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See llms.txt for all machine-readable content.
📊 Description
This workflow allows users to ask questions about past meetings using their voice. It converts the voice question into text, searches stored meeting notes using Pinecone, and replies with a spoken answer generated by AI.
It helps teams quickly recall decisions, tasks, and discussions without reading long meeting notes. 🧠🔊
🔁 What This Template Does
1️⃣ Receives a voice question through a webhook endpoint. 🌐
2️⃣ Converts the audio question into text using speech transcription. 🎤➡️📝
3️⃣ Cleans and prepares the question for searching. ✅
4️⃣ Converts the question into an embedding for semantic search. 🔍
5️⃣ Searches relevant meeting notes from Pinecone using the team namespace. 📚
6️⃣ Combines retrieved meeting context into a single readable format. 🧩
7️⃣ Uses AI to answer the question strictly from the meeting context. 🤖
8️⃣ Converts the AI’s text answer into spoken audio. 🔊
9️⃣ Sends the audio response back to the user via webhook. 🔁
⭐ Key Benefits
✅ Allows hands-free access to meeting information
✅ Saves time searching through meeting notes
✅ Prevents AI hallucinations using RAG
✅ Supports multiple teams using namespaces
✅ Works with voice-based tools and assistants
✅ Improves meeting recall and clarity
🧩 Features
- Voice-based question input
- Speech-to-text transcription
- Semantic search using Pinecone
- Team-based data isolation
- Context-aware AI responses
- Text-to-speech output
- Webhook-driven architecture
🔐 Requirements
- OpenAI API key for transcription, embeddings, and speech generation
- Azure OpenAI credentials for chat responses
- Pinecone API key with a configured index
- Matching embedding model for ingest and query
- Webhook client capable of sending audio files
🎯 Target Audience
- Teams that conduct frequent meetings
- Managers needing quick decision recall
- Remote and distributed teams
- Product, engineering, and operations teams
- Automation builders using n8n
- Organizations adopting voice-based workflows