From 95888f87db5fdd0009518b930745a643f8565927 Mon Sep 17 00:00:00 2001 From: James Codella Date: Thu, 24 Apr 2025 15:52:19 -0400 Subject: [PATCH 1/4] shortened titles, parameter names --- .../default/manifest.json | 24 ++++++++-------- .../default/workflow.json | 6 ++-- .../manifest.json | 4 +-- .../default/manifest.json | 28 +++++++++---------- .../default/workflow.json | 6 ++-- .../manifest.json | 4 +-- .../default/manifest.json | 26 ++++++++--------- .../default/workflow.json | 6 ++-- .../manifest.json | 4 +-- .../default/manifest.json | 28 +++++++++---------- .../default/workflow.json | 6 ++-- .../manifest.json | 4 +-- 12 files changed, 73 insertions(+), 73 deletions(-) diff --git a/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json b/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json index ddd06b7..6275ed1 100644 --- a/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json +++ b/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json @@ -1,16 +1,16 @@ { "id": "default", - "title": "Document ingestion from Azure Blob Storage using Azure Document Intelligence OCR into Azure Cosmos DB for RAG", + "title": "Document ingestion from Azure Blob Storage using Azure Document Intelligence OCR into Azure Cosmos DB", "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", "description": "", - "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You need to [create an Azure Document Intelligence resource](https://learn.microsoft.com/azure/ai-services/document-intelligence/overview). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", + "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You need to [create an Azure Document Intelligence resource](https://learn.microsoft.com/azure/ai-services/document-intelligence/overview). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "kinds": [ "stateful", "stateless" ], "workflows": { "default": { - "name": "ingest-doc-blob-document-intelligence-cosmos" + "name": "cdb-doc-indexer-blob" } }, "artifacts": [ @@ -25,8 +25,8 @@ }, "parameters": [ { - "name": "OpenAI_TextEmbedding_Deployment_#workflowname#", - "displayName": "OpenAI text embedding deployment identifier", + "name": "AOAI_Emb_Dep_#workflowname#", + "displayName": "OpenAI embedding deployment", "type": "String", "description": "Provide the deployment model for Azure OpenAI.", "required": true @@ -40,35 +40,35 @@ }, { "name": "CosmosDB_Account_#workflowname#", - "displayName": "Cosmos DB Account", + "displayName": "Azure Cosmos DB account", "type": "String", "description": "Provide the name for the Azure Cosmos DB Account.", "required": true }, { "name": "CosmosDB_Database_#workflowname#", - "displayName": "Cosmos DB name", + "displayName": "Azure Cosmos DB database", "type": "String", "description": "Provide the name for the Azure Cosmos DB database.", "required": true }, { "name": "CosmosDB_Collection_#workflowname#", - "displayName": "Cosmos DB collection name", + "displayName": "Azure Cosmos DB collection", "type": "String", "description": "Provide the name for the Azure Cosmos DB collection.", "required": true }, { - "name": "CosmosDB_Vector_Path_#workflowname#", - "displayName": "Vector embeddings path or property", + "name": "CDB_Vector_#workflowname#", + "displayName": "Vector embeddings property", "type": "String", "description": "Provide the path or property for the vector embeddings in the Cosmos DB data items.", "required": true }, { - "name": "CosmosDB_Text_Path_#workflowname#", - "displayName": "Property with the document text in data items", + "name": "CDB_Text_#workflowname#", + "displayName": "Document text property", "type": "String", "description": "Provide the property that contains the document text in the Cosmos DB data items.", "required": true diff --git a/ingest-doc-blob-document-intelligence-cosmos/default/workflow.json b/ingest-doc-blob-document-intelligence-cosmos/default/workflow.json index c415500..dbcacba 100644 --- a/ingest-doc-blob-document-intelligence-cosmos/default/workflow.json +++ b/ingest-doc-blob-document-intelligence-cosmos/default/workflow.json @@ -6,7 +6,7 @@ "type": "ServiceProvider", "inputs": { "parameters": { - "deploymentId": "@parameters('OpenAI_TextEmbedding_Deployment_#workflowname#')", + "deploymentId": "@parameters('AOAI_Emb_Dep_#workflowname#')", "input": "@take(body('Chunk_text')?