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athina-ai
rag-cookbooks
Jupyter Notebook2024–2025
athina-ai / rag-cookbooks

rag-cookbooks

This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.

3Distinct people
8Mentions
0Explicit recommenders
2.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week8 dated mentions

Observed mentions: 2024-12-03 11:55:57 UTC to 2025-01-06 18:51:00 UTC. Weeks start Monday, UTC.

012342024-12-02 00:00:00 UTC to 2024-12-09 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 commentsDec 22024-12-09 00:00:00 UTC to 2024-12-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-16 00:00:00 UTC to 2024-12-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsDec 162024-12-23 00:00:00 UTC to 2024-12-30 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2024-12-30 00:00:00 UTC to 2025-01-06 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2025-01-06 00:00:00 UTC to 2025-01-13 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJan 6

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Dec 22024-12-02 00:00:00 UTC2024-12-09 00:00:00 UTC413
Dec 92024-12-09 00:00:00 UTC2024-12-16 00:00:00 UTC000
Dec 162024-12-16 00:00:00 UTC2024-12-23 00:00:00 UTC000
Dec 232024-12-23 00:00:00 UTC2024-12-30 00:00:00 UTC110
Dec 302024-12-30 00:00:00 UTC2025-01-06 00:00:00 UTC220
Jan 62025-01-06 00:00:00 UTC2025-01-13 00:00:00 UTC110

What Hacker News says

8 sources
hbamoria·Jan 3, 2025Submission

Show HN: Open-Source Notebooks for Building Agentic RAG Architectures

Hey HN! We’ve published a series of open-source notebooks showcasing Advanced RAG and Agentic architectures, and we’re excited to share our latest compilation of Agentic RAG techniques! These Colab-ready notebooks are designed to be plug-and-play, making it easy to integrate them into your projects. We're actively expanding the repository and would love your input to shape its future. What Advanced RAG technique should we add next? Drop your ideas in the comments or open an issue on GitHub!
6 points · Open discussion
hbamoria·Dec 4, 2024Submission

Show HN: Open-Source Colab Notebooks to Implement Advanced RAG Techniques

Hey HN fam, We’ve seen developers spend a lot of time implementing advanced RAG techniques from scratch. While these techniques are essential for improving performance, their implementation requires a lot of effort and testing! To help with this process, our team (Athina AI) has released Open-Source Advanced RAG Cookbooks. This is a collection of ready-to-run Google Colab notebooks featuring the most commonly implemented techniques. Please show us some love by starring the repo if you find this useful!
98 points · Open discussion
Large Language Models are trained on a fixed dataset, which limits their ability to handle private or recent information. They can sometimes "hallucinate", providing incorrect yet believable answers. The Retrieval-Augmented Generation (RAG) framework addresses this issue by using external documents to improve the LLM's responses through in-context learning. RAG ensures that the information provided by the LLM is not only contextually relevant but also accurate and up-to-date. If you want to learn more about it along with a code implementation of every technique, you should check this repo out: https://github.com/athina-ai/rag-cookbooks
In What Is Retrieval-Augmented Generation (RAG)?

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