From bdecf8780b511e9cb379e57b69a943dfc8dc16c8 Mon Sep 17 00:00:00 2001 From: XJTUGary <86176423+XJTUGary@users.noreply.github.com> Date: Thu, 19 Sep 2024 22:58:13 +0800 Subject: [PATCH] Update README.md update langchain RAG link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c707556..25770c1 100644 --- a/README.md +++ b/README.md @@ -344,7 +344,7 @@ With RAG, LLMs retrieves contextual documents from a database to improve the acc 📚 **References**: * [Llamaindex - High-level concepts](https://docs.llamaindex.ai/en/stable/getting_started/concepts.html): Main concepts to know when building RAG pipelines. * [Pinecone - Retrieval Augmentation](https://www.pinecone.io/learn/series/langchain/langchain-retrieval-augmentation/): Overview of the retrieval augmentation process. -* [LangChain - Q&A with RAG](https://python.langchain.com/docs/use_cases/question_answering/quickstart): Step-by-step tutorial to build a typical RAG pipeline. +* [LangChain - Tutorials](https://python.langchain.com/docs/tutorials/rag/): Build a Retrieval Augmented Generation (RAG) App. * [LangChain - Memory types](https://python.langchain.com/docs/modules/memory/types/): List of different types of memories with relevant usage. * [RAG pipeline - Metrics](https://docs.ragas.io/en/stable/concepts/metrics/index.html): Overview of the main metrics used to evaluate RAG pipelines.