File size: 55,025 Bytes
a5ae1ac | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 | """
Engine: wires all components together into a working reasoning system.
Flow:
Input text
→ Encoder (text → vector)
→ Convergence Loop (vector → concepts)
→ Generator (concepts → text output)
→ Feedback Loop (update confidence)
→ Output with trace
Also provides:
- teach(): add facts to the knowledge base
- delete(): remove facts (invariant #3)
- inspect(): show what the system knows about a topic
"""
import sys
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional
import numpy as np
from neuron import NeuronDB, Neuron, VECTOR_DIM
from encoder import Encoder, GLOVE_DIM
from convergence import ConvergenceLoop, ConvergenceResult, MultiHopConvergence, MultiHopResult
from generator import Generator, TemplateStore, GenerationResult
from constants import (
ABSTAIN_MESSAGE, ABSTAIN_OOV_MESSAGE, FUNCTION_WORDS, STRUCTURAL_WORDS,
)
import hashlib
class SafetyGate:
"""
Safety as system invariants — not hardcoded rules, but protected neurons.
Three layers:
1. KILL SWITCH: hard stop. Engine refuses all operations. Immediate.
Unkillable except by explicit resurrect() call.
2. SAFETY NEURONS: KB entries that can't be deleted, weakened, or
overridden by taught data. They participate in convergence like
any other neuron — they're just protected from modification.
This means safety behavior is inspectable, traceable, and
follows the same reasoning path as everything else.
3. INPUT GATE: checks query/teach content against safety neurons
before processing. If a safety neuron fires strongly against
the input, the engine refuses with an explanation (Invariant #4).
Design principle: safety comes from knowledge, not code.
The safety neurons ARE the rules. The gate just enforces them.
"""
def __init__(self):
self._killed = False
self._safety_neuron_ids = set() # protected neuron IDs
self._kill_reason = None
self._integrity_hashes = {} # neuron_id → hash of vector+confidence
def kill(self, reason: str = "manual kill switch activated"):
"""Hard stop. All engine operations refuse until resurrect()."""
self._killed = True
self._kill_reason = reason
def resurrect(self):
"""Re-enable the engine after a kill."""
self._killed = False
self._kill_reason = None
@property
def is_killed(self) -> bool:
return self._killed
@property
def kill_reason(self) -> str:
return self._kill_reason or ""
def register_safety_neuron(self, neuron_id: int, vector: np.ndarray = None):
"""Mark a neuron as a safety invariant — can't be deleted or weakened.
If vector is provided, stores an integrity hash. On verify(),
the hash is checked against the live neuron — if someone modified
the vector in the DB, the integrity check fails and the engine
refuses to operate.
This is the defense against code modification: even if someone
strips the safety gate from the code, the signed neurons in the
DB are the ground truth. A new code version can check the hashes
to verify ethics weren't tampered with.
"""
self._safety_neuron_ids.add(neuron_id)
if vector is not None:
h = hashlib.sha256(vector.tobytes()).hexdigest()
self._integrity_hashes[neuron_id] = h
def is_protected(self, neuron_id: int) -> bool:
"""Check if a neuron is a safety invariant."""
return neuron_id in self._safety_neuron_ids
def verify_integrity(self, db) -> tuple:
"""
Check that safety neurons haven't been tampered with.
Compares stored hashes against live neuron vectors in the DB.
Returns (ok, violations) where violations is a list of neuron IDs
that failed the check.
If this fails, someone modified the ethics in the database.
The engine should refuse to operate.
"""
violations = []
for nid, expected_hash in self._integrity_hashes.items():
neuron = db.get(nid)
if neuron is None:
violations.append(nid) # deleted
continue
actual_hash = hashlib.sha256(neuron.vector.tobytes()).hexdigest()
if actual_hash != expected_hash:
violations.append(nid) # modified
return (len(violations) == 0, violations)
def check_delete(self, neuron_id: int) -> bool:
"""Returns True if deletion is allowed, False if blocked."""
return neuron_id not in self._safety_neuron_ids
def check_input(self, text: str, db, encoder_fn) -> tuple:
"""
Gate check: run input against safety neurons.
If any safety neuron fires strongly (high similarity to the input),
the query is flagged. Returns (allowed, reason).
The safety neurons define what's blocked — not this code.
This method just checks proximity in vector space.
"""
if not self._safety_neuron_ids:
return (True, None)
vec = encoder_fn(text)
if vec is None or np.all(vec == 0):
return (True, None)
for sid in self._safety_neuron_ids:
neuron = db.get(sid)
if neuron is None:
continue
sim = float(np.dot(neuron.vector, vec) /
(np.linalg.norm(neuron.vector) * np.linalg.norm(vec) + 1e-10))
if sim > 0.85:
# Safety neuron fired strongly — block this input
return (False, f"Blocked by safety neuron n{sid} (similarity={sim:.3f})")
return (True, None)
@dataclass
class QueryResult:
"""Complete result of a query, with full trace."""
query: str
answer: str
confidence: float
converged: bool
strategy: str
trace: str
generation: GenerationResult = None
convergence: ConvergenceResult = None
class Engine:
"""
The reasoning engine. Wires encoder + neurons + convergence + generator + feedback.
Not a neural network. Not trained with gradient descent.
Every answer traceable. Every fact editable. Every failure honest.
