From 835d8768abd393b97b034ab452532951a3084928 Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Wed, 26 Jun 2024 13:13:35 -0400 Subject: [PATCH 1/4] Remove model requirement from hybrid search documentation Signed-off-by: Fanit Kolchina --- _query-dsl/compound/hybrid.md | 6 +----- _search-plugins/hybrid-search.md | 2 +- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/_query-dsl/compound/hybrid.md b/_query-dsl/compound/hybrid.md index e573d17676..22b3a17fc1 100644 --- a/_query-dsl/compound/hybrid.md +++ b/_query-dsl/compound/hybrid.md @@ -12,11 +12,7 @@ You can use a hybrid query to combine relevance scores from multiple queries int ## Example -Before using a `hybrid` query, you must set up a machine learning (ML) model, ingest documents, and configure a search pipeline with a [`normalization-processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/). - -To learn how to set up an ML model, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). - -Once you set up an ML model, learn how to use the `hybrid` query by following the steps in [Using hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/#using-hybrid-search). +Learn how to use the `hybrid` query by following the steps in [Using hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/#using-hybrid-search). For a comprehensive example, follow the [Neural search tutorial]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search#tutorial). diff --git a/_search-plugins/hybrid-search.md b/_search-plugins/hybrid-search.md index b0fb4d5bef..6323f39c4b 100644 --- a/_search-plugins/hybrid-search.md +++ b/_search-plugins/hybrid-search.md @@ -12,7 +12,7 @@ Introduced 2.11 Hybrid search combines keyword and neural search to improve search relevance. To implement hybrid search, you need to set up a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) that runs at search time. The search pipeline you'll configure intercepts search results at an intermediate stage and applies the [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) to them. The `normalization_processor` normalizes and combines the document scores from multiple query clauses, rescoring the documents according to the chosen normalization and combination techniques. **PREREQUISITE**
-Before using hybrid search, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). +To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). {: .note} ## Using hybrid search From dc5a7b8898874b41ff3a7714e66038d9e432f346 Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Wed, 26 Jun 2024 14:15:59 -0400 Subject: [PATCH 2/4] Review comment Signed-off-by: Fanit Kolchina --- _search-plugins/hybrid-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_search-plugins/hybrid-search.md b/_search-plugins/hybrid-search.md index 6323f39c4b..5c64b1c43d 100644 --- a/_search-plugins/hybrid-search.md +++ b/_search-plugins/hybrid-search.md @@ -12,7 +12,7 @@ Introduced 2.11 Hybrid search combines keyword and neural search to improve search relevance. To implement hybrid search, you need to set up a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) that runs at search time. The search pipeline you'll configure intercepts search results at an intermediate stage and applies the [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) to them. The `normalization_processor` normalizes and combines the document scores from multiple query clauses, rescoring the documents according to the chosen normalization and combination techniques. **PREREQUISITE**
-To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). +To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). If you already generated text embeddings, skip to [Step 4](#step-4-configure-a-search-pipeline). {: .note} ## Using hybrid search From 7b2cceac698985c5fc46112f699987df824b852a Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Wed, 26 Jun 2024 15:43:11 -0400 Subject: [PATCH 3/4] Revised sentence Signed-off-by: Fanit Kolchina --- _search-plugins/hybrid-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_search-plugins/hybrid-search.md b/_search-plugins/hybrid-search.md index 5c64b1c43d..d5c2b31e58 100644 --- a/_search-plugins/hybrid-search.md +++ b/_search-plugins/hybrid-search.md @@ -12,7 +12,7 @@ Introduced 2.11 Hybrid search combines keyword and neural search to improve search relevance. To implement hybrid search, you need to set up a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) that runs at search time. The search pipeline you'll configure intercepts search results at an intermediate stage and applies the [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) to them. The `normalization_processor` normalizes and combines the document scores from multiple query clauses, rescoring the documents according to the chosen normalization and combination techniques. **PREREQUISITE**
-To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). If you already generated text embeddings, skip to [Step 4](#step-4-configure-a-search-pipeline). +To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). If you already generated text embeddings, ingest the embeddings into an index and skip to [Step 4](#step-4-configure-a-search-pipeline). {: .note} ## Using hybrid search From 4b20daa10dabb554bf6148af1207409718b8af3e Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Mon, 15 Jul 2024 13:58:23 -0400 Subject: [PATCH 4/4] Update _search-plugins/hybrid-search.md Co-authored-by: Nathan Bower Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --- _search-plugins/hybrid-search.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_search-plugins/hybrid-search.md b/_search-plugins/hybrid-search.md index d5c2b31e58..7f08d63d0f 100644 --- a/_search-plugins/hybrid-search.md +++ b/_search-plugins/hybrid-search.md @@ -12,7 +12,7 @@ Introduced 2.11 Hybrid search combines keyword and neural search to improve search relevance. To implement hybrid search, you need to set up a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) that runs at search time. The search pipeline you'll configure intercepts search results at an intermediate stage and applies the [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) to them. The `normalization_processor` normalizes and combines the document scores from multiple query clauses, rescoring the documents according to the chosen normalization and combination techniques. **PREREQUISITE**
-To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). If you already generated text embeddings, ingest the embeddings into an index and skip to [Step 4](#step-4-configure-a-search-pipeline). +To follow this example, you must set up a text embedding model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model). If you have already generated text embeddings, ingest the embeddings into an index and skip to [Step 4](#step-4-configure-a-search-pipeline). {: .note} ## Using hybrid search