WikiChat is an improved RAG. It stops the hallucination of large language models by retrieving data from a corpus.

从零搭建大模型知识库(Build LLM RAG Corpus from scratch)

Official code implementation of the paper: QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation

RAG for Vietnamese Wikipedia corpus.

Official implementation for the paper "KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs"

An offline CPU-first low-resource chat application to perform RAG on your corpus of data. Powered by OpenChat and CTranslate2.

RagCheck is a proactive corpus quality assessment tool that analyses RAG application document collections before deployment, identifying content gaps and providing specific recommendations to improve query performance. The platform transforms reactive corpus fixes into proactive quality assurance, helping organisations achieve as high as 85% score.

Automated EDA and Security Audit for RAG Knowledge Bases.

This project aims to develop a Retrieval-Augmented Generation (RAG) based conversational agent that enables users to interact with a corpus of PDF documents through a natural language interface

A Retrieval-Augmented Generation (RAG) based Medical Chatbot. With the base model as Llama2 from Meta, the Retrieval system uses a medical document as corpus to generate context-rich output.

High-throughput RAG ingestion engine in pure Go — 965 MB/s (Mixed Corpus), 2.4 MB heap, zero dependencies.

基于Ollama和AnythingLLM的双语平行语料库管理和问答工具。

A Comprehensive Benchmark Framework for Retrieval-Augmented Generation (RAG) Routing and Query-Corpus Compatibility.

RAG architecture with Databricks Vector Search and Databricks Model Serving. A movie recommender chat bot was developed with LLama-2-7b-hf-chat where a user can type in a movie description (e.g. romantic comedy set in New York) and the chat bot recommends a movie based on a corpus of plot summaries and gives a micro summary.

[NAACL 2025] Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (ACP-Corpus; ACP-QA; ACP-RAG)

OpenAxo Open-Source Dataset: A structured Chinese news corpus for LLM training and RAG, providing real-time Chinese news articles covering topics such as current affairs, technology, automotive, education, finance, healthcare, and more.

A Retrieval-Augmented Generation (RAG) chatbot built in Python using embeddings, PostgreSQL with pgvector, and Hugging Face language models. This project allows a chatbot to answer user queries by retrieving relevant information from a corpus of text documents.

Google ADK agent that creates a RAG corpus of Zettelkasten cards from a source and then use RAG to query the cards.

Document-grounded Conversational Assistant. This system uses the RAG (Retrieval-Augmented Generation) architecture to provide accurate, contextual responses based on your private corpus of documents.

The **Vertex AI RAG Engine Extension** allows the Gemini CLI to interact with the Google Cloud Vertex AI Retrieval-Augmented Generation (RAG) Engine. With this extension, you can manage document corpus, import files, and retrieve context for answering questions based on your uploaded documents.

LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often not well-suited for factual recall and can be costly. Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base

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res is a CLI tool and Typescript library to continually collect web content into a local "reservoir" (organized directory) of markdown files for use as a personal document corpus in RAG pipelines or in search/discovery applications.

A Federated Knowledge Core for astronomical research — decoupling semantic meaning from structural relationships to enable expert-level RAG and autonomous Deep Research agents.

Dameng Corpus for DM-RAG.

Multi-table RAG QA telemetry + decision-grade RAG Ops notebook for retrieval attribution, hallucination risk slicing, and quality×cost×latency trade-offs.

Shows how to use the Fixie Corpus API to provide a proper RAG pipeline to an OpenAI Assistant.

Synthetic multi-table RAG QA telemetry benchmark (corpus→chunks→retrieval→eval): labels for correctness/faithfulness/hallucination + cost/latency for RAG evaluation and dashboards.

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