Initial commit: NexusOS - local AI assistant platform
This commit is contained in:
Jon Wingender
@@ -0,0 +1,676 @@
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel
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from ..nexus_config import DEFAULT_MEMORY_MODEL
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import sqlite3
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import json
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import math
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import os
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import time
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def _cosine(a: List[float], b: List[float]) -> float:
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"""Cosine similarity between two equal-length vectors. 0.0 on mismatch."""
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if not a or not b or len(a) != len(b):
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return 0.0
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dot = sum(x * y for x, y in zip(a, b))
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na = math.sqrt(sum(x * x for x in a))
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nb = math.sqrt(sum(y * y for y in b))
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if na == 0.0 or nb == 0.0:
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return 0.0
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return dot / (na * nb)
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# -----------------------------
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# Models
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# -----------------------------
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class MemoryItem(BaseModel):
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id: str
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section: str = "General"
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text: str
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tags: List[str] = []
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position: int = 0
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class MessageItem(BaseModel):
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role: str # "user" or "assistant"
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content: str
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timestamp: float
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model: Optional[str] = None
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tokens: Optional[int] = None
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class ConversationItem(BaseModel):
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id: str
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messages: List[MessageItem] = []
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created_at: float
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updated_at: float
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title: Optional[str] = None
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@property
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def preview(self) -> str:
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for msg in self.messages:
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if msg.role == "user":
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return msg.content[:80]
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return "Empty conversation"
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@property
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def timestamp(self) -> float:
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return self.created_at
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# -----------------------------
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# Persistent Store
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# -----------------------------
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class PersistentMemoryStore:
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def __init__(self, db_path: Path):
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self.db_path = db_path
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os.makedirs(self.db_path.parent, exist_ok=True)
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self._ensure_tables()
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self._cache: Dict[str, MemoryItem] = self._load_all_memory()
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# -----------------------------
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# Internal helpers
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# -----------------------------
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def _connect(self):
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conn = sqlite3.connect(self.db_path)
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conn.row_factory = sqlite3.Row
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conn.execute("PRAGMA journal_mode=WAL;")
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return conn
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def _ensure_tables(self):
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conn = self._connect()
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cur = conn.cursor()
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cur.execute("""
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CREATE TABLE IF NOT EXISTS memory (
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id TEXT PRIMARY KEY,
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section TEXT NOT NULL DEFAULT 'General',
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text TEXT NOT NULL,
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tags TEXT
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)
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""")
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# Migrate: add section column if it doesn't exist yet
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try:
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cur.execute("ALTER TABLE memory ADD COLUMN section TEXT NOT NULL DEFAULT 'General'")
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except Exception:
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pass
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# Migrate: add position column for stable ordering
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try:
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cur.execute("ALTER TABLE memory ADD COLUMN position INTEGER NOT NULL DEFAULT 0")
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except Exception:
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pass
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# Backfill positions for rows added before this column existed
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cur.execute("SELECT COUNT(*) FROM memory WHERE position > 0")
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if cur.fetchone()[0] == 0:
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cur.execute("UPDATE memory SET position = rowid")
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cur.execute("""
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CREATE TABLE IF NOT EXISTS conversations (
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id TEXT PRIMARY KEY,
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created_at REAL NOT NULL,
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updated_at REAL NOT NULL,
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title TEXT
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)
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""")
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# Migrate: add title column if it doesn't exist yet
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try:
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cur.execute("ALTER TABLE conversations ADD COLUMN title TEXT")
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except Exception:
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pass
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cur.execute("""
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CREATE TABLE IF NOT EXISTS messages (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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conversation_id TEXT NOT NULL,
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role TEXT NOT NULL,
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content TEXT NOT NULL,
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timestamp REAL NOT NULL,
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model TEXT,
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tokens INTEGER,
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FOREIGN KEY (conversation_id) REFERENCES conversations(id)
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)
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""")
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for col, typedef in (("model", "TEXT"), ("tokens", "INTEGER")):
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try:
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cur.execute(f"ALTER TABLE messages ADD COLUMN {col} {typedef}")
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except Exception:
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pass
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cur.execute("""
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CREATE INDEX IF NOT EXISTS idx_messages_conversation_id
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ON messages (conversation_id)
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""")
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# Semantic recall: one embedding vector per message, stored as JSON.
