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

Here's a detailed summary of the YouTube video transcript:

Key Concepts

• Nexa SDK: An open-source tool for running AI models locally on various hardware (GPU, NPU, CPU, mobile).
• Local AI: Running AI models entirely on a user's computer, ensuring data privacy.
• NPU (Neural Processing Unit): Specialized hardware for AI computations.
• MLX: A framework for machine learning on Apple Silicon (NPU).
• Multimodal Support: The ability of an AI model to process and understand different types of data, such as text and images.
• OpenAI Compatible API: An interface that allows applications to interact with AI models in a standardized way, similar to OpenAI's API.
• Olama: Another tool for running AI models locally, but with limitations compared to Nexa SDK in the video.
• GGUF: A file format for storing AI models, commonly used for CPU inference.
• VLM (Visual Language Model): An AI model capable of understanding both text and images.
• CLI (Command Line Interface): A text-based interface for interacting with software.
• Nexa Serve: A command to start a local server for Nexa SDK.
• Retrieval Augmented Generation (RAG): A technique that enhances LLM responses by retrieving relevant information from an external knowledge base.
• Embeddings: Numerical representations of text or other data that capture semantic meaning.
• Vector Database: A database optimized for storing and querying embeddings.
• ChromaDB: A specific vector database used in the demonstration.
• Chunking: The process of dividing large documents into smaller, manageable pieces for processing.
• Chainlit: A Python library for building user interfaces for AI applications.
• Agentic AI: AI systems designed to act autonomously to achieve goals.

Nexa SDK: Local AI with Enhanced Capabilities

The video introduces Nexa SDK as a powerful open-source tool that enables users to run AI models entirely locally on their computers, ensuring data privacy. A key differentiator highlighted is its comprehensive hardware support, including GPUs, NPUs, CPUs, and mobile devices.

Key Features and Advantages:

• Local and Private: All data remains on the user's machine.
• Hardware Agnostic: Supports GPU, NPU, CPU, and mobile.
• NPU and MLX Support: Nexa SDK supports Neural Processing Units (NPUs) and the MLX framework, which is crucial for efficient AI processing on Apple Silicon. This is presented as a significant advantage over tools like Olama, which, at the time of the video, did not support MLX or NPUs.
• Full Multimodal Support: Capable of handling various data types, including audio and images.
• OpenAI Compatible API: Allows seamless integration with applications designed for OpenAI's API.
• Versatile Model Support: Can run the latest Large Language Models (LLMs) and Visual Language Models (VLMs), including models like Quen 3 (VL 4B and 8B) in GGUF format.
• Built-in Server: Nexa SDK includes a server that can be easily started with a single command.

Step-by-Step Installation and Usage of Nexa SDK

The video provides a practical, step-by-step guide to setting up and using Nexa SDK.

Step 1: Download and Install Nexa CLI

1. Download: Users are directed to download the Nexa CLI based on their operating system (Mac OS ARM 64, Windows, or Linux). The presenter uses the ARM 64 version for Mac OS.
2. Installation: The downloaded file is opened, and the installation process is completed with a few clicks.

Step 2: Running AI Models via CLI

1. Open Terminal: A new terminal window is opened.
2. Run nexa info: The command nexa info <model_name> is used to download and load an AI model. For example, nexa info quen-3-vl-8b (though the specific model name might vary in the actual command).
3. Model Download and Loading: Upon execution, the CLI automatically downloads the specified model and loads it.
4. Interaction: Users can then interact with the loaded model by typing questions. The presenter demonstrates this by asking "how are you?" and receiving a response.
5. CLI Features: The CLI offers options for loading conversations, saving conversations, audio transcription (for audio models like Whisper), clearing the session, and exiting.

Example with a Visual Language Model (VLM):

• The presenter demonstrates running a VLM by downloading a model (e.g., Quen 3).
• Image Upload and Query: An image is dragged into the interface, and the user asks, "What is this?".
• VLM Response: The VLM correctly identifies the content of the image as "visual studio code AI test automation."

Step 3: Starting the Nexa Server

1. Command: The command nexa serve is executed in the terminal.
2. Server Information: The terminal displays the local URL where the server is hosted.
3. Accessing the Server: Opening the provided URL in a web browser shows the server's running status and available API endpoints, indicating readiness for application integration.

Building a Chatbot with Nexa SDK and Python

The video then transitions to building a functional chatbot application using Python, leveraging the local Nexa server.

