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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules (such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data) to it and use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer on IoT Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer Edge module you are using.\n\n  | SDK          | Video Analyzer Edge Module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\n## Key concepts\n\n### Pipeline Topology vs Pipeline Instance\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define parameters, sources, and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"description for jsTestTopology\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"description for jsTestLivePipeline\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a pipeline method request\n\nTo create a pipeline method request you will need to get your deviceId and moduleId from your Azure IoT hub.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules (such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data) to it and use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer on IoT Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer Edge module you are using.\n\n  | SDK          | Video Analyzer Edge Module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\n## Key concepts\n\n### Pipeline Topology vs Pipeline Instance\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define parameters, sources, and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"description for jsTestTopology\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"description for jsTestLivePipeline\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a pipeline method request\n\nTo create a pipeline method request you will need to get your deviceId and moduleId from your Azure IoT hub.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: https://github.com/Azure/azure-sdk-for-js/issues\n","browser":{},"engines":{"node":">=8.0.0"},"scripts":{"docs":"typedoc --excludePrivate --excludeNotExported --excludeExternals --stripInternal --mode file --out ./dist/docs ./src","lint":"eslint package.json api-extractor.json src test --ext .ts","pack":"npm pack 2>&1","test":"npm run build:test && npm run unit-test && npm run integration-test","audit":"node ../../../common/scripts/rush-audit.js && rimraf node_modules package-lock.json && npm i --package-lock-only 2>&1 && npm audit","build":"tsc -p . && rollup -c 2>&1 && api-extractor run --local","clean":"rimraf dist dist-* test-dist temp types *.tgz *.log","format":"prettier --write --config ../../../.prettierrc.json --ignore-path ../../../.prettierignore \"src/**/*.ts\" \"test/**/*.ts\" \"*.{js,json}\"","lint:fix":"eslint package.json api-extractor.json src test --ext .ts --fix --fix-type [problem,suggestion]","prebuild":"npm run clean","test:node":"npm run build:test && npm run 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: https://github.com/Azure/azure-sdk-for-js/issues\n","browser":{},"engines":{"node":">=8.0.0"},"scripts":{"docs":"typedoc --excludePrivate --excludeNotExported --excludeExternals --stripInternal --mode file --out ./dist/docs ./src","lint":"eslint package.json api-extractor.json src test --ext .ts","pack":"npm pack 2>&1","test":"npm run build:test && npm run unit-test && npm run integration-test","audit":"node ../../../common/scripts/rush-audit.js && rimraf node_modules package-lock.json && npm i --package-lock-only 2>&1 && npm audit","build":"tsc -p . && rollup -c 2>&1 && api-extractor run --local","clean":"rimraf dist dist-* test-dist temp types *.tgz *.log","format":"prettier --write --config ../../../.prettierrc.json --ignore-path ../../../.prettierignore \"src/**/*.ts\" \"test/**/*.ts\" \"*.{js,json}\"","lint:fix":"eslint package.json api-extractor.json src test --ext .ts --fix --fix-type [problem,suggestion]","prebuild":"npm run clean","test:node":"npm run build:test && npm run 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\n[Product documentation][doc_product] | [Direct methods][doc_direct_methods] | [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods] \n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: https://github.com/Azure/azure-sdk-for-js/issues\n","browser":{},"engines":{"node":">=12.0.0"},"scripts":{"docs":"typedoc --excludePrivate --excludeNotExported --excludeExternals --stripInternal --mode file --out ./dist/docs ./src","lint":"eslint package.json api-extractor.json src test --ext .ts","pack":"npm pack 2>&1","test":"npm run build:test && npm run unit-test && npm run integration-test","audit":"node ../../../common/scripts/rush-audit.js && rimraf node_modules package-lock.json && npm i --package-lock-only 2>&1 && npm audit","build":"tsc -p . && rollup -c 2>&1 && api-extractor run --local","clean":"rimraf dist dist-* test-dist temp types *.tgz *.log","format":"prettier --write --config ../../../.prettierrc.json --ignore-path ../../../.prettierignore \"src/**/*.ts\" \"test/**/*.ts\" \"*.