From 9672370a2a0c87dee77195f4a518db7b511fc2ed Mon Sep 17 00:00:00 2001 From: Aaron Jimenez <aaronjimv@gmail.com> Date: Fri, 10 Nov 2023 01:24:44 -0800 Subject: [PATCH] Adding routers to high level concepts (#8810) * fixing typo (and and -> and) * Adding routers concep * Adding link to router concep --- docs/getting_started/concepts.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/getting_started/concepts.md b/docs/getting_started/concepts.md index e71686753d..3dc1097a54 100644 --- a/docs/getting_started/concepts.md +++ b/docs/getting_started/concepts.md @@ -10,7 +10,7 @@ If you haven't, [install LlamaIndex](/getting_started/installation.md) and compl LLMs are trained on enormous bodies of data but they aren't trained on **your** data. Retrieval-Augmented Generation (RAG) solves this problem by adding your data to the data LLMs already have access to. You will see references to RAG frequently in this documentation. -In RAG, your data is loaded and and prepared for queries or "indexed". User queries act on the index, which filters your data down to the most relevant context. This context and your query then go to the LLM along with a prompt, and the LLM provides a response. +In RAG, your data is loaded and prepared for queries or "indexed". User queries act on the index, which filters your data down to the most relevant context. This context and your query then go to the LLM along with a prompt, and the LLM provides a response. Even if what you're building is a chatbot or an agent, you'll want to know RAG techniques for getting data into your application. @@ -55,6 +55,9 @@ Once you've ingested your data, LlamaIndex will help you index the data into a s [**Retrievers**](/module_guides/querying/retriever/root.md): A retriever defines how to efficiently retrieve relevant context from an index when given a query. Your retrieval strategy is key to the relevancy of the data retrieved and the efficiency with which it's done. +[**Routers**](/module_guides/querying/router/root.md): +A router determines which retriever will be used to retrieve relevant context from the knowledge base. More specifically, the `RouterRetriever` class, are responsible for selecting one or multiple candidate retrievers to execute a query. They use a selector to choose the best option based on each candidate's metadata and the query. + [**Node Postprocessors**](/module_guides/querying/node_postprocessors/root.md): A node postprocessor takes in a set of retrieved nodes and applies transformations, filtering, or re-ranking logic to them. -- GitLab