Simon Willison’s Weblog

• The same model ID no longer needs to be repeated in both the default model and allowed models lists - setting it as a default model automatically adds it to the allowed models list. #6
• Improved documentation for Python API usage.

• The actor who triggers an enrichment is now passed to the llm.mode(... actor=actor) method. #3

• Now uses datasette-llm to manage model configuration, which means you can control which models are available for extraction tasks using the extract purpose and LLM model configuration. #38

• This plugin now uses datasette-llm to configure and manage models. This means it's possible to specify which models should be made available for enrichments, using the new enrichments purpose.

• Removed features relating to allowances and estimated pricing. These are now the domain of datasette-llm-accountant.
• Now depends on datasette-llm for model configuration. #3
• Full prompts and responses and tool calls can now be logged to the llm_usage_prompt_log table in the internal database if you set the new datasette-llm-usage.log_prompts plugin configuration setting.
• Redesigned the /-/llm-usage-simple-prompt page, which now requires the llm-usage-simple-prompt permission.

• The llm_prompt_context() plugin hook wrapper mechanism now tracks prompts executed within a chain as well as one-off prompts, which means it can be used to track tool call loops. #5

• Ability to configure different API keys for models based on their purpose - for example, set it up so enrichments always use gpt-5.4-mini with an API key dedicated to that purpose. #4

I released llm-echo 0.3 to provide an API key testing utility I needed for the tests for this new feature.

LLM plugins can define new models in both sync and async varieties. The async variants are most common for API-backed models - sync variants tend to be things that run the model directly within the plugin.

My llm-mrchatterbox plugin is sync only. I wanted to try it out with various Datasette LLM features (specifically datasette-enrichments-llm) but Datasette can only use async models.

So... I had Claude spin up this plugin that turns sync models into async models using a thread pool. This ended up needing an extra plugin hook mechanism in LLM itself, which I shipped just now in LLM 0.30.

• The register_models() plugin hook now takes an optional model_aliases parameter listing all of the models, async models and aliases that have been registered so far by other plugins. A plugin with @hookimpl(trylast=True) can use this to take previously registered models into account. #1389
• Added docstrings to public classes and methods and included those directly in the documentation.

• Prompts now have the input_tokens and output_tokens fields populated on the response.

• Mechanisms for testing tool calls. #3
• Mechanism for testing raw responses. #4
• New echo-needs-key model for testing model key logic. #7

I'm working on integrating datasette-files into other plugins, such as datasette-extract. This necessitated a new release of the base plugin.

• owners_can_edit and owners_can_delete configuration options, plus the files-edit and files-delete actions are now scoped to a new FileResource which is a child of FileSourceResource. #18
• The file picker UI is now available as a <datasette-file-picker> Web Component. Thanks, Alex Garcia. #19
• New from datasette_files import get_file Python API for other plugins that need to access file data. #20

Adds the ability to configure which LLMs are available for which purpose, which means you can restrict the list of models that can be used with a specific plugin. #3

See Mr. Chatterbox is a (weak) Victorian-era ethically trained model you can run on your own computer.

I added an option to export a Markdown file from my app that lets Showboat incrementally publish updates to a remote server.

• actor is now available to the llm_prompt_context plugin hook. #2

A backend for datasette-files that adds the ability to store and retrieve files using an S3 bucket. This release added a mechanism for fetching S3 configuration periodically from a URL, which means we can use time limited IAM credentials that are restricted to a prefix within a bucket.

New release of the base plugin that makes models from LLM available for use by other Datasette plugins such as datasette-enrichments-llm.

• New register_llm_purposes() plugin hook and get_purposes() function for retrieving registered purpose strings. #1

One of the responsibilities of this plugin is to configure which models are used for which purposes, so you can say in one place "data enrichment uses GPT-5.4-nano but SQL query assistance happens using Sonnet 4.6", for example.

Plugins that depend on this can use model = await llm.model(purpose="enrichment") to indicate the purpose of the prompts they wish to execute against the model. Those plugins can now also use the new register_llm_purposes() hook to register those purpose strings, which means future plugins can list those purposes in one place to power things like an admin UI for assigning models to purposes.

The most interesting alpha of datasette-files yet, a new plugin which adds the ability to upload files directly into a Datasette instance. Here are the release notes in full:

• Columns are now configured using the new column_types system from Datasette 1.0a26. #8
• New file_actions plugin hook, plus ability to import an uploaded CSV/TSV file to a table. #10
• UI for uploading multiple files at once via the new documented JSON upload API. #11
• Thumbnails are now generated for image files and stored in an internal datasette_files_thumbnails table. #13

Datasette now has a mechanism for assigning semantic column types. Built-in column types include url, email, and json, and plugins can register additional types using the new register_column_types() plugin hook.

Adds support for OpenAI's new models gpt-5.4, gpt-5.4-mini, and gpt-5.4-nano.