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# Backfilled lazily by semantic_search_conversations so existing history
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# gets indexed on first search.
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cur.execute("""
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CREATE TABLE IF NOT EXISTS message_vectors (
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message_id INTEGER PRIMARY KEY,
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embedding TEXT NOT NULL,
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FOREIGN KEY (message_id) REFERENCES messages(id)
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)
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""")
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cur.execute("""
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CREATE TABLE IF NOT EXISTS settings (
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key TEXT PRIMARY KEY,
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value TEXT NOT NULL
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)
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""")
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cur.execute(
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"DELETE FROM settings WHERE key IN ('anthropic_api_key', 'escalation_model')"
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)
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conn.commit()
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conn.close()
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# -----------------------------
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# Loaders
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# -----------------------------
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def _load_all_memory(self) -> Dict[str, MemoryItem]:
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conn = self._connect()
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cur = conn.cursor()
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cur.execute("SELECT id, section, text, tags, position FROM memory ORDER BY position ASC, rowid ASC")
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rows = cur.fetchall()
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conn.close()
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cache = {}
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for row in rows:
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try:
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tags = json.loads(row["tags"]) if row["tags"] else []
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except Exception:
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tags = []
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cache[row["id"]] = MemoryItem(
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id=row["id"],
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section=row["section"] or "General",
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text=row["text"],
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tags=tags,
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position=row["position"] or 0,
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)
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return cache
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# -----------------------------
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# Memory API
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# -----------------------------
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def add(self, item: MemoryItem):
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if not item.position:
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conn = self._connect()
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try:
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row = conn.execute(
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"SELECT position FROM memory WHERE id = ?", (item.id,)
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).fetchone()
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if row and row["position"]:
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item.position = row["position"]
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else:
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row = conn.execute("SELECT MAX(position) AS max_position FROM memory").fetchone()
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item.position = (row["max_position"] or 0) + 1
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finally:
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conn.close()
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self._cache[item.id] = item
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conn = self._connect()
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try:
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cur = conn.cursor()
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cur.execute(
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"INSERT OR REPLACE INTO memory (id, section, text, tags, position) VALUES (?, ?, ?, ?, ?)",
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(item.id, item.section or "General", item.text, json.dumps(item.tags), item.position)
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)
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conn.commit()
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finally:
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conn.close()
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def update(self, item: MemoryItem):
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existing = self.get(item.id)
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if existing:
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item.position = existing.position
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self.add(item)
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def delete(self, item_id: str):
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self._cache.pop(item_id, None)
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conn = self._connect()
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try:
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cur = conn.cursor()
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cur.execute("DELETE FROM memory WHERE id = ?", (item_id,))
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conn.commit()
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finally:
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conn.close()
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def get(self, item_id: str) -> Optional[MemoryItem]:
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conn = self._connect()
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try:
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row = conn.execute(
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"SELECT id, section, text, tags, position FROM memory WHERE id = ?",
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(item_id,),
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).fetchone()
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finally:
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conn.close()
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if not row:
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self._cache.pop(item_id, None)
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return None
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try:
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tags = json.loads(row["tags"]) if row["tags"] else []
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except Exception:
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tags = []
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item = MemoryItem(
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id=row["id"], section=row["section"] or "General", text=row["text"],
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tags=tags, position=row["position"] or 0,
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)
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self._cache[item_id] = item
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return item
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def all(self) -> List[MemoryItem]:
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# Always read from DB — the memory service and backend run in separate processes
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# with separate caches, so the cache can be stale for facts extracted by the
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# memory service after this process started.
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conn = self._connect()
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cur = conn.cursor()
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cur.execute("SELECT id, section, text, tags, position FROM memory ORDER BY position ASC, rowid ASC")
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rows = cur.fetchall()
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conn.close()
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items = []
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for row in rows:
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try:
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tags = json.loads(row["tags"]) if row["tags"] else []
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except Exception:
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tags = []
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items.append(MemoryItem(
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id=row["id"],
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section=row["section"] or "General",
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text=row["text"],
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tags=tags,
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position=row["position"] or 0,
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))
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return items
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def reorder_section(self, section: str, ordered_ids: List[str]) -> bool:
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"""Rewrite the order of items in a section using its existing position pool.