['value'], length(body('Chunk_text')['value']))" }, "serviceProviderConfiguration": { @@ -31,9 +31,9 @@ "inputs": { "from": "@range(0, length(body('Chunk_text')['value']))\r\n", "select": { - "@{parameters('CosmosDB_Text_Path_#workflowname#')}": "@{body('Chunk_text')['value'][item()]}", + "@{parameters('CDB_Text_#workflowname#')}": "@{body('Chunk_text')['value'][item()]}", "documentName": "@{triggerBody()?['name']}", - "@{parameters('CosmosDB_Vector_Path_#workflowname#')}": "@body('Get_multiple_embeddings')['embeddings'][item()]", + "@{parameters('CDB_Vector_#workflowname#')}": "@body('Get_multiple_embeddings')['embeddings'][item()]", "fileType": "@{triggerBody()?['properties']?['blobFullPathWithContainer']}", "id": "@{guid()}", "chunkNumber": "@{add(item(),1)}", diff --git a/ingest-doc-blob-document-intelligence-cosmos/manifest.json b/ingest-doc-blob-document-intelligence-cosmos/manifest.json index 4c3d0b7..c09c886 100644 --- a/ingest-doc-blob-document-intelligence-cosmos/manifest.json +++ b/ingest-doc-blob-document-intelligence-cosmos/manifest.json @@ -1,13 +1,13 @@ { "id": "ingest-doc-blob-document-intelligence-cosmos", - "title": "Document ingestion from Azure Blob Storage using Azure Document Intelligence OCR into Azure Cosmos DB for RAG", + "title": "Document ingestion from Azure Blob Storage using Azure Document Intelligence OCR into Azure Cosmos DB", "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", "skus": [ "standard" ], "workflows": { "default": { - "name": "ingest-doc-blob-document-intelligence-cosmos" + "name": "cdb-doc-indexer-blob" } }, "featuredConnectors": [ diff --git a/ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json b/ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json index 4c06b67..a2e3f39 100644 --- a/ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json +++ b/ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json @@ -1,16 +1,16 @@ { "id": "default", - "title": "Document ingestion from SharePoint using Azure Document Intelligence OCR into Azure Cosmos DB for RAG", + "title": "Document ingestion from SharePoint using Azure Document Intelligence OCR into Azure Cosmos DB", "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", "description": "", - "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You need to [create an Azure Document Intelligence resource](https://learn.microsoft.com/azure/ai-services/document-intelligence/overview). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", + "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You need to [create an Azure Document Intelligence resource](https://learn.microsoft.com/azure/ai-services/document-intelligence/overview). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "kinds": [ "stateful", "stateless" ], "workflows": { "default": { - "name": "ingest-doc-sharepoint-document-intelligence-cosmos" + "name": "cdb-doc-indexer-sp" } }, "artifacts": [ @@ -25,22 +25,22 @@ }, "parameters": [ { - "name": "OpenAI_TextEmbedding_Deployment_#workflowname#", - "displayName": "OpenAI text embedding deployment identifier", + "name": "AOAI_Emb_Dep_#workflowname#", + "displayName": "OpenAI embedding deployment", "type": "String", "description": "Provide the deployment model for Azure OpenAI.", "required": true }, { "name": "Sharepoint_Library_Name_#workflowname#", - "displayName": "SharePoint Library Name", + "displayName": "SharePoint Library name", "type": "String", "description": "Provide the name of the SharePoint Library.", "required": true }, { "name": "Sharepoint_Site_Address_#workflowname#", - "displayName": "SharePoint Site address", + "displayName": "SharePoint site address", "type": "String", "description": "Provide the address of the SharePoint Site.", "required": true @@ -53,35 +53,35 @@ "required": true }, { "name": "CosmosDB_Account_#workflowname#", - "displayName": "Cosmos DB Account", + "displayName": "Azure Cosmos DB Account", "type": "String", "description": "Provide the name for the Azure Cosmos DB Account.", "required": true }, { "name": "CosmosDB_Database_#workflowname#", - "displayName": "Azure Cosmos DB name", + "displayName": "Azure Cosmos DB database", "type": "String", "description": "Provide the name for the Azure Cosmos DB database.", "required": true }, { "name": "CosmosDB_Collection_#workflowname#", - "displayName": "Cosmos