From f5b8ff79fd41f13194fd7e877f8a20185b4c4b1f Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Fri, 22 Sep 2023 17:29:58 -0400 Subject: [PATCH] Add score normalization and combination documentation (#4985) * Add search phase results processor Signed-off-by: Fanit Kolchina * Add hybrid query Signed-off-by: Fanit Kolchina * Normalization processor additions Signed-off-by: Fanit Kolchina * Add more details Signed-off-by: Fanit Kolchina * Continue writing Signed-off-by: Fanit Kolchina * Add more query then fetch details and diagram Signed-off-by: Fanit Kolchina * Small rewording Signed-off-by: Fanit Kolchina * Leaner left nav headers Signed-off-by: Fanit Kolchina * Tech review feedback Signed-off-by: Fanit Kolchina * Add semantic search tutorial Signed-off-by: Fanit Kolchina * Reworded prerequisites Signed-off-by: Fanit Kolchina * Removed comma Signed-off-by: Fanit Kolchina * Rewording advanced prerequisites Signed-off-by: Fanit Kolchina * Changed searching for ML model to shorter request Signed-off-by: Fanit Kolchina * Update task type in register model response Signed-off-by: Fanit Kolchina * Changing example Signed-off-by: Fanit Kolchina * Added huggingface prefix to model names Signed-off-by: Fanit Kolchina * Change example responses Signed-off-by: Fanit Kolchina * Added note about huggingface prefix Signed-off-by: Fanit Kolchina * Update _ml-commons-plugin/semantic-search.md Co-authored-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented doc review comments Signed-off-by: Fanit Kolchina * List weights under parameters Signed-off-by: Fanit Kolchina * Remove one-shard warning for normalization processor Signed-off-by: Fanit Kolchina * Apply suggestions from code review Co-authored-by: Nathan Bower Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented editorial comments Signed-off-by: Fanit Kolchina * Change links Signed-off-by: Fanit Kolchina * More editorial feedback Signed-off-by: Fanit Kolchina * Change model-serving framework to ML framework Signed-off-by: Fanit Kolchina * Use get model API to check model status Signed-off-by: Fanit Kolchina * Implemented tech review comments Signed-off-by: Fanit Kolchina * Added neural search description and diagram Signed-off-by: Fanit Kolchina * More editorial comments Signed-off-by: Fanit Kolchina * Add link to profile API Signed-off-by: Fanit Kolchina * Addressed more tech review comments Signed-off-by: Fanit Kolchina * Implemented editorial comments on changes Signed-off-by: Fanit Kolchina --------- Signed-off-by: Fanit Kolchina Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> Co-authored-by: Nathan Bower --- _ml-commons-plugin/api.md | 18 +- _ml-commons-plugin/cluster-settings.md | 2 +- _ml-commons-plugin/model-access-control.md | 2 +- _ml-commons-plugin/pretrained-models.md | 22 +- _ml-commons-plugin/semantic-search.md | 1024 +++++++++++++++++ _query-dsl/compound/bool.md | 2 +- _query-dsl/compound/boosting.md | 2 +- _query-dsl/compound/constant-score.md | 2 +- _query-dsl/compound/disjunction-max.md | 2 +- _query-dsl/compound/function-score.md | 2 +- _query-dsl/compound/hybrid.md | 29 + .../filter-query-processor.md | 2 +- _search-plugins/search-pipelines/index.md | 7 +- .../normalization-processor.md | 117 ++ .../personalize-search-ranking.md | 2 +- .../rename-field-processor.md | 2 +- .../search-pipelines/script-processor.md | 2 +- .../search-pipelines/search-processors.md | 15 + images/neural-search-ingestion.png | Bin 0 -> 56324 bytes images/neural-search-query.png | Bin 0 -> 42753 bytes images/normalization-processor.png | Bin 0 -> 55130 bytes 21 files changed, 1228 insertions(+), 26 deletions(-) create mode 100644 _ml-commons-plugin/semantic-search.md create mode 100644 _query-dsl/compound/hybrid.md create mode 100644 _search-plugins/search-pipelines/normalization-processor.md create mode 100644 images/neural-search-ingestion.png create mode 100644 images/neural-search-query.png create mode 100644 images/normalization-processor.png diff --git a/_ml-commons-plugin/api.md b/_ml-commons-plugin/api.md index 74faa98480..055b66d157 100644 --- a/_ml-commons-plugin/api.md +++ b/_ml-commons-plugin/api.md @@ -518,8 +518,24 @@ The API returns the following: ## Profile -The profile operation returns runtime information on ML tasks and models. The profile operation can help debug issues with models at runtime. +The profile operation returns runtime information about ML tasks and models. The profile operation can help debug model issues at runtime. +### The number of requests returned + +By default, the Profile API monitors the last 100 requests. To change the number of monitoring requests, update the following cluster setting: + +```json +PUT _cluster/settings +{ + "persistent" : { + "plugins.ml_commons.monitoring_request_count" : 1000000 + } +} +``` + +To clear all monitoring requests, set `plugins.ml_commons.monitoring_request_count` to `0`. + +### Path and HTTP methods ```json GET /_plugins/_ml/profile diff --git a/_ml-commons-plugin/cluster-settings.md b/_ml-commons-plugin/cluster-settings.md index 73ca2baf64..f9d02b382c 100644 --- a/_ml-commons-plugin/cluster-settings.md +++ b/_ml-commons-plugin/cluster-settings.md @@ -2,7 +2,7 @@ layout: default title: ML Commons cluster settings has_children: false -nav_order: 160 +nav_order: 10 --- # ML Commons cluster settings diff --git a/_ml-commons-plugin/model-access-control.md b/_ml-commons-plugin/model-access-control.md index 5c76b4da1e..115c610631 100644 --- a/_ml-commons-plugin/model-access-control.md +++ b/_ml-commons-plugin/model-access-control.md @@ -2,7 +2,7 @@ layout: default title: Model access control has_children: false -nav_order: 180 +nav_order: 20 --- # Model access control diff --git a/_ml-commons-plugin/pretrained-models.md b/_ml-commons-plugin/pretrained-models.md index e8bbcf8a4c..c4bea64ae8 100644 --- a/_ml-commons-plugin/pretrained-models.md +++ b/_ml-commons-plugin/pretrained-models.md @@ -39,16 +39,16 @@ The ML Framework supports the following models, categorized by type. All models Sentence transformer models map sentences and paragraphs across a dimensional dense vector space. The number of vectors depends on the model. Use these models for use cases such as clustering and semantic search. -The following table provides a list of sentence transformer models and artifact links to download them. As of OpenSearch 2.6, all artifacts are set to version 1.0.1. +The following table provides a list of sentence transformer models and artifact links you can use to download them. Note that you must prefix the model name with `huggingface/`, as shown in the **Model name** column. As of OpenSearch 2.6, all artifacts are set to version 1.0.1. -| **Model name** | **Vector dimensions** | **Auto-truncation** | **Torchscript artifact** | **ONNX artifact** | +| Model name | Vector dimensions | Auto-truncation | TorchScript artifact | ONNX artifact | |---|---|---|---| -| `sentence-transformers/all-distilroberta-v1` | 768-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/torch_script/sentence-transformers_all-distilroberta-v1-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/onnx/sentence-transformers_all-distilroberta-v1-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/onnx/config.json) | -| `sentence-transformers/all-MiniLM-L6-v2` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/torch_script/sentence-transformers_all-MiniLM-L6-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/onnx/sentence-transformers_all-MiniLM-L6-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/onnx/config.json) | -| `sentence-transformers/all-MiniLM-L12-v2` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/torch_script/sentence-transformers_all-MiniLM-L12-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/sentence-transformers_all-MiniLM-L12-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/config.json) | -| `sentence-transformers/all-mpnet-base-v2` | 768-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/torch_script/sentence-transformers_all-mpnet-base-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/onnx/sentence-transformers_all-mpnet-base-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/onnx/config.json) | -| `sentence-transformers/msmarco-distilbert-base-tas-b` | 768-dimensional dense vector space. Optimized for semantic search. | No | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/torch_script/sentence-transformers_msmarco-distilbert-base-tas-b-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/onnx/sentence-transformers_msmarco-distilbert-base-tas-b-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/onnx/config.json) | -| `sentence-transformers/multi-qa-MiniLM-L6-cos-v1` | 384 dimensional dense vector space. Designed for semantic search and trained on 215 million question/answer pairs. