Using Ollama To Build a FULLY LOCAL "ChatGPT Clone"
TLDRThe video script demonstrates how to build a chatbot using open-source models with the aid of Olama, a tool that simplifies running large language models on a computer. It showcases the ease of downloading Olama, selecting from a variety of models, and running them in parallel. The script also highlights the speed and efficiency of Olama in handling multiple models and provides a step-by-step guide on creating a chat GPT clone using Python and Gradio, emphasizing the customization and integration capabilities of Olama.
Takeaways
- 🚀 The video provides a tutorial on building a chatbot using open-source models with the Olama platform.
- 💻 Olama is a tool that allows users to run large language models on their computers and build applications on top of them.
- 📋 Olama supports running multiple models in parallel, showcasing its capability with popular open-source models like Code Llama, Llama 2, Mistol, and Zephyr.
- 📌 The platform is currently available for MacOS and Linux, with a Windows version in development and potential workaround using WSL for Windows.
- 🔗 Olama's lightweight nature is highlighted by the presence of a small icon in the taskbar, with most operations done through the command line.
- 📈 The video demonstrates the speed and efficiency of Olama by running models sequentially and simultaneously, with quick model swapping.
- 📝 The video shows how to customize the system message prompt by creating a model file and adjusting parameters like temperature and system prompts.
- 🔄 Olama offers a variety of integrations, including web and desktop UIs, terminal integrations, and libraries like Lang chain and Llama index, as well as extensions and plugins.
- 🛠️ The process of building a chat GPT clone using Olama is outlined, starting from setting up a local Python environment to creating a gradio front end for user interaction.
- 📊 The video script includes a practical example of generating a response from the Mistol model by sending a request to the local API with a specific prompt.
- 🔄 The concept of maintaining conversation history is introduced to allow the chatbot to reference previous messages, enhancing the user experience.
Q & A
What is the primary purpose of the tool Ollama mentioned in the script?
-Ollama is a tool designed to run large language models on your computer and build applications on top of them. It allows users to run multiple models in parallel, making it efficient for various tasks.
Which operating systems currently support Ollama, and is there a version for Windows in development?
-Ollama is currently available for Mac OS and Linux. A Windows version is in development and expected to be released soon.
How can Windows users potentially use Ollama before the official Windows version is released?
-Windows users can potentially use WSL (Windows Subsystem for Linux) to run Ollama on their systems.
What is the significance of the models available on Ollama?
-The models available on Ollama, such as Code Llama, Llama 2, Mistol, and others, are popular open-source models that can be used for various tasks. They are constantly being added and updated to provide users with a wide range of options.
How does Ollama handle running multiple models simultaneously?
-Ollama can run multiple models simultaneously by queuing them up and running them sequentially. The swapping between models is fast, allowing for efficient multitasking.
What is a potential use case for Ollama's ability to run multiple models?
-One potential use case is having the right model for the right task. This allows for a centralized model to act as a dispatcher, assigning different tasks to the most appropriate models.
How can users adjust the system message prompt in Ollama?
-Users can adjust the system message prompt by creating a model file, specifying the desired prompt, and then running Ollama with the updated model file.
What integrations does Ollama offer for building applications?
-Ollama offers various integrations, including web and desktop UIs like HTML and chatbot interfaces, terminal integrations, and libraries like Lang chain and Llama index. It also supports extensions and plugins for platforms like Discord.
How is the basic functionality of generating a response using Ollama implemented in Python?
-The basic functionality is implemented by making a POST request to the local URL where Ollama is running, with the appropriate headers and data, including the model name and prompt. The response is then collected and printed.
What is the significance of the 'stream' parameter in the Ollama API?
-The 'stream' parameter controls whether the response is returned as a stream of JSON objects or as a single JSON object containing the full response and additional data about the generation.
How can the chat GPT clone maintain a conversation history?
-The chat GPT clone maintains a conversation history by storing the previous messages in an array. This history is then included in the prompt for subsequent requests to the model, allowing it to generate responses that are aware of the context of the conversation.
Outlines
🚀 Introduction to Building Chatbots with Olama
The paragraph introduces the process of building a chatbot using an open-source model with the help of Olama, a tool that simplifies running large language models on a computer. It highlights Olama's ability to run multiple models in parallel, which was impressive to the speaker. The speaker guides the audience through downloading Olama, which is currently available for Mac OS and Linux, and briefly mentions the upcoming Windows version. The ease of use and lightweight nature of Olama is emphasized, as well as its capability to handle popular open-source models like Code Llama, Mistol, and others. The speaker also demonstrates the speed and efficiency of running models through command line and discusses the potential for using Olama to build applications with multiple models running simultaneously.
📚 Developing Chatbot Functionality with Olama and Mistol
This paragraph delves into the development of chatbot functionality using Olama and Mistol. The speaker explains how to run models through the command line, showcasing the speed and efficiency of the process. A demonstration is provided where the speaker runs two models, Mistol and Llama 2, side by side, and prompts them to write a thousand-word essay about AI. The ability to switch between models quickly and the potential use cases for this capability, such as having the right model for the right task or integrating with autogen, are discussed. The speaker also covers how to adjust the system message prompt by creating a model file and changing settings within it, and then testing the changes with a new model profile, 'Mario', which responds in character.
🛠️ Building a Chat GPT Clone with Olama and Python
The speaker begins by creating a new project folder named 'open chat' and outlines the steps to build a chat GPT clone using open-source models. A new Python file is created, and the speaker uses Python libraries 'requests' and 'json' to interact with a local API running on port 11434. The initial attempt to use Mistol 7B encounters an error, which is resolved by adjusting the syntax. The speaker then refines the code to stream JSON objects and extract the model's response, focusing on the answer rather than additional data. Further, the speaker adds a Gradio front end to allow for browser interaction and conversational capabilities. The paragraph concludes with the speaker enhancing the chatbot to remember previous messages by storing conversation history and incorporating it into the prompt for context-aware responses.
Mindmap
Keywords
💡Chatbot
💡Olama
💡Open-source models
💡Command line
💡Parallel processing
💡Integrations
💡Model file
💡Gradio
💡Conversation history
💡API
💡Streaming
Highlights
The introduction of Olama, an easy-to-use platform for running large language models on your computer.
Olama's ability to run multiple models in parallel, providing a fast and efficient experience.
The demonstration of Olama's lightweight nature, as evidenced by the small taskbar icon.
The availability of popular open-source models like Code Llama, Llama 2, Mistol, and Zephyr on Olama.
A step-by-step guide on how to download and use Olama, including the use of WSL for Windows users.
The process of running a model through the command line using Olama and the speed at which the model operates.
The demonstration of running multiple models simultaneously and the quick swapping between them.
The practical application of having the right model for the right task, acting as a dispatch model for different tasks.
The potential integration of Olama with Autogen for running different models sequentially on the same computer.
The creation of a model file for customization and the ability to adjust the system message prompt.
Olama's extensive integration options, including web and desktop integrations, various UIs, terminal integrations, and plugins.
The process of building a chat GPT clone using open-source models, demonstrated through the creation of a new Python file.
The use of Python libraries 'requests' and 'json' for generating a completion and interacting with the local API.
The implementation of a Gradio front end to enable browser-based interaction with the chat GPT clone.
The addition of conversation history to allow the model to remember and reference previous messages in the chat.
The successful demonstration of a working chat GPT clone powered by the Mistol model, showcasing the capabilities of Olama.
The encouragement for viewers to explore further and build more sophisticated applications using Olama.