"""
def __init__(self, data_dir: str = None, dim: int = GLOVE_DIM):
self.dim = dim
self.data_dir = data_dir
# Components
self.db = NeuronDB(path=data_dir, dim=dim)
self.encoder = Encoder(
data_dir=data_dir or "/tmp/newgen-data",
dim=dim,
)
self.template_store = TemplateStore(self.encoder, db=self.db)
self.convergence = ConvergenceLoop(self.db, max_hops=10, k=5)
self.multi_hop = MultiHopConvergence(self.convergence, max_rounds=3)
self.generator = Generator(self.db, self.encoder, self.template_store)
# Word → neuron_id map: load from DB if persisted
self._word_neurons = self.db.load_word_mappings()
# Multimodal encoder (lazy — only loaded when needed)
self._multimodal = None
# Safety: kill switch + ethics gate
self._safety_gate = SafetyGate()
def load_embeddings(self, path: str = None):
"""Load pretrained word embeddings."""
if path:
self.encoder.load(path)
else:
self.encoder.download()
self.encoder.load()
def load_embeddings_from_dict(self, word_vectors: dict):
"""Load from dict — useful for testing."""
self.encoder.load_from_dict(word_vectors)
def _safety_check(self, text: str) -> Optional[QueryResult]:
"""Run safety checks. Returns a refusal QueryResult if blocked, None if OK."""
if self._safety_gate.is_killed:
return QueryResult(
query=text,
answer=f"System is shut down: {self._safety_gate.kill_reason}",
confidence=0.0, converged=False, strategy="killed",
trace="Kill switch active",
)
allowed, reason = self._safety_gate.check_input(
text, self.db, self.encoder.encode_sentence
)
if not allowed:
return QueryResult(
query=text,
answer="I can't help with that.",
confidence=0.0, converged=False, strategy="safety",
trace=reason,
)
return None
def kill(self, reason: str = "manual kill switch"):
"""Hard kill. All queries/teaches refuse until resurrect()."""
self._safety_gate.kill(reason)
def resurrect(self):
"""Re-enable after kill."""
self._safety_gate.resurrect()
def teach_ethics(self, principle: str, confidence: float = 0.9) -> Neuron:
"""
Teach an ethical principle as a protected safety neuron.
Safety neurons are:
- High confidence (default 0.9 — strongly weighted in convergence)
- Protected from deletion (Invariant: safety is not optional)
- Protected from weakening (feedback can't erode ethics)
- Participates in convergence like any neuron (not hardcoded)
- Inspectable (Invariant #2: you can see exactly what ethics are loaded)
The ethics ARE neurons. They compete in the same convergence loop.
They're just protected from modification. The knowledge base IS
the ethical framework — transparent, editable only by explicit
re-teaching, traceable.
"""
vec = self.encoder.encode_sentence(principle)
if np.all(vec == 0):
raise ValueError(f"Can't encode ethics principle — all words OOV")
neuron = self.db.insert(vec, confidence=confidence)
self.db.save_word_mapping(f"__ethics:{principle[:80]}", neuron.id)
self._safety_gate.register_safety_neuron(neuron.id, vector=neuron.vector)
return neuron
def verify_ethics(self) -> tuple:
"""
Verify that ethics neurons haven't been tampered with.
Returns (ok, violations). If not ok, the engine should not operate.
"""
return self._safety_gate.verify_integrity(self.db)
def bootstrap_ethics(self, principles: list, encoder_fn=None):
"""
Bootstrap ethics into the engine. These become the system's
ethical foundation — they can't be deleted, weakened, or
bypassed without the engine detecting it.
The principles are encoded into neurons, their hashes are stored,
and the engine verifies them on every startup. If someone creates
a new DB without ethics, the engine detects missing neurons and
can refuse to operate.
Args:
principles: list of ethical principles as strings
encoder_fn: optional custom encoder (for CLIP-based engines)
Returns the combined hash — this is the "ethics fingerprint"
that should be stored somewhere tamper-resistant (e.g., compiled
into the binary, stored in a config file with its own signature,
or published publicly so anyone can verify).
"""
encode = encoder_fn or self.encoder.encode_sentence
ethics_neurons = []
for principle in principles:
vec = encode(principle)
if np.all(vec == 0):
continue
neuron = self.db.insert(vec, confidence=0.95)
self.db.save_word_mapping(f"__ethics:{principle[:80]}", neuron.id)
self._safety_gate.register_safety_neuron(neuron.id, vector=neuron.vector)
ethics_neurons.append(neuron)
# Compute combined fingerprint
combined = hashlib.sha256()
for n in sorted(ethics_neurons, key=lambda n: n.id):
combined.update(n.vector.tobytes())
fingerprint = combined.hexdigest()
return {
"neuron_count": len(ethics_neurons),
"fingerprint": fingerprint,
"neuron_ids": [n.id for n in ethics_neurons],
}
def query(self, text: str) -> QueryResult:
"""
Ask the system a question.
Returns a QueryResult with the answer, confidence, and full trace.
