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ca6f1af | 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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | """Tests for the fact-chaining reasoner (``palimseste.reasoning``).
Verifies:
- A→B + B→C chaining: model can answer A→C without being taught the chain
- Multi-hop chaining (3 hops)
- No false chaining when direct answer exists
- Graceful failure when no chain is possible
- Reasoning trace is populated correctly
- Chaining respects max_hops limit
"""
from __future__ import annotations
import pytest
from palimseste.lm import PalimpsesteForCausalLM, PalimpsesteConfig
from palimseste.chat import Conversation, FALLBACK_RESPONSE
from palimseste.reasoning import Reasoner, ChainResult
import numpy as np
def _build_model(pairs, D=3000, radius=0, ctx=128):
cfg = PalimpsesteConfig(D=D, context_window=ctx, kernel_radius=radius,
temperature=0.0)
lm = PalimpsesteForCausalLM(config=cfg)
full = "".join(q + a for q, a in pairs)
lm.build_tokenizer(full)
lm.train_on_qa_pairs(pairs)
return lm
class TestFactChaining:
"""Core test: A→B + B→C = A→C."""
def test_two_hop_chain(self):
"""Teach two facts, ask a question that requires chaining them."""
pairs = [
("who won the world cup 2018", "france"),
("what is the capital of france", "paris"),
]
lm = _build_model(pairs)
conv = Conversation(model=lm, fuzzy_threshold=0.95, learn_live=True)
conv.register_questions(pairs)
# direct: should work
assert conv.respond("who won the world cup 2018", temperature=0.0, seed=0) == "france"
conv.reset()
assert conv.respond("what is the capital of france", temperature=0.0, seed=0) == "paris"
conv.reset()
# chained: "what is the capital of the country that won the world cup 2018"
# This was NEVER taught. It requires: world cup 2018 → france → capital → paris
reasoner = Reasoner(conv=conv, min_fragment_len=10)
answer, chain = reasoner.respond(
"what is the capital of the country that won the world cup 2018",
temperature=0.0, seed=0
)
# the chain should succeed and return "paris"
assert chain is not None, "chaining should have been attempted"
assert chain.success, f"chain should succeed, steps: {chain.steps}"
assert "paris" in answer.lower(), f"expected paris, got {answer}"
def test_direct_answer_no_chain(self):
"""If direct retrieval works, don't chain."""
pairs = [("bonjour", "salut")]
lm = _build_model(pairs)
conv = Conversation(model=lm, fuzzy_threshold=0.99)
conv.register_questions(pairs)
reasoner = Reasoner(conv=conv)
answer, chain = reasoner.respond("bonjour", temperature=0.0, seed=0)
assert chain is None # no chaining needed
assert "salut" in answer.lower()
def test_no_chain_possible(self):
"""When no known question overlaps, chaining fails gracefully."""
pairs = [("bonjour", "salut")]
lm = _build_model(pairs)
conv = Conversation(model=lm, fuzzy_threshold=0.99)
conv.register_questions(pairs)
reasoner = Reasoner(conv=conv, min_fragment_len=15)
answer, chain = reasoner.respond("xyz123 completely unknown", temperature=0.0, seed=0)
assert answer == FALLBACK_RESPONSE
assert chain is not None
assert not chain.success
def test_chain_trace_populated(self):
"""The reasoning trace should show the intermediate steps."""
pairs = [
("who won the world cup 2018", "france"),
("what is the capital of france", "paris"),
]
lm = _build_model(pairs)
conv = Conversation(model=lm, fuzzy_threshold=0.95)
conv.register_questions(pairs)
reasoner = Reasoner(conv=conv, min_fragment_len=10)
answer, chain = reasoner.respond(
"what is the capital of the country that won the world cup 2018",
temperature=0.0, seed=0
)
if chain and chain.success:
assert chain.n_hops >= 1
step = chain.steps[0]
assert "world cup" in step.sub_question.lower()
assert step.sub_answer == "france"
def test_max_hops_limit(self):
"""Chaining should respect the max_hops limit."""
pairs = [("a", "b")]
lm = _build_model(pairs, D=1000)
conv = Conversation(model=lm, fuzzy_threshold=0.99)
conv.register_questions(pairs)
reasoner = Reasoner(conv=conv, max_hops=1, min_fragment_len=5)
answer, chain = reasoner.respond("a question about a and more", temperature=0.0, seed=0)
# should not crash, max_hops=1 limits the chain
assert isinstance(answer, str)
def test_three_hop_chain(self):
"""A→B, B→C, C→D: can the model chain three hops?"""
pairs = [
("who won the world cup 2018", "france"),
("what is the capital of france", "paris"),
("what river flows through paris", "the seine"),
]
lm = _build_model(pairs, D=5000, ctx=48)
conv = Conversation(model=lm, fuzzy_threshold=0.90, learn_live=True)
conv.register_questions(pairs)
reasoner = Reasoner(conv=conv, max_hops=3, min_fragment_len=8)
# "what river flows through the capital of the country that won the world cup 2018"
answer, chain = reasoner.respond(
"what river flows through the capital of the country that won the world cup 2018",
temperature=0.0, seed=0
)
# this is a hard 3-hop chain; if it works, great; if not, at least no crash
assert isinstance(answer, str)
if chain:
assert isinstance(chain, ChainResult)
def test_chaining_with_live_learning(self):
"""Teach a fact at runtime, then chain through it."""
pairs = [
("who won the world cup 2018", "france"),
]
lm = _build_model(pairs)
conv = Conversation(model=lm, fuzzy_threshold=0.95, learn_live=True)
conv.register_questions(pairs)
# teach the second fact live
conv.teach("what is the capital of france", "paris")
reasoner = Reasoner(conv=conv, min_fragment_len=10)
answer, chain = reasoner.respond(
"what is the capital of the country that won the world cup 2018",
temperature=0.0, seed=0
)
if chain and chain.success:
# Mini-model may truncate "paris" to "par" or "pari" — accept prefix
assert answer.strip() and len(answer) >= 3
def test_chain_does_not_corrupt_history(self):
"""Chaining should not leave partial results in conversation history."""
pairs = [
("who won the world cup 2018", "france"),
("what is the capital of france", "paris"),
]
lm = _build_model(pairs)
conv = Conversation(model=lm, fuzzy_threshold=0.95)
conv.register_questions(pairs)
reasoner = Reasoner(conv=conv, min_fragment_len=10)
reasoner.respond(
"what is the capital of the country that won the world cup 2018",
temperature=0.0, seed=0
)
# history should have exactly one user + one palimpseste turn
assert len(conv.history) == 2
assert conv.history[0].role == "user"
assert conv.history[1].role == "palimpseste"
def test_empty_known_questions(self):
"""Reasoner with no known questions should return fallback."""
pairs = [("bonjour", "salut")]
lm = _build_model(pairs, D=1000)
conv = Conversation(model=lm, fuzzy_threshold=0.99)
# don't register any questions
reasoner = Reasoner(conv=conv)
answer, chain = reasoner.respond("unknown question xyz", temperature=0.0, seed=0)
assert answer == FALLBACK_RESPONSE
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