{js,json}\"","lint:fix":"eslint package.json api-extractor.json src test --ext .ts --fix --fix-type [problem,suggestion]","prebuild":"npm run clean","test:node":"npm run build:test && npm run 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods] \n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods] \n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods] \n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: https://github.com/Azure/azure-sdk-for-js/issues\n","browser":{},"engines":{"node":">=12.0.0"},"scripts":{"docs":"typedoc --excludePrivate --excludeNotExported --excludeExternals --stripInternal --mode file --out ./dist/docs ./src","lint":"eslint package.json api-extractor.json src test --ext .ts","pack":"npm pack 2>&1","test":"npm run build:test && npm run unit-test && npm run integration-test","audit":"node ../../../common/scripts/rush-audit.js && rimraf node_modules package-lock.json && npm i --package-lock-only 2>&1 && npm audit","build":"tsc -p . && rollup -c 2>&1 && api-extractor run --local","clean":"rimraf dist dist-* test-dist temp types *.tgz *.log","format":"prettier --write --config ../../../.prettierrc.json --ignore-path ../../../.prettierignore \"src/**/*.ts\" \"test/**/*.ts\" \"*.{js,json}\"","lint:fix":"eslint package.json api-extractor.json src test --ext .ts --fix --fix-type [problem,suggestion]","prebuild":"npm run clean","test:node":"npm run build:test && npm run 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The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods] \n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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Azure Video Analyzer Edge client library for JavaScript\n\nAzure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline.\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods] \n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.x | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n    name: \"rtspSource\",\n    endpoint: {\n      url: \"${rtspUrl}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n      credentials: {\n        username: \"${rtspUserName}\",\n        password: \"${rtspPassword}\",\n        \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n      }\n    } as UnsecuredEndpoint,\n    \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n  };\n\n  const nodeInput: NodeInput = {\n    nodeName: \"rtspSource\"\n  };\n\n  const msgSink: IotHubMessageSink = {\n    name: \"msgSink\",\n    inputs: [nodeInput],\n    hubOutputName: \"${hubSinkOutputName}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.IotHubMessageSink\"\n  };\n\n  const pipelineTopology: PipelineTopology = {\n    name: \"jsTestTopology\",\n    properties: {\n      description: \"Continuous video recording to a Video Analyzer video\",\n      parameters: [\n        { name: \"rtspUserName\", type: \"String\", default: \"dummyUsername\" },\n        { name: \"rtspPassword\", type: \"SecretString\", default: \"dummyPassword\" },\n        { name: \"rtspUrl\", type: \"String\" }\n        { name: \"hubSinkOutputName\", type: \"String\" }\n      ],\n      sources: [rtspSource],\n      sinks: [msgSink]\n    }\n  };\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: pipelineTopologyName,\n  properties: {\n    description: \"Continuous video recording to a Video Analyzer video\",\n    topologyName: \"jsTestTopology\",\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = \"lva-sample-device\";\nconst moduleId = \"mediaEdge\";\nconst connectionString = \"connectionString\";\nconst iotHubClient = Client.fromConnectionString(connectionString);\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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Azure Video Analyzer Edge client library for JavaScript\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods] \n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.3 | 1.1                        |\n  | 1.0.0-beta.2 | 1.0                        |\n  | 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n}\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" },\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n    methodName: pipelineTopologySetRequest.methodName,\n    payload: pipelineTopologySetRequest.payload\n  });\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: https://github.com/Azure/azure-sdk-for-js/issues\n","browser":{},"engines":{"node":">=12.0.0"},"scripts":{"docs":"typedoc --excludePrivate --excludeNotExported --excludeExternals --stripInternal --mode file --out ./dist/docs ./src","lint":"eslint package.json api-extractor.json src test --ext .ts","pack":"npm pack 2>&1","test":"npm run build:test && npm run unit-test && npm run integration-test","audit":"node ../../../common/scripts/rush-audit.js && rimraf node_modules package-lock.json && npm i --package-lock-only 2>&1 && npm audit","build":"npm run clean && tsc -p . && rollup -c 2>&1 && api-extractor run --local","clean":"rimraf dist dist-* test-dist temp types *.tgz *.log","format":"prettier --write --config ../../../.prettierrc.json --ignore-path ../../../.prettierignore \"src/**/*.ts\" \"test/**/*.ts\" \"samples-dev/**/*.ts\" \"*.