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Self-managed multi-tenant vector search with Amazon Aurora PostgreSQL
- Generative AI and ML tools are used to process unstructured data, making vector search essential for efficient data retrieval.
- Amazon Aurora PostgreSQL-Compatible with the pgvector extension facilitates scalable vector store creation.
- A multi-tenant design in SaaS applications requires tenant-aware data retrieval to ensure privacy and security.
- The article discusses a self-managed approach for building a multi-tenant generative AI application using Aurora PostgreSQL-Compatible for vector storage.
- Steps include creating a vector store, ingesting and retrieving vector data, and augmenting data for generative AI.
- Key vector terminologies like vector embeddings, embedding models, and indexing methods are explained.
- Row-level security and tenant isolation are enforced to prevent data breaches in a multi-tenant environment.
- The self-managed approach involves using PostgreSQL for row-level security and ensures tenant-specific data retrieval.
- The article provides code snippets for generating vector embeddings, inserting data into the vector store, and querying the vector database.
- The self-managed approach gives flexibility but adds operational complexity, requiring considerations for performance and indexing strategies.
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