File size: 3,023 Bytes
75263bf | 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 78 79 80 81 82 83 84 85 86 87 88 | """Capability stack: linked ingest + stable compose + substrate decoder."""
from palimseste.lsh import LSHConfig
from palimseste.memory import Memory
from palimseste.phi import Phi, KernelConfig
from palimseste.linked_ingest import LinkedEncoder
from palimseste.compose import analogize, compose_chain, ComposeConfig
from palimseste.substrate_decoder import SubstrateDecoder
def _stack(D=1024):
mem = Memory(D=D, lsh_config=LSHConfig.tune(D=D, target_radius=0.20, recall=0.95))
enc = LinkedEncoder(mem)
phi = Phi(KernelConfig(radius=max(80, D // 8), topk=12, min_weight=1e-6))
dec = SubstrateDecoder(enc, phi)
return enc, phi, dec
CORPUS = {
"capitals": (
"Paris is the capital of France. London is the capital of England. "
"Rome is the capital of Italy."
),
"cortex": (
"Palimpseste is an append-only hypervector cortex. "
"Learning is a single write. "
"Phi reconstructs values from a Hamming neighborhood."
),
}
def test_ingest_links_not_raw_dump():
enc, phi, dec = _stack()
rep = enc.ingest_document("capitals", CORPUS["capitals"], topics=["capital", "city"])
assert rep.n_sentences == 3
assert rep.n_traces > 10
tags = [tr.tag or "" for tr in enc.mem.traces]
assert any(t.startswith("NEXT:") for t in tags)
assert any(t.startswith("PREV:") for t in tags)
assert any(t.startswith("TOPIC:") for t in tags)
assert any(t.startswith("IDX:") for t in tags)
def test_encoder_deterministic():
enc, _, _ = _stack()
a = enc.encode_text("Paris is the capital of France")
b = enc.encode_text("Paris is the capital of France")
assert a == b
def test_analogize_identity():
enc, _, _ = _stack()
a = enc.encode_text("paris")
c = enc.encode_text("london")
assert analogize(a, a, c) == c
def test_decoder_recovers_ingested_tokens():
enc, phi, dec = _stack()
enc.ingest_document("capitals", CORPUS["capitals"], topics=["capital"])
bag = dec.decode_bag(enc.encode_text("Paris is the capital of France"), n=8, min_sim=-1.0)
joined = " ".join(bag.tokens)
assert "paris" in joined or "france" in joined or "capital" in joined
def test_compose_chain_does_not_rot_on_exact_items():
enc, phi, dec = _stack()
enc.ingest_document("cortex", CORPUS["cortex"], topics=["palimpseste", "phi"])
start = enc.encode_text("Palimpseste is an append-only hypervector cortex")
result = compose_chain(
enc.mem,
phi,
start,
steps=[("bundle", enc.encode_text("learning write"), None)],
config=ComposeConfig(floor=-0.2, max_hops=2),
)
assert result.hv is not None
assert result.hops
def test_answer_uses_substrate_not_llm():
enc, phi, dec = _stack()
enc.ingest_document("cortex", CORPUS["cortex"], topics=["palimpseste"])
out = dec.answer("what is palimpseste")
assert isinstance(out.text, str)
# decoder is a codebook peel + NEXT walk — no external model
assert out.tokens is not None
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