feat: switch LLM to OpenWebUI with live model selection

- Point the agent at OpenWebUI's OpenAI-compatible API (LLM_BASE_URL,
  LLM_API_KEY, LLM_MODEL) with GEMMA_* env fallbacks
- Add list_available_models() fetching {base_url}/models, filtering
  non-chat ids (embedding/reranker)
- Rename GemmaLLM -> HopeLLM with update_options(model=...) for runtime
  model switching (mutates _opts.model) and instance-based compaction
- Data channel: agent publishes {type: models} after connect and on
  get_models; set_model switches live and acks model_changed
- Web UI: Brain section in the settings sheet with a model picker that
  persists the choice in localStorage
- Update tests, CI workflow env, and docs for the new endpoint

tests/test_compaction.py

@@ -1,26 +1,27 @@
-"""Tests for GemmaLLM context compaction (agent.GemmaLLM._compact_context).
+"""Tests for HopeLLM context compaction (agent.HopeLLM._compact_context).
 
-Compaction runs a real side LLM call to the xNAS, so these are marked slow.
-The agent module is imported inside the container via docker exec.
+Compaction runs a real side LLM call to the configured endpoint, so these
+are marked slow. The agent module is imported inside the container via
+docker exec.
 """
 
 import pytest
 
-from conftest import GEMMA_BASE_URL, GEMMA_MODEL, docker_exec_python
+from conftest import LLM_BASE_URL, LLM_MODEL, docker_exec_python
 
 # {num_messages} and {do_chat} are filled per test; all other braces are literal.
 TEMPLATE = '''
-import logging, sys
+import logging, os, sys
 from livekit.agents import llm as lk_llm
 sys.path.insert(0, "/opt/voice-agent")
-from agent import GemmaLLM
+from agent import HopeLLM
 
 logging.basicConfig(level=logging.INFO, stream=sys.stdout)
 
-llm = GemmaLLM(
+llm = HopeLLM(
     model="{model}",
     base_url="{base_url}",
-    api_key="not-needed",
+    api_key=os.environ.get("LLM_API_KEY", "not-needed"),
     max_completion_tokens=100,
     extra_body={{"chat_template_kwargs": {{"enable_thinking": False}}}},
 )
@@ -44,7 +45,7 @@ async def main():
     print("AFTER items=" + str(len(compacted.items)))
     for it in compacted.items:
         role = getattr(it, "role", None)
-        content = GemmaLLM._msg_content_text(it)
+        content = HopeLLM._msg_content_text(it)
         print("ITEM " + str(role) + ": " + content[:120])
 
     {do_chat}
@@ -57,8 +58,8 @@ CHAT_SNIPPET = '    stream = llm.chat(chat_ctx=compacted)\n    collected = await
 
 def _run(num_messages: int, *, do_chat: bool = False) -> str:
     code = TEMPLATE.format(
-        model=GEMMA_MODEL,
-        base_url=GEMMA_BASE_URL,
+        model=LLM_MODEL,
+        base_url=LLM_BASE_URL,
         num_messages=num_messages,
         do_chat=CHAT_SNIPPET if do_chat else "pass",
     )