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/torch_script/sentence-transformers_multi-qa-MiniLM-L6-cos-v1-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/onnx/sentence-transformers_multi-qa-MiniLM-L6-cos-v1-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/onnx/config.json) | -| `sentence-transformers/multi-qa-mpnet-base-dot-v1` | 384 dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/torch_script/sentence-transformers_multi-qa-mpnet-base-dot-v1-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/onnx/sentence-transformers_multi-qa-mpnet-base-dot-v1-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/onnx/config.json) | -| `sentence-transformers/paraphrase-MiniLM-L3-v2` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/torch_script/sentence-transformers_paraphrase-MiniLM-L3-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/onnx/sentence-transformers_paraphrase-MiniLM-L3-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/onnx/config.json) | -| `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/torch_script/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/onnx/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/onnx/config.json) | \ No newline at end of file +| `huggingface/sentence-transformers/all-distilroberta-v1` | 768-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/torch_script/sentence-transformers_all-distilroberta-v1-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/onnx/sentence-transformers_all-distilroberta-v1-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-distilroberta-v1/1.0.1/onnx/config.json) | +| `huggingface/sentence-transformers/all-MiniLM-L6-v2` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/torch_script/sentence-transformers_all-MiniLM-L6-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/onnx/sentence-transformers_all-MiniLM-L6-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L6-v2/1.0.1/onnx/config.json) | +| `huggingface/sentence-transformers/all-MiniLM-L12-v2` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/torch_script/sentence-transformers_all-MiniLM-L12-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/sentence-transformers_all-MiniLM-L12-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-MiniLM-L12-v2/1.0.1/onnx/config.json) | +| `huggingface/sentence-transformers/all-mpnet-base-v2` | 768-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/torch_script/sentence-transformers_all-mpnet-base-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/onnx/sentence-transformers_all-mpnet-base-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/all-mpnet-base-v2/1.0.1/onnx/config.json) | +| `huggingface/sentence-transformers/msmarco-distilbert-base-tas-b` | 768-dimensional dense vector space. Optimized for semantic search. | No | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/torch_script/sentence-transformers_msmarco-distilbert-base-tas-b-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/onnx/sentence-transformers_msmarco-distilbert-base-tas-b-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/msmarco-distilbert-base-tas-b/1.0.1/onnx/config.json) | +| `huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1` | 384-dimensional dense vector space. Designed for semantic search and trained on 215 million question/answer pairs. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/torch_script/sentence-transformers_multi-qa-MiniLM-L6-cos-v1-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/onnx/sentence-transformers_multi-qa-MiniLM-L6-cos-v1-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-MiniLM-L6-cos-v1/1.0.1/onnx/config.json) | +| `huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/torch_script/sentence-transformers_multi-qa-mpnet-base-dot-v1-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/onnx/sentence-transformers_multi-qa-mpnet-base-dot-v1-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/multi-qa-mpnet-base-dot-v1/1.0.1/onnx/config.json) | +| `huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/torch_script/sentence-transformers_paraphrase-MiniLM-L3-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/onnx/sentence-transformers_paraphrase-MiniLM-L3-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-MiniLM-L3-v2/1.0.1/onnx/config.json) | +| `huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` | 384-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/torch_script/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/onnx/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2-1.0.1-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2/1.0.1/onnx/config.json) | \ No newline at end of file diff --git a/_ml-commons-plugin/semantic-search.md b/_ml-commons-plugin/semantic-search.md new file mode 100644 index 0000000000..7acdb33e66 --- /dev/null +++ b/_ml-commons-plugin/semantic-search.md @@ -0,0 +1,1024 @@ +--- +layout: default +title: Semantic search +has_children: false +nav_order: 140 +--- + +# Semantic search + +By default, OpenSearch calculates document scores using the [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25) algorithm. BM25 is a keyword-based algorithm that performs well on queries containing keywords but fails to capture the semantic meaning of the query terms. Semantic search, unlike keyword-based search, takes into account the meaning of the query in the search context. Thus, semantic search performs well when a query requires natural language understanding. + +In this tutorial, you'll learn how to: + +- Implement semantic search in OpenSearch. +- Combine semantic search with keyword search to improve search relevance. + +## Terminology + +It's helpful to understand the following terms before starting this tutorial: + +- _Semantic search_: Employs neural search in order to determine the intention of the user's query in the search context and improve search relevance. +- _Neural search_: Facilitates vector search at ingestion time and at search time: + - At ingestion time, neural search uses language models to generate vector embeddings from the text fields in the document. The documents containing both the original text field and the vector embedding of the field are then indexed in a k-NN index, as shown in the following diagram. + + ![Neural search at ingestion time diagram]({{site.url}}{{site.baseurl}}/images/neural-search-ingestion.png) + - At search time, when you then use a _neural query_, the query text is passed through a language model, and the resulting vector embeddings are compared with the document text vector embeddings to find the most relevant results, as shown in the following diagram. + + ![Neural search at search time diagram]({{site.url}}{{site.baseurl}}/images/neural-search-query.png) + +## OpenSearch components for semantic search + +In this tutorial, you'll implement semantic search