AI Agents and Long-Term Memory on Lakebase

Gives AI agents durable memory on Lakebase. Following the lecture-demo pattern, learners distinguish the three memory categories, choose among framework paths, persist a LangGraph conversation with a checkpointer, resume a thread after a restart, isolate per-tier state with branches, and recall facts across threads with a semantic store.

Skill Level

Associate

Duration

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

• Familiarity with LLMs and at least one agent framework — LangGraph (primary) or the OpenAI Agents SDK (shown in parallel).

• Intermediate Python (async code runs in the notebook — nest_asyncio, subprocess-based resume in the lab).

• PostgreSQL / psycopg basics — connecting, running queries, reading information_schema.

• Basic embeddings / vector similarity intuition (cosine similarity, semantic vs. exact-key retrieval).

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Upcoming Public Classes

Migrating to Lakebase

Migrates an existing Postgres workload to Lakebase. Following the lecture-demo pattern, learners use a decision matrix to confirm fit, assemble a pre-migration checklist, execute a pg_dump/pg_restore migration (and its alternatives), and design a cutover strategy with validation queries that catch subtle drift.

Paid & Subscription

Lab

Associate

Production Operations: HA, Read Replicas, Instant Restore

Hardens Lakebase for production. Following the lecture-demo pattern and framed around an incident, learners configure High Availability across zones, add read replicas and route analytical traffic to them, tune the restore window, and recover from data loss with a point-in-time branch.

Paid & Subscription

Lab

Associate

Vector Search and RAG with pgvector on Lakebase

Builds a semantic retrieval layer on pgvector inside Lakebase Postgres, taught against a live "Ask the docs" app that Classroom Setup deploys. Following the lecture-demo pattern, learners decide when pgvector beats Databricks AI Search, design a document/chunk schema, choose and build HNSW and IVFFlat indexes, generate embeddings in batch with ai_query(), and write a hybrid search combining vector similarity, a metadata filter, and full-text ranking (ts_rank_cd cover-density) in one query. An optional closing section surveys Lakebase Search (Beta) and its lakebase_text (BM25) and lakebase_vector (ANN) extensions.

Paid & Subscription

Lab

Associate