Nexa AI: Create Local AI Chatbot for FREE! (EASY Guide)

Quick Overview

The video demonstrates how to create a free, locally-run AI chatbot using the Nexa SDK, which is an open-source developer toolkit that allows running any AI model on any backend across various hardware, including NPUs, GPUs, and CPUs, by following installation steps for the CLI, running a local inference server, and then building a basic web UI using Chainlit and ChromaDB for Retrieval Augmented Generation (RAG) against private data like a PDF.

Key Points: Nexa SDK is an open-source toolkit enabling local inference for any AI model across diverse hardware (NPU, GPU, CPU) and platforms, featuring NPU-first support and compatibility with GGUF, MLX, and Nexa formats. The demonstration involved installing the Nexa CLI, running the local Nexa AI Server on http://127.0.0.1:18181/docs/ui, and successfully running inference on a Gemma model locally (02:57). A Chainlit application was built using an OpenAI-compatible client pointing to the local Nexa server (baseurl="http://127.0.0.1:18181/v1"), enabling streaming responses (04:33). For RAG implementation, necessary packages (PyPDF2, chromadb, sentence-transformers) were installed, and ChromaDB was used as the vector store to index chunks generated from an uploaded PDF file (09:01). The RAG code incorporates functions to chunk text, generate embeddings using 'all-MiniLM-L6-v2', store chunks in ChromaDB, and query the database based on user input before passing context to the LLM for the final answer (08:28, 09:43). The final result shows the Chainlit UI successfully answering a question about the uploaded IBM PDF document using the locally served Nexa AI model (10:58).

Context: The video serves as a tutorial explaining how to leverage the Nexa SDK, an open-source AI inference engine, to build a custom Retrieval Augmented Generation (RAG) chatbot that runs entirely locally on a user's machine. This setup bypasses cloud services by using the local Nexa AI server and integrating it with Python libraries like Chainlit for the front-end UI and ChromaDB for vector storage of private documents.