Upload memory.py

agent/memory/memory.py

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+"""
+NeuraPrompt Agent — Memory Module v7.6 (MongoDB Integrated)
+Properly connects to your existing MongoDB system from main.py
+"""
+
+from typing import List, Dict, Optional
+import logging
+from datetime import datetime, timezone
+
+log = logging.getLogger("agent.memory.v7.6")
+
+# These will be injected from main.py
+long_term_memory_col = None
+chat_history_col = None
+
+
+def set_collections(long_term_col, chat_history_col_ref):
+    """Call this from main.py to inject MongoDB collections"""
+    global long_term_memory_col, chat_history_col
+    long_term_memory_col = long_term_col
+    chat_history_col = chat_history_col_ref
+
+
+class MemoryManager:
+    def __init__(self, user_id: str):
+        self.user_id = user_id
+        self.short_term: List[Dict] = []
+
+    def load_long_term_memory(self) -> Dict:
+        """Load user's long-term memory from MongoDB"""
+        if not long_term_memory_col:
+            return {}
+        try:
+            doc = long_term_memory_col.find_one({"user_id": self.user_id})
+            return doc or {}
+        except Exception as e:
+            log.error(f"Failed to load long-term memory: {e}")
+            return {}
+
+    def load_recent_chat(self, limit: int = 8) -> List[Dict]:
+        """Load recent chat history from MongoDB"""
+        if not chat_history_col:
+            return []
+        try:
+            cursor = chat_history_col.find(
+                {"user_id": self.user_id}
+            ).sort("timestamp", -1).limit(limit)
+            messages = list(cursor)
+            messages.reverse()
+            return messages
+        except Exception as e:
+            log.error(f"Failed to load chat history: {e}")
+            return []
+
+    def get_context_for_agent(self) -> str:
+        """Build rich context string for the agent"""
+        long_term = self.load_long_term_memory()
+        recent = self.load_recent_chat(6)
+
+        context_parts = []
+
+        # Long-term facts
+        if long_term:
+            facts = []
+            for key, value in long_term.items():
+                if key not in ["_id", "user_id", "last_updated"]:
+                    facts.append(f"{key}: {value}")
+            if facts:
+                context_parts.append("Known about user: " + ", ".join(facts[:7]))
+
+        # Recent conversation
+        if recent:
+            chat_lines = []
+            for msg in recent[-5:]:
+                role = msg.get("role", "unknown").capitalize()
+                content = msg.get("content", "")[:180]
+                chat_lines.append(f"{role}: {content}")
+            context_parts.append("Recent conversation:\n" + "\n".join(chat_lines))
+
+        return "\n\n".join(context_parts) if context_parts else "No previous memory available."
+
+    def save_important_fact(self, key: str, value: str):
+        """Save important fact to long-term memory"""
+        if not long_term_memory_col:
+            return
+        try:
+            long_term_memory_col.update_one(
+                {"user_id": self.user_id},
+                {"$set": {
+                    key: value,
+                    "last_updated": datetime.now(timezone.utc)
+                }},
+                upsert=True
+            )
+            log.info(f"Saved fact '{key}' for user {self.user_id}")
+        except Exception as e:
+            log.error(f"Failed to save fact: {e}")
+
+
+def get_memory_manager(user_id: str) -> MemoryManager:
+    return MemoryManager(user_id)