diff --git a/deploy-manage/deploy/cloud-enterprise/ece-ha.md b/deploy-manage/deploy/cloud-enterprise/ece-ha.md index ba5b7c3cba..32d12aed77 100644 --- a/deploy-manage/deploy/cloud-enterprise/ece-ha.md +++ b/deploy-manage/deploy/cloud-enterprise/ece-ha.md @@ -53,7 +53,7 @@ Backing up Zookeeper data directory is also recommended. Refer to [rebuilding a ## External resources accessibility -If you’re using a [private Docker registry server](ece-install-offline-with-registry.md) or hosting any [custom bundles and plugins](../../../solutions/search/full-text/search-with-synonyms.md) on a web server, make sure these resources are accessible from all ECE allocators, so they can continue to be accessed in the event of a network partition or zone outage. +If you're using a [private Docker registry server](ece-install-offline-with-registry.md) or hosting any [custom bundles and plugins](/solutions/search/full-text/search-with-synonyms.md) on a web server, make sure these resources are accessible from all ECE allocators, so they can continue to be accessed in the event of a network partition or zone outage. ## Other recommendations diff --git a/deploy-manage/deploy/elastic-cloud/differences-from-other-elasticsearch-offerings.md b/deploy-manage/deploy/elastic-cloud/differences-from-other-elasticsearch-offerings.md index 2f5232e795..441eac2f80 100644 --- a/deploy-manage/deploy/elastic-cloud/differences-from-other-elasticsearch-offerings.md +++ b/deploy-manage/deploy/elastic-cloud/differences-from-other-elasticsearch-offerings.md @@ -98,7 +98,7 @@ This table compares Elasticsearch capabilities between {{ech}} deployments and S | **Repository management** | ✅ | Managed | Automatically managed by Elastic | | [**Scripted metric aggregations**](elasticsearch://reference/aggregations/search-aggregations-metrics-scripted-metric-aggregation.md) | ✅ | ❌ | Not available in Serverless
The alternative for this in Serverless is [ES|QL](elasticsearch://reference/query-languages/esql.md) | | [**`join` fields**](elasticsearch://reference/elasticsearch/mapping-reference/parent-join.md) | ✅ | ❌ | Not available in Serverless
The alternative for this in Serverless is the ES\|QL [`LOOKUP JOIN`](elasticsearch://reference/query-languages/esql/commands/lookup-join.md) command | -| [**Search applications**](/solutions/search/search-applications.md) | - UI and APIs
- Maintenance mode (beta) | - API-only
- Maintenance mode (beta) | UI not available in Serverless | +| [**Search applications**](/solutions/elasticsearch-solution-project/search-applications.md) | - UI and APIs
- Maintenance mode (beta) | - API-only
- Maintenance mode (beta) | UI not available in Serverless | | **Shard management** | User-configurable | Managed by Elastic | No manual shard allocation in Serverless | | [**Watcher**](/explore-analyze/alerts-cases/watcher.md) | ✅ | ❌ | Use **Kibana Alerts** instead, which provides rich integrations across use cases | | **Web crawler** | ❌ (Managed Elastic Crawler discontinued with Enterprise Search in 9.0) | Self-managed only | Use [**self-managed crawler**](https://github.com/elastic/crawler) | diff --git a/deploy-manage/reference-architectures/hotfrozen-high-availability.md b/deploy-manage/reference-architectures/hotfrozen-high-availability.md index f24c976a02..bdefcb1d92 100644 --- a/deploy-manage/reference-architectures/hotfrozen-high-availability.md +++ b/deploy-manage/reference-architectures/hotfrozen-high-availability.md @@ -27,7 +27,7 @@ This Hot/Frozen – High Availability architecture is intended for organizations * Have a requirement for cost effective long term data storage (many months or years). * Provide insights and alerts using logs, metrics, traces, or various event types to ensure optimal performance and quick issue resolution for applications. * Apply [machine learning anomaly detection](/explore-analyze/machine-learning/anomaly-detection.md) to help detect patterns in time series data to find root cause and resolve problems faster. -* Use an AI assistant ([Observability](/explore-analyze/ai-features/ai-assistant.md), [Security](/solutions/security/ai/ai-assistant.md), or [Playground](/solutions/search/rag/playground.md)) for investigation, incident response, reporting, query generation, or query conversion from other languages using natural language. +* Use an AI assistant ([Observability](/explore-analyze/ai-features/ai-assistant.md), [Security](/solutions/security/ai/ai-assistant.md), or [Playground](/solutions/elasticsearch-solution-project/playground.md)) for investigation, incident response, reporting, query generation, or query conversion from other languages using natural language. * Deploy an architecture model that allows for maximum flexibility between storage cost and performance. ::::{important} diff --git a/explore-analyze/ai-features.md b/explore-analyze/ai-features.md index 41789681d3..2d53f8605d 100644 --- a/explore-analyze/ai-features.md +++ b/explore-analyze/ai-features.md @@ -71,7 +71,7 @@ The [{{es}}](/solutions/search.md) solution view (or project type in {{serverles ### Agent Builder -[Agent Builder](/solutions/search/elastic-agent-builder.md) enables you to create AI agents that can interact with your {{es}} data, run queries, and provide intelligent responses. It provides a complete framework for building conversational AI experiences on top of your search infrastructure. +[Agent Builder](/solutions/elasticsearch-solution-project/elastic-agent-builder.md) enables you to create AI agents that can interact with your {{es}} data, run queries, and provide intelligent responses. It provides a complete framework for building conversational AI experiences on top of your search infrastructure. ### AI assistant for {{es}} @@ -79,11 +79,11 @@ The [{{es}}](/solutions/search.md) solution view (or project type in {{serverles ### Playground -[Playground](/solutions/search/rag/playground.md) enables you to use large language models (LLMs) to understand, explore, and analyze your {{es}} data using retrieval augmented generation (RAG), via a chat interface. Playground is also very useful for testing and debugging your {{es}} queries, using the [retrievers](/solutions/search/retrievers-overview.md) syntax with the `_search` endpoint. +[Playground](/solutions/elasticsearch-solution-project/playground.md) enables you to use large language models (LLMs) to understand, explore, and analyze your {{es}} data using retrieval augmented generation (RAG), via a chat interface. Playground is also very useful for testing and debugging your {{es}} queries, using the [retrievers](/solutions/search/retrievers-overview.md) syntax with the `_search` endpoint. ### Model context protocol -The [Model Context Protocol (MCP)](/solutions/search/mcp.md) lets you connect AI agents and assistants to your {{es}} data to enable natural language interactions with your indices. +The [Model Context Protocol (MCP)](/solutions/elasticsearch-solution-project/mcp.md) lets you connect AI agents and assistants to your {{es}} data to enable natural language interactions with your indices. ## AI-powered features in {{observability}} diff --git a/explore-analyze/machine-learning/machine-learning-in-kibana/inference-processing.md b/explore-analyze/machine-learning/machine-learning-in-kibana/inference-processing.md index 51ac0e20ca..dbba4e7285 100644 --- a/explore-analyze/machine-learning/machine-learning-in-kibana/inference-processing.md +++ b/explore-analyze/machine-learning/machine-learning-in-kibana/inference-processing.md @@ -25,7 +25,7 @@ This feature is not available at all Elastic subscription levels. Refer to the E ### ELSER text expansion [ingest-pipeline-search-inference-elser] -Using Elastic’s [ELSER machine learning model](../nlp/ml-nlp-elser.md) you can easily incorporate text expansion for your queries. This works by using ELSER to provide semantic enrichments to your documents upon ingestion, combined with the power of [Elastic Search Application templates](../../../solutions/search/search-applications.md) to provide automated text expansion at query time. +Using Elastic's [ELSER machine learning model](../nlp/ml-nlp-elser.md) you can easily