# semantic_memory.py
import chromadb
from sentence_transformers import SentenceTransformer
import numpy as np

# نموذج تضمين مجاني وصغير (حجمه ~80 ميجا)
model = SentenceTransformer('all-MiniLM-L6-v2')
client = chromadb.PersistentClient(path="./chroma_memory")
collection = client.get_or_create_collection("semantic_memory")

def store_memory(text: str, user: str, metadata: dict = None):
    """تخزين الذاكرة مع تضمين دلالي"""
    embedding = model.encode(text).tolist()
    doc_id = f"{user}_{hash(text)}_{len(collection.get()['ids'])}"
    collection.add(
        documents=[text],
        embeddings=[embedding],
        metadatas=[{"user": user, "text": text, **(metadata or {})}],
        ids=[doc_id]
    )
    return doc_id

def recall_memory(query: str, user: str, limit: int = 3) -> list:
    """استرجاع أكثر الذكريات تشابهاً دلالياً"""
    embedding = model.encode(query).tolist()
    results = collection.query(query_embeddings=[embedding], n_results=limit, where={"user": user})
    return results['documents'][0] if results['documents'] else []