{js,json}\"","lint:fix":"eslint package.json api-extractor.json src test --ext .ts --fix --fix-type [problem,suggestion]","test:node":"npm run build:test 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Azure Video Analyzer Edge client library for JavaScript\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n-  Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n-  Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods] \n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n-  TypeScript v3.6.\n-  You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n-  To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n-  You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n  | SDK          | Video Analyzer edge module |\n  | ------------ | -------------------------- |\n  | 1.0.0-beta.3 | 1.1                        |\n  | 1.0.0-beta.2 | 1.0                        |\n  | 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n}\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" },\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n    methodName: pipelineTopologySetRequest.methodName,\n    payload: pipelineTopologySetRequest.payload\n  });\n```\n\n## Troubleshooting\n\n-  When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n-  [Samples][samples]\n-  [Azure IoT Device SDK][iot-device-sdk]\n-  [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. 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Azure Video Analyzer Edge client library for JavaScript\n\nDeprecated. We’re retiring the Azure Video Analyzer preview service, you're advised to transition your applications off of Video Analyzer by 01 December 2022. This SDK is not longer maintained.\n\nAzure Video Analyzer is an [Azure Applied AI Service][applied-ai-service] that provides a platform for you to build intelligent video applications that can span both edge and cloud infrastructures. The platform offers the capability to capture, record, and analyze live video along with publishing the results, video and video analytics, to Azure services at the edge or in the cloud. It is designed to be an extensible platform, enabling you to connect different video inferencing edge modules such as Cognitive services modules, or custom inferencing modules that have been trained with your own data using either open-source machine learning or [Azure Machine Learning][machine-learning].\n\nUse the client library for Video Analyzer Edge to:\n\n- Simplify interactions with the [Microsoft Azure IoT SDKs](https://github.com/azure/azure-iot-sdks)\n- Programmatically construct pipeline topologies and live pipelines\n\nKey links:\n\n- [Product documentation][doc_product]\n- [Direct methods][doc_direct_methods]\n- [Source code][source]\n\n## Getting started\n\n### Install the package\n\nInstall the Video Analyzer client library for Typescript with npm:\n\n```bash\nnpm install @azure/video-analyzer-edge\n```\n\n### Prerequisites\n\n- TypeScript v3.6.\n- You need an active [Azure subscription][azure_sub], and a IoT device connection string to use this package.\n- To interact with Azure IoT Hub you will need to run `npm install azure-iothub`\n- You will need to use the version of the SDK that corresponds to the version of the Video Analyzer edge module you are using.\n\n| SDK          | Video Analyzer edge module |\n| ------------ | -------------------------- |\n| 1.0.0-beta.3 | 1.1                        |\n| 1.0.0-beta.2 | 1.0                        |\n| 1.0.0-beta.1 | 1.0                        |\n\n### Creating a pipeline topology and making requests\n\nPlease visit the [Examples](#examples) for starter code.\n\nWe guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.\n\n## Key concepts\n\n### Pipeline topology vs live pipeline\n\nA _pipeline topology_ is a blueprint or template for instantiating live pipelines. It defines the parameters of the pipeline using placeholders as values for them. A _live pipeline_ references a pipeline topology and specifies the parameters. This way you are able to have multiple live pipelines referencing the same topology but with different values for parameters. For more information please visit [pipeline topologies and live pipelines][doc_pipelines].