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feat(deps): Update stack definition member versions (#59)
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terraform-ibm-modules-ops authored Sep 27, 2024
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84 changes: 74 additions & 10 deletions ibm_catalog.json
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"scc"
],
"short_description": "Automate RAG deployment with supporting IBM Cloud and watsonx services, embed your enterprise data in generative AI solutions.",
"long_description": "Utilize data from your enterprise to achieve productivity gains in activities related to question/answer conversations, content search, summarization and generation. RAG can be deployed in multiple configurations and is applicable to various industry use cases and solutions.\n\nThis deployable architecture provides a comprehensive foundation for trust, observability, security, and regulatory compliance by configuring and deploying various services and a sample application for a [RAG pattern](https://cloud.ibm.com/docs/pattern-genai-rag?topic=pattern-genai-rag-genai-pattern), including:\n- Configuring IBM Cloud Account with best practices from [IBM Cloud Framework for Financial Services](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-about)\n- Deploying key and secrets management services for storage and management of encryption keys and secrets\n- Deploying controls for continuous compliance\n- Deploying observability services for application and platform logging and monitoring\n- Deploying a suite of watsonx services to provide generative AI RAG capabilities\n- Deploying content databases for storing vector embeddings of the documents and content search/retrieval\n- Deploying a sample application in a variety of run times including CI/CD/CC pipelines for secure application lifecycle management\n\nThe above configured and deployed services enable a secure and trustworthy deployment of generative AI applications on IBM Cloud.\n\nThe configurations are flexible and be changed to meet the needs for several types of RAG patterns depending on the chosen combination of technologies and services.\n\nThe generative AI RAG pattern services include:\n- [watsonx.ai](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html?context=wx)\n- [watsonx.data](https://cloud.ibm.com/docs/watsonxdata) (with Milvus)\n- [watsonx.governance](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/aiopenscale.html?context=wx)\n- [watsonx Assistant](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-welcome-new-assistant)\n- [watsonx Orchestrate](https://www.ibm.com/docs/en/watsonx/watson-orchestrate/current)\n- [Watson Discovery](https://cloud.ibm.com/docs/discovery-data)\n- [Elasticsearch](https://cloud.ibm.com/docs/databases-for-elasticsearch) Enterprise and Platinum edition\nThe supporting services include:\n- [Secrets Manager](https://cloud.ibm.com/docs/secrets-manager)\n- [Key Protect](https://cloud.ibm.com/docs/key-protect)\n- [Security and Compliance Center](https://cloud.ibm.com/docs/security-compliance)\n- [Event Notifications](https://cloud.ibm.com/docs/event-notifications?topic=event-notifications-getting-started)\n- [Logs](https://cloud.ibm.com/docs/cloud-logs)\n- [Monitoring](https://cloud.ibm.com/docs/monitoring?topic=monitoring-getting-started)\n- [Object Storage](https://cloud.ibm.com/docs/cloud-object-storage?topic=cloud-object-storage-getting-started-cloud-object-storage)\n- [Continuous Delivery](https://cloud.ibm.com/docs/ContinuousDelivery) toolchains\n- [Container Registry](https://cloud.ibm.com/docs/Registry)\n\nA [sample RAG application](https://github.com/IBM/gen-ai-rag-watsonx-sample-application) is deployed to [Code Engine](https://cloud.ibm.com/docs/codeengine) or [Red Hat OpenShift](https://cloud.ibm.com/docs/openshift) cluster.\n\nBy leveraging this architecture, you can accelerate your deployment and tailor it to meet your unique business needs and enterprise goals.",
"long_description": "Utilize data from your enterprise to achieve productivity gains in activities related to question/answer conversations, content search, summarization and generation. RAG can be deployed in multiple configurations and is applicable to various industry use cases and solutions.\n\nThis deployable architecture provides a comprehensive foundation for trust, observability, security, and regulatory compliance by configuring and deploying various services and a sample application for a [RAG pattern](https://cloud.ibm.com/docs/pattern-genai-rag?topic=pattern-genai-rag-genai-pattern), including:\n- Configuring IBM Cloud Account with best practices from [IBM Cloud Framework for Financial Services](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-about)\n- Deploying key and secrets management services for storage and management of encryption keys and secrets\n- Deploying controls for continuous compliance\n- Deploying observability services for application and platform logging and monitoring\n- Deploying a suite of watsonx services to provide generative AI RAG capabilities\n- Deploying content databases for storing vector embeddings of the documents and content search/retrieval\n- Deploying a sample application in a variety of run times including CI/CD/CC pipelines for secure application lifecycle management\n\nThe above configured and deployed services enable a secure and trustworthy deployment of generative AI applications on IBM Cloud.\n\nThe configurations are flexible and be changed to meet the needs for several types of RAG patterns depending on the chosen combination of technologies and services.