Skip to content

shortened titles, parameter names for cosmos db workflow templates #95

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
ccastrotrejo merged 4 commits into from
Apr 25, 2025
Merged

shortened titles, parameter names for cosmos db workflow templates #95

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
95888f8
shortened titles, parameter names
jcodella Apr 24, 2025
4d31eca
added language for feedback
jcodella Apr 25, 2025
677b70a
updated template summary
jcodella Apr 25, 2025
a729714
all summaries should match
jcodella Apr 25, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
26 changes: 13 additions & 13 deletions ingest-doc-blob-document-intelligence-cosmos/default/manifest.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,16 +1,16 @@
{
"id": "default",
"title": "Document ingestion from Azure Blob Storage using Azure Document Intelligence OCR into Azure Cosmos DB for RAG",
"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.",
"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 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](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": [
Expand All @@ -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
Expand All @@ -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
Expand Down
6 changes: 3 additions & 3 deletions ingest-doc-blob-document-intelligence-cosmos/default/workflow.json
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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": {
Expand All @@ -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)}",
Expand Down
6 changes: 3 additions & 3 deletions ingest-doc-blob-document-intelligence-cosmos/manifest.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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",
"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.",
"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 quesstions to CDB4AI@Microsoft.com",
"skus": [
"standard"
],
"workflows": {
"default": {
"name": "ingest-doc-blob-document-intelligence-cosmos"
"name": "cdb-doc-indexer-blob"
}
},
"featuredConnectors": [
Expand Down
30 changes: 15 additions & 15 deletions ingest-doc-sharepoint-document-intelligence-cosmos/default/manifest.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,16 +1,16 @@
{
"id": "default",
"title": "Document ingestion from SharePoint using Azure Document Intelligence OCR into Azure Cosmos DB for RAG",
"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.",
"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. 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](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": [
Expand All @@ -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
Expand All @@ -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
Expand Down
6 changes: 3 additions & 3 deletions ingest-doc-sharepoint-document-intelligence-cosmos/default/workflow.json
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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": {
Expand All @@ -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)}",
Expand Down
6 changes: 3 additions & 3 deletions ingest-doc-sharepoint-document-intelligence-cosmos/manifest.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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",
"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.",
"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. Please send any feedback or questions to CDB4AI@Microsoft.com.",
"skus": [
"standard"
],
"workflows": {
"default": {
"name": "ingest-doc-sharepoint-document-intelligence-cosmos"
"name": "cdb-doc-indexer-sp"
}
},
"featuredConnectors": [
Expand Down
Loading
Loading