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Running on Zero
Running on Zero
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10cf611 128e312 10cf611 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | """Fallback vibe-interpretation: keyword matcher, brief→taxonomy mapping, JSON parse.
These cover the model-free path that runs whenever the LLM (Call 1) is absent or
returns malformed output — the path actually exercised off-GPU.
"""
from __future__ import annotations
from discoverroute.data import taxonomy
from discoverroute.interpret import affinity, keywords, llm_vibe, mapping
def _is_floored_affinity(aff: dict) -> bool:
return (set(aff) == set(taxonomy.CATEGORIES)
and all(0.0 <= v <= 1.0 for v in aff.values())
and abs(max(aff.values()) - 1.0) < 1e-9)
def test_keyword_affinity_matches_books_and_green():
aff = keywords.keyword_affinity("quiet green bookshops")
assert aff is not None and _is_floored_affinity(aff)
# the bookish/green categories should outrank a lively bar
assert aff["bookshop"] > aff["bar_pub"]
assert aff["park_garden"] > aff["bar_pub"]
def test_keyword_affinity_none_when_no_cue():
assert keywords.keyword_affinity("zzzz qwerty") is None
assert keywords.keyword_scores("") is None
def test_brief_scores_to_affinity_shape_and_modifiers():
# a pure "green" modifier should lift the greenest category to the top
aff = mapping.brief_scores_to_affinity({"green": 1.0})
assert _is_floored_affinity(aff)
assert aff["park_garden"] >= max(aff[c] for c in taxonomy.CATEGORIES)
def test_llm_json_extract_and_validate():
good = ('{"cafe":0.9,"park":0.1,"bookshop":0.2,"museum":0.3,"bakery":0.4,'
'"restaurant":0.5,"bar":0.6,"viewpoint":0.7,"market":0.8,"quiet":0.2,'
'"green":0.3,"historic":0.4,"busy":0.5,"detour_budget_multiplier":1.4}')
obj = llm_vibe._validate(llm_vibe._extract_json("noise " + good + " trailing"))
assert obj is not None and obj["cafe"] == 0.9
# missing a required key -> rejected
assert llm_vibe._validate(llm_vibe._extract_json('{"cafe":0.9}')) is None
# not JSON at all -> None
assert llm_vibe._extract_json("sorry, I cannot do that") is None
def test_llm_rejects_degenerate_weights():
"""All-zero / all-equal extractions pass _validate but carry no taste signal,
so _is_degenerate must flag them (the live trace showed a real all-zero row for
'quiet green wander' that the router then ignored)."""
keys = llm_vibe.REQUIRED_KEYS
all_zero = {k: 0.0 for k in keys}
all_zero["detour_budget_multiplier"] = 0.5
assert llm_vibe._is_degenerate(all_zero) is True
all_equal = {k: 0.5 for k in keys}
all_equal["detour_budget_multiplier"] = 1.0
assert llm_vibe._is_degenerate(all_equal) is True
# a real, differentiated weighting is kept
good = {k: 0.1 for k in keys}
good.update(park=0.9, green=0.9, quiet=0.7, detour_budget_multiplier=1.2)
assert llm_vibe._is_degenerate(good) is False
def test_resolve_affinity_neutral_on_empty():
aff, src = affinity.resolve_affinity("")
assert src == "neutral"
assert all(abs(v - 1.0) < 1e-9 for v in aff.values())
def test_resolve_affinity_returns_full_taxonomy():
aff, src = affinity.resolve_affinity("lively cafe crawl")
assert set(aff) == set(taxonomy.CATEGORIES)
assert src in {"llm", "embed", "keyword"}
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