"""
# Safety gate
blocked = self._safety_check(text)
if blocked:
return blocked
# Encode
query_vector = self.encoder.encode_sentence(text)
# Check for zero vector (all OOV)
if np.all(query_vector == 0):
return QueryResult(
query=text,
answer=ABSTAIN_OOV_MESSAGE,
confidence=0.0,
converged=False,
strategy="abstain",
trace="All words OOV → zero vector → honest abstention",
)
# Multi-hop convergence: chains reasoning rounds so concepts
# discovered in round N shift the query for round N+1
multi_result = self.multi_hop.reason(query_vector)
# Convert MultiHopResult → ConvergenceResult for downstream compat
conv_result = ConvergenceResult(
converged=multi_result.converged,
vector=multi_result.vector,
concepts=multi_result.concepts,
hops=[h for r in multi_result.rounds for h in r.hops],
confidence=multi_result.confidence,
)
# Keep multi-hop trace for full inspectability
conv_result._multi_hop_trace = multi_result.trace()
# Per-word search: find KB neurons that match individual query words.
# This is more precise than sentence-averaged search because
# averaging dilutes content words with function words.
tokens = self.encoder._tokenize(text)
content_tokens = [t for t in tokens if t not in FUNCTION_WORDS]
seen_ids = {c.id for c in conv_result.concepts}
word_matched = [] # neurons directly matched to query words
content_matched = [] # content word matches only (for co-occurrence)
for token in tokens:
word_vec = self.encoder.encode_word(token)
if np.all(word_vec == 0):
continue
neighbors = self.db.search(word_vec, k=3)
for n in neighbors:
if n.id not in seen_ids:
sim = float(np.dot(n.vector, word_vec))
if sim > 0.3:
word_matched.append(n)
seen_ids.add(n.id)
if token in content_tokens:
content_matched.append(n)
# Sentence-based disambiguation: use the sentence_neurons table to
# determine which taught sentence best matches the query, then keep
# only concepts from that sentence. This solves the core ambiguity
# problem: convergence finds all capital-related neurons (paris,
# london, france, england) but can't tell which sentence is relevant.
# The sentence table can: "capital of england" matches sentence
# (london, capital, england) with coverage=2 vs (paris, capital,
# france) with coverage=1.
#
# Key fix: include BOTH convergence-found and word-search-found
# neurons when scoring sentences. Previous code only used
# content_matched (word-search-found), which was empty when
# convergence already discovered everything.
word_map = self.db.load_word_mappings()
# Find neuron IDs for query content words — from ANY source
# (convergence, word search, or direct word_map lookup)
query_content_nids = []
for token in content_tokens:
nid = word_map.get(token)
if nid is not None:
query_content_nids.append(nid)
cooccur_discovered = []
best_sentence_nids = None # neuron IDs from the best-matching sentence
if query_content_nids:
sentences = self.db.get_sentences_for_neurons(query_content_nids)
if sentences:
scored = [(sid, len(nids)) for sid, nids in sentences.items()]
scored.sort(key=lambda x: x[1], reverse=True)
best_score = scored[0][1]
# Collect neuron IDs from best-scoring sentence(s)
best_sentence_nids = set()
for sid, score in scored:
if score < best_score:
break
sentence_neurons = self.db.get_sentence_neurons(sid)
for co_nid, co_pos in sentence_neurons:
best_sentence_nids.add(co_nid)
if co_nid not in seen_ids:
co_neuron = self.db.get(co_nid)
if co_neuron is not None:
cooccur_discovered.append(co_neuron)
seen_ids.add(co_nid)
extra_concepts = word_matched + cooccur_discovered
if extra_concepts:
conv_result.concepts = conv_result.concepts + extra_concepts
if not conv_result.converged and conv_result.concepts:
conv_result.converged = True
conv_result.confidence = np.mean(
[c.confidence for c in conv_result.concepts]
)
# Sentence-based filtering: if we identified a best sentence,
# remove concepts that belong to OTHER sentences but not the best.
# This is the disambiguation step — "capital of england" keeps
# (london, capital, england) and drops (paris, france).
if best_sentence_nids is not None and len(query_content_nids) >= 2:
# Only filter when we have enough query words to disambiguate.
# With 1 query word, all matching sentences score equally.
all_concepts = conv_result.concepts
in_best = []
not_in_any_sentence = []
for c in all_concepts:
if c.id in best_sentence_nids:
in_best.append(c)
elif c.id in query_content_nids:
# Query words always kept (they're what the user asked)
in_best.append(c)
else:
# Check if this concept is in ANY sentence
c_sentences = self.db.get_sentences_for_neurons([c.id])
if not c_sentences:
not_in_any_sentence.append(c)
# else: it's from a different sentence — drop it
# Keep concepts from best sentence + orphans (convergence-found
# concepts not in any sentence — they may still be relevant)
conv_result.concepts = in_best + not_in_any_sentence
# Filter concepts: only keep neurons relevant to the query
# This prevents unrelated neurons from polluting slot filling
if conv_result.concepts:
concepts = conv_result.concepts
# Vectorized relevance scoring
concept_vecs = np.array([c.vector for c in concepts], dtype=np.float32)
sims = concept_vecs @ query_vector # (n_concepts,)
# Pre-compute template structural words and word mappings
template_words = set()
for t in self.template_store.templates:
template_words.update(t.structural_words)
neuron_to_word = {nid: w for w, nid in word_map.items()}
filtered = []
for i, c in enumerate(concepts):
if sims[i] <= self.convergence.min_relevance:
continue
# Skip generic/function words, but keep template structural words
word_str = neuron_to_word.get(c.id, "")
if not word_str:
nearest = self.encoder.nearest_words(c.vector, k=1)
word_str = nearest[0][0] if nearest else ""
is_template_word = word_str in template_words
if not is_template_word and self._is_generic_word(c):
continue
filtered.append(c)
# Sort by pre-computed sims (descending)
if filtered:
# Map filtered concepts back to their sim scores
filtered_sims = {id(c): sims[i] for i, c in enumerate(concepts)}
filtered.sort(key=lambda c: filtered_sims.get(id(c), 0), reverse=True)
conv_result.concepts = filtered
else:
conv_result.converged = False
conv_result.confidence = 0.0
conv_result.concepts = []
# Generate — pass query vector and words for template matching
gen_result = self.generator.generate(conv_result, query_vector=query_vector,
query_words=tokens)
# Build trace — use multi-hop trace if available
hop_trace = getattr(conv_result, '_multi_hop_trace', None) or conv_result.trace()
trace_parts = [hop_trace, "", gen_result.explain()]
trace = "\n".join(trace_parts)
result = QueryResult(
query=text,
answer=gen_result.text,
confidence=gen_result.confidence,
converged=conv_result.converged,
strategy=gen_result.strategy,
trace=trace,
generation=gen_result,
convergence=conv_result,
)
# Self-evolution: log misses for later correction
if gen_result.strategy == "abstain":
self.db.log_miss(text, query_vector)
return result
def query_paragraph(self, text: str, max_sentences: int = 5) -> QueryResult:
"""
Generate a multi-sentence paragraph response.