using the following OpenSearch components: + +- [Model group]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control#model-groups) +- [Pretrained language models provided by OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/pretrained-models/) +- [Ingest pipeline]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/) +- [k-NN vector]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector/) +- [Neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/) +- [Search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/) +- [Normalization processor]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) +- [Hybrid query]({{site.url}}{{site.baseurl}}/query-dsl/compound/hybrid/) + +You'll find descriptions of all these components as you follow the tutorial, so don't worry if you're not familiar with some of them. Each link in the preceding list will take you to the documentation section for the corresponding component. + +## Prerequisites + +For this simple setup, you'll use an OpenSearch-provided machine learning (ML) model and a cluster with no dedicated ML nodes. To ensure that this basic local setup works, send the following request to update ML-related cluster settings: + +```json +PUT _cluster/settings +{ + "persistent": { + "plugins": { + "ml_commons": { + "only_run_on_ml_node": "false", + "model_access_control_enabled": "true", + "native_memory_threshold": "99" + } + } + } +} +``` +{% include copy-curl.html %} + +#### Advanced + +For a more advanced setup, note the following requirements: + +- To register a custom model, you need to specify an additional `"allow_registering_model_via_url": "true"` cluster setting. +- In production, it's best practice to separate the workloads by having dedicated ML nodes. On clusters with dedicated ML nodes, specify `"only_run_on_ml_node": "true"` for improved performance. + +For more information about ML-related cluster settings, see [ML Commons cluster settings]({{site.url}}{{site.baseurl}}/ml-commons-plugin/cluster-settings/). + +## Tutorial overview + +This tutorial consists of the following steps: + +1. [**Set up an ML language model**](#step-1-set-up-an-ml-language-model). + 1. [Choose a language model](#step-1a-choose-a-language-model). + 1. [Register a model group](#step-1b-register-a-model-group). + 1. [Register the model to the model group](#step-1c-register-the-model-to-the-model-group). + 1. [Deploy the model](#step-1d-deploy-the-model). +1. [**Ingest data with neural search**](#step-2-ingest-data-with-neural-search). + 1. [Create an ingest pipeline for neural search](#step-2a-create-an-ingest-pipeline-for-neural-search). + 1. [Create a k-NN index](#step-2b-create-a-k-nn-index). + 1. [Ingest documents into the index](#step-2c-ingest-documents-into-the-index). +1. [**Search the data**](#step-3-search-the-data). + - [Search using a keyword search](#search-using-a-keyword-search). + - [Search using a neural search](#search-using-a-neural-search). + - [Search using a combined keyword search and neural search](#search-using-a-combined-keyword-search-and-neural-search). + +Some steps in the tutorial contain optional `Test it` sections. You can ensure that the step was successful by running requests in these sections. + +After you're done, follow the steps in the [Clean up](#clean-up) section to delete all created components. + +## Tutorial + +You can follow this tutorial using your command line or the OpenSearch Dashboards [Dev Tools console]({{site.url}}{{site.baseurl}}/dashboards/dev-tools/run-queries/). + +## Step 1: Set up an ML language model + +Neural search requires a language model in order to generate vector embeddings from text fields, both at ingestion time and query time. + +### Step 1(a): Choose a language model + +For this tutorial, you'll use the [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert) model from Hugging Face. It is one of the pretrained sentence transformer models available in OpenSearch that has shown some of the best results in benchmarking tests (for details, see [this blog post](https://opensearch.org/blog/semantic-science-benchmarks/)). You'll need the name, version, and dimension of the model to register it. You can find this information in the [pretrained model table]({{site.url}}{{site.baseurl}}/ml-commons-plugin/pretrained-models/#sentence-transformers) by selecting the `config_url` link corresponding to the model's TorchScript artifact: + +- The model name is `huggingface/sentence-transformers/msmarco-distilbert-base-tas-b`. +- The model version is `1.0.1`. +- The number of dimensions for this model is `768`. + +#### Advanced: Using a different model + +Alternatively, you can choose to use one of the [pretrained language models provided by OpenSearch]({{site.url}}{{site.baseurl}}/ml-commons-plugin/pretrained-models/) or your own custom model. For information about choosing a model, see [Further reading](#further-reading). For instructions on how to set up a custom model, see [ML framework]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/). + +Take note of the dimensionality of the model because you'll need it when you set up a k-NN index. +{: .important} + +### Step 1(b): Register a model group + +For access control, models are organized into model groups (collections of versions of a particular model). Each model group name in the cluster must be globally unique. Registering a model group ensures the uniqueness of the model group name. + +If you are registering the first version of a model without first registering the model group, a new model group is created automatically. For more information, see [Model access control]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-access-control/). +{: .tip} + +To register a model group with the access mode set to `public`, send the following request: + +```json +POST /_plugins/_ml/model_groups/_register +{ + "name": "NLP_model_group", + "description": "A model group for NLP models", + "access_mode": "public" +} +``` +{% include copy-curl.html %} + +OpenSearch sends back the model group ID: + +```json +{ + "model_group_id": "Z1eQf4oB5Vm0Tdw8EIP2", + "status": "CREATED" +} +``` + +You'll use this ID to register the chosen model to the model group. + +
+ + Test it + + {: .text-delta} + +Search for the newly created model group by providing its model group ID in the request: + +```json +POST /_plugins/_ml/model_groups/_search +{ + "query": { + "match": { + "_id": "Z1eQf4oB5Vm0Tdw8EIP2" + } + } +} +``` +{% include copy-curl.html %} + +The response contains the model group: + +```json +{ + "took": 0, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1, + "hits": [ + { + "_index": ".plugins-ml-model-group", + "_id": "Z1eQf4oB5Vm0Tdw8EIP2", + "_version": 1, + "_seq_no": 14, + "_primary_term": 2, + "_score": 1, + "_source": { + "created_time": 1694357262582, + "access": "public", + "latest_version": 0, + "last_updated_time": 1694357262582, + "name": "NLP_model_group", + "description": "A model group for NLP models" + } + } + ] + } +} +``` +
+ + +### Step 1(c): Register the model to the model group + +To register the model to the model group, provide the model group ID in the register request: + +```json +POST /_plugins/_ml/models/_register +{ + "name": "huggingface/sentence-transformers/msmarco-distilbert-base-tas-b", + "version": "1.0.1", + "model_group_id": "Z1eQf4oB5Vm0Tdw8EIP2", + "model_format": "TORCH_SCRIPT" +} +``` +{% include copy-curl.html %} + +Registering a model is an asynchronous task. OpenSearch sends back a task ID for this task: + +```json +{ + "task_id": "aFeif4oB5Vm0Tdw8yoN7", + "status": "CREATED" +} +``` + +OpenSearch downloads the config file for the model and the model contents from the URL. Because the model is larger than 10 MB in size, OpenSearch splits it into chunks of up to 10 MB and saves those chunks in the model index. You can check the status of the task by using the Tasks API: + +```json +GET /_plugins/_ml/tasks/aFeif4oB5Vm0Tdw8yoN7 +``` +{% include copy-curl.html %} + +Once the task is complete, the task state will be `COMPLETED` and the Tasks API response will contain a model ID for the registered model: + +```json +{ + "model_id": "aVeif4oB5Vm0Tdw8zYO2", + "task_type": "REGISTER_MODEL", + "function_name": "TEXT_EMBEDDING", + "state": "COMPLETED", + "worker_node": [ + "4p6FVOmJRtu3wehDD74hzQ" + ], + "create_time": 1694358489722, + "last_update_time": 1694358499139, + "is_async": true +} +``` + +You'll need the model ID in order to use this model for several of the following steps. + +
