Step-by-Step Guide to Building a Local AI Chatbot with Meta LLaMA 3.2 1B

mage generated with DALL-E with the title Local LLM

While AI chatbots are among the hottest applications, they are often tied to proprietary or expensive online solutions. This tutorial walk-through shows you how to make a local AI chatbot with embedding and text-generation models. SentenceTransformer will be used for embeddings and we will use Meta LLaMA 3.2 (1B parameters) to generate the answers. We will also use Qdrant, a vector database to efficiently handle storing and retrieving context — ideal for enthusiasts or developers looking for local and powerful tools.

Now we will learn the steps one by one.

1. Setting Up the Environment

# Install pdfplumber for PDF text extraction
pip install pdfplumber
# Install Sentence Transformers for embeddings
pip install sentence-transformers
# Install Qdrant Client for vector database interaction
pip install qdrant-client
# Install Hugging Face Transformers for LLaMA and tokenizer
pip install transformers

We’ll use:

• pdfplumber: For extracting text from PDF files.
• SentenceTransformer: For embedding generation.
• Qdrant: For vector database storage and retrieval.
• transformers: This is for loading and using the local LLaMA model.