RAG-Based Agentic Memory: Code Perspective (Chapter 17)

The Memriq AI Inference Brief – Engineering Edition

Unlock how Retrieval-Augmented Generation (RAG) enables AI agents to remember, learn, and personalize over time. In this episode, we explore Chapter 17 of Keith Bourne’s "Unlocking Data with Generative AI and RAG," focusing on implementing agentic memory with the CoALA framework. From episodic and semantic memory distinctions to real-world engineering trade-offs, this discussion is packed with practical insights for AI/ML engineers and infrastructure experts.

In this episode:

- Understand the difference between episodic and semantic memory and their roles in AI agents

- Explore how vector databases like ChromaDB power fast, scalable memory retrieval

- Dive into the architecture and code walkthrough using CoALA, LangChain, LangGraph, and OpenAI APIs

- Discuss engineering challenges including validation, latency, and system complexity

- Hear from author Keith Bourne on the foundational importance of agentic memory

- Review real-world applications and open problems shaping the future of memory-augmented AI

Key tools and technologies mentioned:

- CoALA framework

- LangChain & LangGraph

- ChromaDB vector database

- OpenAI API (embeddings and LLMs)

- python-dotenv

- Pydantic models

Timestamps:

0:00 - Introduction & Episode Overview

2:30 - The Concept of Agentic Memory: Episodic vs Semantic

6:00 - Vector Databases and Retrieval-Augmented Generation (RAG)

9:30 - Coding Agentic Memory: Frameworks and Workflow

13:00 - Engineering Trade-offs and Validation Challenges

16:00 - Real-World Applications and Use Cases

18:30 - Open Problems and Future Directions

20:00 - Closing Thoughts and Resources

Resources:

- "Unlocking Data with Generative AI and RAG" by Keith Bourne - Search for 'Keith Bourne' on Amazon and grab the 2nd edition

- Visit Memriq AI at https://Memriq.ai for more AI engineering deep dives and resources