+ + Test it + + {: .text-delta} + +Search for the newly created model by providing its ID in the request: + +```json +GET /_plugins/_ml/models/aVeif4oB5Vm0Tdw8zYO2 +``` +{% include copy-curl.html %} + +The response contains the model: + +```json +{ + "name": "huggingface/sentence-transformers/msmarco-distilbert-base-tas-b", + "model_group_id": "Z1eQf4oB5Vm0Tdw8EIP2", + "algorithm": "TEXT_EMBEDDING", + "model_version": "1", + "model_format": "TORCH_SCRIPT", + "model_state": "REGISTERED", + "model_content_size_in_bytes": 266352827, + "model_content_hash_value": "acdc81b652b83121f914c5912ae27c0fca8fabf270e6f191ace6979a19830413", + "model_config": { + "model_type": "distilbert", + "embedding_dimension": 768, + "framework_type": "SENTENCE_TRANSFORMERS", + "all_config": """{"_name_or_path":"old_models/msmarco-distilbert-base-tas-b/0_Transformer","activation":"gelu","architectures":["DistilBertModel"],"attention_dropout":0.1,"dim":768,"dropout":0.1,"hidden_dim":3072,"initializer_range":0.02,"max_position_embeddings":512,"model_type":"distilbert","n_heads":12,"n_layers":6,"pad_token_id":0,"qa_dropout":0.1,"seq_classif_dropout":0.2,"sinusoidal_pos_embds":false,"tie_weights_":true,"transformers_version":"4.7.0","vocab_size":30522}""" + }, + "created_time": 1694482261832, + "last_updated_time": 1694482324282, + "last_registered_time": 1694482270216, + "last_deployed_time": 1694482324282, + "total_chunks": 27, + "planning_worker_node_count": 1, + "current_worker_node_count": 1, + "planning_worker_nodes": [ + "4p6FVOmJRtu3wehDD74hzQ" + ], + "deploy_to_all_nodes": true +} +``` + +The response contains the model information. You can see that the `model_state` is `REGISTERED`. Additionally, the model was split into 27 chunks, as shown in the `total_chunks` field. +
+ +#### Advanced: Registering a custom model + +To register a custom model, you must provide a model configuration in the register request. For example, the following is a register request containing the full format for the model used in this tutorial: + +```json +POST /_plugins/_ml/models/_register +{ + "name": "huggingface/sentence-transformers/msmarco-distilbert-base-tas-b", + "version": "1.0.1", + "model_group_id": "Z1eQf4oB5Vm0Tdw8EIP2", + "description": "This is a port of the DistilBert TAS-B Model to sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and is optimized for the task of semantic search.", + "model_task_type": "TEXT_EMBEDDING", + "model_format": "TORCH_SCRIPT", + "model_content_size_in_bytes": 266352827, + "model_content_hash_value": "acdc81b652b83121f914c5912ae27c0fca8fabf270e6f191ace6979a19830413", + "model_config": { + "model_type": "distilbert", + "embedding_dimension": 768, + "framework_type": "sentence_transformers", + "all_config": """{"_name_or_path":"old_models/msmarco-distilbert-base-tas-b/0_Transformer","activation":"gelu","architectures":["DistilBertModel"],"attention_dropout":0.1,"dim":768,"dropout":0.1,"hidden_dim":3072,"initializer_range":0.02,"max_position_embeddings":512,"model_type":"distilbert","n_heads":12,"n_layers":6,"pad_token_id":0,"qa_dropout":0.1,"seq_classif_dropout":0.2,"sinusoidal_pos_embds":false,"tie_weights_":true,"transformers_version":"4.7.0","vocab_size":30522}""" + }, + "created_time": 1676073973126 +} +``` + +For more information, see [ML framework]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/). + +### Step 1(d): Deploy the model + +Once the model is registered, it is saved in the model index. Next, you'll need to deploy the model. Deploying a model creates a model instance and caches the model in memory. To deploy the model, provide its model ID to the `_deploy` endpoint: + +```json +POST /_plugins/_ml/models/aVeif4oB5Vm0Tdw8zYO2/_deploy +``` +{% include copy-curl.html %} + +Like the register operation, the deploy operation is asynchronous, so you'll get a task ID in the response: + +```json +{ + "task_id": "ale6f4oB5Vm0Tdw8NINO", + "status": "CREATED" +} +``` + +You can check the status of the task by using the Tasks API: + +```json +GET /_plugins/_ml/tasks/ale6f4oB5Vm0Tdw8NINO +``` +{% include copy-curl.html %} + +Once the task is complete, the task state will be `COMPLETED`: + +```json +{ + "model_id": "aVeif4oB5Vm0Tdw8zYO2", + "task_type": "DEPLOY_MODEL", + "function_name": "TEXT_EMBEDDING", + "state": "COMPLETED", + "worker_node": [ + "4p6FVOmJRtu3wehDD74hzQ" + ], + "create_time": 1694360024141, + "last_update_time": 1694360027940, + "is_async": true +} +``` + +
+ + Test it + + {: .text-delta} + +Search for the deployed model by providing its ID in the request: + +```json +GET /_plugins/_ml/models/aVeif4oB5Vm0Tdw8zYO2 +``` +{% include copy-curl.html %} + +The response shows the model state as `DEPLOYED`: + +```json +{ + "name": "huggingface/sentence-transformers/msmarco-distilbert-base-tas-b", + "model_group_id": "Z1eQf4oB5Vm0Tdw8EIP2", + "algorithm": "TEXT_EMBEDDING", + "model_version": "1", + "model_format": "TORCH_SCRIPT", + "model_state": "DEPLOYED", + "model_content_size_in_bytes": 266352827, + "model_content_hash_value": "acdc81b652b83121f914c5912ae27c0fca8fabf270e6f191ace6979a19830413", + "model_config": { + "model_type": "distilbert", + "embedding_dimension": 768, + "framework_type": "SENTENCE_TRANSFORMERS", + "all_config": """{"_name_or_path":"old_models/msmarco-distilbert-base-tas-b/0_Transformer","activation":"gelu","architectures":["DistilBertModel"],"attention_dropout":0.1,"dim":768,"dropout":0.1,"hidden_dim":3072,"initializer_range":0.02,"max_position_embeddings":512,"model_type":"distilbert","n_heads":12,"n_layers":6,"pad_token_id":0,"qa_dropout":0.1,"seq_classif_dropout":0.2,"sinusoidal_pos_embds":false,"tie_weights_":true,"transformers_version":"4.7.0","vocab_size":30522}""" + }, + "created_time": 1694482261832, + "last_updated_time": 1694482324282, + "last_registered_time": 1694482270216, + "last_deployed_time": 1694482324282, + "total_chunks": 27, + "planning_worker_node_count": 1, + "current_worker_node_count": 1, + "planning_worker_nodes": [ + "4p6FVOmJRtu3wehDD74hzQ" + ], + "deploy_to_all_nodes": true +} +``` + +You can also receive statistics for all deployed models in your cluster by sending a [Models Profile API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/#profile) request: + +```json +GET /_plugins/_ml/profile/models +``` +
+ +## Step 2: Ingest data with neural search + +Neural search uses a language model to transform text into vector embeddings. During ingestion, neural search creates vector embeddings for the text fields in the request. During search, you can generate vector embeddings for the query text by applying the same model, allowing you to perform vector similarity search on the documents. + +### Step 2(a): Create an ingest pipeline for neural search + +Now that you have deployed a model, you can use this model to configure [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/). First, you need to create an [ingest pipeline]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/) that contains one processor: a task that transforms document fields before documents are ingested into an index. For neural search, you'll set up a `text_embedding` processor that creates vector embeddings from text. You'll need the `model_id` of the model you set up in the previous section and a `field_map`, which specifies the name of the field from which to take the text (`text`) and the name of the field in which to record embeddings (`passage_embedding`): + +```json +PUT /_ingest/pipeline/nlp-ingest-pipeline +{ + "description": "An NLP ingest pipeline", + "processors": [ + { + "text_embedding": { + "model_id": "aVeif4oB5Vm0Tdw8zYO2", + "field_map": { + "text": "passage_embedding" + } + } + } + ] +} +``` +{% include copy-curl.html %} + +