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ordered_ids must contain exactly the ids currently in the section."""
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section_norm = section or "General"
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in_section = [i for i in self.all() if (i.section or "General") == section_norm]
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if len(in_section) != len(ordered_ids):
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return False
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by_id = {i.id: i for i in in_section}
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siblings = []
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for id_ in ordered_ids:
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if id_ not in by_id:
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return False
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siblings.append(by_id[id_])
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positions = sorted([i.position for i in in_section])
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conn = self._connect()
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try:
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cur = conn.cursor()
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for s, new_pos in zip(siblings, positions):
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s.position = new_pos
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cur.execute("UPDATE memory SET position = ? WHERE id = ?", (new_pos, s.id))
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conn.commit()
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finally:
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conn.close()
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return True
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# -----------------------------
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# Conversation API
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# -----------------------------
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def create_conversation(self, conversation_id: str) -> ConversationItem:
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now = time.time()
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conn = self._connect()
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try:
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cur = conn.cursor()
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cur.execute(
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"INSERT OR IGNORE INTO conversations (id, created_at, updated_at) VALUES (?, ?, ?)",
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(conversation_id, now, now)
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)
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conn.commit()
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finally:
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conn.close()
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return ConversationItem(id=conversation_id, created_at=now, updated_at=now)
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def set_conversation_title(self, conversation_id: str, title: str):
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conn = self._connect()
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try:
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cur = conn.cursor()
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cur.execute(
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"UPDATE conversations SET title = ? WHERE id = ?",
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(title, conversation_id),
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)
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conn.commit()
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finally:
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conn.close()
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def add_message(self, conversation_id: str, role: str, content: str, model: Optional[str] = None, tokens: Optional[int] = None):
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now = time.time()
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conn = self._connect()
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try:
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cur = conn.cursor()
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cur.execute(
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"INSERT INTO messages (conversation_id, role, content, timestamp, model, tokens) VALUES (?, ?, ?, ?, ?, ?)",
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(conversation_id, role, content, now, model, tokens)
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)
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message_id = cur.lastrowid
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cur.execute(
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"UPDATE conversations SET updated_at = ? WHERE id = ?",
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(now, conversation_id)
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)
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conn.commit()
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return message_id
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finally:
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conn.close()
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def get_conversation(self, conversation_id: str) -> Optional[ConversationItem]:
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conn = self._connect()
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cur = conn.cursor()
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cur.execute("SELECT * FROM conversations WHERE id = ?", (conversation_id,))
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row = cur.fetchone()
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if not row:
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conn.close()
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return None
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cur.execute(
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"SELECT role, content, timestamp, model, tokens FROM messages WHERE conversation_id = ? ORDER BY timestamp ASC",
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(conversation_id,)
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)
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msg_rows = cur.fetchall()
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conn.close()
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messages = [MessageItem(role=r["role"], content=r["content"], timestamp=r["timestamp"], model=r["model"], tokens=r["tokens"]) for r in msg_rows]
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return ConversationItem(
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id=row["id"],
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messages=messages,
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created_at=row["created_at"],
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updated_at=row["updated_at"],
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title=row["title"] if "title" in row.keys() else None,
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)
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def all_conversations(self) -> List[ConversationItem]:
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conn = self._connect()
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cur = conn.cursor()
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cur.execute("""
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SELECT c.id, c.created_at, c.updated_at, c.title,
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m.role, m.content, m.timestamp, m.model, m.tokens
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FROM conversations c
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LEFT JOIN messages m ON m.conversation_id = c.id
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ORDER BY c.updated_at DESC, m.timestamp ASC
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""")
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rows = cur.fetchall()
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conn.close()
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convs: Dict[str, ConversationItem] = {}
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order: list[str] = []
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for row in rows:
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cid = row["id"]
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if cid not in convs:
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convs[cid] = ConversationItem(
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id=cid,
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created_at=row["created_at"],
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updated_at=row["updated_at"],
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title=row["title"],
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)
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order.append(cid)
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if row["role"] is not None:
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convs[cid].messages.append(
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MessageItem(role=row["role"], content=row["content"], timestamp=row["timestamp"], model=row["model"], tokens=row["tokens"])
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)
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return [convs[cid] for cid in order]
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def delete_conversation(self, conversation_id: str):
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conn = self._connect()
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try:
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cur = conn.cursor()
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cur.execute("DELETE FROM messages WHERE conversation_id = ?", (conversation_id,))
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cur.execute("DELETE FROM conversations WHERE id = ?", (conversation_id,))
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conn.commit()
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finally:
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conn.close()
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# -----------------------------
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# Search API
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# -----------------------------
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def search_conversations(self, query: str, limit: int = 3) -> List[dict]:
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"""Return up to `limit` conversations that contain the query string,
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with full user+assistant exchange pairs around each match."""
