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.
Need help customizing?
Contact me for consulting and support or add me on Linkedin.