Skip to content

Gallery content proposal 36879172128 #95

New issue
New issue
Closed
#96
Closed
Gallery content proposal 36879172128#95
#96
Assignees
jaydestrocopilot-swe-agent

Description

@github-actions
github-actions
bot
opened on Oct 1, 2026

Validated gallery proposal

The automated gallery audit produced the following validated catalog proposal. This workflow does not create a branch or pull request.

Automated gallery content update

Generated: 2026-10-01T14:51:31.298Z

Edit this issue before assigning it to Copilot. Delete recommendations you do not want, revise any item in place, and add implementation notes where needed. The edited issue body is the source of truth.

Additions: 25

  • A1 Add Migrating Elasticsearch Mappings to Azure Cosmos DB
    • Catalog change: Add this card to static/templates.json.
    • Description: This post focuses on one of the most important design tasks when migrating from Elasticsearch to Azure Cosmos DB: translating an Elasticsearch mapping into Azure Cosmos DB container configuration. In Elasticsearch, a mapping defines how document fields are stored and indexed. Azure Cosmos DB is schema-agnostic, so there is no single mapping document to translate. […] The post Migrating Elasticsearch Mappings to Azure Cosmos DB appeared first on Azure Cosmos DB Blog .
    • Preview: coming soon
    • Website: https://devblogs.microsoft.com/cosmosdb/
    • Author: Madeline Egan
    • Source: https://devblogs.microsoft.com/cosmosdb/migrating-elasticsearch-mappings-to-azure-cosmos-db
    • Date: 2026-10-01
    • Tags: blog, microsoft
    • Reason: Teaches how to translate Elasticsearch mappings to Cosmos DB container configuration with practical design decisions.
    • Criteria: direct migration guidance, schema translation walkthrough
  • A2 Add Azure Cosmos DB Migration Guide for Elasticsearch Users
    • Catalog change: Add this card to static/templates.json.
    • Description: Azure Cosmos DB’s full-text search capabilities can improve price performance and simplify application architecture by consolidating operational data and search in a single service. For customers currently using Elasticsearch, these capabilities create a practical path to bringing operational data and search together in Azure Cosmos DB, particularly when the application’s data already lives there. This […] The post Azure Cosmos DB Migration Guide for Elasticsearch Users appeared first on Azure Cosmos DB Blog .
    • Preview: coming soon
    • Website: https://devblogs.microsoft.com/cosmosdb/
    • Author: Madeline Egan
    • Source: https://devblogs.microsoft.com/cosmosdb/azure-cosmos-db-migration-guide-for-elasticsearch-users
    • Date: 2026-09-30
    • Tags: blog, microsoft
    • Reason: Demonstrates consolidating operational data and search in Cosmos DB for Elasticsearch users migrating workloads.
    • Criteria: comprehensive migration path, operational consolidation
  • A3 Add Partitioning and horizontal scaling - Azure Cosmos DB
    • Catalog change: Add this card to static/templates.json.
    • Description: Partitioning and horizontal scaling in Azure Cosmos DB Summarize this article for me In this article Azure Cosmos DB uses partitioning to scale containers in a database to meet your application's performance needs. Both terms refer to the value used to distribute and locate data, but partition key i... This article explains the relationship.
    • Preview: coming soon
    • Website: https://learn.microsoft.com/azure/cosmos-db
    • Author: Azure Cosmos DB Team
    • Source: https://learn.microsoft.com/azure/cosmos-db/partitioning
    • Date: 2026-09-29
    • Tags: documentation, microsoft
    • Reason: Core documentation on partitioning strategy and horizontal scaling in Cosmos DB.
    • Criteria: partitioning fundamentals, horizontal scaling design
  • A9 Add Azure Cosmos DB: Provisioned Throughput vs. Serverless — How to Choose
    • Catalog change: Add this card to static/templates.json.
    • Description: Should you choose provisioned throughput or serverless for Azure Cosmos DB? This quick overview explains how each capacity mode handles database operations, allocates capacity, and bills for usage. Learn when to use manual or autoscale provisioned throughput for sustained traffic and multi-region applications, and when serverless makes sense for development, prototypes, and intermittent workloads. We also cover consumption-based billing, storage charges, and the single-region limitation of serverless. Read the full comparison on Microsoft Learn: https://learn.microsoft.com/azure/cosmos-db/throughput-serverless #AzureCosmosDB #Azure #Serverless
    • Preview: coming soon
    • Website: https://www.youtube.com/@AzureCosmosDB
    • Author: Azure Cosmos DB
    • Source: https://www.youtube.com/watch?v=ftQ67zcA2xc
    • Date: 2026-09-22
    • Tags: video, microsoft
    • Reason: Compares provisioned and serverless throughput modes with guidance on workload fit, billing models, and multi-region limits.
    • Criteria: capacity mode comparison, billing and regional constraints
  • A11 Add Azure Cosmos DB Indexing Policies Explained
    • Catalog change: Add this card to static/templates.json.