\n\n## Examples\n\n### Creating a pipeline topology\n\nTo create a pipeline topology you need to define sources and sinks.\n\n```typescript\nconst rtspSource: RtspSource = {\n  //Create a source for your pipeline topology\n  name: \"rtspSource\",\n  endpoint: {\n    url: \"${rtspUrl}\",\n    \"@type\": \"#Microsoft.VideoAnalyzer.UnsecuredEndpoint\",\n    credentials: {\n      username: \"${rtspUserName}\",\n      password: \"${rtspPassword}\",\n      \"@type\": \"#Microsoft.VideoAnalyzer.UsernamePasswordCredentials\"\n    }\n  } as UnsecuredEndpoint,\n  \"@type\": \"#Microsoft.VideoAnalyzer.RtspSource\"\n};\n\nconst nodeInput: NodeInput = {\n  //Create an input for your sink\n  nodeName: \"rtspSource\"\n};\n\nconst videoSink: VideoSink = {\n  name: \"videoSink\",\n  inputs: [nodeInput],\n  videoName: \"video\",\n  localMediaCachePath: \"/var/lib/videoanalyzer/tmp/\",\n  localMediaCacheMaximumSizeMiB: \"1024\",\n  \"@type\": \"#Microsoft.VideoAnalyzer.VideoSink\"\n};\n\nconst pipelineTopology: PipelineTopology = {\n  name: \"jsTestTopology\",\n  properties: {\n    description: \"description for jsTestTopology\",\n    parameters: [\n      { name: \"rtspUserName\", type: \"String\", default: \"testUsername\" },\n      { name: \"rtspPassword\", type: \"SecretString\", default: \"testPassword\" },\n      { name: \"rtspUrl\", type: \"String\" }\n    ],\n    sources: [rtspSource],\n    sinks: [videoSink]\n  }\n};\n```\n\n### Creating a live pipeline\n\nTo create a live pipeline instance, you need to have an existing pipeline topology.\n\n```typescript\nconst livePipeline: LivePipeline = {\n  name: \"jsLivePipelineTest\",\n  properties: {\n    description: \"description\",\n    topologyName: pipelineTopologyName,\n    parameters: [{ name: \"rtspUrl\", value: \"rtsp://sample.com\" }]\n  }\n};\n```\n\n### Invoking a direct method\n\nTo invoke a direct method on your device you need to first define the request using the Video Analyzer Edge SDK, then send that method request using the IoT SDK's `CloudToDeviceMethod`.\n\n```typescript\nimport { createRequest } from \"@azure/video-analyzer-edge\";\nimport { Client } from \"azure-iothub\";\n\nconst deviceId = process.env.iothub_deviceid;\nconst moduleId = process.env.iothub_moduleid;\nconst connectionString = process.env.iothub_connectionstring;\nconst iotHubClient = Client.fromConnectionString(connectionString); //Connect to your IoT Hub\n\nconst pipelineTopologySetRequest = createRequest(\"pipelineTopologySet\", pipelineTopology);\nconst setPipelineTopResponse = await iotHubClient.invokeDeviceMethod(deviceId, moduleId, {\n  methodName: pipelineTopologySetRequest.methodName,\n  payload: pipelineTopologySetRequest.payload\n});\n```\n\n## Troubleshooting\n\n- When creating a method request remember to check the spelling of the name of the method\n\n## Next steps\n\n- [Samples][samples]\n- [Azure IoT Device SDK][iot-device-sdk]\n- [Azure IoTHub Service SDK][iot-hub-sdk]\n\n## Contributing\n\nThis project welcomes contributions and suggestions. Most contributions require\nyou to agree to a Contributor License Agreement (CLA) declaring that you have\nthe right to, and actually do, grant us the rights to use your contribution.\nFor details, visit https://cla.microsoft.com.\n\nIf you encounter any issues, please open an issue on our [Github][github-page-issues].\n\nWhen you submit a pull request, a CLA-bot will automatically determine whether\nyou need to provide a CLA and decorate the PR appropriately (e.g., label,\ncomment). Simply follow the instructions provided by the bot. You will only\nneed to do this once across all repos using our CLA.\n\nThis project has adopted the\n[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information,\nsee the Code of Conduct FAQ or contact opencode@microsoft.com with any\nadditional questions or comments.\n\n<!-- LINKS -->\n\n[azure_cli]: https://docs.microsoft.com/cli/azure\n[azure_sub]: https://azure.microsoft.com/free/\n[cla]: https://cla.microsoft.com\n[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/\n[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/\n[coc_contact]: mailto:opencode@microsoft.com\n[source]: https://aka.ms/ava/sdk/client/js/source\n[samples]: https://aka.ms/video-analyzer-sample\n[package]: https://aka.ms/ava/sdk/client/js\n[doc_direct_methods]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_product]: https://go.microsoft.com/fwlink/?linkid=2162396\n[doc_pipelines]: https://go.microsoft.com/fwlink/?linkid=2162396\n[iot-device-sdk]: https://www.npmjs.com/package/azure-iot-device\n[iot-hub-sdk]: https://github.com/Azure/azure-iot-sdk-node\n[github-page-issues]: 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