Building AI Apps in Python with Ollama
TLDRIn this informative session, Matt introduces viewers to building applications with Ollama using Python. He assumes familiarity with Ollama and offers a brief introduction for those who need it. The focus is on accessing the Ollama API, which has two main components: the client and the service. Matt explains the REST API endpoints and their uses, such as generating completions, managing models, and creating embeddings. He emphasizes the importance of understanding the underlying API before using the Python library. The video also covers how to generate completions using the 'generate' endpoint, the significance of parameters like 'model', 'prompt', and 'stream', and the differences between the 'generate' and 'chat' endpoints. Matt demonstrates using the Python library to simplify streaming and non-streaming responses, and provides code examples for generating text and describing images. He concludes with a discussion on using Ollama with a remote server, showcasing the ease of adapting local calls to a remote environment. The session is a valuable resource for developers looking to integrate Ollama into their applications.
Takeaways
- 🚀 **Ollama Overview**: Matt introduces Ollama, a tool for developing applications with Python, assuming prior knowledge of Ollama and its basic operations.
- 📚 **API Access**: Ollama consists of a client (used with `ollama run llama2`) and a service (started with `ollama serve`), which runs in the background and publishes the API.
- 🌐 **API Endpoints**: The service offers REST API endpoints documented on GitHub, enabling various operations like model management and completion generation.
- 💬 **Chat vs Generate**: Two endpoints for generating completions are `chat` and `generate`; `generate` is for one-off requests, while `chat` is for managing conversations and context.
- 📈 **Streaming API**: Responses from most endpoints are in a streaming format, providing JSON blobs with tokens, model information, and completion status.
- 🔄 **Image Support**: For multimodal models, images can be included as a base64 encoded array, with the Python library simplifying this process.
- 📏 **API Parameters**: Parameters like `model`, `prompt`, `stream`, `format`, and `keep_alive` control the behavior of the API, with the Python library offering a more straightforward interface.
- 🔗 **Python Library**: The Ollama Python library (`ollama-python`) simplifies API interactions, handling streaming and non-streaming responses with ease.
- 🔑 **Context Management**: The context from one API call can be used in subsequent calls to maintain conversational state, especially important for chat applications.
- 🌟 **Remote Access**: Ollama can be hosted on remote servers, with examples provided for setting up and accessing a remote Ollama instance.
- 📝 **Documentation and Support**: Comprehensive documentation is available on GitHub, and the community can be reached via Discord for further assistance.
Q & A
What are the two main components of Ollama?
-The two main components of Ollama are the client and the service. The client runs when you use the command 'ollama run llama2', and it's the REPL (Read-Eval-Print Loop) that you interact with. The service, which is started with 'ollama serve', typically runs in the background as a service and is responsible for publishing the API.
Where can I find the documentation for the Ollama REST API endpoints?
-You can find the documentation for the Ollama REST API endpoints on the GitHub repository under the 'docs' folder, specifically in the 'api.md' file.
What is the purpose of the 'generate' endpoint in the Ollama API?
-The 'generate' endpoint is used to generate a completion from a model. It is suitable for one-off requests where you want to ask a question to a model and receive an answer without maintaining a conversational context.
How does the 'chat' endpoint differ from the 'generate' endpoint?
-The 'chat' endpoint is designed for situations where you need to have a back-and-forth conversation with the model, managing memory and context. It is more convenient for interactive dialogues, whereas the 'generate' endpoint is better for single requests.
What is the required parameter for the 'generate' endpoint?
-The only required parameter for the 'generate' endpoint is 'model', which specifies the name of the model you want to load.
How can images be used with a multimodal model in Ollama?
-Images can be used with a multimodal model by providing an array of base64 encoded images. The model can only process base64 encoded images, so the conversion must be done beforehand.
What does the 'stream' parameter do in the Ollama API?
-The 'stream' parameter determines whether the API response is a continuous stream of JSON blobs or a single value after the completion of the generation. If set to false, the response will wait until all tokens are generated and then return them in a single response.
How does the Python library simplify working with the Ollama API?
-The Python library simplifies the interaction with the Ollama API by providing function calls that return a single object for non-streaming responses or a Python Generator for streaming responses. It abstracts away some of the complexities of the API and makes it easier to work with in a Python environment.
What is the default behavior for the 'generate' function in the Ollama Python library?
