Building AI Apps in Python with Ollama

Matt Williams
1 Apr 202412:11

TLDRMatt introduces viewers to building applications with Ollama in Python. He explains the two main components of Ollama: the client and the service, and how to access the API. Matt covers the REST API endpoints for generating completions, managing models, and creating embeddings. He then demonstrates how to use the Python library to simplify streaming and non-streaming responses, and provides examples of using both the generate and chat endpoints, including handling images and maintaining conversational context. The video also shows how to work with a remote Ollama server setup. Matt encourages viewers to explore the Ollama Python library and join the Ollama community on Discord for further support.

Takeaways

  • 🚀 **Ollama Introduction**: Matt provides an introduction to developing applications with Ollama in Python, assuming prior knowledge of Ollama.
  • 🔌 **API Access**: Ollama consists of a client and a service, with the service running in the background and publishing the API.
  • 📚 **Documentation**: API endpoints are documented in the GitHub repo under docs/api.md for reference.
  • 🤖 **API Capabilities**: The API allows for model operations like creation, deletion, copying, listing, and information retrieval, as well as generating completions and embeddings.
  • 🗣️ **Chat vs Generate**: Choose between 'chat' and 'generate' endpoints based on whether the interaction requires conversational context.
  • 🌐 **API Endpoint Usage**: The 'generate' endpoint is used for single requests, while 'chat' is for ongoing conversations with the model.
  • 📈 **Streaming API**: Responses from most endpoints are in the form of a streaming API, providing JSON blobs with tokens and other information.
  • 📏 **Parameters and Options**: The 'generate' endpoint requires a 'model' parameter and offers additional parameters like 'prompt', 'images', and 'stream'.
  • 📦 **Python Library**: The Ollama Python library simplifies interaction with the API, handling streaming and non-streaming responses.
  • 🔄 **Context Management**: The context from one API call can be fed into the next to maintain conversational state.
  • 🖼️ **Image Processing**: For multimodal models, the Python library expects images as bytes objects, not base64 encoded strings.
  • 🔗 **Remote Access**: Ollama can be accessed remotely by setting up a server and adjusting the Ollama_host environment variable.
  • 📝 **Code Examples**: The VideoProjects repo contains code examples for various use cases, including non-streaming responses and image description.

Q & A

  • What are the two main components of Ollama?

    -The two main components of Ollama are a client and a service. The client is what runs when you type 'ollama run llama2' and is the REPL that you work with. The service is what 'ollama serve' starts up and runs as a background service that publishes the API.

  • Where can we find the REST API endpoints for Ollama?

    -You can find the REST API endpoints for Ollama at the GitHub repository under the 'docs' folder, specifically in the 'api.md' file.

  • What is the difference between the 'generate' and 'chat' endpoints?

    -Both 'generate' and 'chat' endpoints can generate a completion using the model. The difference lies in the use case: 'generate' is for one-off questions without holding a conversation, while 'chat' is more suitable for managing memory or context in a back-and-forth conversation with the model.

  • What is the 'model' parameter in the 'generate' endpoint?

    -The 'model' parameter in the 'generate' endpoint is the name of the model you want to load. If the model is already loaded, using this parameter will reset the unload timeout to another 5 minutes.

  • How does the 'prompt' parameter work in the 'generate' endpoint?

    -The 'prompt' parameter is the question you want to ask the model. It will be inserted into the actual model request based on the template defined in the model or the template specified in the request.

  • What is the 'stream' parameter for in the 'generate' endpoint?

    -The 'stream' parameter determines whether the response should be a continuous stream of JSON blobs or a single value after the generation is complete. If set to false, the response will be a single value, but you will have to wait until all tokens are generated.

  • How does the 'format' parameter affect the response?

    -The 'format' parameter, when set to 'json', specifies that the response should be in JSON format. It's also recommended to include 'respond as json' in the prompt and provide an example schema to ensure consistent output schema.

  • What is the purpose of the 'keep_alive' parameter?

    -The 'keep_alive' parameter defines how long the model should stay in memory. The default is 5 minutes, but you can set it to any time you like, or use -1 to keep it in memory indefinitely.

  • How does the Python library simplify the use of Ollama?

    -The Python library simplifies the use of Ollama by providing function calls that return a single object when not streaming or a Python Generator when streaming. It also handles the conversion between local and remote Ollama setups more seamlessly.

  • What is the process for using a remote Ollama setup?

    -To use a remote Ollama setup, you need to set up a server, install Ollama and llama2, configure tailscale with the server's machine name, set the Ollama_host environment variable to 0.0.0.0, and restart Ollama. Then, in your local code, change the Ollama import to point to the remote host.

  • What is the benefit of using the 'chat' endpoint over the 'generate' endpoint for conversations?

    -The 'chat' endpoint is more convenient for conversations as it allows for managing memory and context more effectively. It replaces the 'context', 'system', and 'prompt' parameters in 'generate' with 'messages', which is an array of message objects that can include various roles and content.

  • How does the Python library handle multimodal models with images?

    -Unlike the REST API, which expects base64 encoded strings for images, the Python library expects the image as a bytes object. It does not work with a base64 encoded string, simplifying the process of working with multimodal models.

Outlines

00:00

🚀 Introduction to Ollama API and Python Development

Matt introduces the audience to developing applications with Ollama using Python, assuming prior knowledge of Ollama. He offers a 10-minute intro for beginners and then delves into accessing the Ollama API, which has two main components: the client and the service. The client is used for interactive work, while the service runs in the background and publishes the API. Matt explains the REST API endpoints and their documentation location, emphasizing the importance of understanding the API before using the Python library. He outlines various actions possible with the API, such as generating completions, managing models, and creating embeddings. Matt also discusses the differences between the 'chat' and 'generate' endpoints, their use cases, and provides a detailed look at how to generate a completion with the 'generate' endpoint, including parameters and response format.

