A LLM semantic caching system aiming to enhance user experience by reducing response time via cached query-result pairs.

Redis Vector Library (RedisVL) -- the AI-native Python client for Redis.

Semantic caching layer for your LLM applications. Reuse responses and reduce token usage.

Reliable and Efficient Semantic Prompt Caching with vCache

Redis Vector Library (RedisVL) -- the AI-native Java client for Redis.

This is a RAG based chatbot in which semantic cache and guardrails have been incorporated.

This repository contains sample code demonstrating how to implement a verified semantic cache using Amazon Bedrock Knowledge Bases to prevent hallucinations in Large Language Model (LLM) responses while improving latency and reducing costs.

High-performance LLM query cache with semantic search. Reduce API costs 80% and latency from 8.5s to 1ms using Redis + Qdrant vector DB. Multi-provider support (OpenAI, Anthropic).

Enhance LLM retrieval performance with Azure Cosmos DB Semantic Cache. Learn how to integrate and optimize caching strategies in real-world web applications.

Redis Vector Similarity Search, Semantic Caching, Recommendation Systems and RAG

A ChatBot using Redis Vector Similarity Search, which can recommend blogs based on user prompt

Optimized RAG Retrieval with Indexing, Quantization, Hybrid Search and Caching

Ultra-fast Semantic Cache Proxy written in pure C

🚀 Optimize LLM usage with PromptCache, a smart middleware that cuts costs and speeds up responses by caching repetitive queries.

Redis Database offers unique capability to keep your data fresh while serving through LLM chatbot

Semantic cache for your LLM apps in Go!

Episodic memory and semantic cache proxy for LLM APIs with ~40% token savings

Zero-dependency, type-safe Node.js client for Vecs Semantic Cache.

🔍 Optimize RAG systems by exploring Lexical, Semantic, and Hybrid Search methods for better context retrieval and improved LLM responses.