+ + Test it + + {: .text-delta} + +Search for the created ingest pipeline by using the Ingest API: + +```json +GET /_ingest/pipeline +``` +{% include copy-curl.html %} + +The response contains the ingest pipeline: + +```json +{ + "nlp-ingest-pipeline": { + "description": "An NLP ingest pipeline", + "processors": [ + { + "text_embedding": { + "model_id": "aVeif4oB5Vm0Tdw8zYO2", + "field_map": { + "text": "passage_embedding" + } + } + } + ] + } +} +``` +
+ +### Step 2(b): Create a k-NN index + +Now you'll create a k-NN index with a field named `text`, which contains an image description, and a [`knn_vector`]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector/) field named `passage_embedding`, which contains the vector embedding of the text. Additionally, set the default ingest pipeline to the `nlp-ingest-pipeline` you created in the previous step: + + +```json +PUT /my-nlp-index +{ + "settings": { + "index.knn": true, + "default_pipeline": "nlp-ingest-pipeline" + }, + "mappings": { + "properties": { + "id": { + "type": "text" + }, + "passage_embedding": { + "type": "knn_vector", + "dimension": 768, + "method": { + "engine": "lucene", + "space_type": "l2", + "name": "hnsw", + "parameters": {} + } + }, + "text": { + "type": "text" + } + } + } +} +``` +{% include copy-curl.html %} + +Setting up a k-NN index allows you to later perform a vector search on the `passage_embedding` field. + +
+ + Test it + + {: .text-delta} + +Use the following requests to get the settings and the mappings of the created index: + +```json +GET /my-nlp-index/_settings +``` +{% include copy-curl.html %} + +```json +GET /my-nlp-index/_mappings +``` +{% include copy-curl.html %} + +
+ +### Step 2(c): Ingest documents into the index + +In this step, you'll ingest several sample documents into the index. The sample data is taken from the [Flickr image dataset](https://www.kaggle.com/datasets/hsankesara/flickr-image-dataset). Each document contains a `text` field corresponding to the image description and an `id` field corresponding to the image ID: + +```json +PUT /my-nlp-index/_doc/1 +{ + "text": "A West Virginia university women 's basketball team , officials , and a small gathering of fans are in a West Virginia arena .", + "id": "4319130149.jpg" +} +``` +{% include copy-curl.html %} + +```json +PUT /my-nlp-index/_doc/2 +{ + "text": "A wild animal races across an uncut field with a minimal amount of trees .", + "id": "1775029934.jpg" +} +``` +{% include copy-curl.html %} + +```json +PUT /my-nlp-index/_doc/3 +{ + "text": "People line the stands which advertise Freemont 's orthopedics , a cowboy rides a light brown bucking bronco .", + "id": "2664027527.jpg" +} +``` +{% include copy-curl.html %} + +```json +PUT /my-nlp-index/_doc/4 +{ + "text": "A man who is riding a wild horse in the rodeo is very near to falling off .", + "id": "4427058951.jpg" +} +``` +{% include copy-curl.html %} + +```json +PUT /my-nlp-index/_doc/5 +{ + "text": "A rodeo cowboy , wearing a cowboy hat , is being thrown off of a wild white horse .", + "id": "2691147709.jpg" +} +``` +{% include copy-curl.html %} + +When the documents are ingested into the index, the `text_embedding` processor creates an additional field that contains vector embeddings and adds that field to the document. To see an example document that is indexed, search for document 1: + +```json +GET /my-nlp-index/_search/1 +``` +{% include copy-curl.html %} + +The response shows the document `_source` containing the original `text` and `id` fields and the added `passage_embeddings` field: + +```json +{ + "_index": "my-nlp-index", + "_id": "1", + "_version": 1, + "_seq_no": 0, + "_primary_term": 1, + "found": true, + "_source": { + "passage_embedding": [ + 0.04491629, + -0.34105563, + 0.036822468, + -0.14139028, + ... + ], + "text": "A West Virginia university women 's basketball team , officials , and a small gathering of fans are in a West Virginia arena .", + "id": "4319130149.jpg" + } +} +``` + +## Step 3: Search the data + +Now you'll search the index using keyword search, neural search, and a combination of the two. + +### Search using a keyword search + +To search using a keyword search, use a `match` query. You'll exclude embeddings from the results: + +```json +GET /my-nlp-index/_search +{ + "_source": { + "excludes": [ + "passage_embedding" + ] + }, + "query": { + "match": { + "text": { + "query": "wild west" + } + } + } +} +``` +{% include copy-curl.html %} + +Document 3 is not returned because it does not contain the specified keywords. Documents containing the words `rodeo` and `cowboy` are scored lower because semantic meaning is not considered: + +
+ + Results + + {: .text-delta} + +```json +{ + "took": 647, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 4, + "relation": "eq" + }, + "max_score": 1.7878418, + "hits": [ + { + "_index": "my-nlp-index", + "_id": "1", + "_score": 1.7878418, + "_source": { + "text": "A West Virginia university women 's basketball team , officials , and a small gathering of fans are in a West Virginia arena .", + "id": "4319130149.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "2", + "_score": 0.58093566, + "_source": { + "text": "A wild animal races across an uncut field with a minimal amount of trees .", + "id": "1775029934.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "5", + "_score": 0.55228686, + "_source": { + "text": "A rodeo cowboy , wearing a cowboy hat , is being thrown off of a wild white horse .", + "id": "2691147709.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "4", + "_score": 0.53899646, + "_source": { + "text": "A man who is riding a wild horse in the rodeo is very near to falling off .", + "id": "4427058951.jpg" + } + } + ] + } +} +``` +
+ +### Search using a neural search + +To search using a neural search, use a `neural` query and provide the model ID of the model you set up earlier so that vector embeddings for the query text are generated with the model used at ingestion time: + +```json +GET /my-nlp-index/_search +{ + "_source": { + "excludes": [ + "passage_embedding" + ] + }, + "query": { + "neural": { + "passage_embedding": { + "query_text": "wild west", + "model_id": "aVeif4oB5Vm0Tdw8zYO2", + "k": 5 + } + } + } +} +``` +{% include copy-curl.html %} + +This time, the response not only contains all five documents, but the document order is also improved because neural search considers semantic meaning: + +
+ + Results + + {: .text-delta} + +```json +{ + "took": 25, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 5, + "relation": "eq" + }, + "max_score": 0.01585195, + "hits": [ + { + "_index": "my-nlp-index", + "_id": "4", + "_score": 0.01585195, + "_source": { + "text": "A man who is riding a wild horse in the rodeo is very near to falling off .", + "id": "4427058951.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "2", + "_score": 0.015748845, + "_source": { + "text": "A wild animal races across an uncut field with a minimal amount of trees.", + "id": "1775029934.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "5", + "_score": 0.015177963, + "_source": { + "text": "A rodeo cowboy , wearing a cowboy hat , is being thrown off of a wild white horse .", + "id": "2691147709.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "1", + "_score": 0.013272902, + "_source": { + "text": "A West Virginia university women 's basketball team , officials , and a small gathering of fans are in a West Virginia arena .", + "id": "4319130149.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "3", + "_score": 0.011347735, + "_source": { + "text": "People line the stands which advertise Freemont 's orthopedics , a cowboy rides a light brown bucking bronco .", + "id": "2664027527.jpg" + } + } + ] + } +} +``` +