05:06

📚 Working with Ollama's Python Library

The paragraph covers the advantages of using the Ollama Python library, which simplifies the process of switching between streaming and non-streaming responses. Matt guides the audience through installing the library and provides a step-by-step coding example. He demonstrates how to use the 'ollama.generate' function with different parameters, including setting up a prompt and handling the response stream. The summary also touches on additional parameters like 'format', 'context', 'system', 'template', and 'keep_alive'. Matt then transitions to the 'chat' endpoint, explaining the structure of message objects and their roles. He provides examples of using the chat endpoint with messages and formatting the output as JSON. The paragraph concludes with a more complex example that includes an example schema and formatted outputs.

10:07

🌐 Remote Ollama API Usage and Conclusion

Matt discusses the possibility of setting up a remote Ollama server and using it as a client from a different machine. He walks through the process of installing Ollama on a Linux box, using tailscale for network configuration, and setting environment variables for the Ollama host. The paragraph includes instructions for modifying the Ollama client in the code to point to the remote host. Matt assures that other endpoints should be intuitive to use, based on the provided documentation and examples in the VideoProjects repository. He invites feedback in the comments for any unclear parts and encourages joining the Ollama community on Discord before concluding the video with a thank you message.

Mindmap

Keywords

💡Ollama

Ollama is a software application mentioned in the video that is used for developing AI applications. It is assumed that viewers already have some knowledge of it. In the context of the video, Ollama is used to demonstrate how to access its API and build applications that leverage its capabilities. It's a central theme around which the video's content is structured.

💡API

API, or Application Programming Interface, refers to a set of rules and protocols that allows different software applications to communicate and interact with each other. In the video, Matt discusses how to access the Ollama API, which has two main components: a client and a service, and how to use its REST API endpoints for various functionalities like generating completions, managing models, and more.

💡Client

In the context of the Ollama software, the client is the component that runs when the command 'ollama run llama2' is executed. It provides a Read-Eval-Print Loop (REPL) for interactive use. The client is a key part of how users interact with Ollama for developing applications.

💡Service

The service component in Ollama is what is started with the 'ollama serve' command. Unlike the client, the service operates in the background as a daemon, publishing the API that other components or external applications can use to interact with Ollama.

💡REPL

REPL stands for Read-Eval-Print Loop, which is an interactive programming environment where users can type in commands that are read, evaluated, and then the result is printed out. In the video, the client is described as a REPL that developers work with when using the Ollama application.

💡REST API Endpoints

REST API endpoints are the specific URLs designed for clients to interact with the web service through the HTTP protocol. In the video, Matt explains that Ollama's service publishes several REST API endpoints that can be used for different operations such as generating completions, managing models, and more.

💡Streaming API

A streaming API is a type of API that returns data in a continuous stream rather than as a single response. In the context of the video, Matt discusses how most of the endpoints in Ollama's API respond as a streaming API, providing JSON blobs that include tokens and other information about the model's response.

💡Multimodal Model

A multimodal model is a type of AI model that can process and understand multiple types of data, such as text and images. In the video, Matt mentions the use of the 'images' parameter when working with a multimodal model like Llava, where base64 encoded images can be provided for the model to process.

💡Python Library

The Python library mentioned in the video refers to a set of Python modules that have been developed to simplify interaction with the Ollama API. It allows developers to more easily work with Ollama's functionalities within a Python programming environment, making it simpler to switch between streaming and non-streaming responses.

💡Context

In the context of AI and the Ollama API, context refers to the information that is retained between interactions to maintain a coherent conversation or logical flow. Matt explains how to use the context from one API call to inform subsequent calls, especially when using the chat endpoint for ongoing dialogues with the model.

💡Keep Alive

The 'keep alive' parameter in the video determines how long a model should remain in memory after use. The default setting is 5 minutes, but it can be adjusted by the user to any desired duration, or set to -1 to keep the model loaded indefinitely. This is important for managing the performance and memory usage of applications using the Ollama API.

💡Discord

Discord is a communication platform that allows for text, voice, and video conversations. In the video, Matt invites viewers to join a Discord community related to Ollama at 'discord.gg/ollama'. This is a place where users can discuss, ask questions, and share knowledge about using Ollama for AI application development.

Highlights

Matt introduces building applications with Ollama using Python.

Assumption that viewers already know what Ollama is and how to work with it.

Introduction to Ollama available for those who need to catch up on basics.

Explanation of how to access the Ollama API.

Description of Ollama's two main components: the client and the service.

The service runs in the background and publishes the API.

Differentiation between the chat and generate endpoints based on use case.

Requirement of a 'model' parameter for the generate endpoint.

Usage of 'prompt' parameter to ask a question to the model.

Capability to work with multimodal models using the 'images' parameter.

Details on the response format and streaming API.

Option to set 'stream' to false for a single value response.

Importance of understanding the underlying API before using the Python library.

Overview of the Python library's ability to simplify streaming.

Demonstration of installing the Ollama Python library using pip.

Code examples illustrating the use of Ollama's generate function.

Explanation of how to use the 'context' parameter for continued conversations.

Process of describing an image using the Python module with a bytes object.

Usage of the chat endpoint in the Python library with message arrays.

Example of using format JSON for structured responses.

Setup and usage of a remote Ollama server for API calls.

Invitation to join the Ollama community on Discord for further support.