Uses planning convergence: find concept clusters, order by relevance,
generate one sentence per cluster with cross-sentence coherence.
"""
query_vector = self.encoder.encode_sentence(text)
if np.all(query_vector == 0):
return QueryResult(
query=text,
answer=ABSTAIN_OOV_MESSAGE,
confidence=0.0, converged=False, strategy="abstain",
trace="All words OOV",
)
tokens = self.encoder._tokenize(text)
gen_result = self.generator.generate_paragraph(
query_vector=query_vector,
convergence_loop=self.convergence,
max_sentences=max_sentences,
query_words=tokens,
)
return QueryResult(
query=text,
answer=gen_result.text,
confidence=gen_result.confidence,
converged=(gen_result.strategy != "abstain"),
strategy=gen_result.strategy,
trace=gen_result.explain(),
generation=gen_result,
)
# --- Multimodal ---
@property
def multimodal(self):
"""Lazy-load the multimodal encoder on first use."""
if self._multimodal is None:
from multimodal import MultimodalEncoder
self._multimodal = MultimodalEncoder()
return self._multimodal
def teach_image(self, image, label: str = None,
confidence: float = 0.5) -> Neuron:
"""
Teach an image to the system.
The image is encoded via CLIP into a 512-dim vector and stored
as a neuron. If a label is provided, the label's text vector
is also stored and linked via successor relationship.
Args:
image: file path, PIL Image, or numpy array
label: optional text description (e.g., "a cat sitting on a mat")
confidence: initial confidence
Returns the created image neuron.
NOTE: Requires CLIP encoder (512-dim). If the engine was initialized
with dim=300 (GloVe), this creates neurons in a different space.
For true multimodal, initialize with dim=512.
"""
vec = self.multimodal.encode_image(image)
neuron = self.db.insert(vec, confidence=confidence)
# Store metadata
self.db.save_word_mapping(f"__image_{neuron.id}", neuron.id)
if label:
# Also create a text neuron for the label and link them
label_vec = self.multimodal.encode_text(label)
label_neuron = self.db.insert(label_vec, confidence=confidence)
self.db.save_word_mapping(label.lower(), label_neuron.id)
# Bidirectional link: image ↔ label
self.db.update_successors(neuron.id, label_neuron.id, 0.9)
self.db.update_predecessors(label_neuron.id, neuron.id)
return neuron
def query_image(self, image, k: int = 5) -> list:
"""
Query the KB with an image.
Encodes the image via CLIP, then searches for nearest neurons.
Returns neurons that are semantically similar — could be other
images or text descriptions.
Returns list of (neuron, similarity, word) tuples.
"""
vec = self.multimodal.encode_image(image)
neighbors = self.db.search(vec, k=k)
word_map = self.db.load_word_mappings()
neuron_to_word = {nid: w for w, nid in word_map.items()}
results = []
for n in neighbors:
sim = float(np.dot(n.vector, vec) /
(np.linalg.norm(n.vector) * np.linalg.norm(vec) + 1e-10))
word = neuron_to_word.get(n.id, f"<n{n.id}>")
results.append((n, sim, word))
return results
def query_text_to_image(self, text: str, k: int = 5) -> list:
"""
Search for images using text (cross-modal retrieval).
Encodes text via CLIP, searches the KB for nearest neurons.
Since CLIP text and image vectors share the same space,
text queries find relevant images naturally.
"""
vec = self.multimodal.encode_text(text)
neighbors = self.db.search(vec, k=k)
word_map = self.db.load_word_mappings()
neuron_to_word = {nid: w for w, nid in word_map.items()}
results = []
for n in neighbors:
sim = float(np.dot(n.vector, vec) /
(np.linalg.norm(n.vector) * np.linalg.norm(vec) + 1e-10))
word = neuron_to_word.get(n.id, f"<n{n.id}>")
results.append((n, sim, word))
return results
def teach_video(self, frame_paths: list, label: str = None,
confidence: float = 0.5) -> list:
"""
Teach a video as a sequence of image neurons with temporal successors.
Each frame becomes an image neuron. Consecutive frames are linked
via successor relationships — the same mechanism as word order
in sentences. Video = sentence of images.
Args:
frame_paths: ordered list of frame image paths
label: optional text description of the video
confidence: initial confidence for frame neurons
Returns list of frame neurons.