    • Description: Which fields does your application actually query? Learn how to tune Azure Cosmos DB indexing policies around your workload. This short walkthrough covers automatic indexing, including and excluding property paths, composite indexes for multi-property sorting, and specialized index options. See how indexing choices affect write costs, storage, and query behavior—and why you should measure the results before making further changes. Learn more: https://learn.microsoft.com/azure/cosmos-db/index-policy #AzureCosmosDB #Azure #DatabasePerformance
    • Preview: coming soon
    • Website: https://www.youtube.com/@AzureCosmosDB
    • Author: Azure Cosmos DB
    • Source: https://www.youtube.com/watch?v=sMLpszK94xU
    • Date: 2026-09-18
    • Tags: video, microsoft
    • Reason: Shows how to configure indexing policies around actual query patterns, with guidance on write costs, storage, and measurement.
    • Criteria: indexing policy tuning walkthrough, cost and query impact
  • A12 Add Why Build AI Apps with Azure Cosmos DB?
    • Catalog change: Add this card to static/templates.json.
    • Description: Your AI application needs useful context and a database that handles everyday traffic. See how Azure Cosmos DB stores application data and vector embeddings together to support both. Follow a shopping assistant example to learn how vector search, full-text search, and filters retrieve relevant products for a language model. We also cover DiskANN, capacity options, multi-region replication for provisioned accounts, Azure integrations, and tenant isolation. Learn more: https://learn.microsoft.com/azure/cosmos-db/gen-ai/why-cosmos-ai #AzureCosmosDB #GenerativeAI #VectorSearch
    • Preview: coming soon
    • Website: https://www.youtube.com/@AzureCosmosDB
    • Author: Azure Cosmos DB
    • Source: https://www.youtube.com/watch?v=embY5yxjCoU
    • Date: 2026-09-18
    • Tags: video, microsoft
    • Reason: Teaches how to design AI apps with Cosmos DB for both data and embeddings, covering vector search, full-text search, DiskANN, and tenant isolation.
    • Criteria: AI application architecture, search and retrieval patterns
  • A14 Add Emulator (Docker/local) - Azure Cosmos DB
    • Catalog change: Add this card to static/templates.json.
    • Description: What is the Azure Cosmos DB emulator. Summarize this article for me In this article The Azure Cosmos DB emulator provides a local environment that emulates the Azure Cosmos DB service and is designed for development purposes. You can use it for local development and offline testing without creating an Azure subscription or incurring any service.
    • Preview: coming soon
    • Website: https://learn.microsoft.com/azure/cosmos-db
    • Author: Azure Cosmos DB Team
    • Source: https://learn.microsoft.com/azure/cosmos-db/emulator
    • Date: 2026-09-01
    • Tags: documentation, microsoft
    • Reason: Documentation for configuring Cosmos DB Emulator for local development and offline testing.
    • Criteria: emulator setup and local development
  • A16 Add Databases, containers, and items - Azure Cosmos DB
    • Catalog change: Add this card to static/templates.json.
    • Description: Databases, containers, and items in Azure Cosmos DB Summarize this article for me In this article Azure Cosmos DB is a fully managed platform as a service (PaaS). To begin using Azure Cosmos DB, create an Azure Cosmos DB account in an Azure resource group in your subscription. Then, create databases and containers within the account. Azure Cosmos.
    • Preview: coming soon
    • Website: https://learn.microsoft.com/azure/cosmos-db
    • Author: Azure Cosmos DB Team
    • Source: https://learn.microsoft.com/azure/cosmos-db/resource-model
    • Date: 2026-08-25
    • Tags: documentation, microsoft
    • Reason: Foundational documentation on Cosmos DB resource model: accounts, databases, containers, and items.
    • Criteria: resource model fundamentals
  • A17 Add Service Quotas and Default Limits - Azure Cosmos DB
    • Catalog change: Add this card to static/templates.json.
    • Description: Resource Limit Maximum RUs per container (dedicated throughput provisioned mode) 1,000,000 ¹ Maximum RUs per database (shared throughput provisioned mode) 1,000,000 ¹ Maximum RUs per partition (logical & physical) 10,000 Maximum storage across all items per (logical) partition 20 GB ² Maximum number of distinct (logical) partition keys Unlimited.
    • Preview: coming soon
    • Website: https://learn.microsoft.com/azure/cosmos-db
    • Author: Azure Cosmos DB Team
    • Source: https://learn.microsoft.com/azure/cosmos-db/concepts-limits
    • Date: 2026-08-25
    • Tags: documentation, microsoft
    • Reason: Official Cosmos DB limits and quotas documentation essential for capacity planning and constraint awareness.
    • Criteria: authoritative limits reference
  • A18 Add Powering Memory in Foundry Agent Service, with Azure Cosmos DB
    • Catalog change: Add this card to static/templates.json.
    • Description: Move from a locally composed memory-enabled agent to a deployed Foundry Agent Service agent, with keyless Azure infrastructure and an interactive browser sample. In Native Agent Memory for Microsoft Agent Framework, Powered by Azure Cosmos DB, we introduced CosmosMemoryContextProvider: a Microsoft Agent Framework context provider that extracts useful information from conversations, stores it in Azure Cosmos DB, […] The post Powering Memory in Foundry Agent Service, with Azure Cosmos DB appeared first on Azure Cosmos DB Blog .