-In the Ollama Python library, the 'generate' function defaults to not streaming, meaning it returns a single response object. This is different from the REST API, which defaults to streaming.
How can you manage conversational context when using the Ollama Python library?
-You can manage conversational context by saving the value of the context from the last response and feeding it into the context of the next call to the 'generate' endpoint in the Python library.
What is the role of the 'keep_alive' parameter in the Ollama API?
-The 'keep_alive' parameter determines how long a model should stay in memory after a request. It can be set to any duration or -1 to keep the model in memory indefinitely. The default is 5 minutes.
How can you use the Ollama API with a remote server?
-You can use the Ollama API with a remote server by setting up the Ollama host environment variable to point to the remote host's address. The Python library allows you to create a new Ollama client that targets the remote host, enabling you to interact with the API as if it were local.
Outlines
🚀 Introduction to Ollama and API Access
Matt introduces the audience to developing applications with Ollama using Python. He assumes prior knowledge of Ollama and focuses on leveraging it for application development. The video outlines how to access the Ollama API, which consists of a client and a service component. The client is used for interactive sessions, while the service runs in the background and publishes the API. The API offers various functionalities, including generating completions, managing models, and creating embeddings. Two main endpoints, 'chat' and 'generate', are introduced, each suitable for different use cases. The 'generate' endpoint is preferred for one-off requests, while 'chat' is more convenient for ongoing conversations. The video also covers the parameters required for using these endpoints and how responses are structured.
📚 Understanding API Parameters and Python Library
The paragraph delves into the nuances of using the Ollama API, emphasizing the importance of understanding the underlying API before working with the Python library. It discusses various parameters like 'model', 'prompt', 'images', and 'stream', and their roles in API requests. The 'format' parameter and its use for specifying JSON responses are also explained. The paragraph then transitions to the Python library, which simplifies the process of switching between streaming and non-streaming responses. Matt demonstrates how to install and use the Ollama Python library, showing examples of generating completions, handling contexts, and describing images using the library. He also touches on the use of the 'chat' endpoint in the Python library.
🌐 Remote Ollama Setup and Further Exploration
Matt demonstrates how to set up and work with a remote Ollama server, which is particularly useful when the development machine is not the same as the server hosting Ollama. He walks through the process of setting up a Linux box, installing Ollama, and using tools like tailscale for network configuration. The video concludes with a discussion on how to adapt the local Ollama client to point to a remote host and how this allows the code to function seamlessly across different machines. Matt also invites viewers to explore additional examples in the provided code repository and to reach out with any questions or for clarification.
Mindmap
Keywords
💡Ollama
💡API
💡Client
💡Service
💡REPL
💡REST API Endpoints
💡Model
💡Completion
💡Streaming API
💡Python Library
💡Multimodal Model
💡Context
Highlights
Matt introduces Ollama, a tool for developing applications with Python.
Ollama has two main components: a client and a service.
The client is the REPL interface, while the service runs in the background and publishes the API.
The CLI is an API client that uses the standard public API.
The service offers REST API endpoints documented on GitHub.
API capabilities include generating completions, managing models, and generating embeddings.
Two endpoints for generating completions: 'chat' and 'generate', chosen based on use case.
The 'generate' endpoint is suitable for one-off requests without conversational context.
The 'chat' endpoint is more convenient for managing memory and context in ongoing conversations.
The 'generate' endpoint requires a 'model' parameter and optionally a 'prompt'.
Images can be used with multimodal models and must be base64 encoded.
Responses are streamed as JSON blobs, including model, created_at, response, and done.
The 'stream' parameter can be set to false for a single value response after generation.
The 'format' parameter allows specifying the output format, with JSON being a common choice.
The Python library simplifies the use of Ollama, handling streaming and non-streaming responses.
The 'ollama.generate' function is used for generating completions with a given model and prompt.
The context from a previous 'generate' call can be fed into a subsequent call to maintain conversation state.
Images can be described using the Python library by providing them as bytes objects.
The 'chat' endpoint in the Python library uses an array of message objects with roles and content.
The 'format json' option can be used to specify the expected JSON schema in the prompt for consistent responses.
Ollama can be hosted remotely, and the Python library can connect to a remote Ollama instance by changing the host variable.
The video includes a walkthrough of code examples and usage of Ollama's Python library.
Join the Ollama community on Discord for further questions and support.