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A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to augment the FM's responses. The AI assistant must identify semantic relationships between legal concepts, specific legal terminology, and citations. The AI assistant must perform quickly and return precise results.
Which solution will meet these requirements?
A
Configure an Amazon Bedrock knowledge base to use a default vector search configuration. Use Amazon Bedrock to expand queries to improve retrieval for legal documents based on specific terminology and citations.
B
Use Amazon OpenSearch Service to deploy a hybrid search architecture that combines vector search with keyword search. Apply an Amazon Bedrock reranker model to optimize result relevance.
C
Enable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to perform post-processing of search results to identify semantic similarity in the documents and to produce precise results.
D
Use Amazon OpenSearch Service with vector search and Amazon Bedrock Titan Embeddings to index and search legal documents. Use custom AWS Lambda functions to merge results with keyword-based filters that are stored in an Amazon RDS database.