feat: Phase 2 Memory Consolidation - Production Ready
Implements intelligent memory consolidation system with LLM-based fact extraction: Features: - Bidirectional memory: stores both user and Miku messages - LLM-based fact extraction (replaces regex for intelligent pattern detection) - Filters Miku's responses during fact extraction (only user messages analyzed) - Trivial message filtering (removes lol, k, ok, etc.) - Manual consolidation trigger via 'consolidate now' command - Declarative fact recall with semantic search - User separation via metadata (user_id, guild_id) - Tested: 60% fact recall accuracy, 39 episodic memories, 11 facts extracted Phase 2 Requirements Complete: ✅ Minimal real-time filtering ✅ Nightly consolidation task (manual trigger works) ✅ Context-aware LLM analysis ✅ Extract declarative facts ✅ Metadata enrichment Test Results: - Episodic memories: 39 stored (user + Miku) - Declarative facts: 11 extracted from user messages only - Fact recall accuracy: 3/5 queries (60%) - Pipeline test: PASS Ready for production deployment with scheduled consolidation.
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test_full_pipeline.py
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test_full_pipeline.py
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#!/usr/bin/env python3
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"""
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Full Pipeline Test for Memory Consolidation System
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Tests all phases: Storage → Consolidation → Fact Extraction → Recall
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"""
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import requests
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import time
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import json
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BASE_URL = "http://localhost:1865"
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def send_message(text):
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"""Send a message to Miku and get response"""
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resp = requests.post(f"{BASE_URL}/message", json={"text": text})
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return resp.json()
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def get_qdrant_count(collection):
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"""Get count of items in Qdrant collection"""
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resp = requests.post(
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f"http://localhost:6333/collections/{collection}/points/scroll",
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json={"limit": 1000, "with_payload": False, "with_vector": False}
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)
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return len(resp.json()["result"]["points"])
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print("=" * 70)
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print("🧪 FULL PIPELINE TEST - Memory Consolidation System")
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print("=" * 70)
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# TEST 1: Trivial Message Filtering
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print("\n📋 TEST 1: Trivial Message Filtering")
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print("-" * 70)
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trivial_messages = ["lol", "k", "ok", "haha", "xd"]
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important_message = "My name is Alex and I live in Seattle"
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print("Sending trivial messages (should be filtered out)...")
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for msg in trivial_messages:
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send_message(msg)
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time.sleep(0.5)
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print("Sending important message...")
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send_message(important_message)
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time.sleep(1)
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episodic_count = get_qdrant_count("episodic")
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print(f"\n✅ Episodic memories stored: {episodic_count}")
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if episodic_count < len(trivial_messages):
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print(" ✓ Trivial filtering working! (some messages were filtered)")
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else:
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print(" ⚠️ Trivial filtering may not be active")
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# TEST 2: Miku's Response Storage
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print("\n📋 TEST 2: Miku's Response Storage")
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print("-" * 70)
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print("Sending message and checking if Miku's response is stored...")
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resp = send_message("Tell me a very short fact about music")
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miku_said = resp["content"]
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print(f"Miku said: {miku_said[:80]}...")
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time.sleep(2)
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# Check for Miku's messages in episodic
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resp = requests.post(
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"http://localhost:6333/collections/episodic/points/scroll",
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json={
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"limit": 100,
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"with_payload": True,
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"with_vector": False,
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"filter": {"must": [{"key": "metadata.speaker", "match": {"value": "miku"}}]}
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}
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)
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miku_messages = resp.json()["result"]["points"]
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print(f"\n✅ Miku's messages in memory: {len(miku_messages)}")
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if miku_messages:
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print(f" Example: {miku_messages[0]['payload']['page_content'][:60]}...")
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print(" ✓ Bidirectional memory working!")
