HTTP Request and Vector Store Question Answer Tool integration

The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. Ideal for shops, commercial activities and restaurants How it works: Here's how it operates: Webhook Activation: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node. AI Agent Processing: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database. Knowledge Base Retrieval: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user’s query. Text Generation: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user’s question. Response Delivery: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user. Continuous Interaction: For ongoing conversations, the Window Buffer Memory ensures context retention by maintaining a history of interactions, enhancing the conversational flow. Set up steps: To configure this workflow effectively, follow these detailed setup instructions: ElevenLabs Agent Creation: Begin by creating an agent on ElevenLabs (e.g., named 'test_n8n'). Customize the first message and define the system prompt specific to your use case, such as portraying a character like a waiter at "Pizzeria da Michele". Add a Webhook tool labeled 'test_chatbot_elevenlabs' configured to receive questions via POST requests. Qdrant Collection Initialization: Utilize the HTTP Request nodes ('Create collection' and 'Refresh collection') to initialize and clear existing collections in Qdrant. Ensure you update placeholders QDRANTURL and COLLECTION accordingly. Document Vectorization: Use Google Drive integration to fetch documents from a designated folder. These documents are then downloaded and processed for embedding. Employ the Embeddings OpenAI node to generate embeddings for the downloaded files before storing them into Qdrant via the Qdrant Vector Store node. AI Agent Configuration: Define the system prompt for the AI Agent node which guides its behavior and responses based on the nature of queries expected (e.g., product details, troubleshooting tips). Link necessary models and tools including OpenAI language models and memory buffers to enhance interaction quality. Testing Workflow: Execute test runs of the entire workflow by clicking 'Test workflow' in n8n alongside initiating tests on the ElevenLabs side to confirm all components interact seamlessly. Monitor logs and outputs closely during testing phases to ensure accurate data flow between systems. Integration with Website: Finally, integrate the chatbot widget onto your business website replacing placeholder AGENT_ID with the actual identifier created earlier on ElevenLabs. By adhering to these comprehensive guidelines, users can successfully deploy a sophisticated voice-driven chatbot capable of delivering precise answers utilizing advanced retrieval-augmented generation techniques powered by OpenAI and ElevenLabs technologies.

The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot. How it works: Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages. Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent. AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base. Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations. Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o-mini) and sends it back to the user via WhatsApp. Set up steps: Create Qdrant Collection: Update the QDRANTURL and COLLECTION variables in the workflow. Use the Create collection HTTP request node to initialize the collection in Qdrant. Vectorize Documents: Configure the Get folder and Download Files nodes to fetch documents from a specified Google Drive folder. Use the Embeddings OpenAI node to generate embeddings for the downloaded files. Store the vectorized documents in Qdrant using the Qdrant Vector Store node. Configure Webhooks: Ensure both Verify and Respond webhooks have the same URL. Set the Verify webhook to use the GET HTTP method and the Respond webhook to use the POST HTTP method. Set Up AI Agent: Define the system prompt for the AI Agent, specifying guidelines for product information, technical support, customer service, and knowledge base usage. Link the AI Agent to the OpenAI chat model and configure any additional tools as needed. Test Workflow: Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all components are functioning correctly. Monitor the flow of data through the nodes and verify that responses are being generated and sent accurately. By following these steps, the workflow will be fully operational, enabling a robust AI-powered chatbot capable of handling customer inquiries via WhatsApp.

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!

An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via voice messages, which are transcribed and processed just like text queries. How It Works Message Capture: When an employee sends a message to the chatbot in WhatsApp or Telegram (text or audio), the chatbot captures the input. Audio Transcription: For audio messages, the chatbot transcribes the content into text using an AI-powered transcription service (e.g., Whisper, Google Cloud Speech-to-Text). Query Processing: The transcribed text (or directly entered text) is sent to an AI service (e.g., OpenAI) to generate embeddings. These embeddings are used to search a vector database (e.g., Supabase or Qdrant) containing the company’s internal HR and IT documentation. The most relevant data is retrieved and sent back to the AI service to compose a concise and helpful response. Response Delivery: The chatbot sends the final response back to the employee, whether the input was text or audio. Set Up Steps Estimated Time**: 20–25 minutes Prerequisites**: Create an account with an AI provider (e.g., OpenAI). Connect WhatsApp or Telegram credentials in n8n. Set up a transcription service (e.g., Whisper or Google Cloud Speech-to-Text). Configure a vector database (e.g., Supabase or Qdrant) and add your internal HR and IT documentation. Import the workflow template into n8n and update environment variables for your credentials.

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

Video Guide I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n. Youtube Link Who is this for? This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive. What problem does this workflow solve? Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files. What this workflow does The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include: Fetching and comparing files to avoid duplicate processing. Handling file downloads and extracting content based on the file type. Converting documents into vectorized data for contextual information retrieval. Storing and querying vectorized data from a Supabase vector store. File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content. Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions. Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot. Setup N8N Workflow Fetch File List from Supabase: Use Supabase to retrieve the stored file list from a specified bucket. Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing. Compare and Filter Files: Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table. Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled. Handle File Downloads: Download new files using detailed storage configurations for public/private access. Adjust the storage settings and GET requests to match your Supabase setup. File Type Processing: Use a Switch node to target specific file types (e.g., PDFs or text files). Employ relevant tools to process the content: For PDFs, extract embedded content. For text files, directly process the text data. Content Chunking: Break large text data into smaller chunks using the Text Splitter node. Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks. Vector Embedding Creation: Generate vectorized embeddings for the processed content using OpenAI's embedding tools. Ensure metadata, such as file ID, is included for easy data retrieval. Store Vectorized Data: Save the vectorized information into a dedicated Supabase vector store. Use the default schema and table provided by Supabase for seamless setup. AI Chatbot Integration: Add a chatbot node to handle user input and retrieve relevant document chunks. Use metadata like file ID for targeted queries, especially when multiple documents are involved. Testing Upload sample files to your Supabase bucket. Verify if files are processed and stored successfully in the vector store. Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?"). Test for accuracy and contextual relevance of retrieved results.