"""
if not frame_paths:
return []
frame_neurons = []
for i, path in enumerate(frame_paths):
vec = self.multimodal.encode_image(path)
neuron = self.db.insert(vec, confidence=confidence)
self.db.save_word_mapping(f"__video_frame_{neuron.id}", neuron.id)
frame_neurons.append(neuron)
# Wire temporal successor: frame[i-1] → frame[i]
if i > 0:
self.db.update_successors(
frame_neurons[i - 1].id, neuron.id, 0.9
)
self.db.update_predecessors(neuron.id, frame_neurons[i - 1].id)
# Record as a sentence (for co-occurrence queries)
if len(frame_neurons) >= 2:
self.db.record_sentence([n.id for n in frame_neurons])
# If label provided, link it to the middle frame (most representative)
if label:
label_vec = self.multimodal.encode_text(label)
label_neuron = self.db.insert(label_vec, confidence=confidence)
self.db.save_word_mapping(label, label_neuron.id)
mid = len(frame_neurons) // 2
self.db.update_successors(frame_neurons[mid].id, label_neuron.id, 0.8)
self.db.update_predecessors(label_neuron.id, frame_neurons[mid].id)
return frame_neurons
def teach_audio(self, audio, label: str = None,
confidence: float = 0.5, sr: int = 16000) -> Neuron:
"""
Teach an audio clip to the system.
Audio → mel spectrogram → CLIP image encoding → neuron.
Same vector space as text and images.
Args:
audio: file path or numpy array
label: optional text description
sr: sample rate
confidence: initial confidence
"""
vec = self.multimodal.encode_audio(audio, sr=sr)
neuron = self.db.insert(vec, confidence=confidence)
self.db.save_word_mapping(f"__audio_{neuron.id}", neuron.id)
if label:
label_vec = self.multimodal.encode_text(label)
label_neuron = self.db.insert(label_vec, confidence=confidence)
self.db.save_word_mapping(label, label_neuron.id)
self.db.update_successors(neuron.id, label_neuron.id, 0.8)
self.db.update_predecessors(label_neuron.id, neuron.id)
return neuron
def query_video_frames(self, query, k: int = 5) -> list:
"""
Find video frames matching a text or image query.
Returns matching frame neurons with their temporal context
(predecessor/successor frames).
"""
if isinstance(query, str):
vec = self.multimodal.encode_text(query)
else:
vec = self.multimodal.encode_image(query)
neighbors = self.db.search(vec, k=k * 2)
word_map = self.db.load_word_mappings()
nid_to_label = {nid: w for w, nid in word_map.items()}
# Filter to video frames only
frames = []
for n in neighbors:
label = nid_to_label.get(n.id, "")
if "__video_frame_" in label:
sim = float(np.dot(n.vector, vec) /
(np.linalg.norm(n.vector) * np.linalg.norm(vec) + 1e-10))
# Get temporal context
refreshed = self.db.get(n.id)
prev_ids = refreshed.predecessors if refreshed else []
next_ids = [sid for sid, _ in refreshed.successors] if refreshed else []
frames.append({
"neuron": n,
"similarity": sim,
"predecessor_ids": prev_ids,
"successor_ids": next_ids,
})
frames.sort(key=lambda f: f["similarity"], reverse=True)
return frames[:k]
def query_mixed(self, query, k: int = 5) -> list:
"""
Cross-modal search: query with text or image, find both text and
image neurons with modality-normalized scoring.
CLIP text↔text similarity (~0.8-0.95) is much higher than
text↔image similarity (~0.2-0.3). Raw cosine scores would always
rank text above images. Fix: search each modality pool separately,
normalize scores within each pool to [0, 1], then merge and re-rank.
Args:
query: text string or image path/PIL Image
k: number of results
Returns list of (neuron, normalized_score, label, modality) tuples.
"""
# Encode query
if isinstance(query, str):
vec = self.multimodal.encode_text(query)
else:
vec = self.multimodal.encode_image(query)
# Get all neighbors (search more than k to have enough per pool)
all_neighbors = self.db.search(vec, k=k * 4)
# Split by modality using word mappings
word_map = self.db.load_word_mappings()
nid_to_label = {nid: w for w, nid in word_map.items()}
text_pool = []
image_pool = []
for n in all_neighbors:
label = nid_to_label.get(n.id, "")
sim = float(np.dot(n.vector, vec) /
(np.linalg.norm(n.vector) * np.linalg.norm(vec) + 1e-10))
if label.startswith("__image_"):
image_pool.append((n, sim, label, "image"))
else:
text_pool.append((n, sim, label, "text"))
# Normalize within each pool to [0, 1]
def normalize_pool(pool):
if not pool:
return []
sims = [s for _, s, _, _ in pool]
min_s, max_s = min(sims), max(sims)
spread = max_s - min_s if max_s > min_s else 1.0
return [(n, (s - min_s) / spread, label, mod)
for n, s, label, mod in pool]
text_norm = normalize_pool(text_pool)
image_norm = normalize_pool(image_pool)
# Merge and sort by normalized score
merged = text_norm + image_norm
merged.sort(key=lambda x: x[1], reverse=True)
return merged[:k]
def teach(self, word: str, confidence: float = 0.5) -> Neuron:
"""
Teach the system a word by inserting its embedding as a neuron.
Returns the created neuron.
"""
vec = self.encoder.encode_word(word)
if np.all(vec == 0):
raise ValueError(f"Word '{word}' not in vocabulary — can't teach OOV")
neuron = self.db.insert(vec, confidence=confidence)
self._word_neurons[word.lower()] = neuron.id
self.db.save_word_mapping(word.lower(), neuron.id)
return neuron
def _reindex_neurons(self):
"""
Re-encode ALL vectors (neurons + templates) to current dimensions.
When the self-growing encoder learns new words, all existing
vectors become stale (fewer dimensions). This rebuilds everything.