incorporate text expansion for your queries. This works by using ELSER to provide semantic enrichments to your documents upon ingestion, combined with the power of [Elastic Search Application templates](/solutions/elasticsearch-solution-project/search-applications.md) to provide automated text expansion at query time. ### Named entity recognition (NER) [ingest-pipeline-search-inference-ner] diff --git a/explore-analyze/query-filter/tools/playground.md b/explore-analyze/query-filter/tools/playground.md index 8dedbcc4cd..c86468593b 100644 --- a/explore-analyze/query-filter/tools/playground.md +++ b/explore-analyze/query-filter/tools/playground.md @@ -16,6 +16,6 @@ Use the Search Playground to test and edit {{es}} queries visually in the UI. Th Find Playground in the {{es-serverless}} UI under **{{es}} > Build > Playground**. ::::{note} -ℹ️ For more details, check the full [Playground documentation](../../../solutions/search/rag/playground.md). +ℹ️ For more details, check the full [Playground documentation](/solutions/elasticsearch-solution-project/playground.md). :::: diff --git a/get-started/evaluate-elastic.md b/get-started/evaluate-elastic.md index 81da74b59c..298141b7ad 100644 --- a/get-started/evaluate-elastic.md +++ b/get-started/evaluate-elastic.md @@ -172,7 +172,7 @@ Once data is flowing, use the trial to validate the features that will determine | Feature | Why it matters | How to try it | |---------|----------------|---------------| | Vector search and hybrid search | Combine semantic understanding with keyword precision | [Semantic search quickstart](/solutions/search/get-started/semantic-search.md) | -| Relevance tuning | Ensure users find the most relevant results | [Query rules](/solutions/search/query-rules-ui.md) | +| Relevance tuning | Ensure users find the most relevant results | [Query rules](/solutions/elasticsearch-solution-project/query-rules-ui.md) | | Search analytics | Understand what users search for and what they find | [Search relevance](/solutions/search/full-text/search-relevance.md) | | Performance at scale | Validate response times with production-like volumes | Index a representative dataset and benchmark queries | diff --git a/get-started/introduction.md b/get-started/introduction.md index 26fc8d969a..98c573f730 100644 --- a/get-started/introduction.md +++ b/get-started/introduction.md @@ -17,7 +17,7 @@ Whether you're building a search experience, monitoring your infrastructure, or | Your need | Recommended solution | Best for | |-----------|-------------------|----------| -| Build powerful, scalable searches to quickly search, analyze, and visualize large amounts of data for real-time insights| [{{es}}](/solutions/search.md)
• [Get started](/solutions/search/get-started.md)| Developers, architects, data engineers | +| Build powerful, scalable searches to quickly search, analyze, and visualize large amounts of data for real-time insights| [{{es}}](/solutions/search.md)[^search-note]
• [Get started](/solutions/search/get-started.md)| Developers, architects, data engineers | | Observe and monitor system health and performance, or send telemetry data | [Elastic {{observability}}](/solutions/observability.md)
• [Get started](/solutions/observability/get-started.md) | DevOps, SREs, IT operations | | Monitor data for anomalous activity, detect, prevent, and respond to security incidents | [{{elastic-sec}}](/solutions/security.md)
• [Get started](/solutions/security/get-started.md)| SOC teams, security analysts, IT security admins | @@ -29,6 +29,8 @@ Each of our solutions is available as a fully managed {{serverless-short}} proje If you're new to Elastic, you can find quickstarts and introductory steps for each solution within [](/solutions/index.md). +[^search-note]: The core [{{es}} search capabilities](/solutions/search.md) are available across all deployment types, solutions, and project types through APIs and client libraries. The [{{es}} solution](/solutions/elasticsearch-solution-project.md) adds UI tools on top of these capabilities to help you build search applications faster. +