diff --git a/site/en/integrations/integrate_with_langfuse.md b/site/en/integrations/integrate_with_langfuse.md index 1f91fce58..9044e06ef 100644 --- a/site/en/integrations/integrate_with_langfuse.md +++ b/site/en/integrations/integrate_with_langfuse.md @@ -1,16 +1,20 @@ --- id: integrate_with_langfuse.md summary: This is a simple cookbook that demonstrates how to use the LlamaIndex Langfuse integration. It uses Milvus Lite to store the documents and Query. -title: Cookbook LlamaIndex & Milvus Integration +title: Using Langfuse to Evaluate RAG Quality --- -# Cookbook - LlamaIndex & Milvus Integration +# Using Langfuse to Trace Queries in RAG Open In Colab -This is a simple cookbook that demonstrates how to use the [LlamaIndex Langfuse integration](https://langfuse.com/docs/integrations/llama-index/get-started). It uses Milvus Lite to store the documents and Query. +This is a simple cookbook that demonstrates how to use Langfuse to trace your queries in RAG. The RAG pipeline is implemented with LlamaIndex and Milvus Lite to store and retrieve the documents. + +In this quickstart, we’ll show you how to set up a LlamaIndex application using Milvus Lite as the vector store. We’ll also show you how to use the Langfuse LlamaIndex integration to trace your application. + +[Langfuse](https://github.com/langfuse/langfuse) is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications. All platform features are natively integrated to accelerate the development workflow. [Milvus Lite](https://github.com/milvus-io/milvus-lite/) is the lightweight version of Milvus, an open-source vector database that powers AI applications with vector embeddings and similarity search.