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RAG & Knowledge Systems

Cag

Cag is an open-source Python research project implementing Cache-Augmented Generation, a language-model approach that preloads relevant knowledge into the context and caches runtime parameters to generate responses without real-time retrieval. The repository provides separate scripts for CAG and RAG experiments.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Cag — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to study or prototype knowledge-grounded generation when the relevant material can fit in the model’s context window, or to compare that approach with a retrieval baseline.

Who it is for

  • Researchers comparing retrieval-free generation with retrieval-augmented generation.
  • Developers exploring knowledge-grounded language-model experiments using SQuAD or HotpotQA.

Documented uses

  • Explore generation using knowledge preloaded into model context.
  • Compare CAG and RAG experiments with configurable dataset and knowledge-size settings.
  • Investigate how context length and knowledge volume affect a retrieval-free workflow.
Before you choose

Requirements & limits

The public GitHub repository is identified as open source under the MIT license.

  • CAG preloads relevant knowledge and avoids retrieval at generation time; the provided RAG script offers an experimental baseline.
  • The entire relevant knowledge source must fit within the context window, limiting suitability for very large collections.
  • The README warns that model performance may degrade with very long contexts.
  • The README documents Python requirements, dataset download preparation and a .env file with required keys.
  • The documented CAG example exposes settings including dataset, similarity measure, model name, knowledge count and output path.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the CAG experiment environment

Prepare the repository’s documented dependencies, datasets and environment file for its experiments.

Before you start

  • Python project environment
  • Repository files, including requirements.txt, downloads.sh and .env.template
  • Required environment keys, including HF_TOKEN for the documented Docker workflow

Steps

  1. Install the requirements with pip install -r ./requirements.txt.

  2. Download the SQuAD and HotpotQA datasets by running sh ./downloads.sh.

  3. Create the local environment file with cp ./.env.template ./.env and populate the required keys.

Expected outcome

The documented dependencies and experiment data are prepared, and the required environment configuration file is in place.

Guide limitations

  • This guide covers preparation only; it does not verify that an experiment runs successfully.
  • The README identifies SQuAD and HotpotQA training datasets for the provided experiments.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
hhhuang/CAG · Official homepage title · retained in its source language
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