**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 discusses the importance of real-time access for Retrieval Augmented Generation (RAG) and how Redis can enable this through its real-time vector database, semantic cache, and LLM memory capabilities, leading to faster and more accurate responses in GenAI applications.
Explore how semantic caching, which understands the meaning behind user queries, can boost performance and relevance in AI applications by storing and retrieving data based on intent.
The author describes building a GitHub repository assistant capable of answering user issues using Large Language Models (LLMs), specifically Gemini, and Redis.
