Mastering Ollama: Build Private Local LLM Apps with Python

Establish the development environment by installing Python and a code editor, with VS Code as the example, and consult the Kinsta knowledge base to install Python if needed.

Explore practical tips to enhance your Udemy learning experience by using playback controls, notes, and progress tracking, and by navigating course content, Q&A, and announcements.

Access the course code by downloading resources or cloning the git repository, then follow along as the instructor codes throughout the course.

Explore Ollama, an open source tool that runs large language models locally via a CLI, enabling private, cost-efficient, low-latency models and retrieval augmented generation via embeddings.

Explore Ollama’s model management to download and switch between different large language models locally, with a unified interface, extensibility, and gpu-accelerated performance for private local llm apps in Python.

Install and set up Ollama locally, following the installation steps. Ensure your Mac, Linux, or Windows system has at least 10 GB of free storage and a modern CPU.

Download and install llama 3.2 with Ollama on mac, Linux, or Windows, run it locally, and interact via the shell to view model info and manage sessions.

Browse the Ollama models page to filter, test, and download local LLMs like llama 3.2 and 3.21b, choosing the best model for summarization, instruction following, and tool use.

Explore how Ollama parameters and architecture, from 3.2 billion weights to 131,072 token context and 3072 embedding dimensions, enable private local llm apps with four-bit quantization.

Explore how model parameters, disk size, and computation shape private local llm apps, compare models spanning billions to hundreds of billions of parameters, and assess benchmarks and practical resource needs.

List, remove, pull, and run models with Ollama commands to manage local models. Test Gemma by running it and testing a simple Python function.

Explore the Llava multimodal model that combines a vision encoder with vicuna to read images, describe scenes like flower_1.png, and save conversation history for a private local LLM workflow.

Demonstrate sentiment analysis and summarization with a llama 3.2 model, and customize behavior via a model file, adjusting temperature and system prompts to create a tailored local llm.

Harness the Ollama rest api to access generate and chat endpoints on localhost 11434, using curl with streaming off to receive full responses from a llama 3.2 model.

Explore how to request json mode with the Ollama rest api, forcing json responses, and use cli and rest endpoints to run, modify, and manage private local llm models.

Ollama lets you run local language models and supports text generation, code generation, and multimodal tasks using llama variants, code llama, and multimodal lava.

Explore different ways to interact with Ollama models, including the CLI, a UI-based interface, and the REST API, then leverage the Python library to build private local llm apps.

Learn to run private local LLMs with Ollama in Misty, enable one-click setup, offline private models, and build a RAG chat with documents using embeddings.

Explore interacting with llama models via CLI, REST API, and a UI interface with Misty, and learn to use the Ollama Python library to build local LLM applications.

Learn to interact with llama 3.2 locally using Ollama and the REST API in Python, including setting up a virtual environment, installing requests, and streaming responses.

Learn how to enable streaming in a chat function, pass messages and set stream to true, and print streamed responses in a loop using Python with Ollama.

Explore the Ollama show function and rest api wrappers in Python, using endpoints for chat, generate, and show to access model details and responses.

Create a custom model in code with Ollama, set temperature, and generate responses to build private local llm apps; manage models with the Python SDK and REST API.

Build a local llm app in Python that categorizes a grocery list with llama 3.2 and olama, sorts items alphabetically within categories like produce and dairy, and saves categorized lists.

Master retrieval augmented generation with Ollama to build private local llm apps. Understand how documents are chunked, embedded, stored in vector stores, and retrieved with Lang Chain to answer queries.

Explore how documents are loaded and split, converted into embeddings, and stored in a vector database; learn retrieval and how an LLM uses similar vectors to answer queries.

Build pdf RAG system using link chains to load pdfs, split text, and embed with Ollama into a database; retrieve with multi-query retriever and generate answers with llama 3.2.

Ingest a pdf with the unstructured pdf loader, split into 1200-token chunks with a 300-token overlap, generate olama embeddings, and build a chroma db vector store for a rag system.

Build a retrieval augmented generation system with LangChain and a multi query retriever to fetch documents from a vector database and drive answers with a rag prompt.

Explore how a cleaner, modular pdf rag implementation uses ingest pdf, split documents, create a vector db, retrieve data, and run a chain to answer questions efficiently.

Build a Streamlit UI for a retrieval augmented generation system that loads a vector DB and answers document questions, like beneficial ownership and filing penalties.

Explore function calling and tools to enhance private local LLM apps, enabling the model to fetch price and nutrition data and recipes from a grocery list.

Leverage function calling and tools to fetch a recipe for a random category using a private local llm. Build the product that processes prompts, calls tools, and returns a recipe.

Implement voice rag overview that loads pdfs, splits text, stores embeddings in chroma, uses a multi query retriever, runs llama 3.2 models, and converts responses to audio with 11 labs.

Securely obtain a free 11 labs api key, load multiple pdfs with the pdf lumber loader, and summarize documents using llama 3.2 to build private local llm apps with python.

Utilize recursive character text splitter to chunk ingested content, attach metadata, generate fast embeddings, store in chroma db, and perform multi-query rag retrieval with an llm for answering queries.

Discover how to use Eleven Labs to convert text responses from a local LLM app into spoken audio, including env setup, API key management, streaming, and optional audio file saving.