A company provides a service that helps users from around the world discover new restaurants. The
service has 50 million monthly active users. The company wants to implement a semantic search
solution across a database that contains 20 million restaurants and 200 million reviews. The company
currently stores the data in PostgreSQL.
The solution must support complex natural language queries and return results for at least 95% of
queries within 500 ms. The solution must maintain data freshness for restaurant details that update
hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?
A.
Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, features, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.
B.
Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When users submit natural language queries, convert the queries to embeddings by using the same FM. Perform k-nearest neighbors (k-NN) searches to find semantically similar results.
C.
Keep the restaurant data in PostgreSQL and implement a pgvector extension. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant data. Store the vector embeddings directly in PostgreSQL. Create an AWS Lambda function to convert natural language queries to vector representations by using the same FM. Configure the Lambda function to perform similarity searches within the database.
D.
Migrate restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipeline.
Configure the knowledge base to automatically generate embeddings from restaurant information. Use
the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base
directly by using natural language input.
Correct Answer: B
Explanation:
Option B best satisfies the requirements while minimizing development effort by combining managed
semantic search capabilities with fully managed foundation models. AWS Generative AI guidance
describes semantic search as a vector-based retrieval pattern where both documents and user queries
are embedded into a shared vector space. Similarity search (such as k-nearest neighbors) then retrieves
results based on meaning rather than exact keywords.
Amazon OpenSearch Service natively supports vector indexing and k-NN search at scale. This makes it
well suited for large datasets such as 20 million restaurants and 200 million reviews while still achieving
sub-second latency for the majority of queries. Because OpenSearch is a distributed, managed service,
it automatically scales during peak traffic periods and provides cost-effective performance compared with
building and tuning custom vector search pipelines on relational databases.
Using Amazon Bedrock to generate embeddings significantly reduces development complexity. AWS
manages the foundation models, eliminates the need for custom model hosting, and ensures
consistency by using the same FM for both document embeddings and query embeddings. This aligns
directly with AWS-recommended semantic search architectures and removes the need for model
lifecycle management.
Hourly updates to restaurant data can be handled efficiently through incremental re-indexing in
OpenSearch without disrupting query performance. This approach cleanly separates transactional data storage from search workloads, which is a best practice in AWS architectures.
Option A does not meet the semantic search requirement because keyword-based search cannot reliably
interpret complex natural language intent.
Option C introduces scalability and performance risks by running large-scale vector similarity searches
inside PostgreSQL, which increases operational complexity.
Option D adds unnecessary ingestion and abstraction layers intended for retrieval-augmented
generation, not high-throughput semantic search.
Therefore, Option B provides the optimal balance of performance, scalability, data freshness, and
minimal development effort using AWS Generative AI services