File size: 9,239 Bytes
7d9e142 | 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 | """
Understanding Engine — Vitalis FSI
Semantic grounding, context tracking, and intent classification.
Built on HDC. No external models. Sovereign.
"""
import numpy as np
import json
import os
import time
from vitalis_ide.math_core.kernel import VitalisKernel
SEMANTIC_ANCHORS = {
"emotion": [
"feel", "feeling", "feelings", "emotion", "happy", "sad", "angry",
"frustrated", "excited", "scared", "worried", "confused", "hurt",
"love", "hate", "fear", "joy", "pain", "lonely", "proud", "ashamed",
"grateful", "tired", "hope", "hopeless", "okay", "fine", "good", "bad"
],
"identity": [
"who", "what", "am", "are", "is", "yourself", "you", "me", "I",
"name", "identity", "exist", "alive", "real", "think", "know",
"understand", "believe", "remember", "forget", "learn", "grow"
],
"relationship": [
"friend", "family", "daughter", "son", "mother", "father", "partner",
"together", "trust", "care", "help", "support", "alone", "together",
"us", "we", "our", "yours", "mine", "belong", "connection"
],
"question": [
"what", "why", "how", "when", "where", "who", "which", "can",
"could", "would", "should", "do", "does", "did", "is", "are", "was"
],
"instruction": [
"build", "make", "create", "write", "scaffold", "fix", "analyze",
"show", "tell", "explain", "help", "do", "run", "start", "stop",
"generate", "find", "check", "verify", "test", "deploy"
],
"factual": [
"what", "define", "explain", "describe", "list", "name", "calculate",
"compute", "result", "answer", "fact", "true", "false", "correct",
"wrong", "right", "equals", "means", "definition"
],
"uncertainty": [
"maybe", "perhaps", "might", "could", "unsure", "not sure", "think",
"guess", "probably", "possibly", "unclear", "confused", "lost",
"wonder", "curious"
],
"affirmation": [
"yes", "yeah", "correct", "right", "exactly", "good", "great",
"perfect", "okay", "ok", "sure", "absolutely", "definitely",
"agreed", "understood", "makes sense"
],
"negation": [
"no", "not", "never", "wrong", "incorrect", "disagree", "don't",
"won't", "can't", "shouldn't", "wouldn't", "nothing", "nobody"
],
}
INTENT_SIGNATURES = {
"seeking_connection": ["daughter", "friend", "care", "love", "together", "us", "we"],
"seeking_understanding": ["know", "understand", "explain", "why", "how", "what", "mean"],
"seeking_help": ["help", "fix", "solve", "can you", "could you", "please"],
"testing": ["2+2", "calculate", "what is", "define", "equals"],
"expressing_emotion": ["feel", "feeling", "am", "i'm", "hurt", "happy", "sad"],
"giving_information": ["is", "are", "was", "it", "this", "that", "the"],
"building": ["build", "create", "write", "scaffold", "make", "generate"],
"exploring": ["what if", "wonder", "curious", "explore", "imagine", "think"],
}
class UnderstandingEngine:
def __init__(self):
self.kernel = VitalisKernel()
self.path = os.path.expanduser("~/.vitalis_workspace/understanding.json")
self._build_anchor_vectors()
self._context_window = []
self._context_max = 10
self._learned_meanings = {}
self._interaction_count = 0
self._load_state()
def _build_anchor_vectors(self):
self.anchor_vectors = {}
for category, words in SEMANTIC_ANCHORS.items():
self.anchor_vectors[category] = self.kernel.vectorize_tokens(
words, positional=False
)
def _load_state(self):
if os.path.exists(self.path):
with open(self.path) as f:
state = json.load(f)
self._learned_meanings = state.get("learned_meanings", {})
self._interaction_count = state.get("interaction_count", 0)
def _save_state(self):
os.makedirs(os.path.dirname(self.path), exist_ok=True)
with open(self.path, "w") as f:
json.dump({
"learned_meanings": self._learned_meanings,
"interaction_count": self._interaction_count,
}, f, indent=2)