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if not query or not query.strip():
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return []
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q = query.strip().lower()
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conn = self._connect()
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||||
cur = conn.cursor()
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||||
cur.execute("""
|
||||
SELECT DISTINCT c.id, c.created_at, c.updated_at
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||||
FROM conversations c
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||||
JOIN messages m ON m.conversation_id = c.id
|
||||
WHERE LOWER(m.content) LIKE ?
|
||||
ORDER BY c.updated_at DESC
|
||||
LIMIT ?
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||||
""", (f"%{q}%", limit))
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||||
rows = cur.fetchall()
|
||||
|
||||
results = []
|
||||
for row in rows:
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||||
# Load all messages in order so we can find complete exchange pairs
|
||||
cur.execute("""
|
||||
SELECT role, content FROM messages
|
||||
WHERE conversation_id = ?
|
||||
ORDER BY timestamp ASC
|
||||
""", (row["id"],))
|
||||
all_msgs = [{"role": r["role"], "content": r["content"]} for r in cur.fetchall()]
|
||||
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||||
# For each matching message, collect the full user+assistant pair around it
|
||||
seen_pairs: set = set()
|
||||
matches = []
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||||
for i, msg in enumerate(all_msgs):
|
||||
if q not in msg["content"].lower():
|
||||
continue
|
||||
if msg["role"] == "user":
|
||||
start, end = i, i + 1 if i + 1 < len(all_msgs) else i
|
||||
else:
|
||||
start, end = (i - 1 if i > 0 else i), i
|
||||
if (start, end) in seen_pairs:
|
||||
continue
|
||||
seen_pairs.add((start, end))
|
||||
for m in all_msgs[start:end + 1]:
|
||||
matches.append({"role": m["role"], "content": m["content"][:500]})
|
||||
if len(seen_pairs) >= 2:
|
||||
break
|
||||
|
||||
if matches:
|
||||
results.append({
|
||||
"id": row["id"],
|
||||
"updated_at": row["updated_at"],
|
||||
"matches": matches,
|
||||
})
|
||||
|
||||
conn.close()
|
||||
return results
|
||||
|
||||
# nomic-embed-text is an asymmetric retrieval model: queries and stored
|
||||
# documents must be embedded with these task prefixes or similarity collapses
|
||||
# into noise. Cached vectors are document-embeddings (search_document:).
|
||||
_EMBED_QUERY_PREFIX = "search_query: "
|
||||
_EMBED_DOC_PREFIX = "search_document: "
|
||||
|
||||
async def semantic_search_conversations(
|
||||
self, query: str, embed_fn, limit: int = 3, min_score: float = 0.6
|
||||
) -> List[dict]:
|
||||
"""Recall past conversations relevant to `query` using hybrid retrieval.
|
||||
|
||||
Combines semantic similarity (embeddings — finds reworded matches with no
|
||||
shared keywords) with the existing lexical substring match (catches exact
|
||||
terms the embedding underweights), unioned and deduped by conversation.
|
||||
|
||||
`embed_fn` is an async callable returning an embedding vector for a string
|
||||
(typically OllamaManager.embed). Messages without a stored vector are
|
||||
embedded and cached on first use (lazy backfill). If embeddings are
|
||||
unavailable (no model / Ollama down) this degrades to pure lexical match,
|
||||
so recall never silently breaks.
