Agent Memory & State Management
This program explores different types of agent memory (short-term, long-term), techniques for managing conversational state, and integrating external memory systems (e.g., vector databases for RAG – Retrieval Augmented Generation).
Pre-Requisite
- Building Simple AI Agents with Python, or equivalent hands-on experience with agent development.
Course Outline
- 3 Sections
- 12 Lessons
- 3 Days
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- DAY 1 - Short-Term & Conversational Memory4
- DAY 2 - Long-Term Memory & Introduction to RAG4
- 2.1Concept of Long-Term Memory for Agents (Knowledge Bases, Databases)
- 2.2Introduction to Retrieval Augmented Generation (RAG)
- 2.3Vector Embeddings and Vector Databases (Conceptual overview)
- 2.4Hands-on: Setting up a basic RAG system with a local text corpus and simple vector search (e.g., using faiss or simple cosine similarity).
- DAY 3 - Advanced Memory Patterns & State Persistence4
- 3.1Integrating External Data Sources (Databases, APIs for persistent memory)
- 3.2Hybrid Memory Systems (Combining short-term and long-term memory)
- 3.3Techniques for persisting agent state across sessions
- 3.4Hands-on: Building an agent that leverages both conversational history and an external knowledge base for its responses.
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