From ff2152b115c641b6d91853967dc2bf2dac269358 Mon Sep 17 00:00:00 2001 From: ccurme Date: Fri, 8 Nov 2024 14:47:32 -0500 Subject: [PATCH] docs: update tutorials index and add get started guides (#27996) --- docs/docs/tutorials/index.mdx | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/docs/docs/tutorials/index.mdx b/docs/docs/tutorials/index.mdx index 6e8fc50e9a..817acf0441 100644 --- a/docs/docs/tutorials/index.mdx +++ b/docs/docs/tutorials/index.mdx @@ -7,25 +7,25 @@ sidebar_class_name: hidden New to LangChain or LLM app development in general? Read this material to quickly get up and running. ## Basics -- [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain) -- [Build a Chatbot](/docs/tutorials/chatbot) -- [Build vector stores and retrievers](/docs/tutorials/retrievers) -- [Build an Agent](/docs/tutorials/agents) +- [LLM applications](/docs/tutorials/llm_chain): Build and deploy a simple LLM application. +- [Chatbots](/docs/tutorials/chatbot): Build a chatbot that incorporates memory. +- [Vector stores](/docs/tutorials/retrievers): Build vector stores and use them to retrieve data. +- [Agents](/docs/tutorials/agents): Build an agent that interacts with external tools. ## Working with external knowledge -- [Build a Retrieval Augmented Generation (RAG) Application](/docs/tutorials/rag) -- [Build a Conversational RAG Application](/docs/tutorials/qa_chat_history) -- [Build a Question/Answering system over SQL data](/docs/tutorials/sql_qa) -- [Build a Query Analysis System](/docs/tutorials/query_analysis) -- [Build a local RAG application](/docs/tutorials/local_rag) -- [Build a Question Answering application over a Graph Database](/docs/tutorials/graph) -- [Build a PDF ingestion and Question/Answering system](/docs/tutorials/pdf_qa/) +- [Retrieval Augmented Generation (RAG)](/docs/tutorials/rag): Build an application that uses your own documents to inform its responses. +- [Conversational RAG](/docs/tutorials/qa_chat_history): Build a RAG application that incorporates a memory of its user interactions. +- [Question-Answering with SQL](/docs/tutorials/sql_qa): Build a question-answering system that executes SQL queries to inform its responses. +- [Query Analysis](/docs/tutorials/query_analysis): Build a RAG application that analyzes questions to generate filters and other structured queries. +- [Local RAG](/docs/tutorials/local_rag): Build a RAG application using LLMs running locally on your machine. +- [Question-Answering with Graph Databases](/docs/tutorials/graph): Build a question-answering system that queries a graph database to inform its responses. +- [Question-Answering with PDFs](/docs/tutorials/pdf_qa/): Build a question-answering system that ingests PDFs and uses them to inform its responses. ## Specialized tasks -- [Build an Extraction Chain](/docs/tutorials/extraction) -- [Generate synthetic data](/docs/tutorials/data_generation) -- [Classify text into labels](/docs/tutorials/classification) -- [Summarize text](/docs/tutorials/summarization) +- [Extraction](/docs/tutorials/extraction): Extract structured data from text and other unstructured media. +- [Synthetic data](/docs/tutorials/data_generation): Generate synthetic data using LLMs. +- [Classification](/docs/tutorials/classification): Classify text into categories or labels. +- [Summarization](/docs/tutorials/summarization): Generate summaries of (potentially long) texts. ## LangGraph