DB collection name", + "displayName": "Azure Cosmos DB collection", "type": "String", "description": "Provide the name for the Azure Cosmos DB collection.", "required": true }, { - "name": "CosmosDB_Vector_Path_#workflowname#", - "displayName": "Vector embeddings path or property", + "name": "CDB_Vector_#workflowname#", + "displayName": "Vector embeddings property", "type": "String", "description": "Provide the path or property for the vector embeddings in the Cosmos DB data items.", "required": true }, { - "name": "CosmosDB_Text_Path_#workflowname#", - "displayName": "Property with the document text in data items", + "name": "CDB_Text_#workflowname#", + "displayName": "Property with document text", "type": "String", "description": "Provide the property that contains the document text in the Cosmos DB data items.", "required": true diff --git a/ingest-doc-sharepoint-document-intelligence-cosmos/default/workflow.json b/ingest-doc-sharepoint-document-intelligence-cosmos/default/workflow.json index ba7e2c8..47bdef4 100644 --- a/ingest-doc-sharepoint-document-intelligence-cosmos/default/workflow.json +++ b/ingest-doc-sharepoint-document-intelligence-cosmos/default/workflow.json @@ -5,7 +5,7 @@ "type": "ServiceProvider", "inputs": { "parameters": { - "deploymentId": "@parameters('OpenAI_TextEmbedding_Deployment_#workflowname#')", + "deploymentId": "@parameters('AOAI_Emb_Dep_#workflowname#')", "input": "@take(body('Chunk_text')?['value'], length(body('Chunk_text')['value']))" }, "serviceProviderConfiguration": { @@ -30,9 +30,9 @@ "inputs": { "from": "@range(0, length(body('Chunk_text')['value']))\r\n", "select": { - "@{parameters('CosmosDB_Text_Path_#workflowname#')}": "@{body('Chunk_text')['value'][item()]}", + "@{parameters('CDB_Text_#workflowname#')}": "@{body('Chunk_text')['value'][item()]}", "documentName": "@{triggerBody()?['{FilenameWithExtension}']}", - "@{parameters('CosmosDB_Vector_Path_#workflowname#')}": "@body('Get_multiple_embeddings')['embeddings'][item()]", + "@{parameters('CDB_Vector_#workflowname#')}": "@body('Get_multiple_embeddings')['embeddings'][item()]", "fileLink": "@{triggerBody()?['{Link}']}", "id": "@{guid()}", "chunkNumber": "@{add(item(),1)}", diff --git a/ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json b/ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json index 34ec843..9f7215b 100644 --- a/ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json +++ b/ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json @@ -1,13 +1,13 @@ { "id": "ingest-doc-sharepoint-document-intelligence-cosmos", - "title": "Document ingestion from SharePoint using Azure Document Intelligence OCR into Azure Cosmos DB for RAG", + "title": "Document ingestion from SharePoint using Azure Document Intelligence OCR into Azure Cosmos DB", "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", "skus": [ "standard" ], "workflows": { "default": { - "name": "ingest-doc-sharepoint-document-intelligence-cosmos" + "name": "cdb-doc-indexer-sp" } }, "featuredConnectors": [ diff --git a/ingest-document-blob-openai-cosmos/default/manifest.json b/ingest-document-blob-openai-cosmos/default/manifest.json index e72b1d6..0559ca0 100644 --- a/ingest-document-blob-openai-cosmos/default/manifest.json +++ b/ingest-document-blob-openai-cosmos/default/manifest.json @@ -1,16 +1,16 @@ { "id": "default", - "title": "Document ingestion from Azure Blob Storage into Azure Cosmos DB for RAG", + "title": "Document ingestion from Azure Blob Storage into Azure Cosmos DB", "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs and Markdown, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", "description": "", - "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", + "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "kinds": [ "stateful", "stateless" ], "workflows": { "default": { - "name": "ingest-document-blob-openai-cosmos" + "name": "cdb-doc-indexer-blob" } }, "artifacts": [ @@ -25,22 +25,22 @@ }, "parameters": [ { - "name": "OpenAI_TextEmbedding_Deployment_#workflowname#", - "displayName": "OpenAI text embedding deployment identifier", + "name": "AOAI_Emb_Dep_#workflowname#", + "displayName": "OpenAI embedding deployment", "type": "String", "description": "Provide the deployment model for Azure OpenAI.", "required": true }, { "name": "Blob_Path_#workflowname#", - "displayName": "Blob Storage documents path_#workflowname#", + "displayName": "Blob Storage documents path", "type": "String", "description": "Provide the path to the documents in Blob Storage.", "required": true }, { "name": "CosmosDB_Account_#workflowname#", - "displayName": "Cosmos DB Account", + "displayName": "Azure Cosmos DB account", "type": "String", "description": "Provide the name for the Azure Cosmos DB Account.", "required": true @@ -48,28 +48,28 @@ { "name": "CosmosDB_Database_#workflowname#", - "displayName": "Cosmos DB name", + "displayName": "Azure Cosmos DB database", "type": "String", "description": "Provide the name for the Azure Cosmos DB database.", "required": true }, { "name": "CosmosDB_Collection_#workflowname#", - "displayName": "Cosmos DB collection name", + "displayName": "Azure Cosmos DB collection", "type": "String", "description": "Provide the name for the Azure Cosmos DB collection.", "required": true }, { - "name": "CosmosDB_Vector_Path_#workflowname#", - "displayName": "Vector embeddings path or property", + "name": "CDB_Vector_#workflowname#", + "displayName": "Vector embeddings property", "type": "String", "description": "Provide the path or property for the vector embeddings in the Cosmos DB data items.", "required": true }, { - "name": "CosmosDB_Text_Path_#workflowname#", - "displayName": "Property with the document text in data items", + "name": "CDB_Text_#workflowname#", + "displayName": "Document text property", "type": "String", "description": "Provide the property that contains the document text in the Cosmos DB data items.", "required": true diff --git a/ingest-document-blob-openai-cosmos/default/workflow.json b/ingest-document-blob-openai-cosmos/default/workflow.json index 6cfd9f4..d2f0eb6 100644 --- a/ingest-document-blob-openai-cosmos/default/workflow.json +++ b/ingest-document-blob-openai-cosmos/default/workflow.json @@ -20,7 +20,7 @@ "type": "ServiceProvider", "inputs": { "parameters": { - "deploymentId": "@parameters('OpenAI_TextEmbedding_Deployment_#workflowname#')", + "deploymentId": "@parameters('AOAI_Emb_Dep_#workflowname#')", "input": "@take(body('Chunk_text')?['value'], length(body('Chunk_text')['value']))" }, "serviceProviderConfiguration": { @@ -66,9 +66,9 @@ "inputs": { "from": "@range(0, length(body('Chunk_text')['value']))\r\n", "select": { - "@{parameters('CosmosDB_Text_Path_#workflowname#')}": "@body('Chunk_text')['value'][item()]", + "@{parameters('CDB_Text_#workflowname#')}": "@body('Chunk_text')['value'][item()]", "documentName": "@triggerBody()?['name']", - "@{parameters('CosmosDB_Vector_Path_#workflowname#')}": "@body('Get_multiple_embeddings')['embeddings'][item()]", + "@{parameters('CDB_Vector_#workflowname#')}": "@body('Get_multiple_embeddings')['embeddings'][item()]", "sourceFile": "@triggerBody()?['name']", "fileType": "@slice(triggerBody()?['name'],-3)", "id": "@guid()", diff --git a/ingest-document-blob-openai-cosmos/manifest.json b/ingest-document-blob-openai-cosmos/manifest.json index 016a5cd..4296fac 100644 --- a/ingest-document-blob-openai-cosmos/manifest.json +++ b/ingest-document-blob-openai-cosmos/manifest.json @@ -1,13 +1,13 @@ { "id": "ingest-document-blob-openai-cosmos", - "title": "Document ingestion from Azure Blob Storage into Azure Cosmos DB for RAG", + "title": "Document ingestion from Azure Blob Storage into Azure Cosmos DB", "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs and Markdown, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", "skus": [ "standard" ], "workflows": { "default": { - "name": "ingest-document-blob-openai-cosmos" + "name": "cdb-doc-indexer-blob" } }, "featuredConnectors": [ diff --git a/ingest-document-sharepoint-openai-cosmos/default/manifest.json b/ingest-document-sharepoint-openai-cosmos/default/manifest.json index 86fb683..5f031bf 100644 --- a/ingest-document-sharepoint-openai-cosmos/default/manifest.json +++ b/ingest-document-sharepoint-openai-cosmos/default/manifest.json @@ -1,12 +1,12 @@ { "id": "default", - "title": "Document ingestion from SharePoint into Azure Cosmos DB for RAG", + "title": "Document ingestion from SharePoint into Azure Cosmos DB", "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs, text