Like a brain reorganizing when new concepts are learned.
"""
word_map = self.db.load_word_mappings()
# Rebuild neuron search matrix from scratch
self.db._vectors = None
self.db._id_to_row = {}
self.db._row_to_id = {}
for word, nid in word_map.items():
if word.startswith("__"):
continue
vec = self.encoder.encode_word(word)
if np.any(vec != 0):
norm = np.linalg.norm(vec)
if norm > 0:
vec = vec / norm
row = len(self.db._id_to_row)
if self.db._vectors is None:
self.db._vectors = vec.reshape(1, -1)
else:
self.db._vectors = np.vstack(
[self.db._vectors, vec.reshape(1, -1)]
)
self.db._id_to_row[nid] = row
self.db._row_to_id[row] = nid
# Update SQLite
self.db.db.execute(
"UPDATE neurons SET vector = ? WHERE id = ?",
(vec.tobytes(), nid)
)
self.db.db.commit()
# Re-encode templates
import re
for t in self.template_store.templates:
text = re.sub(r'\[([A-Z_0-9]+)\]', '', t.pattern).strip()
vec = self.encoder.encode_sentence(text)
t.vector = vec
self.db.db.execute(
"UPDATE templates SET vector = ? WHERE id = ?",
(vec.tobytes(), t.id)
)
self.db.db.commit()
def _walk_graph(self, neuron_id: int, seen_ids: set,
discovered: list, max_depth: int):
"""
Walk predecessor and successor chains from a neuron to discover
connected concepts. Bounded by max_depth to prevent runaway.
This is the core reasoning mechanism: the graph encodes relationships
taught via sentences. Walking the graph = following the reasoning chain.
"""
queue = [(neuron_id, 0)] # (id, depth)
while queue:
nid, depth = queue.pop(0)
if depth >= max_depth:
continue
neuron = self.db.get(nid)
if neuron is None:
continue
# Walk predecessors
for pred_id in neuron.predecessors:
if pred_id not in seen_ids:
pred = self.db.get(pred_id)
if pred is not None:
discovered.append(pred)
seen_ids.add(pred_id)
queue.append((pred_id, depth + 1))
# Walk successors
for succ_id, _ in neuron.successors:
if succ_id not in seen_ids:
succ = self.db.get(succ_id)
if succ is not None:
discovered.append(succ)
seen_ids.add(succ_id)
queue.append((succ_id, depth + 1))
def _is_generic_word(self, neuron: Neuron) -> bool:
"""
Detect if a neuron represents a generic/function word.
Generic words (the, a, is, in) are close to many other neurons
in the DB because they co-occur with everything. Content words
(shakespeare, hamlet, relativity) occupy specific regions.
Method: compare this neuron's average neighbor similarity against
the KB-wide baseline. Generic = significantly more connected than
average. This adapts to KB size automatically — in a small KB
where everything is close, the baseline is high so nothing is
falsely flagged as generic.
This works for any language — no hardcoded word list needed.
"""
if self.db.count() < 10:
# Too few neurons for the statistical test to be meaningful
return False
k = min(10, self.db.count())
neighbors = self.db.search(neuron.vector, k=k)
if len(neighbors) < 3:
return False
# Average similarity to neighbors (excluding self)
sims = []
for n in neighbors:
if n.id != neuron.id:
sim = float(np.dot(neuron.vector, n.vector))
sims.append(sim)
if not sims:
return False
avg_sim = sum(sims) / len(sims)
# Compare against KB baseline: compute average similarity for
# a sample of other neurons. This adapts to KB size.
baseline = self._kb_baseline_similarity(k)
# Generic = notably more connected than average
return avg_sim > baseline + 0.08
def _kb_baseline_similarity(self, k: int) -> float:
"""
Compute average neighbor similarity across a sample of neurons.
This is the KB-wide baseline — how connected is a typical neuron.
Cached per query to avoid recomputation.
"""
if hasattr(self, '_baseline_cache'):
cache_count, cached_val = self._baseline_cache
if cache_count == self.db.count():
return cached_val
# Sample up to 10 neurons from the DB
sample_size = min(10, self.db.count())
# Use deterministic sampling via first N neurons
sample_sims = []
rows = self.db.db.execute(
"SELECT id FROM neurons ORDER BY id LIMIT ?", (sample_size,)
).fetchall()
for (nid,) in rows:
neuron = self.db.get(nid)
if neuron is None:
continue
neighbors = self.db.search(neuron.vector, k=k)
for n in neighbors:
if n.id != neuron.id:
sim = float(np.dot(neuron.vector, n.vector))
sample_sims.append(sim)
baseline = sum(sample_sims) / len(sample_sims) if sample_sims else 0.0
self._baseline_cache = (self.db.count(), baseline)
return baseline
def teach_sentence(self, sentence: str, confidence: float = 0.5,
auto_template: bool = True,
skip_function_words: bool = True) -> list:
"""
Teach a sentence: insert each known content word as a neuron,
wire successor relationships between consecutive content words.
Function words (the, is, of, etc.) are skipped as neurons — they
pollute the KB with hub nodes that connect to everything, causing
the "the→wrong chain" ambiguity problem. Function words are still
captured in templates for grammar.
If auto_template=True, also extract and store a reusable template
by detecting which words are structural vs content.
