Memory retrieval strategies in Agentic AI

Introduction

Memory retrieval strategies help AI systems find and use stored information quickly and correctly. This makes AI smarter and more helpful.

When an AI assistant needs to remember past conversations to answer questions better.

When a recommendation system looks up user preferences stored earlier.

When a chatbot retrieves facts from a knowledge base to reply accurately.

When a search engine finds relevant documents from a large database.

When a robot recalls past actions to improve future decisions.

Syntax

Agentic AI

class MemoryRetrieval:
    def __init__(self, memory_store):
        self.memory_store = memory_store

    def retrieve(self, query):
        # Simple retrieval by matching query keywords
        results = [item for item in self.memory_store if query in item]
        return results

This example shows a simple memory retrieval class in Python.

The retrieve method looks for items containing the query word.

Examples

Retrieves 'banana' from the memory list.

Agentic AI

memory = ['apple', 'banana', 'cherry']
retriever = MemoryRetrieval(memory)
print(retriever.retrieve('banana'))

Edge case: empty memory returns an empty list.

Agentic AI

memory = []
retriever = MemoryRetrieval(memory)
print(retriever.retrieve('apple'))

Edge case: memory with one item returns that item if it matches.

Agentic AI

memory = ['apple']
retriever = MemoryRetrieval(memory)
print(retriever.retrieve('apple'))

Query not found returns an empty list.

Agentic AI

memory = ['apple', 'banana', 'cherry']
retriever = MemoryRetrieval(memory)
print(retriever.retrieve('berry'))

Sample Model

This program creates a simple memory list and retrieves items containing specific words.

It shows how retrieval works with matches and no matches.

Agentic AI

class MemoryRetrieval:
    def __init__(self, memory_store):
        self.memory_store = memory_store

    def retrieve(self, query):
        results = [item for item in self.memory_store if query in item]
        return results

# Create memory with some items
memory_items = ['cat', 'dog', 'parrot', 'doghouse', 'caterpillar']
retriever = MemoryRetrieval(memory_items)

print('Memory before retrieval:', memory_items)

# Retrieve items containing 'dog'
found_items = retriever.retrieve('dog')
print('Retrieved items for query "dog":', found_items)

# Retrieve items containing 'cat'
found_items_cat = retriever.retrieve('cat')
print('Retrieved items for query "cat":', found_items_cat)

# Retrieve items with no match
found_items_none = retriever.retrieve('fish')
print('Retrieved items for query "fish":', found_items_none)

Important Notes

Time complexity is O(n) because it checks each memory item once.

Space complexity is O(k) where k is the number of matched items returned.

Common mistake: forgetting to handle empty memory or no matches, which should return an empty list.

Use simple retrieval for small memory stores; for large data, use indexing or search algorithms.

Summary

Memory retrieval strategies help AI find stored information quickly.

Simple retrieval checks each item for a match and returns results.

Handle empty memory and no matches gracefully to avoid errors.