5ee47827f3
Spoken (TTS) replies are 1-2 sentences, so an unbounded num_predict only exposes the worst case where the chat model rambles or loops. Add an ollama_num_predict config (default 512, 0 disables) wired into the reply loop's chat call on both the native- and text-tool paths. The 512-token headroom stays well above this app's short tool-call JSON, so capping never truncates a tool call. This keeps the user's quality model instead of downgrading it. Configurable in the container via OLLAMA_NUM_PREDICT. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
"""Tests for the ``ollama_num_predict`` chat-output cap.
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The cap bounds worst-case reply latency by limiting how many tokens the chat
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model may generate per turn. Spoken (TTS) answers are 1-2 sentences, so the
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default headroom never clips a normal answer and stays above tool-call JSON.
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These tests verify behaviour:
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- the config default is present,
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- the value is threaded into the Ollama request as the ``num_predict`` option,
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- the reply loop forwards it to the chat call (and disables it at 0).
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"""
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from unittest.mock import Mock, patch
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import pytest
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from src.jarvis.config import get_default_config
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from src.jarvis.memory.conversation import DialogueMemory
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from src.jarvis.reply.engine import run_reply_engine
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# ---------------------------------------------------------------------------
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# Config default
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# ---------------------------------------------------------------------------
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def test_default_config_has_num_predict_cap():
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config = get_default_config()
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assert config["ollama_num_predict"] == 512
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# ---------------------------------------------------------------------------
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# Transport: extra_options.num_predict reaches the Ollama payload options
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# ---------------------------------------------------------------------------
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@patch("jarvis.llm.requests.post")
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def test_chat_with_messages_forwards_num_predict(mock_post):
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from jarvis.llm import chat_with_messages
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mock_resp = Mock()
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mock_resp.status_code = 200
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mock_resp.json.return_value = {"message": {"content": "ok"}}
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mock_resp.raise_for_status = Mock()
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mock_post.return_value = mock_resp
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chat_with_messages(
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"http://localhost:11434",
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"test-large",
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[{"role": "user", "content": "hi"}],
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extra_options={"num_predict": 512},
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)
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_, kwargs = mock_post.call_args
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options = (kwargs.get("json") or {}).get("options") or {}
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assert options.get("num_predict") == 512
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# ---------------------------------------------------------------------------
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# Reply loop wiring
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# ---------------------------------------------------------------------------
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def _mock_cfg(num_predict):
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cfg = Mock()
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cfg.ollama_base_url = "http://localhost:11434"
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cfg.ollama_chat_model = "test-large" # avoid SMALL-model text-tool path
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cfg.ollama_num_predict = num_predict
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cfg.voice_debug = False
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cfg.llm_tools_timeout_sec = 8.0
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cfg.llm_embed_timeout_sec = 10.0
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cfg.llm_chat_timeout_sec = 45.0
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cfg.llm_digest_timeout_sec = 8.0
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cfg.memory_enrichment_max_results = 5
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cfg.memory_enrichment_source = "diary"
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cfg.memory_digest_enabled = False
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cfg.tool_result_digest_enabled = False
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cfg.location_ip_address = None
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cfg.location_auto_detect = False
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cfg.location_enabled = False
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cfg.agentic_max_turns = 8
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cfg.tool_search_max_calls = 3
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cfg.tool_selection_strategy = "all"
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cfg.tool_carryover_max_turns = 2
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cfg.tool_carryover_per_entry_chars = 1200
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cfg.mcps = {}
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cfg.llm_thinking_enabled = False
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cfg.tts_engine = "none"
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cfg.ollama_embed_model = "test-embed"
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return cfg
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def _run_single_turn(cfg):
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"""Drive one reply turn that answers in plain text and capture the
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chat call's extra_options."""
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with patch("src.jarvis.reply.engine.plan_query", return_value=[]), \
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patch("src.jarvis.reply.engine.extract_search_params_for_memory", return_value={}), \
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patch("src.jarvis.reply.engine.extract_text_from_response", return_value="Hello."), \
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patch("src.jarvis.reply.engine.chat_with_messages") as mock_chat:
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mock_chat.return_value = {"message": {"content": "Hello."}}
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run_reply_engine(db=Mock(), cfg=cfg, tts=None,
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text="hi", dialogue_memory=DialogueMemory())
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assert mock_chat.called
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return mock_chat.call_args.kwargs.get("extra_options")
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@pytest.mark.unit
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def test_reply_loop_caps_output_when_enabled():
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extra = _run_single_turn(_mock_cfg(512))
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assert extra == {"num_predict": 512}
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@pytest.mark.unit
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def test_reply_loop_no_cap_when_zero():
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extra = _run_single_turn(_mock_cfg(0))
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assert extra is None
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