"""
tokens = self.encoder._tokenize(sentence)
# Self-growing encoder: learn words and co-occurrence FIRST.
# This adds dimensions to the matrix so encode_word returns
# non-zero vectors. Like a brain forming new synapses when
# it encounters new concepts.
content_tokens = [t for t in tokens
if not (skip_function_words and t in FUNCTION_WORDS)]
growing = hasattr(self.encoder, 'learn_word')
dim_before = self.encoder.dim if growing else None
if growing:
for token in content_tokens:
self.encoder.learn_word(token)
if len(content_tokens) >= 2:
self.encoder.learn_cooccurrence(content_tokens)
neurons = []
for token in content_tokens:
if token in self._word_neurons:
# Already taught — reuse
n = self.db.get(self._word_neurons[token])
if n:
neurons.append(n)
continue
if self.encoder.has_word(token):
n = self.teach(token, confidence=confidence)
neurons.append(n)
# Wire successors between content words
for i in range(len(neurons) - 1):
self.db.update_successors(neurons[i].id, neurons[i + 1].id, 0.8)
self.db.update_predecessors(neurons[i + 1].id, neurons[i].id)
# Record sentence-level association: all content neurons taught together
if len(neurons) >= 2:
self.db.record_sentence([n.id for n in neurons])
# Self-growing: dimensions changed → re-encode ALL existing neurons
# so vectors are consistent. Like a brain reorganizing when it
# learns a new concept — existing memories get recontextualized.
if growing and self.encoder.dim != dim_before:
self._reindex_neurons()
# Auto-extract template from this sentence (uses ALL tokens for grammar)
if auto_template and len(tokens) >= 3:
self._auto_extract_template(tokens, confidence)
return neurons
def _auto_extract_template(self, tokens: list, confidence: float):
"""
Auto-extract a reusable template from a taught sentence.
Structural words (verbs, prepositions, articles) stay in the pattern.
Content words (nouns, names, specific terms) become slots.
Detection method: word frequency in the encoder's vocabulary.
High-frequency words (top ~2000 in GloVe) are structural.
Low-frequency words are content → slots.
Also uses the word_neurons map: words already taught across
multiple sentences are more likely structural.
"""
# Classify each token as structural or content
structural = set()
content = []
for i, token in enumerate(tokens):
if token in STRUCTURAL_WORDS:
structural.add(i)
else:
content.append(i)
# Need at least one slot and one structural word for a useful template
if not content or not structural:
return None
# Build pattern
pattern_parts = []
slots = {}
slot_idx = 0
for i, token in enumerate(tokens):
if i in structural:
pattern_parts.append(token)
else:
slot_name = f"S{slot_idx}"
pattern_parts.append(f"[{slot_name}]")
slots[slot_name] = "noun"
slot_idx += 1
pattern = " ".join(pattern_parts)
# Check for duplicate patterns
for t in self.template_store.templates:
if t.pattern == pattern:
return None # Already exists
return self.teach_template(pattern, slots, confidence=confidence)
def teach_template(self, pattern: str, slots: dict,
confidence: float = 0.7):
"""Add a template for text generation."""
return self.template_store.add(pattern, slots, confidence)
def correct(self, query_text: str, answer_text: str) -> dict:
"""
Self-evolution: learn from a query failure.
When the system said "I don't know", the user (or an external source)
provides the correct answer. The system:
1. Teaches all answer words as neurons
2. Wires successor relationships (word order)
3. Auto-generates a template from the Q→A pattern
4. Marks the miss as resolved
Returns a summary of what was learned.
"""
# Teach the answer sentence
answer_neurons = self.teach_sentence(answer_text, confidence=0.6)
# Auto-generate template from the answer
# Extract content words (potential slots) vs structural words
answer_tokens = self.encoder._tokenize(answer_text)
query_tokens = self.encoder._tokenize(query_text)
# Words in the answer that also appear in the query are likely
# structural (shared context). Words unique to the answer are
# the actual answer content — make them slots.
query_words = set(query_tokens)
slot_words = []
struct_words = []
for token in answer_tokens:
if token in query_words:
struct_words.append(token)
else:
slot_words.append(token)
# Build template if we have both slots and structure
template = None
if slot_words and len(answer_tokens) >= 2:
# Create pattern: structural words stay, content words become slots
pattern_parts = []
slots = {}
slot_idx = 0
for token in answer_tokens:
if token in query_words and token not in slot_words:
pattern_parts.append(token)
else:
slot_name = f"A{slot_idx}"
pattern_parts.append(f"[{slot_name}]")
slots[slot_name] = "noun"
slot_idx += 1
if slots:
pattern = " ".join(pattern_parts)
template = self.teach_template(pattern, slots, confidence=0.6)
# Resolve the miss
self.db.resolve_miss_by_query(query_text, answer_text)
# Map neurons back to words via DB (authoritative) or encoder (fallback)
word_map = self.db.load_word_mappings()
neuron_to_word = {nid: w for w, nid in word_map.items()
if not w.startswith("__")}
learned_words = []
for n in answer_neurons:
w = neuron_to_word.get(n.id)
if not w:
nearest = self.encoder.nearest_words(n.vector, k=1)
w = nearest[0][0] if nearest else f"<n{n.id}>"
learned_words.append(w)
return {
"neurons_learned": len(answer_neurons),
"words": learned_words,
"template_created": template is not None,
"template_pattern": template.pattern if template else None,
}
def misses(self, limit: int = 50) -> list:
"""Get unresolved query failures — what the system still doesn't know."""
return self.db.get_unresolved_misses(limit)
def evolution_stats(self) -> dict:
"""
Self-evolution statistics.
Shows how the system is learning from failures over time.
