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

Description

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?
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