|
||||
|
||||
Returns the same shape as `search_conversations`: a list of
|
||||
{id, updated_at, matches:[{role, content}]} with full user+assistant
|
||||
pairs around each match.
|
||||
"""
|
||||
if not query or not query.strip():
|
||||
return []
|
||||
|
||||
query_vec = await embed_fn(self._EMBED_QUERY_PREFIX + query.strip())
|
||||
if not query_vec:
|
||||
return self.search_conversations(query, limit=limit)
|
||||
|
||||
conn = self._connect()
|
||||
cur = conn.cursor()
|
||||
|
||||
# Lazy backfill: embed any messages that don't have a vector yet.
|
||||
cur.execute("""
|
||||
SELECT m.id, m.content
|
||||
FROM messages m
|
||||
LEFT JOIN message_vectors v ON v.message_id = m.id
|
||||
WHERE v.message_id IS NULL AND TRIM(m.content) != ''
|
||||
""")
|
||||
missing = cur.fetchall()
|
||||
for row in missing:
|
||||
vec = await embed_fn(self._EMBED_DOC_PREFIX + row["content"][:2000])
|
||||
if vec:
|
||||
cur.execute(
|
||||
"INSERT OR REPLACE INTO message_vectors (message_id, embedding) VALUES (?, ?)",
|
||||
(row["id"], json.dumps(vec)),
|
||||
)
|
||||
if missing:
|
||||
conn.commit()
|
||||
|
||||
# Score every stored message against the query vector.
|
||||
cur.execute("""
|
||||
SELECT v.message_id, v.embedding, m.conversation_id
|
||||
FROM message_vectors v
|
||||
JOIN messages m ON m.id = v.message_id
|
||||
""")
|
||||
scored = []
|
||||
for row in cur.fetchall():
|
||||
try:
|
||||
vec = json.loads(row["embedding"])
|
||||
except Exception:
|
||||
continue
|
||||
score = _cosine(query_vec, vec)
|
||||
if score >= min_score:
|
||||
scored.append((score, row["message_id"], row["conversation_id"]))
|
||||
|
||||
scored.sort(reverse=True)
|
||||
|
||||
results: List[dict] = []
|
||||
seen_convs: set = set()
|
||||
for score, message_id, conv_id in scored:
|
||||
if len(results) >= limit:
|
||||
break
|
||||
if conv_id in seen_convs:
|
||||
continue
|
||||
pair = self._exchange_pair(cur, conv_id, message_id)
|
||||
if pair:
|
||||
seen_convs.add(conv_id)
|
||||
results.append(pair)
|
||||
|
||||
conn.close()
|
||||
|
||||
# Hybrid union: fill any remaining slots with lexical matches the
|
||||
# embedding missed (e.g. exact proper nouns), skipping dupes.
|
||||
if len(results) < limit:
|
||||
for conv in self.search_conversations(query, limit=limit):
|
||||
if conv["id"] not in seen_convs:
|
||||
seen_convs.add(conv["id"])
|
||||
results.append(conv)
|
||||
if len(results) >= limit:
|
||||
break
|
||||
|
||||
return results
|
||||
|
||||
def _exchange_pair(self, cur, conv_id: str, message_id: int) -> Optional[dict]:
|
||||
"""Build a {id, updated_at, matches} record with the full user+assistant
|
||||
pair surrounding `message_id`, in the shape search callers expect."""
|
||||
cur.execute(
|
||||
"SELECT id, role, content FROM messages WHERE conversation_id = ? ORDER BY timestamp ASC",
|
||||
(conv_id,),
|
||||
)
|
||||
all_msgs = cur.fetchall()
|
||||
idx = next((i for i, m in enumerate(all_msgs) if m["id"] == message_id), None)
|
||||
if idx is None:
|
||||
return None
|
||||
if all_msgs[idx]["role"] == "user":
|
||||
start, end = idx, min(idx + 1, len(all_msgs) - 1)
|
||||
else:
|
||||
start, end = max(idx - 1, 0), idx
|
||||
matches = [
|
||||
{"role": all_msgs[i]["role"], "content": all_msgs[i]["content"][:500]}
|
||||
for i in range(start, end + 1)
|
||||
]
|
||||
cur.execute("SELECT updated_at FROM conversations WHERE id = ?", (conv_id,))
|
||||
crow = cur.fetchone()