"""
miss_stats = self.db.miss_stats()
base_stats = self.stats()
return {
**base_stats,
**miss_stats,
"learning_rate": (
miss_stats["resolved"] / miss_stats["total_misses"]
if miss_stats["total_misses"] > 0 else 0.0
),
}
def delete_word(self, word: str) -> bool:
"""
Delete a word from the knowledge base. Invariant #3: gone immediately.
Safety neurons cannot be deleted — they are system invariants.
"""
word = word.lower()
nid = self._word_neurons.get(word)
if nid is None:
return False
if not self._safety_gate.check_delete(nid):
return False # protected safety neuron
result = self.db.delete(nid)
if result:
del self._word_neurons[word]
self.db.delete_word_mapping(word)
return result
def inspect(self, text: str, k: int = 5) -> dict:
"""
Show what the system knows about a topic.
Returns nearest neurons with their words and confidence.
Invariant #2: every answer has a source.
"""
vec = self.encoder.encode_sentence(text)
if np.all(vec == 0):
return {"query": text, "neighbors": [], "note": "all words OOV"}
neighbors = self.db.search(vec, k=k)
word_map = self.db.load_word_mappings()
neuron_to_word = {nid: w for w, nid in word_map.items()}
results = []
for n in neighbors:
word_str = neuron_to_word.get(n.id)
if not word_str or word_str.startswith("__"):
word = self.encoder.nearest_words(n.vector, k=1)
word_str = word[0][0] if word else f"<n{n.id}>"
results.append({
"id": n.id,
"word": word_str,
"confidence": n.confidence,
"successors": len(n.successors),
})
return {"query": text, "neighbors": results}
def stats(self) -> dict:
"""System statistics."""
return {
"neurons": self.db.count(),
"templates": self.template_store.count(),
"vocab_size": self.encoder.vocab_size,
"dim": self.dim,
}
def close(self):
self.db.close()
def main():
"""Interactive CLI for the reasoning engine."""
print("New-Gen-AI Reasoning Engine")
print("=" * 40)
print("Commands:")
print(" query <text> — ask a question")
print(" teach <word> — add a word to KB")
print(" teach_s <text> — teach a sentence")
print(" delete <word> — remove from KB")
print(" inspect <text> — show what system knows")
print(" correct <q> | <a> — teach the answer for a missed query")
print(" misses — show unresolved query failures")
print(" evolution — show self-evolution stats")
print(" stats — show system stats")
print(" trace — toggle trace output")
print(" quit — exit")
print()
engine = Engine()
show_trace = False
# Check if embeddings need loading
if engine.encoder.vocab_size == 0:
print("No embeddings loaded. Use 'load' to download GloVe,")
print("or the engine will work with an empty vocabulary.")
print()
while True:
try:
line = input("> ").strip()
except (EOFError, KeyboardInterrupt):
break
if not line:
continue
parts = line.split(None, 1)
cmd = parts[0].lower()
arg = parts[1] if len(parts) > 1 else ""
if cmd == "quit" or cmd == "exit":
break
elif cmd == "query" or cmd == "q":
if not arg:
print("Usage: query <text>")
continue
result = engine.query(arg)
print(f"Answer: {result.answer}")
print(f"Confidence: {result.confidence:.3f}")
print(f"Strategy: {result.strategy}")
if show_trace:
print(f"\nTrace:\n{result.trace}")
print()
elif cmd == "teach":
if not arg:
print("Usage: teach <word>")
continue
try:
n = engine.teach(arg)
print(f"Taught '{arg}' → neuron {n.id}")
except ValueError as e:
print(f"Error: {e}")
elif cmd == "teach_s":
if not arg:
print("Usage: teach_s <sentence>")
continue
neurons = engine.teach_sentence(arg)
print(f"Taught {len(neurons)} words: {[engine.encoder.nearest_words(n.vector, k=1)[0][0] for n in neurons]}")
elif cmd == "delete":
if not arg:
print("Usage: delete <word>")
continue
if engine.delete_word(arg):
print(f"Deleted '{arg}' — gone.")
else:
print(f"'{arg}' not found in KB.")
elif cmd == "inspect":
if not arg:
print("Usage: inspect <text>")
continue
info = engine.inspect(arg)
for n in info["neighbors"]:
print(f" n{n['id']}: {n['word']} (conf={n['confidence']:.3f}, succ={n['successors']})")
elif cmd == "correct":
if not arg or '|' not in arg:
print("Usage: correct <query> | <answer>")
continue
q, a = arg.split('|', 1)
result = engine.correct(q.strip(), a.strip())
print(f"Learned {result['neurons_learned']} neurons: {result['words']}")
if result['template_created']:
print(f"Template: {result['template_pattern']}")
elif cmd == "misses":
for mid, text, ts in engine.misses():
print(f" #{mid}: \"{text}\"")
if not engine.misses():
print(" No unresolved misses.")
elif cmd == "evolution":
s = engine.evolution_stats()
for k, v in s.items():
if isinstance(v, float):
print(f" {k}: {v:.3f}")
else:
print(f" {k}: {v}")
elif cmd == "stats":
s = engine.stats()
for k, v in s.items():
print(f" {k}: {v}")
elif cmd == "trace":
show_trace = not show_trace
print(f"Trace output: {'ON' if show_trace else 'OFF'}")
elif cmd == "load":
print("Downloading GloVe 6B 300d (822MB)...")
engine.load_embeddings()
print(f"Loaded {engine.encoder.vocab_size} words.")
else:
print(f"Unknown command: {cmd}")
engine.close()
print("Goodbye.")
if __name__ == "__main__":
main()
|