feat: integrate production-grade FAISS MemoryEngine and services singleton

memory_engine.py

@@ -0,0 +1,42 @@
+import json
+import os
+import threading
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+import faiss
+import numpy as np
+
+def _ensure_numpy(vec: np.ndarray, dim: int) -> np.ndarray:
+    if not isinstance(vec, np.ndarray): raise TypeError("Vector must be a numpy.ndarray")
+    if vec.ndim != 1: raise ValueError("Vector must be 1-dimensional")
+    if vec.shape[0] != dim: raise ValueError(f"Vector length {vec.shape[0]} does not match dim={dim}")
+    return np.ascontiguousarray(vec.astype(np.float32))
+
+class MemoryEngine:
+    def __init__(self, dim: int, index_factory: str = "Flat", metric: str = "l2"):
+        self.dim = dim
+        self._lock = threading.RLock()
+        self.metric = faiss.METRIC_L2 if metric == "l2" else faiss.METRIC_INNER_PRODUCT
+        self.index = faiss.index_factory(dim, index_factory, self.metric)
+        self._metadata: Dict[int, Dict[str, Any]] = {}
+
+    def add(self, vector: np.ndarray, meta: Dict[str, Any] | None = None) -> int:
+        vec = _ensure_numpy(vector, self.dim)
+        with self._lock:
+            self.index.add(np.expand_dims(vec, axis=0))
+            new_id = self.index.ntotal - 1
+            self._metadata[new_id] = meta or {}
+            return new_id
+
+    def query(self, vector: np.ndarray, k: int = 5) -> List[Tuple[int, float, Dict[str, Any]]]:
+        vec = _ensure_numpy(vector, self.dim)
+        with self._lock:
+            distances, ids = self.index.search(np.expand_dims(vec, axis=0), k)
+        return [(int(idx), float(dist), self._metadata.get(int(idx), {})) 
+                for idx, dist in zip(ids[0], distances[0]) if idx != -1]
+
+    def save(self, folder: str):
+        path = Path(folder)
+        path.mkdir(parents=True, exist_ok=True)
+        faiss.write_index(self.index, str(path / "faiss.index"))
+        with (path / "metadata.json").open("w") as f: json.dump(self._metadata, f)

services.py

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+from pathlib import Path
+from memory_engine import MemoryEngine
+
+EMBED_DIM = 768
+_memory_engine = None
+
+def get_memory_engine():
+    global _memory_engine
+    if _memory_engine is None:
+        _memory_engine = MemoryEngine(dim=EMBED_DIM)
+        if Path("data/memory_store").is_dir():
+            _memory_engine.load("data/memory_store")
+    return _memory_engine