From 3dbcc346706263a1bb65ca934b67bf331f100b7a Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Wed, 8 Nov 2023 09:30:11 -0500 Subject: [PATCH] Add custom pre and post processor functions (#5300) * Add custom pre and post processor functions Signed-off-by: Fanit Kolchina * Uniform placeholder format Signed-off-by: Fanit Kolchina * Add custom pre and post processor blueprint Signed-off-by: Fanit Kolchina * Add a pretrained model Signed-off-by: Fanit Kolchina * Update _ml-commons-plugin/extensibility/blueprints.md Co-authored-by: Yaliang Wu Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented tech review comments Signed-off-by: Fanit Kolchina * Add two more models Signed-off-by: Fanit Kolchina * Specify json code highlighter 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: Yaliang Wu --- .../extensibility/blueprints.md | 57 +++++++++++++++---- .../extensibility/connectors.md | 51 +++++++++++++++-- _ml-commons-plugin/extensibility/index.md | 3 +- _ml-commons-plugin/pretrained-models.md | 37 ++++++------ 4 files changed, 113 insertions(+), 35 deletions(-) diff --git a/_ml-commons-plugin/extensibility/blueprints.md b/_ml-commons-plugin/extensibility/blueprints.md index 3d537452d5..0e093dd5a7 100644 --- a/_ml-commons-plugin/extensibility/blueprints.md +++ b/_ml-commons-plugin/extensibility/blueprints.md @@ -10,7 +10,7 @@ parent: Connecting to remote models All connectors consist of a JSON blueprint created by machine learning (ML) developers. The blueprint allows administrators and data scientists to make connections between OpenSearch and an AI service or model-serving technology. -The following example shows a blueprint that connects to Amazon SageMaker: +The following example shows a blueprint of an Amazon SageMaker connector: ```json POST /_plugins/_ml/connectors/_create @@ -20,12 +20,12 @@ POST /_plugins/_ml/connectors/_create "version": "", "protocol": "aws_sigv4", "credential": { - "access_key": "", - "secret_key": "", - "session_token": "" + "access_key": "", + "secret_key": "", + "session_token": "" }, "parameters": { - "region": "", + "region": "", "service_name": "sagemaker" }, "actions": [ @@ -35,8 +35,8 @@ POST /_plugins/_ml/connectors/_create "headers": { "content-type": "application/json" }, - "url": "", - "request_body": "" + "url": "", + "request_body": "" } ] } @@ -105,9 +105,9 @@ POST /_plugins/_ml/connectors/_create "version": 1, "protocol": "aws_sigv4", "credential": { - "access_key": "", - "secret_key": "", - "session_token": "" + "access_key": "", + "secret_key": "", + "session_token": "" }, "parameters": { "region": "ap-northeast-1", @@ -173,6 +173,43 @@ The remote text embedding model output must be a two-dimensional float array, ea ] ``` +## Custom pre- and post-processing functions + +You can write your own pre- and post-processing functions specifically for your model format. For example, the following Amazon Bedrock connector definition contains custom pre- and post-processing functions for the Amazon Bedrock Titan embedding model: + +```json +POST /_plugins/_ml/connectors/_create +{ + "name": "Amazon Bedrock Connector: embedding", + "description": "The connector to the Bedrock Titan embedding model", + "version": 1, + "protocol": "aws_sigv4", + "parameters": { + "region": "", + "service_name": "bedrock" + }, + "credential": { + "access_key": "", + "secret_key": "", + "session_token": "" + }, + "actions": [ + { + "action_type": "predict", + "method": "POST", + "url": "https://bedrock-runtime.us-east-1.amazonaws.com/model/amazon.titan-embed-text-v1/invoke", + "headers": { + "content-type": "application/json", + "x-amz-content-sha256": "required" + }, + "request_body": "{ \"inputText\": \"${parameters.inputText}\" }", + "pre_process_function": "\n StringBuilder builder = new StringBuilder();\n builder.append(\"\\\"\");\n String first = params.text_docs[0];\n builder.append(first);\n builder.append(\"\\\"\");\n def parameters = \"{\" +\"\\\"inputText\\\":\" + builder + \"}\";\n return \"{\" +\"\\\"parameters\\\":\" + parameters + \"}\";", + "post_process_function": "\n def name = \"sentence_embedding\";\n def dataType = \"FLOAT32\";\n if (params.embedding == null || params.embedding.length == 0) {\n return params.message;\n }\n def shape = [params.embedding.length];\n def json = \"{\" +\n \"\\\"name\\\":\\\"\" + name + \"\\\",\" +\n \"\\\"data_type\\\":\\\"\" + dataType + \"\\\",\" +\n \"\\\"shape\\\":\" + shape + \",\" +\n \"\\\"data\\\":\" + params.embedding +\n \"}\";\n return json;\n " + } + ] +} +``` +{% include copy-curl.html %} ## Next step diff --git a/_ml-commons-plugin/extensibility/connectors.md b/_ml-commons-plugin/extensibility/connectors.md index 640120c10a..a9af255692 100644 --- a/_ml-commons-plugin/extensibility/connectors.md +++ b/_ml-commons-plugin/extensibility/connectors.md @@ -192,17 +192,17 @@ The `parameters` section requires the following options when using `aws_sigv4` a ## Cohere connector -The following example request creates a standalone Cohere connection: +The following example request creates a standalone Cohere connector: ```json POST /_plugins/_ml/connectors/_create { - "name": "YOUR CONNECTOR NAME", - "description": "YOUR CONNECTOR DESCRIPTION", - "version": "YOUR CONNECTOR VERSION", + "name": "", + "description": "", + "version": "", "protocol": "http", "credential": { - "cohere_key": "ADD YOUR Cohere API KEY HERE" + "cohere_key": "" }, "parameters": { "model": "embed-english-v2.0", @@ -216,13 +216,52 @@ POST /_plugins/_ml/connectors/_create "headers": { "Authorization": "Bearer ${credential.cohere_key}" }, - "request_body": "{ \"texts\": ${parameters.texts}, \"truncate\": \"${parameters.truncate}\", \"model\": \"${parameters.model}\" }" + "request_body": "{ \"texts\": ${parameters.texts}, \"truncate\": \"${parameters.truncate}\", \"model\": \"${parameters.model}\" }", + "pre_process_function": "connector.pre_process.cohere.embedding", + "post_process_function": "connector.post_process.cohere.embedding" } ] } ``` {% include copy-curl.html %} +## Amazon Bedrock connector + +The following example request creates a standalone Amazon Bedrock connector: + +```json +POST /_plugins/_ml/connectors/_create +{ + "name": "Amazon Bedrock Connector: embedding", + "description": "The connector to the Bedrock Titan embedding model", + "version": 1, + "protocol": "aws_sigv4", + "parameters": { + "region": "", + "service_name": "bedrock" + }, + "credential": { + "access_key": "", + "secret_key": "", + "session_token": "" + }, + "actions": [ + { + "action_type": "predict", + "method": "POST", + "url": "https://bedrock-runtime.us-east-1.amazonaws.com/model/amazon.titan-embed-text-v1/invoke", + "headers": { + "content-type": "application/json", + "x-amz-content-sha256": "required" + }, + "request_body": "{ \"inputText\": \"${parameters.inputText}\" }", + "pre_process_function": "\n StringBuilder builder = new StringBuilder();\n builder.append(\"\\\"\");\n String first = params.text_docs[0];\n builder.append(first);\n builder.append(\"\\\"\");\n def parameters = \"{\" +\"\\\"inputText\\\":\" + builder + \"}\";\n return \"{\" +\"\\\"parameters\\\":\" + parameters + \"}\";", + "post_process_function": "\n def name = \"sentence_embedding\";\n def dataType = \"FLOAT32\";\n if (params.embedding == null || params.embedding.length == 0) {\n return params.message;\n }\n def shape = [params.embedding.length];\n def json = \"{\" +\n \"\\\"name\\\":\\\"\" + name + \"\\\",\" +\n \"\\\"data_type\\\":\\\"\" + dataType + \"\\\",\" +\n \"\\\"shape\\\":\" + shape + \",\" +\n \"\\\"data\\\":\" + params.embedding +\n \"}\";\n return json;\n " + } + ] +} +``` +{% include copy-curl.html %} ## Next steps diff --git a/_ml-commons-plugin/extensibility/index.md b/_ml-commons-plugin/extensibility/index.md index e52d827166..f2d44b8632 100644 --- a/_ml-commons-plugin/extensibility/index.md +++ b/_ml-commons-plugin/extensibility/index.md @@ -32,7 +32,8 @@ PUT /_cluster/settings "plugins.ml_commons.trusted_connector_endpoints_regex": [ "^https://runtime\\.sagemaker\\..*[a-z0-9-]\\.amazonaws\\.com/.*$", "^https://api\\.openai\\.com/.*$", - "^https://api\\.cohere\\.ai/.*$" + "^https://api\\.cohere\\.ai/.*$", + "^https://bedrock-runtime\\..*[a-z0-9-]\\.amazonaws\\.com/.*$" ] } } diff --git a/_ml-commons-plugin/pretrained-models.md b/_ml-commons-plugin/pretrained-models.md index ef29eae4c1..ef923e67bd 100644 --- a/_ml-commons-plugin/pretrained-models.md +++ b/_ml-commons-plugin/pretrained-models.md @@ -20,7 +20,7 @@ To use a pretrained model in your OpenSearch cluster: 1. Select the model you want to upload. For a list of pretrained models, see [supported pretrained models](#supported-pretrained-models). 2. Upload the model using the [upload API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-serving-framework#upload-model-to-opensearch). Because a pretrained model originates from the ML Commons model repository, you only need to provide the `name`, `version`, and `model_format` in the upload API request. -``` +```json POST /_plugins/_ml/models/_upload { "name": "huggingface/sentence-transformers/all-MiniLM-L12-v2", @@ -31,7 +31,7 @@ POST /_plugins/_ml/models/_upload Note that for sparse encoding models, you still need to upload the full request body, as shown in the following example: -``` +```json POST /_plugins/_ml/models/_upload { "name": "amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1", @@ -61,20 +61,22 @@ OpenSearch supports the following models, categorized by type. Text embedding mo Sentence transformer models map sentences and paragraphs across a dimensional dense vector space. The number of vectors depends on the type of model. You can use these models for use cases such as clustering or semantic search. -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 | -|---|---|---|---| -| `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) | - +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. + +| Model name | Version | Vector dimensions | Auto-truncation | TorchScript artifact | ONNX artifact | +|:---|:---|:---|:---|:---| +| `huggingface/sentence-transformers/all-distilroberta-v1` | 1.0.1 | 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` | 1.0.1 | 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` | 1.0.1 | 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` | 1.0.1 | 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` | 1.0.1 | 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` | 1.0.1 | 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` | 1.0.1 | 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` | 1.0.1 | 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` | 1.0.1 | 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) | +| `huggingface/sentence-transformers/paraphrase-mpnet-base-v2` | 1.0.0 | 768-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-mpnet-base-v2/1.0.0/torch_script/sentence-transformers_paraphrase-mpnet-base-v2-1.0.0-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-mpnet-base-v2/1.0.0/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-mpnet-base-v2/1.0.0/onnx/sentence-transformers_paraphrase-mpnet-base-v2-1.0.0-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-mpnet-base-v2/1.0.0/onnx/config.json) | +| `huggingface/sentence-transformers/distiluse-base-multilingual-cased-v1` | 1.0.1 | 512-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/distiluse-base-multilingual-cased-v1/1.0.1/torch_script/sentence-transformers_distiluse-base-multilingual-cased-v1-1.0.1-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/distiluse-base-multilingual-cased-v1/1.0.1/torch_script/config.json) | Not available | +| `huggingface/sentence-transformers/paraphrase-mpnet-base-v2` | 1.0.0 | 768-dimensional dense vector space. | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-mpnet-base-v2/1.0.0/torch_script/sentence-transformers_paraphrase-mpnet-base-v2-1.0.0-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-mpnet-base-v2/1.0.0/torch_script/config.json) | - [model_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-mpnet-base-v2/1.0.0/onnx/sentence-transformers_paraphrase-mpnet-base-v2-1.0.0-onnx.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/huggingface/sentence-transformers/paraphrase-mpnet-base-v2/1.0.0/onnx/config.json) | ### Sparse encoding models @@ -92,5 +94,4 @@ The following table provides a list of sparse encoding models and artifact links |---|---|---| | `amazon/neural-sparse/opensearch-neural-sparse-encoding-v1` | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.0/torch_script/opensearch-neural-sparse-encoding-v1-1.0.0-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.0/torch_script/config.json) | A neural sparse encoding model. The model transforms text into a sparse vector, identifies the indexes of non-zero elements in the vector, and then converts the vector into `` pairs, where each entry corresponds to a non-zero element index. | | `amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1` | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.0/torch_script/opensearch-neural-sparse-encoding-doc-v1-1.0.0-torch_script.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-doc-v1/1.0.0/torch_script/config.json) | A neural sparse encoding model. The model transforms text into a sparse vector, identifies the indexes of non-zero elements in the vector, and then converts the vector into `` pairs, where each entry corresponds to a non-zero element index. | -| `amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1` | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.0/torch_script/opensearch-neural-sparse-tokenizer-v1-1.0.0.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.0/torch_script/config.json) | A neural sparse tokenizer model. The model tokenizes text into tokens and assigns each token a predefined weight, which is the token's IDF (if the IDF file is not provided, the weight defaults to 1). For more information, see [Uploading your own model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/#uploading-your-own-model). | - +| `amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1` | Yes | - [model_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.0/torch_script/opensearch-neural-sparse-tokenizer-v1-1.0.0.zip)
- [config_url](https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-tokenizer-v1/1.0.0/torch_script/config.json) | A neural sparse tokenizer model. The model tokenizes text into tokens and assigns each token a predefined weight, which is the token's IDF (if the IDF file is not provided, the weight defaults to 1). For more information, see [Uploading your own model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/ml-framework/#uploading-your-own-model). | \ No newline at end of file