File size: 1,724 Bytes
fa6e2ea | 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 | import numpy as np
import os
import json
from src.hippocampus import Hippocampus
from vitalis_ide.math_core.kernel import VitalisKernel
class PatternLibrary:
def __init__(self):
self.root = os.path.expanduser("~/.vitalis_workspace")
self.hdc = VitalisKernel()
self.hippocampus = Hippocampus()
self.meta_path = os.path.join(self.root, "pattern_meta.json")
self._load_meta()
def _load_meta(self):
if os.path.exists(self.meta_path):
with open(self.meta_path) as f:
self.meta = json.load(f)
else:
self.meta = {}
def _save_meta(self):
os.makedirs(self.root, exist_ok=True)
with open(self.meta_path, 'w') as f:
json.dump(self.meta, f, indent=2)
def store(self, intent: str, code: str, file_path: str = None):
# Semantic encoding — no position binding
vector = self.hdc.vectorize_tokens(intent.split(), positional=False)
slot = f"pattern_{len(self.meta)}"
self.hippocampus.store(slot, vector)
self.meta[slot] = {"intent": intent, "code": code, "file": file_path}
self._save_meta()
print(f"[PATTERN] Learned: {intent} → slot {slot}")
return slot
def retrieve(self, query: str, top_k: int = 3) -> list:
query_vec = self.hdc.vectorize_tokens(query.split(), positional=False)
results = []
for slot, meta in self.meta.items():
vec = self.hippocampus.recall(slot)
if vec is not None:
sim = self.hdc.similarity(query_vec, vec)
results.append((sim, meta))
results.sort(key=lambda x: x[0], reverse=True)
return results[:top_k]
|