Ashwin Bharambe 68a2dfbad7 feat(ollama): periodically refresh models (#2805) ... For self-hosted providers like Ollama (or vLLM), the backing server is running a set of models. That server should be treated as the source of truth and the Stack registry should just be a cache for those models. Of course, in production environments, you may not want this (because you know what model you are running statically) hence there's a config boolean to control this behavior. _This is part of a series of PRs aimed at removing the requirement of needing to set `INFERENCE_MODEL` env variables for running Llama Stack server._ ## Test Plan Copy and modify the starter.yaml template / config and enable `refresh_models: true, refresh_models_interval: 10` for the ollama provider. Then, run: ``` LLAMA_STACK_LOGGING=all=debug \ ENABLE_OLLAMA=ollama uv run llama stack run --image-type venv /tmp/starter.yaml ``` See a gargantuan amount of logs, but verify that the provider is periodically refreshing models. Stop and prune a model from ollama server, restart the server. Verify that the model goes away when I call `uv run llama-stack-client models list`		2025-07-18 12:20:36 -07:00
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__init__.py	chore: remove nested imports (#2515)	2025-06-26 08:01:05 +05:30
event_logger.py	pre-commit lint	2024-09-28 16:04:41 -07:00
inference.py	feat(ollama): periodically refresh models (#2805)	2025-07-18 12:20:36 -07:00