+ +### Search using a combined keyword search and neural search + +To combine keyword search and neural 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. + +#### Step 1: Configure a search pipeline + +To configure a search pipeline with a `normalization_processor`, use the following request. The normalization technique in the processor is set to `min_max`, and the combination technique is set to `arithmetic_mean`. The `weights` array specifies the weights assigned to each query clause as decimal percentages: + +```json +PUT /_search/pipeline/nlp-search-pipeline +{ + "description": "Post processor for hybrid search", + "phase_results_processors": [ + { + "normalization-processor": { + "normalization": { + "technique": "min_max" + }, + "combination": { + "technique": "arithmetic_mean", + "parameters": { + "weights": [ + 0.3, + 0.7 + ] + } + } + } + } + ] +} +``` +{% include copy-curl.html %} + +#### Step 2: Search with the hybrid query + +You'll use the [`hybrid` query]({{site.url}}{{site.baseurl}}/query-dsl/compound/hybrid/) to combine the `match` and `neural` query clauses. Make sure to apply the previously created `nlp-search-pipeline` to the request in the query parameter: + +```json +GET /my-nlp-index/_search?search_pipeline=nlp-search-pipeline +{ + "_source": { + "exclude": [ + "passage_embedding" + ] + }, + "query": { + "hybrid": { + "queries": [ + { + "match": { + "text": { + "query": "cowboy rodeo bronco" + } + } + }, + { + "neural": { + "passage_embedding": { + "query_text": "wild west", + "model_id": "aVeif4oB5Vm0Tdw8zYO2", + "k": 5 + } + } + } + ] + } + } +} +``` +{% include copy-curl.html %} + +Not only does OpenSearch return documents that match the semantic meaning of `wild west`, but now the documents containing words related to the wild west theme are also scored higher relative to the others: + +
+ + Results + + {: .text-delta} + +```json +{ + "took": 27, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 5, + "relation": "eq" + }, + "max_score": 0.86481035, + "hits": [ + { + "_index": "my-nlp-index", + "_id": "5", + "_score": 0.86481035, + "_source": { + "text": "A rodeo cowboy , wearing a cowboy hat , is being thrown off of a wild white horse .", + "id": "2691147709.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "4", + "_score": 0.7003, + "_source": { + "text": "A man who is riding a wild horse in the rodeo is very near to falling off .", + "id": "4427058951.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "2", + "_score": 0.6839765, + "_source": { + "text": "A wild animal races across an uncut field with a minimal amount of trees.", + "id": "1775029934.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "3", + "_score": 0.3007, + "_source": { + "text": "People line the stands which advertise Freemont 's orthopedics , a cowboy rides a light brown bucking bronco .", + "id": "2664027527.jpg" + } + }, + { + "_index": "my-nlp-index", + "_id": "1", + "_score": 0.29919013, + "_source": { + "text": "A West Virginia university women 's basketball team , officials , and a small gathering of fans are in a West Virginia arena .", + "id": "4319130149.jpg" + } + } + ] + } +} +``` +
+ +Instead of specifying the search pipeline in every request, you can set it as a default search pipeline for the index as follows: + +```json +PUT /my-nlp-index/_settings +{ + "index.search.default_pipeline" : "nlp-search-pipeline" +} +``` +{% include copy-curl.html %} + +You can now experiment with different weights, normalization techniques, and combination techniques. For more information, see the [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) and [`hybrid` query]({{site.url}}{{site.baseurl}}/query-dsl/compound/hybrid/) documentation. + +#### Advanced + +You can parameterize the search by using search templates. Search templates hide implementation details, reducing the number of nested levels and thus the query complexity. For more information, see [search templates]({{site.url}}{{site.baseurl}}/search-plugins/search-template/). + +### Clean up + +After you're done, delete the components you've created in tutorial from the cluster: + +```json +DELETE /my-nlp-index +``` +{% include copy-curl.html %} + +```json +DELETE /_search/pipeline/nlp-search-pipeline +``` +{% include copy-curl.html %} + +```json +DELETE /_ingest/pipeline/nlp-ingest-pipeline +``` +{% include copy-curl.html %} + +```json +POST /_plugins/_ml/models/aVeif4oB5Vm0Tdw8zYO2/_undeploy +``` +{% include copy-curl.html %} + +```json +DELETE /_plugins/_ml/models/aVeif4oB5Vm0Tdw8zYO2 +``` +{% include copy-curl.html %} + +```json +DELETE /_plugins/_ml/model_groups/Z1eQf4oB5Vm0Tdw8EIP2 +``` +{% include copy-curl.html %} + +## Further reading + +- Read about the basics of OpenSearch semantic search in [Building a semantic search engine in OpenSearch](https://opensearch.org/blog/semantic-search-solutions/). +- Read about the benefits of combining keyword and neural search, the normalization and combination technique options, and benchmarking tests in [The ABCs of semantic search in OpenSearch: Architectures, benchmarks, and combination strategies](https://opensearch.org/blog/semantic-science-benchmarks/). \ No newline at end of file diff --git a/_query-dsl/compound/bool.md b/_query-dsl/compound/bool.md index 6e29cea382..d5d1794450 100644 --- a/_query-dsl/compound/bool.md +++ b/_query-dsl/compound/bool.md @@ -1,6 +1,6 @@ --- layout: default -title: Boolean queries +title: Boolean parent: Compound queries grand_parent: Query DSL nav_order: 10 diff --git a/_query-dsl/compound/boosting.md b/_query-dsl/compound/boosting.md index 7ccb40177b..d283a0540e 100644 --- a/_query-dsl/compound/boosting.md +++ b/_query-dsl/compound/boosting.md @@ -1,6 +1,6 @@ --- layout: default -title: Boosting queries +title: Boosting parent: Compound queries grand_parent: Query DSL nav_order: 30 diff --git a/_query-dsl/compound/constant-score.md b/_query-dsl/compound/constant-score.md index 63906e079e..15279ef50a 100644 --- a/_query-dsl/compound/constant-score.md +++ b/_query-dsl/compound/constant-score.md @@ -1,6 +1,6 @@ --- layout: default -title: Constant score queries +title: Constant score parent: Compound queries grand_parent: Query DSL nav_order: 40 diff --git a/_query-dsl/compound/disjunction-max.md b/_query-dsl/compound/disjunction-max.md index e03b8d0fb4..b6397c173c 100644 --- a/_query-dsl/compound/disjunction-max.md +++ b/_query-dsl/compound/disjunction-max.md @@ -1,6 +1,6 @@ --- layout: default -title: Disjunction max queries +title: Disjunction max parent: Compound queries grand_parent: Query DSL nav_order: 50 diff --git a/_query-dsl/compound/function-score.md b/_query-dsl/compound/function-score.md index e030f0475e..f9f7300371 100644 --- a/_query-dsl/compound/function-score.md +++ b/_query-dsl/compound/function-score.md @@ -1,6 +1,6 @@ --- layout: default -title: Function score queries +title: Function score parent: Compound queries grand_parent: Query DSL nav_order: 60 diff --git a/_query-dsl/compound/hybrid.md b/_query-dsl/compound/hybrid.md new file mode 100644 index 0000000000..f317205ca2 --- /dev/null +++ b/_query-dsl/compound/hybrid.md @@ -0,0 +1,29 @@ +--- +layout: default +title: Hybrid +parent: Compound queries +grand_parent: Query DSL +nav_order: 70 +--- + +# Hybrid query + +You can use a hybrid query to combine relevance scores from multiple queries into one score for a given document. A hybrid query contains a list of one or more queries and independently calculates document scores at the shard level for each subquery. The subquery rewriting is performed at the coordinating node level in order to avoid duplicate computations. + +## Example + +Before using a `hybrid` query, you must configure a search pipeline with a [`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor/) (see [this example]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/normalization-processor#example)). + +To try out the example, follow the [Semantic search tutorial]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search#tutorial). + +## Parameters + +The following table lists all top-level parameters supported by `hybrid` queries. + +Parameter | Description +:--- | :--- +`queries` | An array of one or more query clauses that are used to match documents. A document must match at least one query clause in order to be returned in the results. The documents' relevance scores from all query clauses are combined into one score by applying a [search pipeline]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/). The maximum number of query clauses is 5. Required. + +## Disabling hybrid queries + +By default, hybrid queries are enabled. To disable hybrid queries in your cluster, set the `plugins.neural_search.hybrid_search_disabled` setting to `true` in `opensearch.yml`. \ No newline at end of file diff --git a/_search-plugins/search-pipelines/filter-query-processor.md b/_search-plugins/search-pipelines/filter-query-processor.md index b358bbb542..1fe396eb20 100644 --- a/_search-plugins/search-pipelines/filter-query-processor.md +++ b/_search-plugins/search-pipelines/filter-query-processor.md @@ -1,6 +1,6 @@ --- layout: default -title: Filter query processor +title: Filter query nav_order: 10 has_children: false parent: Search processors diff --git a/_search-plugins/search-pipelines/index.md b/_search-plugins/search-pipelines/index.md index 26f663704a..a9ff3cd18e 100644 --- a/_search-plugins/search-pipelines/index.md +++ b/_search-plugins/search-pipelines/index.md @@ -14,9 +14,10 @@ You can use _search pipelines_ to build new or reuse existing result rerankers, The following is a list of search pipeline terminology: -* _Search request processor_: A component that takes a search request (the query and the metadata passed in the request), performs an operation with or on the search request, and returns a search request. -* _Search response processor_: A component that takes a search response and search request (the query, results, and metadata passed in the request), performs an operation with or on the search response, and returns a search response. -* _Processor_: Either a search request processor or a search response processor. +* [_Search request processor_]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/search-processors#search-request-processors): A component that intercepts a search request (the query and the metadata passed in the request), performs an operation with or on the search request, and returns the search request. +* [_Search response processor_]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/search-processors#search-response-processors): A component that intercepts a search response and search request (the query, results, and metadata passed in the request), performs an operation with or on the search response, and returns the search response. +* [_Search phase results processor_]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/search-processors#search-phase-results-processors): A component that runs between search phases at the coordinating node level. A search phase results processor intercepts the results retrieved from one search phase and transforms them before passing them to the next search phase. +* [_Processor_]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/search-processors/): Either a search request processor or a search response processor. * _Search pipeline_: An ordered list of processors that is integrated into OpenSearch. The pipeline intercepts a query, performs processing on the query, sends it to OpenSearch, intercepts the results, performs processing on the results, and returns them to the calling application, as shown in the following diagram. ![Search processor diagram]({{site.url}}{{site.baseurl}}/images/search-pipelines.png) diff --git a/_search-plugins/search-pipelines/normalization-processor.md b/_search-plugins/search-pipelines/normalization-processor.md new file mode 100644 index 0000000000..be854d1319 --- /dev/null +++ b/_search-plugins/search-pipelines/normalization-processor.md @@ -0,0 +1,117 @@ +--- +layout: default +title: Normalization +nav_order: 15 +has_children: false +parent: Search processors +grand_parent: Search pipelines +--- + +# Normalization processor + +The `normalization_processor` is a search phase results processor that runs between the query and fetch phases of search execution. It intercepts the query phase results and then normalizes and combines the document scores from different query clauses before passing the documents to the fetch phase. + +## Score normalization and combination + +Many applications require both keyword matching and semantic understanding. For example, BM25 accurately provides relevant search results for a query containing keywords, and neural networks perform well when a query requires natural language understanding. Thus, you might want to combine BM25 search results with the results of a k-NN or neural search. However, BM25 and k-NN search use different scales to calculate relevance scores for the matching documents. Before combining the scores from multiple queries, it is beneficial to normalize them so that they are on the same scale, as shown by experimental data. For further reading about score normalization and combination, including benchmarks and various techniques, see [this semantic search blog post](https://opensearch.org/blog/semantic-science-benchmarks/). + +## Query then fetch + +OpenSearch supports two search types: `query_then_fetch` and `dfs_query_then_fetch`. The following diagram outlines the query-then-fetch process, which includes a normalization processor. + +![Normalization processor flow diagram]({{site.url}}{{site.baseurl}}/images/normalization-processor.png) + +When you send a search request to a node, the node becomes a _coordinating node_. During the first phase of search, the _query phase_, the coordinating node routes the search request to all shards in the index, including primary and replica shards. Each shard then runs the search query locally and returns metadata about the matching documents, which includes their document IDs and relevance scores. The `normalization_processor` then normalizes and combines scores from different query clauses. The coordinating node merges and sorts the local lists of results, compiling a global list of top documents that match the query. After that, search execution enters a _fetch phase_, in which the coordinating node requests the documents in the global list from the shards where they reside. Each shard returns the documents' `_source` to the coordinating node. Finally, the coordinating node sends a search response containing the results back to you. + +## Request fields + +The following table lists all available request fields. + +Field | Data type | Description +:--- | :--- | :--- +`normalization.technique` | String | The technique for normalizing scores. Valid values are [`min_max`](https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization)) and [`l2`](https://en.wikipedia.org/wiki/Cosine_similarity#L2-normalized_Euclidean_distance). Optional. Default is `min_max`. +`combination.technique` | String | The technique for combining scores. Valid values are [`arithmetic_mean`](https://en.wikipedia.org/wiki/Arithmetic_mean), [`geometric_mean`](https://en.wikipedia.org/wiki/Geometric_mean), and [`harmonic_mean`](https://en.wikipedia.org/wiki/Harmonic_mean). Optional. Default is `arithmetic_mean`. +`combination.parameters.weights` | Array of floating-point values | Specifies the weights to use for each query. Valid values are in the [0.0, 1.0] range and signify decimal percentages. The closer the weight is to 1.0, the more weight is given to a query. The number of values in the `weights` array must equal the number of queries. The sum of the values in the array must equal 1.0. Optional. If not provided, all queries are given equal weight. +`tag` | String | The processor's identifier. Optional. +`description` | String | A description of the processor. Optional. +`ignore_failure` | Boolean | For this processor, this value is ignored. If the processor fails, the pipeline always fails and returns an error. + +## Example + +The following example demonstrates using a search pipeline with a `normalization_processor`. To try out this example, follow the [Semantic search tutorial]({{site.url}}{{site.baseurl}}/ml-commons-plugin/semantic-search#tutorial). + +### Creating a search pipeline + +The following request creates a search pipeline containing a `normalization_processor` that uses the `min_max` normalization technique and the `arithmetic_mean` combination technique: + +```json +PUT /_search/pipeline/nlp-search-pipeline +{ + "description": "Post processor for hybrid search", + "phase_results_processors": [ + { + "normalization-processor": { + "normalization": { + "technique": "min_max" + }, + "combination": { + "technique": "arithmetic_mean", + "parameters": { + "weights": [ + 0.3, + 0.7 + ] + } + } + } + } + ] +} +``` +{% include copy-curl.html %} + +### Using a search pipeline + +Provide the query clauses that you want to combine in a `hybrid` query and apply the search pipeline created in the previous section so that the scores are combined using the chosen techniques: + +```json +GET /my-nlp-index/_search?search_pipeline=nlp-search-pipeline +{ + "_source": { + "exclude": [ + "passage_embedding" + ] + }, + "query": { + "hybrid": { + "queries": [ + { + "match": { + "text": { + "query": "horse" + } + } + }, + { + "neural": { + "passage_embedding": { + "query_text": "wild west", + "model_id": "aVeif4oB5Vm0Tdw8zYO2", + "k": 5 + } + } + } + ] + } + } +} +``` +{% include copy-curl.html %} + +For more information, see [Hybrid query]({{site.url}}{{site.baseurl}}/query-dsl/compound/hybrid/). + +## Search tuning recommendations + +To improve search relevance, we recommend increasing the sample size. + +If the hybrid query does not return some expected results, it may be because the subqueries return too few documents. The `normalization_processor` only transforms the results returned by each subquery; it does not perform any additional sampling. During our experiments, we used [nDCG@10](https://en.wikipedia.org/wiki/Discounted_cumulative_gain) to measure quality of information retrieval depending on the number of documents returned (the size). We have found that a size in the [100, 200] range works best for datasets of up to 10M documents. We do not recommend increasing the size beyond the recommended values because higher size values do not improve search relevance but increase search latency. diff --git a/_search-plugins/search-pipelines/personalize-search-ranking.md b/_search-plugins/search-pipelines/personalize-search-ranking.md index c008bb155b..64b2ef2017 100644 --- a/_search-plugins/search-pipelines/personalize-search-ranking.md +++ b/_search-plugins/search-pipelines/personalize-search-ranking.md @@ -1,6 +1,6 @@ --- layout: default -title: Personalize search ranking processor +title: Personalize search ranking nav_order: 40 has_children: false parent: Search processors diff --git a/_search-plugins/search-pipelines/rename-field-processor.md b/_search-plugins/search-pipelines/rename-field-processor.md index 47d445f093..5ad8a367dc 100644 --- a/_search-plugins/search-pipelines/rename-field-processor.md +++ b/_search-plugins/search-pipelines/rename-field-processor.md @@ -1,6 +1,6 @@ --- layout: default -title: Rename field processor +title: Rename field nav_order: 20 has_children: false parent: Search processors diff --git a/_search-plugins/search-pipelines/script-processor.md b/_search-plugins/search-pipelines/script-processor.md index 4c25dd490e..f4bc7d43db 100644 --- a/_search-plugins/search-pipelines/script-processor.md +++ b/_search-plugins/search-pipelines/script-processor.md @@ -1,6 +1,6 @@ --- layout: default -title: Script processor +title: Script nav_order: 30 has_children: false parent: Search processors diff --git a/_search-plugins/search-pipelines/search-processors.md b/_search-plugins/search-pipelines/search-processors.md index d35f243bbf..3bf4061cd9 100644 --- a/_search-plugins/search-pipelines/search-processors.md +++ b/_search-plugins/search-pipelines/search-processors.md @@ -13,9 +13,12 @@ Search processors can be of the following types: - [Search request processors](#search-request-processors) - [Search response processors](#search-response-processors) +- [Search phase results processors](#search-phase-results-processors) ## Search request processors +A search request processor intercepts a search request (the query and the metadata passed in the request), performs an operation with or on the search request, and submits the search request to the index. + The following table lists all supported search request processors. Processor | Description | Earliest available version @@ -25,6 +28,8 @@ Processor | Description | Earliest available version ## Search response processors +A search response processor intercepts a search response and search request (the query, results, and metadata passed in the request), performs an operation with or on the search response, and returns the search response. + The following table lists all supported search response processors. Processor | Description | Earliest available version @@ -32,6 +37,16 @@ Processor | Description | Earliest available version [`rename_field`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/rename-field-processor/)| Renames an existing field. | 2.8 [`personalize_search_ranking`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/personalize-search-ranking/) | Uses [Amazon Personalize](https://aws.amazon.com/personalize/) to rerank search results (requires setting up the Amazon Personalize service). | 2.9 +## Search phase results processors + +A search phase results processor runs between search phases at the coordinating node level. It intercepts the results retrieved from one search phase and transforms them before passing them to the next search phase. + +The following table lists all supported search request processors. + +Processor | Description | Earliest available version +:--- | :--- | :--- +[`normalization_processor`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/script-processor/) | Intercepts the query phase results and normalizes and combines the document scores before passing the documents to the fetch phase. | 2.10 + ## Viewing available processor types You can use the Nodes Search Pipelines API to view the available processor types: diff --git a/images/neural-search-ingestion.png b/images/neural-search-ingestion.png new file mode 100644 index 0000000000000000000000000000000000000000..9e69d80f548e8cbecbc7162bd36b6df977897acc GIT binary patch literal 56324 zcmeFYWmKHY(gunL4esvl?hrJ%yF0<%-C>YGun>Z~y9IZ5x8UyXcaptx&i>B6fA0Uw 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