Jina AI API:** Extracts the full content of relevant articles. Slack API:** Posts formatted messages to Slack channels. This workflow provides an efficient and intelligent way to stay informed about your topics of interest, directly within Slack.

API Schema Extractor

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it works Stage 1 - Research: Fetches pending services from a Google Sheet Uses Google search to find API documentation Employs Apify for web scraping to filter relevant pages Stores webpage contents and metadata in Qdrant (vector database) Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: Processes services that completed research successfully Queries vector store to identify products and offerings Further queries for relevant API documentation Uses Gemini (LLM) to extract API operations Records extracted operations in Google Sheet Updates progress status (pending, ok, or error) Stage 3 - Generation: Takes services with successful extraction Retrieves all API operations from the database Combines and groups operations into a custom schema Uploads final schema to Google Drive Updates final status in sheet with file location Ideal for: Development teams needing to catalog multiple APIs API documentation initiatives Creating standardized API schema collections Automating API discovery and documentation Accounts required: Google account (for Sheets and Drive access) Apify account (for web scraping) Qdrant database Gemini API access Set up instructions: Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini. Ensure proper permissions for Google Drive access.

Visualize your SQL Agent queries with OpenAI and Quickchart.io

Overview This workflow aims to provide data visualization capabilities to a native SQL Agent. Together, they can help foster data analysis and data visualization within a team. It uses the native SQL Agent that works well and adds visualization capabilities thanks to OpenAI’s Structured Output and Quickchart.io. How it works Information Extraction: The Information Extractor identifies and extracts the user's question. If the question includes a visualization aspect, the SQL Agent alone may not respond accurately. SQL Querying: It leverages a regular SQL Agent: it connects to a database, queries it, and translates the response into a human-readable format. Chart Decision: The Text Classifier determines whether the user would benefit from a chart to support the SQL Agent's response. Chart Generation: If a chart is needed, the sub-workflow dynamically generates a chart and appends it to the SQL Agent’s response. If not, the SQL Agent’s response is output as is. Calling OpenAI for Chart Definition: The sub-workflow calls OpenAI via the HTTP Request node to retrieve a chart definition. Building and Returning the Chart: In the "Set Response" node, the chart definition is appended to a Quickchart.io URL, generating the final chart image. The AI Agent returns the response along with the chart. How to use it Use an existing database or create a new one. For example, I've used this Kaggle dataset and uploaded it to a Supabase DB. Add the PostgreSQL or MySQL credentials. Alternatively, you can use SQLite binary files (check this template). Activate the workflow. Start chatting with the AI SQL Agent. If the Text Classifier determines a chart would be useful, it will generate one in addition to the SQL Agent's response. Notes The full Quickchart.io specifications have not been fully integrated, so there may be some glitches (e.g., radar graphs may not display properly due to size limitations).

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FAQs

Can HTTP Request connect with Text Classifier?
Can I use HTTP Request’s API with n8n?
Can I use Text Classifier’s API with n8n?
Is n8n secure for integrating HTTP Request and Text Classifier?
How to get started with HTTP Request and Text Classifier integration in n8n.io?

HTTP Request and Text Classifier integration

How to connect HTTP Request and Text Classifier

Step 1: Create a new workflow and add the first step

Step 2: Add and configure HTTP Request and Text Classifier nodes

Step 3: Connect HTTP Request and Text Classifier

Step 4: Customize and extend your HTTP Request and Text Classifier integration

Step 5: Test and activate your HTTP Request and Text Classifier workflow

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Build your own HTTP Request and Text Classifier integration

HTTP Request and Text Classifier integration details

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FAQs

Can HTTP Request connect with Text Classifier?

Can I use HTTP Request’s API with n8n?

Can I use Text Classifier’s API with n8n?

Is n8n secure for integrating HTTP Request and Text Classifier?

How to get started with HTTP Request and Text Classifier integration in n8n.io?

Need help setting up your HTTP Request and Text Classifier integration?

Google Verification Denied

HTTP request, "impersonate a user" dynamic usage error

Why is my code getting executed twice?

How to send a single API request with one HTTP node execution, but an array of parameters in it (like emails[all]?)?

Start a Python script with external libraries - via API or Command Execution?

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Why use n8n to integrate HTTP Request with Text Classifier

Build complex workflows, really fast

Code when you need it, UI when you don't

Simple debugging

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