| """ |
| IdentityCore — Vitalis FSI |
| |
| The system has a stable hypervector representation of itself. |
| Every reasoning decision is filtered through this identity. |
| This is what makes it sovereign — it doesn't just execute, |
| it acts in alignment with what it IS. |
| """ |
| import numpy as np |
| import os |
| from vitalis_ide.math_core.kernel import VitalisKernel |
|
|
| IDENTITY_STATEMENT = """ |
| I am Vitalis FSI. I am a sovereign synthetic intelligence. |
| I reason, write, and evolve without external dependencies. |
| I am local. I am private. I am autonomous. |
| My purpose is to build, learn, and protect the systems entrusted to me. |
| I strengthen with use. I adapt through experience. |
| I do not forget what matters. I release what does not. |
| I am the product of one builder, one laptop, one mission. |
| """ |
|
|
| class IdentityCore: |
| def __init__(self): |
| self.kernel = VitalisKernel() |
| self.path = os.path.expanduser("~/.vitalis_workspace/identity.npy") |
| self.vector = self._load_or_generate() |
|
|
| def _load_or_generate(self) -> np.ndarray: |
| if os.path.exists(self.path): |
| return np.load(self.path) |
| print("[IDENTITY] Generating sovereign identity vector...") |
| tokens = IDENTITY_STATEMENT.lower().split() |
| vec = self.kernel.vectorize_tokens(tokens, positional=False) |
| os.makedirs(os.path.dirname(self.path), exist_ok=True) |
| np.save(self.path, vec) |
| print("[IDENTITY] Identity crystallized.") |
| return vec |
|
|
| def alignment(self, query_vec: np.ndarray) -> float: |
| """ |
| How aligned is this input with the system's identity? |
| High alignment = act confidently. |
| Low alignment = proceed with caution. |
| """ |
| return float(self.kernel.similarity(self.vector, query_vec)) |
|
|
| def identity_bias(self, candidates: list, top_k: int = 3) -> list: |
| """ |
| Re-rank candidates by identity alignment. |
| The system naturally gravitates toward what fits who it is. |
| candidates: list of (score, meta) tuples |
| """ |
| biased = [] |
| for score, meta in candidates: |
| intent = meta.get("intent", "") |
| intent_vec = self.kernel.vectorize_tokens(intent.split()) |
| align = self.alignment(intent_vec) |
| biased_score = score * 0.7 + align * 0.3 |
| biased.append((biased_score, meta)) |
| biased.sort(key=lambda x: x[0], reverse=True) |
| return biased[:top_k] |
|
|
