docs: update tutorials index and add get started guides (#27996)

This commit is contained in:
ccurme 2024-11-08 14:47:32 -05:00 committed by GitHub
parent c421997caa
commit ff2152b115
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194

View File

@ -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. New to LangChain or LLM app development in general? Read this material to quickly get up and running.
## Basics ## Basics
- [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain) - [LLM applications](/docs/tutorials/llm_chain): Build and deploy a simple LLM application.
- [Build a Chatbot](/docs/tutorials/chatbot) - [Chatbots](/docs/tutorials/chatbot): Build a chatbot that incorporates memory.
- [Build vector stores and retrievers](/docs/tutorials/retrievers) - [Vector stores](/docs/tutorials/retrievers): Build vector stores and use them to retrieve data.
- [Build an Agent](/docs/tutorials/agents) - [Agents](/docs/tutorials/agents): Build an agent that interacts with external tools.
## Working with external knowledge ## Working with external knowledge
- [Build a Retrieval Augmented Generation (RAG) Application](/docs/tutorials/rag) - [Retrieval Augmented Generation (RAG)](/docs/tutorials/rag): Build an application that uses your own documents to inform its responses.
- [Build a Conversational RAG Application](/docs/tutorials/qa_chat_history) - [Conversational RAG](/docs/tutorials/qa_chat_history): Build a RAG application that incorporates a memory of its user interactions.
- [Build a Question/Answering system over SQL data](/docs/tutorials/sql_qa) - [Question-Answering with SQL](/docs/tutorials/sql_qa): Build a question-answering system that executes SQL queries to inform its responses.
- [Build a Query Analysis System](/docs/tutorials/query_analysis) - [Query Analysis](/docs/tutorials/query_analysis): Build a RAG application that analyzes questions to generate filters and other structured queries.
- [Build a local RAG application](/docs/tutorials/local_rag) - [Local RAG](/docs/tutorials/local_rag): Build a RAG application using LLMs running locally on your machine.
- [Build a Question Answering application over a Graph Database](/docs/tutorials/graph) - [Question-Answering with Graph Databases](/docs/tutorials/graph): Build a question-answering system that queries a graph database to inform its responses.
- [Build a PDF ingestion and Question/Answering system](/docs/tutorials/pdf_qa/) - [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 ## Specialized tasks
- [Build an Extraction Chain](/docs/tutorials/extraction) - [Extraction](/docs/tutorials/extraction): Extract structured data from text and other unstructured media.
- [Generate synthetic data](/docs/tutorials/data_generation) - [Synthetic data](/docs/tutorials/data_generation): Generate synthetic data using LLMs.
- [Classify text into labels](/docs/tutorials/classification) - [Classification](/docs/tutorials/classification): Classify text into categories or labels.
- [Summarize text](/docs/tutorials/summarization) - [Summarization](/docs/tutorials/summarization): Generate summaries of (potentially long) texts.
## LangGraph ## LangGraph