files, Markdown, etc. from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", "description": "", - "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", + "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "workflows": { "default": { - "name": "ingest-document-sharepoint-openai-cosmos" + "name": "cdb-doc-indexer-sp" } }, "kinds": [ @@ -25,22 +25,22 @@ }, "parameters": [ { - "name": "OpenAI_TextEmbedding_Deployment_#workflowname#", - "displayName": "OpenAI text embedding deployment identifier", + "name": "AOAI_Emb_Dep_#workflowname#", + "displayName": "OpenAI embedding deployment", "type": "String", "description": "Provide the deployment model for Azure OpenAI.", "required": true }, { "name": "Sharepoint_Library_Name_#workflowname#", - "displayName": "SharePoint Library Name", + "displayName": "SharePoint Library name", "type": "String", "description": "Provide the name of the SharePoint Library.", "required": true }, { "name": "Sharepoint_Site_Address_#workflowname#", - "displayName": "SharePoint Site address", + "displayName": "SharePoint site address", "type": "String", "description": "Provide the address of the SharePoint Site.", "required": true @@ -54,35 +54,35 @@ }, { "name": "CosmosDB_Account_#workflowname#", - "displayName": "Cosmos DB Account", + "displayName": "Azure Cosmos DB account", "type": "String", "description": "Provide the name for the Azure Cosmos DB Account.", "required": true }, { "name": "CosmosDB_Database_#workflowname#", - "displayName": "Azure Cosmos DB name", + "displayName": "Azure Cosmos DB database", "type": "String", "description": "Provide the name for the Azure Cosmos DB database.", "required": true }, { "name": "CosmosDB_Collection_#workflowname#", - "displayName": "Cosmos DB collection name", + "displayName": "Azure Cosmos DB collection", "type": "String", "description": "Provide the name for the Azure Cosmos DB collection.", "required": true }, { - "name": "CosmosDB_Vector_Path_#workflowname#", - "displayName": "Vector embeddings path or property", + "name": "CDB_Vector_#workflowname#", + "displayName": "Vector embeddings property", "type": "String", "description": "Provide the path or property for the vector embeddings in the Cosmos DB data items.", "required": true }, { - "name": "CosmosDB_Text_Path_#workflowname#", - "displayName": "Property with the document text in data items", + "name": "CDB_Text_#workflowname#", + "displayName": "Document text property", "type": "String", "description": "Provide the property that contains the document text in the Cosmos DB data items.", "required": true diff --git a/ingest-document-sharepoint-openai-cosmos/default/workflow.json b/ingest-document-sharepoint-openai-cosmos/default/workflow.json index f75ed08..3bbd139 100644 --- a/ingest-document-sharepoint-openai-cosmos/default/workflow.json +++ b/ingest-document-sharepoint-openai-cosmos/default/workflow.json @@ -20,7 +20,7 @@ "type": "ServiceProvider", "inputs": { "parameters": { - "deploymentId": "@parameters('OpenAI_TextEmbedding_Deployment_#workflowname#')", + "deploymentId": "@parameters('AOAI_Emb_Dep_#workflowname#')", "input": "@take(body('Chunk_text')?['value'], length(body('Chunk_text')['value']))" }, "serviceProviderConfiguration": { @@ -72,9 +72,9 @@ "inputs": { "from": "@range(0, length(body('Chunk_text')['value']))\r\n", "select": { - "@{parameters('CosmosDB_Text_Path_#workflowname#')}": "@{body('Chunk_text')['value'][item()]}", + "@{parameters('CDB_Text_#workflowname#')}": "@{body('Chunk_text')['value'][item()]}", "documentName": "@{triggerBody()?['{FilenameWithExtension}']}", - "@{parameters('CosmosDB_Vector_Path_#workflowname#')}": "@body('Get_multiple_embeddings')['embeddings'][item()]", + "@{parameters('CDB_Vector_#workflowname#')}": "@body('Get_multiple_embeddings')['embeddings'][item()]", "fileLink": "@{triggerBody()?