1. Setting up the Development Environment:

• Install Packages: The necessary Python packages are installed using pip:
  • pip install openai (for interacting with the OpenAI-compatible API)
  • pip install chainlit (for creating a user interface)

2. Basic Chatbot Application (app.py):

• Code Structure: A Python file (app.py) is created.
• Initialization: The openai library is imported, and an OpenAI client is initialized with the Nexa server's base URL and a placeholder API key.

  from openai import OpenAI
  client = OpenAI(
      base_url="http://localhost:8000/v1", # Example URL from nexa serve
      api_key="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Placeholder
  )
  

• Creating a Chat Completion: The client.chat.completions.create method is used to send a prompt to the AI model.
  • model: Specifies the name of the downloaded AI model.
  • messages: A list containing system and user messages.
    • System message: "You are a helpful assistant."
    • User message: "Give me a meal plan for me today."
• Output: The response from the AI model is printed.

3. Implementing Streaming Responses:

• Modification: To provide a more user-friendly experience, the code is modified to enable streaming.
  • stream=True is added to the create call.
  • A loop iterates through the streamed response chunks and prints them as they arrive.
• Running the Streaming Code: A new file (e.g., stream.py) is created with this modification and run using python stream.py. This demonstrates the AI's response being generated and displayed word by word.

4. Creating a User Interface with Chainlit (ui.py):

• Code Modification: The Python code is further adapted to use Chainlit for a web-based UI.
  • import chainlit as cl is added.
  • Decorators @cl.on_chat_start and @cl.on_message are used to manage chat events.
• @cl.on_chat_start: This function runs when a new chat session begins. It can be used to set up initial messages or load conversation history.
• @cl.on_message: This function is triggered when the user sends a message.
  • It takes the user's message as input.
  • It calls client.chat.completions.create with the user's message dynamically.
  • It streams the response back to the UI.
  • It saves message history to maintain context.
• Running the UI Code: The Chainlit application is launched using chainlit run ui.py.
• Web Interface: A URL is provided to access the chatbot's web interface. Users can type prompts, and the chatbot responds in real-time.

Implementing Retrieval Augmented Generation (RAG)

The video demonstrates how to implement RAG to allow the chatbot to answer questions based on private documents.

RAG Process:

1. Data Ingestion (Step 1):

   • Chunking: Uploaded documents (e.g., PDFs) are divided into smaller chunks.
   • Embeddings: Each chunk is converted into numerical embeddings using a model like "all-MiniLM-L6-v2" (from sentence-transformers).
   • Vector Database Storage: These embeddings are stored in a vector database (ChromaDB in this example).

2. Querying and Generation (Step 2):

   • Semantic Search: When a user asks a question, the question is also converted into an embedding.
   • Retrieval: The vector database is queried to find the most semantically similar chunks to the question's embedding.
   • Contextualization: The retrieved chunks are combined with the user's question and sent to the LLM as context.
   • Accurate Response: The LLM generates a more accurate and context-aware answer based on the provided information.

Implementation Details:

• Required Packages: Additional packages are installed:
  • pip install pypdf (for PDF text extraction)
  • pip install chromadb (for the vector database)
  • pip install sentence-transformers (for embedding models)
• Code Modifications (rag.py):
  • Embedding Model: The get_embedding function is created to generate embeddings.
  • Chunking Function: A chunk_text function is implemented to divide text into smaller parts.
  • ChromaDB Integration: ChromaDB is initialized, and a collection is created to store the embeddings.
  • @cl.on_chat_start: This function handles document upload. It reads the PDF, chunks the text, generates embeddings, and stores them in ChromaDB.
  • @cl.on_message: This function queries ChromaDB for relevant information based on the user's question and then passes this context to the LLM for response generation.

Demonstration of RAG:

1. Run RAG Code: The RAG application is launched using chainlit run rag.py.
2. Access UI: The application is accessible via a provided URL.
3. Upload Document: A PDF file (e.g., about "agentic AI") is uploaded. The system indexes the document, creating 12 chunks.
4. Ask Question: The user asks, "Tell me what is written in this IBM file." (Note: The presenter mentions "IBM file" but the uploaded file was about "agentic AI," suggesting a slight discrepancy or a general example).
5. Backend Processing: The video shows the backend processing, where chunks are converted to embeddings and stored.
6. RAG Chatbot Response: The chatbot provides a summary of the text, demonstrating its ability to answer questions based on the uploaded document.

Conclusion and Call to Action

The presenter expresses strong satisfaction with Nexa SDK, highlighting its capabilities for local AI development. They encourage viewers to try Nexa SDK and share their feedback in the comments. The video concludes with a standard call to action for liking, sharing, and subscribing.

Key Takeaways:

• Nexa SDK offers a robust and flexible solution for running AI models locally, prioritizing data privacy.
• Its support for NPUs and MLX makes it particularly valuable for modern hardware.
• The SDK simplifies the process of deploying and interacting with AI models through its CLI and built-in server.
• Integrating Nexa SDK with Python frameworks like Chainlit allows for the rapid development of user-friendly AI applications.
• The implementation of RAG with Nexa SDK enables chatbots to leverage private data for more informed responses.