\n\nThe generative AI RAG pattern services include:\n- [watsonx.ai](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/welcome-main.html?context=wx)\n- [watsonx.data](https://cloud.ibm.com/docs/watsonxdata) (with Milvus)\n- [watsonx.governance](https://dataplatform.cloud.ibm.com/docs/content/svc-welcome/aiopenscale.html?context=wx)\n- [watsonx Assistant](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-welcome-new-assistant)\n- [watsonx Orchestrate](https://www.ibm.com/docs/en/watsonx/watson-orchestrate/current)\n- [Watson Discovery](https://cloud.ibm.com/docs/discovery-data)\n- [Elasticsearch](https://cloud.ibm.com/docs/databases-for-elasticsearch) Enterprise and Platinum edition\n\nThe supporting services include:\n- [Secrets Manager](https://cloud.ibm.com/docs/secrets-manager)\n- [Key Protect](https://cloud.ibm.com/docs/key-protect)\n- [Security and Compliance Center](https://cloud.ibm.com/docs/security-compliance)\n- [Event Notifications](https://cloud.ibm.com/docs/event-notifications?topic=event-notifications-getting-started)\n- [Logs](https://cloud.ibm.com/docs/cloud-logs)\n- [Monitoring](https://cloud.ibm.com/docs/monitoring?topic=monitoring-getting-started)\n- [Object Storage](https://cloud.ibm.com/docs/cloud-object-storage?topic=cloud-object-storage-getting-started-cloud-object-storage)\n- [Continuous Delivery](https://cloud.ibm.com/docs/ContinuousDelivery) toolchains\n- [Container Registry](https://cloud.ibm.com/docs/Registry)\n\nA [sample RAG application](https://github.com/IBM/gen-ai-rag-watsonx-sample-application) is deployed to [Code Engine](https://cloud.ibm.com/docs/codeengine) or [Red Hat OpenShift](https://cloud.ibm.com/docs/openshift) cluster.\n\nBy leveraging this architecture, you can accelerate your deployment and tailor it to meet your unique business needs and enterprise goals.",
"offering_docs_url": "https://github.com/terraform-ibm-modules/stack-retrieval-augmented-generation/blob/main/README.md",
"offering_icon_url": "https://globalcatalog.cloud.ibm.com/api/v1/1082e7d2-5e2f-0a11-a3bc-f88a8e1931fc/artifacts/solution.svg",
"provider_name": "IBM",
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"profiles": [
{
"profile_name": "AI Security Guardrails 2.0",
"profile_version": "1.0.0"
"profile_version": "1.1.0"
}
]
},
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"architecture": {
"features": [
{
"title": "Enables:",
"description": "1.Code Engine for containerized and serverless workloads\n2.Elasticsearch Enterprise for building and storing dense vector indexes or keyword search indexes\n3.watsonx.ai in-memory vector store for RAG trial and exploration\n4.watsonx.ai UI to upload documents\n5.watsonx.ai Prompt Lab for inferencing and Prompt Templates\n6.watsonx Assistant Conversational Search with embedded LLM\n7.Build your own data processing, ingestion pipeline and indexes"
"title": " ",
"description": "Enables:"
},
{
"title": "1. Code Engine for containerized and serverless workloads",
"description": " "
},
{
"title": "2. Elasticsearch Enterprise for building and storing dense vector indexes or keyword search indexes",
"description": " "
},
{
"title": "3. watsonx.ai in-memory vector store for RAG trial and exploration",
"description": " "
},
{
"title": "4. watsonx.ai UI to upload documents",
"description": " "
},
{
"title": "5. watsonx.ai Prompt Lab for inferencing and Prompt Templates",
"description": " "
},
{
"title": "6. watsonx Assistant Conversational Search with embedded LLM",
"description": " "
},
{
"title": "7. Build your own data processing, ingestion pipeline and indexes",
"description": " "
}
],
"diagrams": [
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"required": false
},
{
"key": "enable_platform_logs_metrics",
"key": "enable_platform_metrics",
"type": "boolean",
"default_value": false,
"description": "Whether to provision logging and monitoring instances are configured to receive all platform logs and metrics in the target region. There can only be one instance per region provisioned for platform logs/metrics.",
"description": "Whether the monitoring instance is configured to receive all platform metrics in the target region. There can only be one instance per region provisioned for platform metrics.",
"required": false
},
{
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"architecture": {
"features": [
{
"title": "Enables:",
"description": "1.Red Hat OpenShift cluster for microservices workloads\n2.Elasticsearch Platinum for building and storing sparse vectors, dense vector indexes or keyword search indexes\n - watsonx.ai use of Elasticsearch ELSER2 vector index for RAG\n - watsonx Assistant Conversational Search with UI feature for uploading documents to create or use Elasticsearch ELSER2 vector index for RAG\n3.watsonx.ai in-memory vector store for RAG trial and exploration\n4.watsonx.ai UI to upload documents\n5.watsonx.ai Prompt Lab for inferencing and Prompt Templates\n6.watsonx Assistant Conversational Search with embedded LLM\n7.Build your own data processing, ingestion pipeline and indexes"
"title": " ",
"description": "Enables:"
},
{
"title": "1. Red Hat OpenShift cluster for microservices workloads",
"description": " "
},
{
"title": "2. Elasticsearch Platinum for building and storing sparse vectors, dense vector indexes or keyword search indexes",
"description": " "
},
{
"title": "- watsonx.ai use of Elasticsearch ELSER2 vector index for RAG",
"description": " "
},
{
"title": "- watsonx Assistant Conversational Search with UI feature for uploading documents to create or use Elasticsearch ELSER2 vector index for RAG",
"description": " "
},
{
"title": "3. watsonx.ai in-memory vector store for RAG trial and exploration",
"description": " "
},
{
"title": "4. watsonx.ai UI to upload documents",
"description": " "
},
{
"title": "5. watsonx.ai Prompt Lab for inferencing and Prompt Templates",
"description": " "
},
{
"title": "6. watsonx Assistant Conversational Search with embedded LLM",
"description": " "
},
{
"title": "7. Build your own data processing, ingestion pipeline and indexes",
"description": " "
}
],
"diagrams": [
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"required": false
},
{
"key": "enable_platform_logs_metrics",
"key": "enable_platform_metrics",
"type": "boolean",
"default_value": false,
"description": "Whether to provision logging and monitoring instances are configured to receive all platform logs and metrics in the target region. There can only be one instance per region provisioned for platform logs/metrics.",
"description": "Whether the monitoring instance is configured to receive all platform metrics in the target region. There can only be one instance per region provisioned for platform metrics.",
"required": false
},
{
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