AWS prescriptive-guidance documentation change
Summary
Fixed broken Amazon OpenSearch Service URL by removing duplicate URL fragment.
Security assessment
Change corrects a documentation link error. No security-related content was added or modified.
Diff
diff --git a/prescriptive-guidance/latest/rag-healthcare-use-cases/use-cases-talent-mgmt.md b/prescriptive-guidance/latest/rag-healthcare-use-cases/use-cases-talent-mgmt.md index f7f70f492..b5c1d5b35 100644 --- a//prescriptive-guidance/latest/rag-healthcare-use-cases/use-cases-talent-mgmt.md +++ b//prescriptive-guidance/latest/rag-healthcare-use-cases/use-cases-talent-mgmt.md @@ -80 +80 @@ The following image shows the steps to build a knowledge graph from source data. -In this step, you accurately compute the proximity between the current state of a healthcare professional and potential future state roles. To do this, you perform a skill affinity analysis by comparing the individual's skills sets with the job role. In an [Amazon OpenSearch Service](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/what-is.htmlhttps:/docs.aws.amazon.com/opensearch-service/latest/developerguide/what-is.html) vector database, you store skill taxonomy information and skill metadata, such as the skill description, skill type, and skill clusters. Use an Amazon Bedrock embedding model, such as [Amazon Titan Text Embeddings models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html), to embed the identified key skill into vectors. Through a vector search, you retrieve the descriptions of current state skills and target state skills and perform an ontology analysis. The analysis provides proximity scores between the current and target state skill pairs. For each pair, you use the computed ontology scores to identify the gaps in skill affinities. Then, you recommend the optimal path for upskilling, which the candidate can consider during role transitions. +In this step, you accurately compute the proximity between the current state of a healthcare professional and potential future state roles. To do this, you perform a skill affinity analysis by comparing the individual's skills sets with the job role. In an [Amazon OpenSearch Service](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/what-is.html) vector database, you store skill taxonomy information and skill metadata, such as the skill description, skill type, and skill clusters. Use an Amazon Bedrock embedding model, such as [Amazon Titan Text Embeddings models](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html), to embed the identified key skill into vectors. Through a vector search, you retrieve the descriptions of current state skills and target state skills and perform an ontology analysis. The analysis provides proximity scores between the current and target state skill pairs. For each pair, you use the computed ontology scores to identify the gaps in skill affinities. Then, you recommend the optimal path for upskilling, which the candidate can consider during role transitions.