# Next steps and resources
<a name="next-steps"></a>

After reviewing this guide, consider the following actions to move from understanding to implementation:

1. Evaluate your current needs:
   + Assess your existing database infrastructure and expertise.
   + Document your specific vector search requirements.
   + Define your performance, scaling, and cost targets.

1. Choose one of the following options to test vector database options:
   + **Option 1:** Set up a proof of concept using your preferred vector database solution.
   + **Option 2: **Experiment with sample datasets in Amazon Bedrock Knowledge Bases. Try the quick-create experience for an Amazon Bedrock Knowledge Base. For an example, see [Quick create an Aurora PostgreSQL Knowledge Base for Amazon Bedrock](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL.quickcreatekb.html) in the Aurora documentation.

1. Review additional [resources](#resources).

1. Get expert help:
   + Contact your AWS account team or AWS Solutions Architects for implementation guidance.
   + [Engage with AWS Partners ](https://partners.amazonaws.com/)that specialize in vector databases.

1. Plan your production deployment:
   + Create a migration strategy if moving from existing databases.
   + Develop a scaling plan for your chosen solution.
   + Design your monitoring and maintenance procedures.

## Resources
<a name="resources"></a>

The following resources can help you in choosing a vector database.

### AWS blog posts
<a name="blog-posts"></a>
+ [Accelerate your generative AI application development with Amazon Bedrock Knowledge Bases Quick Create and Amazon Aurora Serverless](https://aws.amazon.com/blogs/database/accelerate-your-generative-ai-application-development-with-amazon-bedrock-knowledge-bases-quick-create-and-amazon-aurora-serverless/)
+ [Amazon OpenSearch Service's vector database capabilities explained](https://aws.amazon.com/blogs/big-data/amazon-opensearch-services-vector-database-capabilities-explained/)
+ [Dive deep into vector data stores using Amazon Bedrock Knowledge Bases](https://aws.amazon.com/blogs/machine-learning/dive-deep-into-vector-data-stores-using-amazon-bedrock-knowledge-bases/)
+ [Leverage pgvector and Amazon Aurora PostgreSQL for Natural Language Processing, Chatbots and Sentiment Analysis](https://aws.amazon.com/blogs/database/leverage-pgvector-and-amazon-aurora-postgresql-for-natural-language-processing-chatbots-and-sentiment-analysis/)

### AWS service documentation
<a name="service-docs"></a>
+ [Choosing an AWS database service](https://docs.aws.amazon.com/decision-guides/latest/databases-on-aws-how-to-choose/databases-on-aws-how-to-choose.html)
+ [How Amazon Bedrock knowledge bases work](https://docs.aws.amazon.com/bedrock/latest/userguide/kb-how-it-works.html)
+ [Neptune Analytics documentation](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html)
+ [Overview of Amazon Web Services: Databases](https://docs.aws.amazon.com/whitepapers/latest/aws-overview/database.html)
+ [Using Aurora PostgreSQL as a Knowledge Base for Amazon Bedrock](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL.VectorDB.html)
+ [Working with Amazon Aurora PostgreSQL](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.AuroraPostgreSQL.html)
+ [Amazon DocumentDB](https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html)
+ [Amazon MemoryDB](https://docs.aws.amazon.com/memorydb/latest/devguide/vector-search.html)
+ [Amazon S3 Vectors](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html)

### Other AWS resources
<a name="other-aws-resources"></a>
+ [Amazon Bedrock Knowledge Bases](https://aws.amazon.com/bedrock/knowledge-bases/)
+ [Vector Databases & Embeddings](https://aws.amazon.com/solutions/databases/vector-databases-and-embeddings/)
+ [Vector Databases for generative AI applications](https://community.aws/content/2f5dkpj96MDM6Y9lumYPjZAB8SX/vector-databases-for-generative-ai-applications)
+ [What are Embeddings in Machine Learning?](https://aws.amazon.com/what-is/embeddings-in-machine-learning/)

### Other resources
<a name="other-resources"></a>
+ [About PostgreSQL](https://www.postgresql.org/about/)
+ [pgvector documentation](https://github.com/pgvector/pgvector)
+ [Pinecone as a Knowledge Base for Amazon Bedrock](https://docs.pinecone.io/integrations/amazon-bedrock)
+ [Redis Enterprise Cloud on AWS](https://redis.io/docs/latest/integrate/aws-redis-cloud/)