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fc3e70a | 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 | # This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
"""NLP subsystem — tokenization, entity extraction, intent classification, BERT interface.
BERT operates as an internal interpretive tool only. Output is SemanticCandidate,
never executable code. The FSM remains the admissibility gate.
"""
from __future__ import annotations
import re
from dataclasses import dataclass, field
from typing import Optional
from .types import IntentClass, NormalizedInput, RawInput, SemanticCandidate
# ── Controlled vocabulary for intent ─────────────────────────────────────────
_INTENT_PATTERNS: list[tuple[IntentClass, list[str]]] = [
(IntentClass.BUILD, ["build", "create", "make", "construct", "implement", "write"]),
(IntentClass.TRANSFORM, ["transform", "convert", "translate", "map", "rewrite", "change"]),
(IntentClass.VALIDATE, ["validate", "verify", "check", "confirm", "ensure", "test"]),
(IntentClass.GENERATE, ["generate", "produce", "emit", "output", "render"]),
(IntentClass.PARSE, ["parse", "read", "decode", "lex", "scan", "interpret"]),
(IntentClass.COMPUTE, ["compute", "calculate", "multiply", "add", "subtract", "solve"]),
(IntentClass.RECONSTRUCT, ["reconstruct", "restore", "recover", "rebuild", "regenerate"]),
]
_OPERATOR_PATTERNS = re.compile(
r"\b(multiply|add|subtract|divide|transform|convert|validate|parse|build|generate|"
r"compute|verify|reconstruct|normalize|encode|decode|serialize|deserialize)\b",
re.IGNORECASE,
)
_ENTITY_PATTERNS = re.compile(
r"\b(matrix|vector|parser|backend|service|function|module|component|token|"
r"format|stream|buffer|table|schema|graph|tree|list|array|string|integer|"
r"boolean|packet|message|frame|field|register)\b",
re.IGNORECASE,
)
_CONSTRAINT_PATTERNS = re.compile(
r"\b(deterministic|immutable|idempotent|commutative|associative|invertible|"
r"ordered|typed|bounded|validated|correct|minimal|canonical|stable)\b",
re.IGNORECASE,
)
_RELATIONSHIP_PATTERN = re.compile(
r"(\w+)\s+(of|from|to|into|for|with|by|through|via|as)\s+(\w+)",
re.IGNORECASE,
)
# ── Tokenizer ─────────────────────────────────────────────────────────────────
def tokenize(text: str) -> list[str]:
return re.findall(r"\w+|[^\w\s]", text)
def segment_sentences(text: str) -> list[str]:
return [s.strip() for s in re.split(r"(?<=[.!?])\s+", text) if s.strip()]
# ── Rule-based NLP (no external model required) ───────────────────────────────
def classify_intent(text: str) -> tuple[IntentClass, float]:
lower = text.lower()
scores: dict[IntentClass, int] = {}
for intent, keywords in _INTENT_PATTERNS:
for kw in keywords:
if kw in lower:
scores[intent] = scores.get(intent, 0) + 1
if not scores:
return IntentClass.UNKNOWN, 0.3
best = max(scores, key=lambda k: scores[k])
confidence = min(0.5 + scores[best] * 0.15, 0.95)
return best, confidence
def extract_entities(text: str) -> list[str]:
return list({m.group(0).lower() for m in _ENTITY_PATTERNS.finditer(text)})
def extract_operators(text: str) -> list[str]:
return list({m.group(0).lower() for m in _OPERATOR_PATTERNS.finditer(text)})
def extract_constraints(text: str) -> list[str]:
return list({m.group(0).lower() for m in _CONSTRAINT_PATTERNS.finditer(text)})
def extract_relationships(text: str) -> list[tuple[str, str, str]]:
return [(m.group(1).lower(), m.group(2).lower(), m.group(3).lower())
for m in _RELATIONSHIP_PATTERN.finditer(text)]
def detect_ambiguity(text: str) -> bool:
ambiguous_markers = re.compile(
r"\b(it|this|that|they|something|somehow|maybe|perhaps|possibly|"
r"or something|etc|and so on)\b",
re.IGNORECASE,
)
return bool(ambiguous_markers.search(text))
# ── BERT interface ────────────────────────────────────────────────────────────
class BertDecoder:
"""
Internal BERT interface for semantic interpretation.
If the `transformers` package is available and a model is configured,
uses real BERT embeddings. Otherwise falls back to rule-based analysis.
BERT output is always SemanticCandidate — never executable code.
The FSM is the admissibility gate regardless of BERT confidence.
"""
def __init__(self, model_name: Optional[str] = None):
self._model = None
self._tokenizer = None
self._use_bert = False
if model_name:
try:
from transformers import AutoModel, AutoTokenizer # type: ignore
import torch # type: ignore
self._tokenizer = AutoTokenizer.from_pretrained(model_name)
self._model = AutoModel.from_pretrained(model_name)
self._torch = torch
self._use_bert = True
except ImportError:
pass # fall back to rule-based
def _bert_embeddings(self, text: str) -> Optional[list[float]]:
if not self._use_bert:
return None
inputs = self._tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with self._torch.no_grad():
outputs = self._model(**inputs)
cls_embedding = outputs.last_hidden_state[:, 0, :].squeeze().tolist()
return cls_embedding if isinstance(cls_embedding, list) else [cls_embedding]
def decode(self, normalized: NormalizedInput) -> SemanticCandidate:
text = normalized.text
intent, confidence = classify_intent(text)
entities = extract_entities(text)
operators = extract_operators(text)
constraints = extract_constraints(text)
rels = extract_relationships(text)
ambiguous = detect_ambiguity(text)
embeddings = self._bert_embeddings(text)
return SemanticCandidate(
input_ref=normalized.original.input_id,
intent=intent,
entities=entities,
operators=operators,
constraints=constraints,
relationships=rels,
confidence=confidence,
ambiguous=ambiguous,
raw_embeddings=embeddings,
)
# ── Input normalization ───────────────────────────────────────────────────────
def normalize_input(raw: RawInput) -> NormalizedInput:
text = raw.text.strip()
text = re.sub(r"\s+", " ", text)
tokens = tuple(tokenize(text))
sentences = tuple(segment_sentences(text))
return NormalizedInput(original=raw, text=text, tokens=tokens, sentences=sentences)
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