**Redis Iris** is a context and memory platform designed for agentic pull architectures. It replaces static RAG with dynamic, live-synced data, semantic tool access, and session management to handle high-frequency AI agent requests at scale.
* Delivers petabyte-scale retrieval with sub-millisecond latency by optimizing costs (99% flash/SSD, 1% RAM).
* Auto-generates MCP tools via Pydantic models, enabling agents to query business data directly with row-level access controls.
* Uses CDC pipelines for continuous synchronization with sources like Snowflake, Databricks, and Postgres.
* Delivers petabyte-scale retrieval with sub-millisecond latency by optimizing costs (99% flash/SSD, 1% RAM).
* Auto-generates MCP tools via Pydantic models, enabling agents to query business data directly with row-level access controls.
* Uses CDC pipelines for continuous synchronization with sources like Snowflake, Databricks, and Postgres.
This article details how to automate embedding generation and updates in Postgres using Supabase Vector, Queues, Cron, and pg_net extension with Edge Functions, addressing the issues of drift, latency, and complexity found in traditional external embedding pipelines.
pg_timeseries is an open-source PostgreSQL extension focused on creating a cohesive user experience around the creation, maintenance, and use of time-series tables. It allows users to create time-series tables, configure the compression and retention of older data, monitor time-series partitions, and run complex time-series analytics functions with a user-friendly syntax.
