mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-28 14:07:55 +00:00
Elliot SluskyandGitHub
433d10db5e
Adds the openjarvis.memory package (LocalFactStore, FactExtractor, background MemoryService), starts/stops it in the jarvis serve and jarvis chat lifecycle, feeds completed non-streaming exchanges to it, adds [memory] config support, and adds jarvis memory list/clear CLI commands. Extraction runs on a background thread and degrades to a no-op on any failure (BrokenPipe, timeouts, unparseable output) so it can never block a reply or crash the host. Disabled by default. Closes #393, #571, #572, #573.
106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
"""Tests for the LLM-backed fact extractor (openjarvis.memory.extractor)."""
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from __future__ import annotations
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from openjarvis.memory.extractor import FactExtractor
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class FakeEngine:
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"""Engine stub returning a canned completion (or raising)."""
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def __init__(self, content="", *, raises=None):
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self._content = content
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self._raises = raises
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self.calls = []
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def generate(self, messages, *, model, temperature=0.7, max_tokens=1024, **kwargs):
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self.calls.append((messages, model, temperature, max_tokens))
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if self._raises is not None:
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raise self._raises
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return {"content": self._content}
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def test_parses_json_array():
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engine = FakeEngine('["User likes coffee", "User lives in Berlin"]')
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extractor = FactExtractor(engine, "qwen3:14b")
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facts = extractor.extract("I like coffee and live in Berlin", "Noted.")
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assert facts == ["User likes coffee", "User lives in Berlin"]
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def test_parses_json_array_wrapped_in_prose():
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engine = FakeEngine('Sure! Here are the facts:\n["Fact A", "Fact B"]\nDone.')
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extractor = FactExtractor(engine, "m")
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assert extractor.extract("hi", "hello") == ["Fact A", "Fact B"]
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def test_empty_array_returns_no_facts():
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engine = FakeEngine("[]")
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extractor = FactExtractor(engine, "m")
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assert extractor.extract("just chatting", "ok") == []
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def test_line_fallback_for_bullets():
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engine = FakeEngine("- User is a teacher\n- User has two kids\n")
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extractor = FactExtractor(engine, "m")
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assert extractor.extract("about me", "noted") == [
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"User is a teacher",
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"User has two kids",
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]
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def test_dedupe_within_turn():
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engine = FakeEngine('["likes tea", "Likes Tea", "likes tea"]')
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extractor = FactExtractor(engine, "m")
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assert extractor.extract("x", "y") == ["likes tea"]
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def test_cap_facts_per_turn():
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items = [f'"fact {i}"' for i in range(20)]
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engine = FakeEngine("[" + ", ".join(items) + "]")
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extractor = FactExtractor(engine, "m", max_facts_per_turn=3)
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assert len(extractor.extract("x", "y")) == 3
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def test_truncates_long_facts():
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long_fact = "z" * 500
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engine = FakeEngine(f'["{long_fact}"]')
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extractor = FactExtractor(engine, "m", max_fact_chars=50)
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facts = extractor.extract("x", "y")
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assert len(facts) == 1
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assert len(facts[0]) == 50
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def test_empty_user_text_skips_engine():
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engine = FakeEngine('["should not be called"]')
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extractor = FactExtractor(engine, "m")
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assert extractor.extract(" ", "y") == []
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assert engine.calls == []
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def test_broken_pipe_returns_empty():
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engine = FakeEngine(raises=BrokenPipeError("client gone"))
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extractor = FactExtractor(engine, "m")
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# Must not raise — extraction is best-effort.
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assert extractor.extract("hi", "hello") == []
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def test_generic_exception_returns_empty():
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engine = FakeEngine(raises=RuntimeError("ollama exploded"))
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extractor = FactExtractor(engine, "m")
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assert extractor.extract("hi", "hello") == []
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def test_handles_non_dict_result():
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class StrEngine:
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def generate(self, *a, **k):
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return '["plain string result"]'
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extractor = FactExtractor(StrEngine(), "m")
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assert extractor.extract("x", "y") == ["plain string result"]
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def test_filters_non_fact_tokens():
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engine = FakeEngine('["none", "N/A", "Real fact"]')
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extractor = FactExtractor(engine, "m")
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assert extractor.extract("x", "y") == ["Real fact"]
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