Cache-Augmented Generation: A Simple, Efficient Alternative to RAG

An open-source, AI-powered application using Agentic CAG to chat with any public GitHub repository or developer profile, offering deep code analysis, visual architecture maps and security audits

A Demo of Cache-Augmented Generation (CAG) in an LLM

Kusto and Log Analytics MCP server help you execute a KQL (Kusto Query Language) query within an AI prompt, analyze, and visualize the data.

A local LLM chatbot with CAG / Context Caching for PDF input files

computer aided governance map and process

RAG framework that combines structured knowledge representation using hypergraphs with efficient response generation via semantic caching.

Integrate Anyparser's powerful content extraction capabilities with LangChain for enhanced AI workflows. This integration package enables seamless use of Anyparser's document processing and data extraction features within your LangChain applications.

AI-Suite - n8n, Open WebUI, OpenCode, Llama.cpp/Ollama, Flowise, Langfuse, MCP Gateway and more!

This repository demonstrates Cache-Augmented Generation (CAG) using the Mistral-7B model.

AI-powered agent is designed to compare the performance of these two cutting-edge approaches, providing insights into their strengths, weaknesses, and real-world applications.

Supercharge your AI workflows by combining Anyparser’s advanced content extraction with Crew AI. With this integration, you can effortlessly leverage Anyparser’s document processing and data extraction tools within your Crew AI applications.

Your AI-Powered Intelligent Search Assistant for Insurance Documents

AI-Powered Git Repository Assistant. (An AI tool to answer questions about any GitHub repo)

A cost-optimization case study comparing RAG and CAG architectures, showcasing efficient API usage and local CPU deployment with Google Gemini and Llama 3.2 via Ollama.

An LLM-powered augmented generation suite leveraging LangChain, Ollama, and vector databases to enhance response quality through caching, contextual memory, and retrieval-based methods. This collection of Jupyter notebooks showcases modular techniques for building intelligent, memory-efficient generative systems with real-time semantic awareness.

Instantly access Anyparser's robust document processing and data extraction capabilities directly within your LlamaIndex workflows. Enhance your AI applications with superior content understanding and data quality.

Your AI-Powered Intelligent Search Assistant for Policy Documents

educational overview and technical walkthrough of three key techniques used to enhance the capabilities of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG), Cache-Augmented Generation (CAG), and Fine-Tuning

AI-powered research assistant that ingests PDFs, summarizes content, and supports chat-style queries over your documents. Frontend: Vite/React UI for uploads, summaries, and chat. Backend: FastAPI with embeddings-based retrieval, caching, and summarization endpoints.