Build Real-Time AI Voice Assistant With RAG Pipeline And Memory | Mistral LLM | Ollama | LlamaIndex

Start your journey from ground zero and master the creation of a real-time AI voice assistant using Python's RAG pipeline. This comprehensive tutorial guides you through the process of building an advanced assistant capable of handling voice interactions, transcribing speech, and generating intelligent responses from scratch. Ideal for those eager to dive into AI development, this guide offers a solid foundation for creating powerful voice-enabled applications. Perfect for call centers, customer support, and virtual receptionist applications.
In this comprehensive tutorial, you'll learn how to integrate top AI technologies:
✅ Faster Whisper: A reimplementation of Whisper from OpenAI Speech-to-Text API, ensuring faster and precise real-time transcription.
✅ TTS (Google Text-to-Speech): Harness Google Translate's text-to-speech API with ease using Python.
✅ Qdrant Vector DB: Leverage vector data storage for efficient processing.
✅ LlamaIndex: Master this premier data framework for robust LLM applications.
✅ Ollama: Unleash the power of large language models locally, streamlining your workflow.
✅ Mistral AI Model: 7B quantized version model by Mistral AI
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github.com/ayaansh-roy/voice_...
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Timestamps:
00:00 - Intro
00:05 - Highlights from Demo
00:35 - Components in pipeline
00:55 - Faster Whisper
04:53 - GTTS
07:05 - Code Explanation
17:57 - Ollama
19:55 - Qdrant Vector DB
22:54 - Demo
Follow along as we build a Python application that seamlessly integrates these tools, enabling your AI assistant to comprehend speech, generate contextually relevant responses, and interact with users in real-time.
Use Case:
Imagine a customer calling Bangalore Kitchen restaurant and engaging with a voice assistant bot to place orders effortlessly. This tutorial transforms that vision into reality.
Why Watch This Tutorial ?
✅ Master the creation of a state-of-the-art real-time AI voice assistant with Python's RAG pipeline
✅ Explore seamless integration of RAG with chat memory through llamaindex's ChatMemoryBuffer
✅ Gain practical expertise to implement advanced AI concepts into your projects
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[@jayakrishnanp5988]

Amazingly simplified ❤ Kindly make a terraform file too ❤ And if integrated with advanced ai voices from specialised vendors like 11 labs it will enhance this much more . ❤❤❤

[@mernik5599]

Great video man! Comments were disabled on your latest video so came here to appreciate. You're making unique application! Also just wanted to ask if it's possible to add internet access/function calling to ollama run web ui? Im hosting the open web ui server using ngrok and using it on my phone, would be incredibly useful if we could enhance the functionality using function calling

[@alfierimorillo]

Hello, what a good job! Thanks for sharing it, I have a question, and that is, does this work with other languages? Is it possible to make it work with other languages?

[@user-pr6nm2di6d]

I seriously don't know how KZbin didn't recommend your channel. Ur awesome boss. This project is really awesome. Better than most of them outhere.. Kudos, keep posting more. I request you to make one video to save chat history memory with Redis cache or supabase or any open-source DB that nakes more sense in practical use case ❤

[@ayaanshroy]

Thank you so much for your incredibly kind words and support! 🌟 I'm truly grateful for your encouragement and appreciation of the project. I appreciate your suggestion on saving chat history memory with Redis cache or Supabase, or any open-source database. It's an excellent idea and aligns well with practical use cases. I'll definitely consider creating a video on this topic in the near future. Stay tuned for more content!

[@tharunbhaskar6795]

Noiceee. You can replace the Google TTS with an open source TTS model like the SpeechT5. I have made a similar project but have used Microsoft SpeechT5 as I wanted everything to be local and not sending any information outside my host

[@ayaanshroy]

Hey thanks for watching the video, using an open-source TTS model like SpeechT5 is a fantastic idea for keeping everything local and maintaining privacy. I will definitely try it in my future experiments ;)

[@AkshatGupta-kw9tp]

that's really awesome. Waiting for you to drop a link to the codebase. Dope!

[@ayaanshroy]

Thanks a lot, soon sharing the GIT link in the description!

[@anthonycadden120]

looking forward to the link @@ayaanshroy

[@zsoltilibai3417]

hey! i have this problem : ModuleNotFoundError: No module named 'llama_index.vector_stores

[@ayaanshroy]

Hi I recently updated the requirements.txt please get an update, it should resolve the issue!

[@techgiant__]

This is really cool, I've always wanted to do this. Can this run with something like LM studio quantized models?

[@ayaanshroy]

Thank you! I'm glad you found it cool. Yes, the system can indeed run with quantized models on LM Studio, infact Ollama and LM Studio both these frameworks use quantized GGUF models

[@irraz1]

Hi! It seems that the code is not usable, it gives error due to update: "llama-index, ServiceContext has to be migrated to Settings"

[@ayaanshroy]

Hi I recently updated the requirements.txt please get an update, it should resolve the issue!

[@irraz1]

@@ayaanshroy Thank you very much, commenting pywin32 has worked for me now. Although after the first AI response, I get an OSError: [Errno -9981] Input overflowed