Building a Living Memory System Using Agentic AI
In today’s rapidly evolving tech landscape, harnessing the power of Agentic AI can notably enhance how we manage information. This blog post will guide you through the process of building a “Zettelkasten” memory system, a dynamic architecture designed to mimic the human brain while organizing information efficiently.
Understanding Agentic AI
Agentic AI refers to advanced artificial intelligence systems capable of autonomous learning and decision-making. By leveraging this technology, we can move beyond standard retrieval methods and create a knowledge graph that organizes information dynamically.
Step-by-Step Guide to Creating a Robust Zettelkasten System
Importing Essential Libraries
To kick off our project, we need to import several crucial libraries for graph management and interaction with the AI model. Securing our API key is also essential to ensure smooth operations.
python
!pip install -q -U google-generativeai networkx pyvis scikit-learn numpy
import os
import json
import uuid
import time
import getpass
import random
import networkx as nx
import numpy as np
import google.generativeai as genai
from dataclasses import dataclass, field
from typing import List
Defining the Memory Structure
We use data classes to create a structure for our MemoryNode. This organizes content, types, and vector embeddings cohesively.
python
@dataclass
class MemoryNode:
id: str
content: str
type: str
embedding: List[float] = field(default_factory=list)
timestamp: int = 0
Building the RobustZettelkasten Class
The main class for our Zettelkasten system initializes a graph and integrates the embedding model for semantic search capabilities.
python
class RobustZettelkasten:
def init(self):
self.graph = nx.Graph()
self.model = genai.GenerativeModel(MODEL_NAME)
self.step_counter = 0
Atomizing User Input
To construct a seamless ingestion pipeline, we break down complex user inputs into atomic facts, ensuring no information is lost.
python
def _atomize_input(self, text):
prompt = f”””
Break the following text into independent atomic facts.
Output JSON: {{ “facts”: [“fact1”, “fact2”] }}
Text: “{text}”
“””
response = retry_with_backoff(
self.model.generate_content,
prompt,
generation_config={“response_mime_type”: “application/json”}
)
return json.loads(response.text).get(“facts”, []) if response else [text]
Linking Related Facts
Our system identifies existing nodes and creates semantic links, effectively building a real-time knowledge graph that resembles human associative memory.
python
def add_memory(self, user_input):
self.step_counter += 1
facts = self._atomize_input(user_input)
…
Memory Consolidation and Querying
To enhance the cognitive capabilities of our agent, we implement a “sleep” phase that consolidates dense memory clusters into higher-order insights.
python
def consolidate_memory(self):
…
The agent can also traverse linked paths to answer complex questions based on context.
python
def answer_query(self, query):
…
Visualizing the Memory Graph
Finally, we wrap things up with a function that generates an interactive HTML graph of the agent’s memory, allowing for easy inspection of nodes and connections.
python
def show_graph(self):
…
Conclusion
Through this tutorial, we’ve created a fully functional “Living Memory” prototype that surpasses traditional database systems. By enabling our agent to autonomously link related concepts during a consolidation phase, we effectively address the challenge of fragmented context in AI interactions. This pioneering system highlights the potential of combining processing power with a structured, evolving memory in AI development.
Related Keywords
- Agentic AI
- Zettelkasten memory system
- Knowledge graph
- Semantic search
- AI memory architecture
- Machine learning
- Cognitive computing
