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- 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
95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
"""Tests for HopeLLM context compaction (agent.HopeLLM._compact_context).
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Compaction runs a real side LLM call to the configured endpoint, so these
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are marked slow. The agent module is imported inside the container via
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docker exec.
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"""
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import pytest
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from conftest import LLM_BASE_URL, LLM_MODEL, docker_exec_python
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# {num_messages} and {do_chat} are filled per test; all other braces are literal.
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TEMPLATE = '''
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import logging, os, sys
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from livekit.agents import llm as lk_llm
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sys.path.insert(0, "/opt/voice-agent")
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from agent import HopeLLM
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logging.basicConfig(level=logging.INFO, stream=sys.stdout)
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llm = HopeLLM(
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model="{model}",
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base_url="{base_url}",
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api_key=os.environ.get("LLM_API_KEY", "not-needed"),
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max_completion_tokens=100,
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extra_body={{"chat_template_kwargs": {{"enable_thinking": False}}}},
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)
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items = [lk_llm.ChatMessage(role="system", content=["You are Hope, a voice assistant."])]
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for i in range({num_messages}):
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items.append(lk_llm.ChatMessage(role="user", content=["user message number " + str(i)]))
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items.append(lk_llm.ChatMessage(role="assistant", content=["agent reply number " + str(i)]))
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ctx = lk_llm.ChatContext(items)
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print("BUILT ctx with " + str(len(ctx.items)) + " items")
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import asyncio
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async def main():
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try:
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compacted = llm._compact_context(ctx, None)
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except Exception as e:
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print("COMPACT_ERROR " + repr(e))
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return
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print("AFTER items=" + str(len(compacted.items)))
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for it in compacted.items:
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role = getattr(it, "role", None)
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content = HopeLLM._msg_content_text(it)
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print("ITEM " + str(role) + ": " + content[:120])
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{do_chat}
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asyncio.run(main())
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'''
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CHAT_SNIPPET = ' stream = llm.chat(chat_ctx=compacted)\n collected = await stream.collect()\n print("CHAT_OK text_len=" + str(len(collected.text)))'
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def _run(num_messages: int, *, do_chat: bool = False) -> str:
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code = TEMPLATE.format(
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model=LLM_MODEL,
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base_url=LLM_BASE_URL,
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num_messages=num_messages,
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do_chat=CHAT_SNIPPET if do_chat else "pass",
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)
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proc = docker_exec_python(code, timeout=120, check=False)
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return proc.stdout + (("\n" + proc.stderr) if proc.stderr else "")
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@pytest.mark.slow
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def test_compaction_triggers_on_size():
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"""A context with > 24 items must compact without crashing and chat() must still work."""
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# 13 pairs = 26 non-system + 1 system = 27 items (> threshold of 24)
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out = _run(13, do_chat=True)
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assert "BUILT ctx with 27 items" in out
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assert "Context compacted:" in out
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assert "CHAT_OK text_len=" in out
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@pytest.mark.slow
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def test_compaction_preserves_system_prompt():
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out = _run(28)
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lines = [l for l in out.splitlines() if l.startswith("ITEM system:")]
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assert lines, f"no system message survived compaction:\n{out}"
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assert "You are Hope" in lines[0]
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def test_short_context_no_compaction():
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"""Context with non_sys <= KEEP_RECENT must NOT be compacted."""
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# system + 4 user/assistant pairs = 9 items, non_sys=8 <= KEEP_RECENT(10)
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out = _run(4)
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assert "BUILT ctx with 9 items" in out
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# _compact_context should return the context unchanged (early return)
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assert "AFTER items=9" in out, f"expected no compaction:\n{out}"
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