llama-stack-mirror/llama_stack/providers
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
..
inline chore: add mypy inference parallel utils (#2670) 2025-07-18 12:01:10 +02:00
registry fix: only load mcp when enabled in tool_group (#2621) 2025-07-04 20:27:05 +05:30
remote feat(ollama): periodically refresh models (#2805) 2025-07-18 12:20:36 -07:00
utils feat: create dynamic model registration for OpenAI and Llama compat remote inference providers (#2745) 2025-07-16 12:49:38 -04:00
__init__.py API Updates (#73) 2024-09-17 19:51:35 -07:00
datatypes.py docs: auto generated documentation for providers (#2543) 2025-06-30 15:13:20 +02:00