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

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

computer aided governance map and process

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

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

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

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.

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 Insurance Documents

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.

Original Repo for Cache-Augmented Generation (CAG)

Your AI-Powered Intelligent Search Assistant for Policy Documents

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.

dockerApplicationServer