Build intelligent voice conversations with dual AI models
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Workflow Description
Automation that receives voice requests via webhook and processes them using OpenAI and Google Gemini models, maintaining conversation context in memory to ensure coherent and contextually aware responses throughout extended dialogues.
How it works
- 1.Receive voice request via webhook and identify the sender
- 2.Store previous conversation context in memory manager
- 3.Process text through LLM chain and route to optimal AI model
- 4.Generate response from OpenAI or Gemini based on context
- 5.Send processed reply back via webhook to the client
Use cases
- Self-service customer support with context-aware sequential conversations
- Intelligent assistant chatbots that retain memory of extended interactions
Requirements
- Valid API keys for OpenAI and Google Gemini
- Configured webhook endpoint to receive voice requests
- Sufficient memory capacity to store multiple conversation contexts
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (11)
Get Chat
Memory Manager
Insert Chat
Memory Manager
Sticky Note5
Sticky Note
Sticky Note
Sticky Note
Aggregate
Aggregate
Window Buffer Memory
Memory Buffer Window
Google Gemini Chat Model
Gemini Model
Respond to Webhook
Webhook
ElevenLabs - Generate Audio
HTTP Request
Sticky Note2
Sticky Note
Sticky Note1
Sticky Note
Limit
Limit
Basic LLM Chain
LLM Chain
Webhook
Webhook
OpenAI - Speech to Text
OpenAI
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