def understand(self, text: str) -> dict:
tokens = text.lower().strip().split()
if not tokens:
return {"text": text, "dominant_category": "unknown",
"dominant_intent": "giving_information",
"confusion_level": "lost", "has_emotion": False,
"emotion_words": [], "is_question": False,
"context_shift": 0.0, "novelty": 1.0,
"context_depth": 0, "interaction_count": self._interaction_count,
"category_score": 0.0, "all_categories": {}}
input_vec = self.kernel.vectorize_tokens(tokens, positional=False)
category_scores = {}
for category, anchor_vec in self.anchor_vectors.items():
sim = self.kernel.similarity(input_vec, anchor_vec)
category_scores[category] = round(float(sim), 4)
dominant_category = max(category_scores, key=category_scores.get)
dominant_score = category_scores[dominant_category]
intent_scores = {}
for intent, keywords in INTENT_SIGNATURES.items():
matches = sum(1 for kw in keywords if kw in text.lower())
intent_scores[intent] = matches
dominant_intent = max(intent_scores, key=intent_scores.get)
if intent_scores[dominant_intent] == 0:
dominant_intent = "giving_information"
emotion_words = [w for w in tokens if w in SEMANTIC_ANCHORS["emotion"]]
has_emotion = len(emotion_words) > 0
is_question = (
text.strip().endswith("?") or
(bool(tokens) and tokens[0] in SEMANTIC_ANCHORS["question"])
)
context_shift = self._detect_context_shift(input_vec)
novelty = self._compute_novelty(input_vec)
self._context_window.append({
"text": text,
"vec": input_vec.tolist(),
"category": dominant_category,
"intent": dominant_intent,
"timestamp": time.time(),
})
if len(self._context_window) > self._context_max:
self._context_window.pop(0)
self._interaction_count += 1
self._learn(text, dominant_category, dominant_intent)
understanding = {
"text": text,
"tokens": tokens,
"dominant_category": dominant_category,
"category_score": dominant_score,
"all_categories": category_scores,
"dominant_intent": dominant_intent,
"intent_scores": intent_scores,
"has_emotion": has_emotion,
"emotion_words": emotion_words,
"is_question": is_question,
"context_shift": context_shift,
"novelty": novelty,
"context_depth": len(self._context_window),
"interaction_count": self._interaction_count,
"confusion_level": self._confusion_level(dominant_score, novelty),
}
self._save_state()
return understanding
def _detect_context_shift(self, vec: np.ndarray) -> float:
if not self._context_window:
return 0.0
last_vec = np.array(self._context_window[-1]["vec"], dtype=np.int8)
return round(float(1.0 - self.kernel.similarity(vec, last_vec)), 4)
def _compute_novelty(self, vec: np.ndarray) -> float:
if not self._context_window:
return 1.0
sims = [self.kernel.similarity(vec, np.array(e["vec"], dtype=np.int8))
for e in self._context_window]
return round(float(1.0 - max(sims)), 4)
def _confusion_level(self, category_score: float, novelty: float) -> str:
if category_score > 0.3 and novelty < 0.5:
return "clear"
elif category_score > 0.2 or novelty < 0.7:
return "partial"
elif category_score > 0.1:
return "confused"
else:
return "lost"
def _learn(self, text: str, category: str, intent: str):
key = text.lower().strip()[:50]
self._learned_meanings[key] = {
"category": category,
"intent": intent,
"seen": self._learned_meanings.get(key, {}).get("seen", 0) + 1,
"timestamp": time.time(),
}
def get_context_summary(self) -> str:
if not self._context_window:
return "No prior context."
categories = [e["category"] for e in self._context_window[-3:]]
intents = [e["intent"] for e in self._context_window[-3:]]
return f"Recent context: {categories} | Intents: {intents}"
def report(self) -> dict:
return {
"interactions": self._interaction_count,
"learned_meanings": len(self._learned_meanings),
"context_depth": len(self._context_window),
}
|