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Remove experimental feature labels and flags for OS Assistant (opensearch-project#6745)
* Remove experimental feature labels and flags from OS Assistant-related pages Signed-off-by: Fanit Kolchina <[email protected]> * Change generally available to introduced Signed-off-by: Fanit Kolchina <[email protected]> * Remove ML settings for OS Assistant Signed-off-by: Fanit Kolchina <[email protected]> * Query assistant is enabled by default Signed-off-by: Fanit Kolchina <[email protected]> * Revised per SME comments Signed-off-by: Fanit Kolchina <[email protected]> --------- Signed-off-by: Fanit Kolchina <[email protected]>
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_automating-configurations/index.md

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# Automating configurations
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**Introduced 2.12**
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**Introduced 2.13**
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{: .label .label-purple }
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You can automate complex OpenSearch setup and preprocessing tasks by providing templates for common use cases. For example, automating machine learning (ML) setup tasks streamlines the use of OpenSearch ML offerings.

_dashboards/dashboards-assistant/index.md

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has_toc: false
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This is an experimental feature and is not recommended for use in a production environment. For updates on the feature's progress or to leave feedback, go to the [`dashboards-assistant` repository](https://github.com/opensearch-project/dashboards-assistant) on GitHub or the associated [OpenSearch forum thread](https://forum.opensearch.org/t/feedback-opensearch-assistant/16741).
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{: .warning}
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Note that machine learning models are probabilistic and that some may perform better than others, so the OpenSearch Assistant may occasionally produce inaccurate information. We recommend evaluating outputs for accuracy as appropriate to your use case, including reviewing the output or combining it with other verification factors.
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# OpenSearch Assistant for OpenSearch Dashboards
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Introduced 2.12
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**Introduced 2.13**
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The OpenSearch Assistant toolkit helps you create AI-powered assistants for OpenSearch Dashboards without requiring you to have specialized query tools or skills.
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<img width="700" src="{{site.url}}{{site.baseurl}}/images/dashboards/opensearch-assistant-full-frame.png" alt="OpenSearch Assistant interface">
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For more information about ways to enable experimental features, see [Experimental feature flags]({{site.url}}{{site.baseurl}}/install-and-configure/configuring-opensearch/experimental/).
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{: .note}
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## Configuring OpenSearch Assistant
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You can use the OpenSearch Dashboards interface to configure OpenSearch Assistant. Go to the [Getting started guide](https://github.com/opensearch-project/dashboards-assistant/blob/main/GETTING_STARTED_GUIDE.md) for step-by-step instructions. For the chatbot template, go to the [Flow Framework plugin](https://github.com/opensearch-project/flow-framework) documentation. You can modify this template to use your own model and customize the chatbot tools.

_ml-commons-plugin/agents-tools/agents-tools-tutorial.md

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# Agents and tools tutorial
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The following tutorial illustrates creating a flow agent for retrieval-augmented generation (RAG). A flow agent runs its configured tools sequentially, in the order specified. In this example, you'll create an agent with two tools:
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1. `VectorDBTool`: The agent will use this tool to retrieve OpenSearch documents relevant to the user question. You'll ingest supplementary information into an OpenSearch index. To facilitate vector search, you'll deploy a text embedding model that translates text into vector embeddings. OpenSearch will translate the ingested documents into embeddings and store them in the index. When you provide a user question to the agent, the agent will construct a query from the question, run vector search on the OpenSearch index, and pass the relevant retrieved documents to the `MLModelTool`.

_ml-commons-plugin/agents-tools/index.md

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# Agents and tools
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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You can automate machine learning (ML) tasks using agents and tools. An _agent_ orchestrates and runs ML models and tools. A _tool_ performs a set of specific tasks. Some examples of tools are the `VectorDBTool`, which supports vector search, and the `CATIndexTool`, which executes the `cat indices` operation. For a list of supported tools, see [Tools]({{site.url}}{{site.baseurl}}/ml-commons-plugin/agents-tools/tools/index/).
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## Agents
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It is important to provide thorough descriptions of the tools so that the LLM can decide in which situations to use those tools.
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## Enabling the feature
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To enable agents and tools, configure the following setting:
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```yaml
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plugins.ml_commons.agent_framework_enabled: true
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```
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{% include copy.html %}
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For conversational agents, you also need to enable RAG for use in conversational search. To enable RAG, configure the following setting:
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```yaml
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plugins.ml_commons.rag_pipeline_feature_enabled: true
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```
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{% include copy.html %}
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For more information about ways to enable experimental features, see [Experimental feature flags]({{site.url}}{{site.baseurl}}/install-and-configure/configuring-opensearch/experimental/).
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## Next steps
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- For a list of supported tools, see [Tools]({{site.url}}{{site.baseurl}}/ml-commons-plugin/agents-tools/tools/index/).

_ml-commons-plugin/agents-tools/tools/agent-tool.md

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# Agent tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `AgentTool` runs any agent.
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## Step 1: Set up an agent for AgentTool to run

_ml-commons-plugin/agents-tools/tools/cat-index-tool.md

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# CAT Index tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `CatIndexTool` retrieves index information for the OpenSearch cluster, similarly to the [CAT Indices API]({{site.url}}{{site.baseurl}}/api-reference/cat/cat-indices/).
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## Step 1: Register a flow agent that will run the CatIndexTool

_ml-commons-plugin/agents-tools/tools/index-mapping-tool.md

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# Index Mapping tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `IndexMappingTool` retrieves mapping and setting information for indexes in your cluster.
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## Step 1: Register a flow agent that will run the IndexMappingTool

_ml-commons-plugin/agents-tools/tools/index.md

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# Tools
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A _tool_ performs a set of specific tasks. The following table lists all tools that OpenSearch supports.

