📦 AWS Services for Storing Embeddings in Vector Databases

When building Retrieval-Augmented Generation (RAG) or semantic search applications, storing and querying embeddings (numerical representations of text, images, or audio) is essential. AWS provides several services that support vector search and storage.

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📌 1. Amazon OpenSearch Service

• ✅ Built-in vector support with KNN (k-nearest neighbors) indexing and ANN (approximate nearest neighbor) algorithms.
• ✅ Integrated semantic search using embeddings (e.g., from BERT or other LLMs).
• ✅ OpenSearch Dashboards: Visualize and explore search results.
• ✅ Serverless option: OpenSearch Serverless with Vector Engine for scalable embedding workloads.

Use Case:

• Implement RAG
• Build recommendation systems
• Power intelligent search for websites and apps

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📌 2. Amazon RDS for PostgreSQL (with pgvector extension)

• ✅ Supports the pgvector extension to store, index, and search embeddings.
• ✅ Use SQL syntax to filter, search, and rank based on vector similarity.
• ✅ Fully managed PostgreSQL experience with automated backups and replication.

Use Case:

• Add vector search to existing relational apps.
• Perform similarity queries alongside structured data (e.g., customer profiles + embeddings).

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3. 🔗 Amazon Aurora (PostgreSQL-compatible)

• ✅ Also supports the pgvector extension.
• ✅ Provides higher performance and scalability over standard PostgreSQL.
• ✅ Ideal for applications requiring high throughput and low latency.

Use Case:

• Scalable semantic search within high-traffic apps.
• Store large volumes of embeddings from documents, chat history, or logs.

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4. 📄 Amazon DocumentDB (with MongoDB Compatibility)

• ✅ Document-oriented storage of embeddings in JSON format.
• ✅ Supports flexible schemas, making it easy to store varied embedding payloads.
• ✅ Can be paired with external libraries/tools for vector similarity search.

Use Case:

• Embed documents and perform vector-based search with minimal schema constraints.
• Store content and embeddings together in the same document.

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5. 🗃️ Amazon Neptune

• ✅ Graph database service with support for property graphs and RDF.
• ✅ Integrated with ML models for knowledge graph embeddings (e.g., via SageMaker).
• ✅ Stores relationship-based embeddings (e.g., user A → likes → product B).

Use Case:

• Graph-based recommendation engines.
• Semantic reasoning over entity relationships (e.g., fraud detection, social graphs).

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🚀 Summary Table

AWS Service	Vector Support	Ideal Use Case
OpenSearch Service	Native KNN / ANN	Semantic search, dashboards, RAG
RDS PostgreSQL	pgvector extension	Combine SQL + vector search
Aurora PostgreSQL	pgvector + scalable	High-perf apps with vector needs
Amazon Neptune	Graph embeddings	Entity relationships, knowledge graphs
DocumentDB (Mongo)	JSON-based embeddings	Schema-flexible, doc-centric applications

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tip

You can generate embeddings using Amazon SageMaker, store them in these vector stores, and query them via Amazon Bedrock Knowledge Bases to build Retrieval-Augmented Generation (RAG) applications at scale.