['{Link}']}", "id": "@{guid()}", "chunkNumber": "@{add(item(),1)}", diff --git a/ingest-document-sharepoint-openai-cosmos/manifest.json b/ingest-document-sharepoint-openai-cosmos/manifest.json index b0ab749..af803be 100644 --- a/ingest-document-sharepoint-openai-cosmos/manifest.json +++ b/ingest-document-sharepoint-openai-cosmos/manifest.json @@ -1,13 +1,13 @@ { "id": "ingest-document-sharepoint-openai-cosmos", - "title": "Document ingestion from SharePoint into Azure Cosmos DB for RAG", + "title": "Document ingestion from SharePoint into Azure Cosmos DB", "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs, text files, Markdown, etc. from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", "skus": [ "standard" ], "workflows": { "default": { - "name": "ingest-document-sharepoint-openai-cosmos" + "name": "cdb-doc-indexer-sp" } }, "featuredConnectors": [ From 4d31ecaa15af1ff5531fafcb9a2eeaa05e0b58e1 Mon Sep 17 00:00:00 2001 From: James Codella Date: Fri, 25 Apr 2025 09:39:29 -0400 Subject: [PATCH 2/4] added language for feedback --- .../default/manifest.json | 2 +- ingest-doc-blob-document-intelligence-cosmos/manifest.json | 2 +- .../default/manifest.json | 2 +- .../manifest.json | 2 +- ingest-document-blob-openai-cosmos/manifest.json | 2 +- ingest-document-sharepoint-openai-cosmos/default/manifest.json | 2 +- ingest-document-sharepoint-openai-cosmos/manifest.json | 2 +- 7 files changed, 7 insertions(+), 7 deletions(-) diff --git a/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json b/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json index 6275ed1..3f42c36 100644 --- a/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json +++ b/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json @@ -1,7 +1,7 @@ { "id": "default", "title": "Document ingestion from Azure Blob Storage using Azure Document Intelligence OCR into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or questions to CDB4AI@Microsoft.com.", "description": "", "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You need to [create an Azure Document Intelligence resource](https://learn.microsoft.com/azure/ai-services/document-intelligence/overview). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "kinds": [ diff --git a/ingest-doc-blob-document-intelligence-cosmos/manifest.json b/ingest-doc-blob-document-intelligence-cosmos/manifest.json index c09c886..a1826c2 100644 --- a/ingest-doc-blob-document-intelligence-cosmos/manifest.json +++ b/ingest-doc-blob-document-intelligence-cosmos/manifest.json @@ -1,7 +1,7 @@ { "id": "ingest-doc-blob-document-intelligence-cosmos", "title": "Document ingestion from Azure Blob Storage using Azure Document Intelligence OCR into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or quesstions to CDB4AI@Microsoft.com", "skus": [ "standard" ], diff --git a/ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json b/ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json index a2e3f39..1193b84 100644 --- a/ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json +++ b/ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json @@ -1,7 +1,7 @@ { "id": "default", "title": "Document ingestion from SharePoint using Azure Document Intelligence OCR into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or questions to CDB4AI@Microsoft.com.", "description": "", "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You need to [create an Azure Document Intelligence resource](https://learn.microsoft.com/azure/ai-services/document-intelligence/overview). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "kinds": [ diff --git a/ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json b/ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json index 9f7215b..52cb042 100644 --- a/ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json +++ b/ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json @@ -1,7 +1,7 @@ { "id": "ingest-doc-sharepoint-document-intelligence-cosmos", "title": "Document ingestion from SharePoint using Azure Document Intelligence OCR into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or questions to CDB4AI@Microsoft.com.", "skus": [ "standard" ], diff --git a/ingest-document-blob-openai-cosmos/manifest.json b/ingest-document-blob-openai-cosmos/manifest.json index 4296fac..f8812ae 100644 --- a/ingest-document-blob-openai-cosmos/manifest.json +++ b/ingest-document-blob-openai-cosmos/manifest.json @@ -1,7 +1,7 @@ { "id": "ingest-document-blob-openai-cosmos", "title": "Document ingestion from Azure Blob Storage into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs and Markdown, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs and Markdown, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or questions to CDB4AI@Microsoft.com.", "skus": [ "standard" ], diff --git a/ingest-document-sharepoint-openai-cosmos/default/manifest.json