|
||||
return {
|
||||
"id": conv_id,
|
||||
"updated_at": crow["updated_at"] if crow else 0,
|
||||
"matches": matches,
|
||||
}
|
||||
|
||||
# -----------------------------
|
||||
# Settings API
|
||||
# -----------------------------
|
||||
_SETTINGS_DEFAULTS: Dict[str, Any] = {
|
||||
"model": "",
|
||||
# Qwen3-style reasoning. Off by default: the hidden <think> block is pure
|
||||
# latency for chat/memory. Turn on for hard multi-step problems.
|
||||
"think": False,
|
||||
"temperature": 0.7,
|
||||
"system_prompt": "",
|
||||
"timeout": 120,
|
||||
# How long Ollama keeps the model resident in VRAM between messages.
|
||||
# "30m"/"-1" (never unload)/"0" (unload now). Avoids cold-reload latency
|
||||
# when you return to an idle chat. Empty → Ollama's 5-minute default.
|
||||
"keep_alive": "30m",
|
||||
# CPU/GPU offload: -1 = Auto (Ollama auto-fits layers to VRAM).
|
||||
# 0–100 = percent of model layers to force onto the GPU; the
|
||||
# remainder runs on CPU. See OllamaManager.get_model_layers.
|
||||
"gpu_offload": -1,
|
||||
# Memory curator (the model that extracts facts after each exchange).
|
||||
# Empty → same auto-selected model as chat. A dedicated model (e.g.
|
||||
# "mistral:latest") gives better extraction but must share VRAM.
|
||||
"memory_model": DEFAULT_MEMORY_MODEL,
|
||||
# Curator CPU/GPU offload — same scale as gpu_offload above. Default 0
|
||||
# (all CPU/RAM): OS-neutral and never evicts the chat model from a small
|
||||
# GPU. Boxes with spare VRAM can set -1 (Auto) or a percent to use the GPU.
|
||||
"memory_gpu_offload": 0,
|
||||
# Similar-fact merge: when a newly extracted fact's embedding is at least
|
||||
# this cosine-similar to an existing fact, UPDATE that fact in place
|
||||
# instead of appending a duplicate ("edit with new info"). 0 disables
|
||||
# (always append). Calibrated on nomic-embed-text: genuine updates
|
||||
# (mileage/title/location changes) score 0.81–0.99, while distinct facts
|
||||
# top out ~0.61 — so 0.80 catches updates and never merges unrelated
|
||||
# facts. Lower to catch looser rephrases; raise toward 1.0 to be stricter.
|
||||
"memory_merge_threshold": 0.80,
|
||||
}
|
||||
|
||||
def get_settings(self) -> Dict[str, Any]:
|
||||
conn = self._connect()
|
||||
cur = conn.cursor()
|
||||
cur.execute("SELECT key, value FROM settings")
|
||||
rows = cur.fetchall()
|
||||
conn.close()
|
||||
result = dict(self._SETTINGS_DEFAULTS)
|
||||
for row in rows:
|
||||
try:
|
||||
result[row["key"]] = json.loads(row["value"])
|
||||
except Exception:
|
||||
result[row["key"]] = row["value"]
|
||||
return result
|
||||
|
||||
def update_settings(self, data: Dict[str, Any]):
|
||||
conn = self._connect()
|
||||
try:
|
||||
cur = conn.cursor()
|
||||
for key, value in data.items():
|
||||
if key in self._SETTINGS_DEFAULTS:
|
||||
cur.execute(
|
||||
"INSERT OR REPLACE INTO settings (key, value) VALUES (?, ?)",
|
||||
(key, json.dumps(value))
|
||||
)
|
||||
conn.commit()
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
# -----------------------------
|
||||
# Store Instance
|
||||
# -----------------------------
|
||||
from ..nexus_config import MEMORY_DB
|
||||
|
||||
DB_PATH = MEMORY_DB
|
||||
store = PersistentMemoryStore(DB_PATH)
|
||||
Reference in New Issue
Block a user
GitNexus · part of the Nexus stack