    • Preview: coming soon
    • Website: https://devblogs.microsoft.com/cosmosdb/
    • Author: Theo van Kraay
    • Source: https://devblogs.microsoft.com/cosmosdb/powering-memory-in-foundry-agent-service-with-azure-cosmos-db
    • Date: 2026-08-24
    • Tags: blog, microsoft
    • Reason: Demonstrates deploying Foundry Agent Service with Cosmos DB as persistent memory store, including keyless infrastructure setup.
    • Criteria: agent memory implementation, production deployment example
  • A19 Add foundry-cosmos-memory
    • Catalog change: Add this card to static/templates.json.
    • Description: Deployable Microsoft Foundry Agent Service sample with long-term, user-scoped memory powered by Azure Cosmos DB and Microsoft Agent Framework.
    • Preview: coming soon
    • Website: https://github.com/AzureCosmosDB
    • Author: Azure Cosmos DB Team
    • Source: https://github.com/AzureCosmosDB/foundry-cosmos-memory
    • Date: 2026-08-19
    • Tags: example, microsoft
    • Reason: Runnable Foundry Agent Service sample with persistent, user-scoped memory powered by Cosmos DB.
    • Criteria: deployable agent memory sample, Microsoft Agent Framework integration
  • A20 Add Stale Embeddings Hurt Search Quality in RAG: How to Fix It
    • Catalog change: Add this card to static/templates.json.
    • Description: Application data changes, but its vector embeddings do not update by themselves. Stale embeddings can reduce semantic-search relevance and hurt the quality of results returned by RAG applications. Some techniques include: - Generate embeddings in the application's write path - Regenerate them asynchronously using a change feed and background worker - Let the data platform manage updates (for instance, with Integrated Embeddings in Azure Cosmos DB) Learn more: https://aka.ms/embeddings-cosmosdb 00:00 Why embeddings need to stay synchronized 00:27 Option 1: Generate embeddings on write 00:45 Option 2: Update embeddings asynchronously 01:29 Option 3: Managed updates with Azure Cosmos DB 02:28 Choosing the right approach #RAG #VectorSearch #AzureCosmosDB
    • Preview: coming soon
    • Website: https://www.youtube.com/@AzureCosmosDB
    • Author: Azure Cosmos DB
    • Source: https://www.youtube.com/watch?v=ad9XIL0YzkU
    • Date: 2026-08-18
    • Tags: video, microsoft
    • Reason: Explains stale embedding impact on search quality and teaches three approaches: write-path generation, async regeneration, and integrated embeddings.
    • Criteria: RAG embedding staleness problem and solutions
  • A22 Add spec-kit-cosmosdb
    • Catalog change: Add this card to static/templates.json.
    • Description: Best-practice Azure Cosmos DB code generation, review, and design guidance for any AI coding agent.
    • Preview: coming soon
    • Website: https://github.com/AzureCosmosDB
    • Author: Azure Cosmos DB Team
    • Source: https://github.com/AzureCosmosDB/spec-kit-cosmosdb
    • Date: 2026-08-07
    • Tags: example, microsoft
    • Reason: Provides best-practice code generation, review rules, and design guidance for AI coding agents working with Cosmos DB.
    • Criteria: AI-assisted code generation guidance, best practices enforcement
  • A24 Add eve-cosmos-memory
    • Catalog change: Add this card to static/templates.json.
    • Description: An eve AI agent that authors a Next.js/Vercel blog and remembers across conversations using Azure Cosmos DB vector search as its semantic memory store
    • Preview: coming soon
    • Website: https://github.com/AzureCosmosDB
    • Author: Azure Cosmos DB Team
    • Source: https://github.com/AzureCosmosDB/eve-cosmos-memory
    • Date: 2026-07-07
    • Tags: example, microsoft
    • Reason: Demonstrates AI agent authoring a Next.js blog with semantic memory stored as Cosmos DB vector search.
    • Criteria: semantic memory pattern, persistent agent state

URL updates: 0

Retirements: 3

Skipped high-confidence additions: 0

Assign this issue to Copilot only after the issue body reflects the desired catalog changes. The resulting draft pull request requires human approval before merge.

Operator handoff

Edit the proposals above before assigning this issue to an implementation agent. Delete unwanted proposals, correct retained card fields and destinations, and add any implementation notes.

Treat the edited issue body as the complete source of truth. The assigned agent must implement exactly the additions, URL updates, retirements, and notes that remain; ignore deleted recommendations. Modify static/templates.json and static/retired-templates.json as directed, run npm run test:gallery-audit and npm run build, and open a draft pull request to main. Do not add unrelated changes, approve, merge, or enable automerge.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Metadata

Metadata

Assignees

Labels

No labels
No labels

Type

No type

Projects

No projects

    Milestone

    No milestone

    Relationships

    None yet

    Development

    No branches or pull requests

    Issue actions