b/ingest-document-sharepoint-openai-cosmos/default/manifest.json index 5f031bf..a01b5a0 100644 --- a/ingest-document-sharepoint-openai-cosmos/default/manifest.json +++ b/ingest-document-sharepoint-openai-cosmos/default/manifest.json @@ -1,7 +1,7 @@ { "id": "default", "title": "Document ingestion from SharePoint into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs, text files, Markdown, etc. from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs, text files, Markdown, etc. from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or questions to CDB4AI@Microsoft.com.", "description": "", "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "workflows": { diff --git a/ingest-document-sharepoint-openai-cosmos/manifest.json b/ingest-document-sharepoint-openai-cosmos/manifest.json index af803be..069cc57 100644 --- a/ingest-document-sharepoint-openai-cosmos/manifest.json +++ b/ingest-document-sharepoint-openai-cosmos/manifest.json @@ -1,7 +1,7 @@ { "id": "ingest-document-sharepoint-openai-cosmos", "title": "Document ingestion from SharePoint into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs, text files, Markdown, etc. from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs, text files, Markdown, etc. from Microsoft SharePoint, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or questions to CDB4AI@Microsoft.com.", "skus": [ "standard" ], From 677b70a220df0f2229c87b2ee46de832219d6e8a Mon Sep 17 00:00:00 2001 From: James Codella Date: Fri, 25 Apr 2025 12:17:11 -0400 Subject: [PATCH 3/4] updated template summary --- ingest-document-blob-openai-cosmos/default/manifest.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ingest-document-blob-openai-cosmos/default/manifest.json b/ingest-document-blob-openai-cosmos/default/manifest.json index 0559ca0..6348ee5 100644 --- a/ingest-document-blob-openai-cosmos/default/manifest.json +++ b/ingest-document-blob-openai-cosmos/default/manifest.json @@ -1,7 +1,7 @@ { "id": "default", "title": "Document ingestion from Azure Blob Storage into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs and Markdown, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents, such as PDFs and Markdown, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or questions to CDB4AI@Microsoft.com.", "description": "", "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "kinds": [ From a7297149f00d9148c060ff3808c5ad4270c53767 Mon Sep 17 00:00:00 2001 From: James Codella Date: Fri, 25 Apr 2025 12:19:00 -0400 Subject: [PATCH 4/4] all summaries should match --- .../default/manifest.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json b/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json index 3f42c36..239c27c 100644 --- a/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json +++ b/ingest-doc-blob-document-intelligence-cosmos/default/manifest.json @@ -1,7 +1,7 @@ { "id": "default", "title": "Document ingestion from Azure Blob Storage using Azure Document Intelligence OCR into Azure Cosmos DB", - "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or questions to CDB4AI@Microsoft.com.", + "summary": "To support search and retrieval purposes for the retrieval-augmented generation (RAG) pattern, this workflow reads documents and images, using OCR powered by Azure Document Intelligence, from Azure Blob Storage, chunks the text, creates vector embeddings, formats the resulting data into a JSON document, and inserts that content into Azure Cosmos DB for NoSQL. Please send any feedback or quesstions to CDB4AI@Microsoft.com", "description": "", "prerequisites": "Before you use this template, you need to configure an Azure Open AI instance with an embedding model. For more detailed prerequisites, see the [Azure Logic Apps project sample on GitHub - Create a Chat with Your Data](https://github.com/Azure/logicapps/tree/master/LogicApps-AI-RAG-Demo). You need to [create an Azure Document Intelligence resource](https://learn.microsoft.com/azure/ai-services/document-intelligence/overview). You also need to [create an Azure Cosmos DB database](https://learn.microsoft.com/azure/cosmos-db/nosql/quickstart-dotnet), and create a connection by using the keys and endpoint. Also, get more familiar with [Vector Search in Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/nosql/vector-search).", "kinds": [