diff --git a/templates/AI-Develop RAG pipeline using SQL database in Fabric/AI-Develop RAG pipeline using SQL database in Fabric.json b/templates/AI-Develop RAG pipeline using SQL database in Fabric/AI-Develop RAG pipeline using SQL database in Fabric.json index 4bf5fe9f..65b5a529 100644 --- a/templates/AI-Develop RAG pipeline using SQL database in Fabric/AI-Develop RAG pipeline using SQL database in Fabric.json +++ b/templates/AI-Develop RAG pipeline using SQL database in Fabric/AI-Develop RAG pipeline using SQL database in Fabric.json @@ -9,7 +9,7 @@ "variables": {}, "resources": [ { - "name": "ai_rag_pipeline_using_sql_database_in_fabric", + "name": "AI-Develop RAG pipeline using SQL database in Fabric", "description": "This Retrieval Augmented Generation (RAG) data pipeline will get your data ready for building Generative AI and Agentic AI applications. Triggered by Azure Blob Storage events, the pipeline copies the file to the Lakehouse, extracts the content from the file, chunks the content, redacts any PII information, generates embeddings, and stores the chunks and embeddings in SQL Database in Fabric. Documentation: https://github.com/Azure-Samples/fabric-sqldb-ai-ragpipeline", "type": "pipelines", "apiVersion": "2018-06-01", diff --git a/templates/AI-Develop RAG pipeline using SQL database in Fabric/manifest.json b/templates/AI-Develop RAG pipeline using SQL database in Fabric/manifest.json index 8015f380..9a8f4a52 100644 --- a/templates/AI-Develop RAG pipeline using SQL database in Fabric/manifest.json +++ b/templates/AI-Develop RAG pipeline using SQL database in Fabric/manifest.json @@ -1,5 +1,5 @@ { - "name": "ai_rag_pipeline_using_sql_database_in_fabric", + "name": "AI-Develop RAG pipeline using SQL database in Fabric", "description":"Use this Retrieval Augmented Generation (RAG) data pipeline template to get your data ready in SQL Database in fabric for building Generative AI and Agentic AI applications. \n\n Triggered by Azure Blob Storage events, the pipeline copies the file to the Lakehouse, extracts the content from the file, chunks the content, redacts any PII information, generates embeddings, and stores the chunks and embeddings in SQL Database in Fabric.\n\n As a part of configuring the pipeline, you will be required to provide values for predefined variables such as \"apiKey\", \"cognitiveServiceEndpoint\", \"openAIEndpoint\", \"openAIKey\" etc., by selecting the pipeline canvas and navigating to the variables menu. Additionally, the pipeline configuration will also depend on Python Notebook and UserDataFunction. \n\nThe source files and documentation for this pipeline can can be found at:\n https://github.com/Azure-Samples/fabric-sqldb-ai-ragpipeline", "image": "Notebookazureblob_to_lakehouseFunctionsExtract TextIf ConditionText ExtractionResultsTrue+FalseTextExtractio...TextExtractio...+FunctionsGenerate ChunksFunctionsRedact PII DataIf ConditionPII Reaction ResultsTrue+FalseRedactionFailure...TextRedactio...+FunctionsGenerateEmbeddingsIf ConditionGenerateEmbeddings...True+FalseGenerateEmbeddi...GenerateEmbeddi...+FunctionsCreate DatabaseObjectsFunctionsSave Data", "icons": [