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FullStackRetrieval.com

Retrieval is the process of bringing extra information or context to your language models. Retrieval Augmented Generation (RAG) is a system that augments a Large Language Model (LLM) by adding extra context or information from another source.

Retrieval is hyped (2nd to only to agents), but it is still underrated.

This guide has two aims:

  1. Help you understand the full landscape of retrieval in context aware applications
  2. Explore options for to tune, tinker, and improve your retrieval process

Start Here 👉 Retrieval Overview​

New to retrieval?

Glad to have you! Start off by checking out our Retrieval Overview, this will be the home base for this guide

Advanced Retrieval?

We still recommend getting a lay of the land on our Retrieval Overview then branching to the section that interests you. Then head over to the Retrieval Inventory to explore more.

Why do we need a guide on retrieval?​

Connecting your AI applications to the outside world (via retrieval) are a core part of what makes your applications rich. It's often the magic moment to your users.

But retrieval can be confusing and misunderstood, especially when we lack a 30K ft view of the retrieval process.

This guide will start from the basics, work our way towards a mental framework to think about retrieval, then dive in deep with code, videos, evaluations, and examples.

What format will this guide be in?​

The #1 goal is to help you ramp up to speed on retrieval. This means we are mostly agnostic

  • We'll be package agnostic - All popular libraries (LangChain, Llama Index, etc.) will be featured
  • No sponsorship allowed - We will make quality content, not biased content
  • Heavy preference towards python
  • We'll mostly be using OpenAI because it is easy, but also mix in open sourced models

Who's behind this?​

Most of the content is made by Greg Kamradt with amazing help from other community members.

Have Questions?​

Drop us a note on Twitter or contact us