Learn how to build an end-to-end RAG pipeline with monitoring and evaluation using Langchain, Azure AI Search, OpenAI, Langfuse, Nemo-gaurdrails, and ragas.
Table of contents
Code ImplementationImporting libraries and Setting up environment variablesCollecting DatasetGenerating Chunking and Adding MetadataCreating Index in Azure AI SearchIntegrating with LangfuseBuilding RAG ChainAdding Guardrails to the RAG chainBuilding ‘Golden Dataset’ to evaluate RAG pipelineEvaluate on ‘Golden dataset’ using ragas metricsDisplay the performance/score in LangfuseSort: