Voice AI Chatbot with OpenAI, RAG (Qdrant) & Guardrails for WordPress

This workflow implements a complete Voice AI Chatbot system for Wordress that integrates speech recognition, guardrails for safety, retrieval-augmented generation (RAG), Qdrant vector search, and audio responses. It is designed to be connected to a WordPress Voicebot AI plugin through a webhook endpoint.

Key Advantages

✅ Complete Voice AI Pipeline** The workflow handles: audio input STT intelligent processing TTS output All within a single automated process.

✅ Safe and Policy-Compliant Thanks to the Guardrails module, the system automatically: detects harmful or disallowed requests blocks them responds safely This protects both the user and the business.

✅ Contextual and Memory-Based Conversations The Window Buffer Memory tied to unique session IDs enables: continuous conversation flow natural dialogue better understanding of context

✅ Company-Specific Knowledge via RAG By integrating Qdrant as a vector store, the system can: retrieve business documentation give accurate and up-to-date answers support personalized content This makes the chatbot far more powerful than a standard LLM.

✅ Modular and Extensible Architecture Because everything is modular inside n8n, you can: swap OpenAI with other models add new tools or knowledge sources change prompts or capabilities without redesigning the entire workflow.

✅ **Easy WordPress Integration The workflow connects directly to a WordPress Voicebot plugin, meaning: no custom backend development simple deployment fast integration for websites

✅ Automatic Indexing of Documents The second workflow section: fetches Google Drive files converts them into embeddings indexes them into Qdrant This lets you maintain your knowledge base with almost no manual work.

How It Works

This workflow creates a Wordpress voice-enabled AI chatbot that processes audio inputs and provides contextual responses using RAG (Retrieval-Augmented Generation) from a Qdrant vector database. The system operates as follows:

Audio Processing Pipeline: Receives audio input via webhook and converts speech to text using OpenAI's STT (Speech-to-Text) Applies guardrails to detect inappropriate content or jailbreak attempts using a separate GPT-4.1-mini model Routes safe queries to the AI agent and blocks unsafe content with a default response

AI Agent with Contextual Memory: Uses OpenAI Chat Model with window buffer memory to maintain conversation context Equips the agent with two tools: Calculator for computations and RAG tool for business knowledge retrieval The RAG system queries Qdrant vector store containing company documents using OpenAI embeddings

Response Generation: Generates appropriate text responses based on query type and available knowledge Converts approved responses to audio using OpenAI's TTS (Text-to-Speech) with "onyx" voice Returns binary audio responses to the webhook caller

Set Up Steps

Vector Database Preparation: Create Qdrant collection via HTTP request with specified vector configuration Clear existing collection data before adding new documents Set up Google Drive integration to source documents from specific folders

Document Processing Pipeline: Search and retrieve files from Google Drive folder "Test Negozio" Process documents through recursive text splitting (500 chunk size, 50 overlap) Generate embeddings using OpenAI and store in Qdrant vector store Implement batch processing with 5-second delays between operations

System Configuration: Configure webhook endpoint for receiving audio inputs Set up multiple OpenAI accounts for different functions (STT, TTS, guardrails, main agent) Establish Qdrant API connections for vector storage and retrieval Implement session-based memory management using session IDs from webhook headers

WordPress Integration: Install the provided Voicebot AI Agent WordPress plugin Configure the plugin with the webhook URL to connect to this n8n workflow The system is now ready to receive audio queries and respond with voice answers

The workflow handles both real-time voice queries and background document processing, creating a comprehensive voice assistant solution with business-specific knowledge retrieval capabilities.

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