# Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Google Axion processors

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/rag/)
- [Demo](https://learn.arm.com/learning-paths/servers-and-cloud-computing/rag/_demo/)
- [Set up a RAG based LLM Chatbot](https://learn.arm.com/learning-paths/servers-and-cloud-computing/rag/rag_llm/)
- [Deploy a RAG-based LLM backend server](https://learn.arm.com/learning-paths/servers-and-cloud-computing/rag/backend/)
- [Deploy RAG-based LLM frontend server](https://learn.arm.com/learning-paths/servers-and-cloud-computing/rag/frontend/)
- [The RAG Chatbot and its Performance](https://learn.arm.com/learning-paths/servers-and-cloud-computing/rag/chatbot/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/rag/_next-steps/)

## About this Learning Path

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| Skill level: | Advanced |
| Reading time: | 45 min |
| Last updated: | 09 Sep 2026 |

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| Author: | Nobel Chowdary Mandepudi , Arm |
| Arm IP: | [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors) |
| Tags: | [ML](https://learn.arm.com/tag/ml) [Google Axion](https://learn.arm.com/tag/google-axion) [Linux](https://learn.arm.com/tag/linux) [Python](https://learn.arm.com/tag/python) [Streamlit](https://learn.arm.com/tag/streamlit) [Google Axion](https://learn.arm.com/tag/google-axion) [Demo](https://learn.arm.com/tag/demo) [Hugging Face](https://learn.arm.com/tag/hugging-face) |

### Who is this for?
This Learning Path is for software developers, ML engineers, and those looking to deploy production-ready LLM chatbots with Retrieval Augmented Generation (RAG) capabilities, knowledge base integration, and performance optimization for Arm Architecture.

### What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Set up llama-cpp-python optimized for Arm servers.
- Implement RAG architecture using the Facebook AI Similarity Search (FAISS) vector database.
- Optimize model performance through 4-bit quantization.
- Build a web interface for document upload and chat.
- Monitor and analyze inference performance metrics.

### Prerequisites
Before starting, you'll need the following prerequisites:
- A Google Cloud Axion (or other Arm) compute instance with at least 16 cores, 8GB of RAM, and 32GB disk space.
- Basic understanding of Python and ML concepts.
- Familiarity with REST APIs and web services.
- Basic knowledge of vector databases.
- Understanding of LLM fundamentals.