_ml-commons-plugin/agents-tools/tools/ml-model-tool.md

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# ML Model tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `MLModelTool` runs a machine learning (ML) model and returns inference results.
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## Step 1: Create a connector for a model

_ml-commons-plugin/agents-tools/tools/neural-sparse-tool.md

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# Neural Sparse Search tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `NeuralSparseSearchTool` performs sparse vector retrieval. For more information about neural sparse search, see [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
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## Step 1: Register and deploy a sparse encoding model

_ml-commons-plugin/agents-tools/tools/ppl-tool.md

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# PPL tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `PPLTool` translates natural language into a PPL query. The tool provides an `execute` flag to specify whether to run the query. If you set the flag to `true`, the `PPLTool` runs the query and returns the query and the results.
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## Prerequisite

_ml-commons-plugin/agents-tools/tools/rag-tool.md

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# RAG tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `RAGTool` performs retrieval-augmented generation (RAG). For more information about RAG, see [Conversational search]({{site.url}}{{site.baseurl}}/search-plugins/conversational-search/).
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RAG calls a large language model (LLM) and supplements its knowledge by providing relevant OpenSearch documents along with the user question. To retrieve relevant documents from an OpenSearch index, you'll need a text embedding model that facilitates vector search.

_ml-commons-plugin/agents-tools/tools/search-alerts-tool.md

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# Search Alerts tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `SearchAlertsTool` retrieves information about generated alerts. For more information about alerts, see [Alerting]({{site.url}}{{site.baseurl}}/observing-your-data/alerting/index/).
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## Step 1: Register a flow agent that will run the SearchAlertsTool

_ml-commons-plugin/agents-tools/tools/search-anomaly-detectors.md

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# Search Anomaly Detectors tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `SearchAnomalyDetectorsTool` retrieves information about anomaly detectors set up on your cluster. For more information about anomaly detectors, see [Anomaly detection]({{site.url}}{{site.baseurl}}/observing-your-data/ad/index/).
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## Step 1: Register a flow agent that will run the SearchAnomalyDetectorsTool

_ml-commons-plugin/agents-tools/tools/search-anomaly-results.md

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# Search Anomaly Results tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `SearchAnomalyResultsTool` retrieves information about anomaly detector results. For more information about anomaly detectors, see [Anomaly detection]({{site.url}}{{site.baseurl}}/observing-your-data/ad/index/).
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## Step 1: Register a flow agent that will run the SearchAnomalyResultsTool

_ml-commons-plugin/agents-tools/tools/search-index-tool.md

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# Search Index tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `SearchIndexTool` searches an index using a query written in query domain-specific language (DSL) and returns the query results.
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## Step 1: Register a flow agent that will run the SearchIndexTool

_ml-commons-plugin/agents-tools/tools/search-monitors-tool.md

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# Search Monitors tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `SearchMonitorsTool` retrieves information about alerting monitors set up on your cluster. For more information about alerting monitors, see [Monitors]({{site.url}}{{site.baseurl}}/observing-your-data/alerting/monitors/).
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## Step 1: Register a flow agent that will run the SearchMonitorsTool

_ml-commons-plugin/agents-tools/tools/vector-db-tool.md

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# Vector DB tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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The `VectorDBTool` performs dense vector retrieval. For more information about OpenSearch vector database capabilities, see [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/).
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## Step 1: Register and deploy a sparse encoding model

_ml-commons-plugin/agents-tools/tools/visualization-tool.md

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# Visualization tool
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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## Step 1: Register a flow agent that will run the VisualizationTool

_ml-commons-plugin/api/agent-apis/delete-agent.md

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# Delete an agent
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## Path and HTTP methods

_ml-commons-plugin/api/agent-apis/execute-agent.md

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# Execute an agent
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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### Path and HTTP methods

_ml-commons-plugin/api/agent-apis/get-agent.md

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# Get an agent
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## Path and HTTP methods

_ml-commons-plugin/api/agent-apis/index.md

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# Agent APIs
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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You can automate machine learning (ML) tasks using agents and tools. An _agent_ orchestrates and runs ML models and tools. For more information, see [Agents and tools]({{site.url}}{{site.baseurl}}/ml-commons-plugin/agents-tools/index/).
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ML Commons supports the following agent-level APIs:

_ml-commons-plugin/api/agent-apis/register-agent.md

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# Register an agent
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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Use this API to register an agent.
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Agents may be of the following types:

_ml-commons-plugin/api/agent-apis/search-agent.md

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# Search for an agent
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This is an experimental feature and is not recommended for use in a production environment. For updates on the progress of the feature or if you want to leave feedback, see the associated [GitHub issue](https://github.com/opensearch-project/ml-commons/issues/1161).
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Use this command to search for agents you've already created. You can provide any OpenSearch search query in the request body.
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_ml-commons-plugin/custom-local-models.md

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# Custom local models
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To use a custom model locally, you can upload it to the OpenSearch cluster.

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