Refs #776
* Implemented new llm prompt --schema and model.prompt(schema=)
* Log schema to responses.schema_id and schemas table
* Include schema in llm logs Markdown output
* Test for schema=pydantic_model
* Initial --schema CLI documentation
* Python docs for schema=
* Advanced plugin docs on schemas
- https://github.com/simonw/llm/issues/507#issuecomment-2458639308
* register_model is now async aware
Refs https://github.com/simonw/llm/issues/507#issuecomment-2458658134
* Refactor Chat and AsyncChat to use _Shared base class
Refs https://github.com/simonw/llm/issues/507#issuecomment-2458692338
* fixed function name
* Fix for infinite loop
* Applied Black
* Ran cog
* Applied Black
* Add Response.from_row() classmethod back again
It does not matter that this is a blocking call, since it is a classmethod
* Made mypy happy with llm/models.py
* mypy fixes for openai_models.py
I am unhappy with this, had to duplicate some code.
* First test for AsyncModel
* Still have not quite got this working
* Fix for not loading plugins during tests, refs #626
* audio/wav not audio/wave, refs #603
* Black and mypy and ruff all happy
* Refactor to avoid generics
* Removed obsolete response() method
* Support text = await async_mock_model.prompt("hello")
* Initial docs for llm.get_async_model() and await model.prompt()
Refs #507
* Initial async model plugin creation docs
* duration_ms ANY to pass test
* llm models --async option
Refs https://github.com/simonw/llm/pull/613#issuecomment-2474724406
* Removed obsolete TypeVars
* Expanded register_models() docs for async
* await model.prompt() now returns AsyncResponse
Refs https://github.com/simonw/llm/pull/613#issuecomment-2475157822
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Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
* change type of cli "embed -c" to be STR not FILE
The type of the "content" argument was erroneously set to be FILE with a "type=click.Path(...)" argument, which content is a string.
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Co-authored-by: Simon Willison <swillison@gmail.com>
* Embeddings plugin hook + OpenAI implementation
* llm.get_embedding_model(name) function
* llm embed command, for returning embeddings or saving them to SQLite
* Tests using an EmbedDemo embedding model
* llm embed-models list and emeb-models default commands
* llm embed-db path and llm embed-db collections commands
