From 6dc9b63f8b6b859c1fc72c00a0f9b7e5ffdc6a70 Mon Sep 17 00:00:00 2001 From: Jongseob Jeon <aiden.jongseob@gmail.com> Date: Sun, 10 Nov 2024 07:39:05 +0900 Subject: [PATCH] fix not valid redirections in docs `getting_started/concepts` (#16892) --- docs/docs/getting_started/concepts.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/docs/getting_started/concepts.md b/docs/docs/getting_started/concepts.md index 5a02fc70de..60e92d254d 100644 --- a/docs/docs/getting_started/concepts.md +++ b/docs/docs/getting_started/concepts.md @@ -4,7 +4,7 @@ This is a quick guide to the high-level concepts you'll encounter frequently whe ## Large Language Models (LLMs) -LLMs are the fundamental innovation that launched LlamaIndex. They are an artificial intelligence (AI) computer system that can understand, generate, and manipulate natural language, including answering questions based on their training data or data provided to them at query time. You can [learn more about using LLMs](../understanding/using_llms/using_llms/). +LLMs are the fundamental innovation that launched LlamaIndex. They are an artificial intelligence (AI) computer system that can understand, generate, and manipulate natural language, including answering questions based on their training data or data provided to them at query time. You can [learn more about using LLMs](../understanding/using_llms/using_llms.md). ## Retrieval Augmented Generation (RAG) @@ -12,13 +12,13 @@ Retrieval-Augmented Generation (RAG) is a core technique for building data-backe ## Agents -An agent is a piece of software that semi-autonomously performs tasks by combining LLMs with other tools. You can [learn more about agents](../understanding/agents/index.md). +An agent is a piece of software that semi-autonomously performs tasks by combining LLMs with other tools. You can [learn more about agents](../understanding/agent/index.md). ## Use cases There are endless use cases for data-backed LLM applications but they can be roughly grouped into four categories: -[**Structured Data Extraction**](../use_cases/extraction.md/) +[**Structured Data Extraction**](../use_cases/extraction.md) Pydantic extractors allow you to specify a precise data structure to extract from your data and use LLMs to fill in the missing pieces in a type-safe way. This is useful for extracting structured data from unstructured sources like PDFs, websites, and more, and is key to automating workflows. [**Query Engines**](../module_guides/deploying/query_engine/index.md): -- GitLab