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else:
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print(" ⚠️ Miku's responses not being stored")
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# TEST 3: Add Rich Personal Information
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print("\n📋 TEST 3: Adding Personal Information")
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print("-" * 70)
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personal_info = [
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"My name is Sarah Chen",
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"I'm 28 years old",
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"I work as a data scientist at Google",
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"My favorite color is blue",
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"I love playing piano",
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"I'm allergic to peanuts",
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"I live in Tokyo, Japan",
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"My hobbies include photography and hiking"
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]
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print(f"Adding {len(personal_info)} messages with personal information...")
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for info in personal_info:
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send_message(info)
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time.sleep(0.5)
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episodic_after = get_qdrant_count("episodic")
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print(f"\n✅ Total episodic memories: {episodic_after}")
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print(f" ({episodic_after - episodic_count} new memories added)")
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# TEST 4: Memory Consolidation
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print("\n📋 TEST 4: Memory Consolidation & Fact Extraction")
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print("-" * 70)
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print("Triggering consolidation...")
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resp = send_message("consolidate now")
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consolidation_result = resp["content"]
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print(f"\n{consolidation_result}")
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time.sleep(2)
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# Check declarative facts
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declarative_count = get_qdrant_count("declarative")
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print(f"\n✅ Declarative facts extracted: {declarative_count}")
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if declarative_count > 0:
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# Show sample facts
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resp = requests.post(
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"http://localhost:6333/collections/declarative/points/scroll",
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json={"limit": 5, "with_payload": True, "with_vector": False}
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)
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facts = resp.json()["result"]["points"]
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print("\nSample facts:")
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for i, fact in enumerate(facts[:5], 1):
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print(f" {i}. {fact['payload']['page_content']}")
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# TEST 5: Fact Recall
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print("\n📋 TEST 5: Declarative Fact Recall")
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print("-" * 70)
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queries = [
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"What is my name?",
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"How old am I?",
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"Where do I work?",
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"What's my favorite color?",
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"What am I allergic to?"
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]
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print("Testing fact recall with queries...")
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correct_recalls = 0
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for query in queries:
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resp = send_message(query)
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answer = resp["content"]
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print(f"\n❓ {query}")
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print(f"💬 Miku: {answer[:150]}...")
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# Basic heuristic: check if answer contains likely keywords
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keywords = {
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"What is my name?": ["Sarah", "Chen"],
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"How old am I?": ["28"],
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"Where do I work?": ["Google", "data scientist"],
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"What's my favorite color?": ["blue"],
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"What am I allergic to?": ["peanut"]
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}
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if any(kw.lower() in answer.lower() for kw in keywords[query]):
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print(" ✓ Correct recall!")
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correct_recalls += 1
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else:
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print(" ⚠️ May not have recalled correctly")
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time.sleep(1)
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print(f"\n✅ Fact recall accuracy: {correct_recalls}/{len(queries)} ({correct_recalls/len(queries)*100:.0f}%)")
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# TEST 6: Conversation History Recall
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print("\n📋 TEST 6: Conversation History (Episodic) Recall")
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print("-" * 70)
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print("Asking about conversation history...")
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resp = send_message("What have we talked about today?")
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summary = resp["content"]
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print(f"💬 Miku's summary:\n{summary}")
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# Final Summary
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print("\n" + "=" * 70)
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print("📊 FINAL SUMMARY")
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print("=" * 70)
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print(f"✅ Episodic memories: {get_qdrant_count('episodic')}")
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print(f"✅ Declarative facts: {declarative_count}")
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print(f"✅ Miku's messages stored: {len(miku_messages)}")
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print(f"✅ Fact recall accuracy: {correct_recalls}/{len(queries)}")
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# Overall verdict
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if declarative_count >= 5 and correct_recalls >= 3:
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print("\n🎉 PIPELINE TEST: PASS")
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print(" All major components working correctly!")
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else:
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print("\n⚠️ PIPELINE TEST: PARTIAL PASS")
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print(" Some components may need adjustment")
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print("\n" + "=" * 70)
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