Vector Database:
Brief: https://www.pinecone.io/learn/vector-database/
Sample Database: https://www.pinecone.io/
Apache Cassandra: Cassandra is a distributed NoSQL database that can be used to store and query vector data. It's often used for real-time applications and is known for its scalability.
Elasticsearch: Elasticsearch is a search and analytics engine that can be used to store and search vector data efficiently. It's commonly used for text-based vectors and is great for full-text search.
Faiss: Faiss is an open-source library developed by Facebook AI Research for similarity search and clustering of dense vectors. It's highly optimized for efficient vector storage and search.
Milvus: Milvus is an open-source vector database designed specifically for similarity search and data retrieval using vector data. It's widely used in applications like image and product similarity search.
Redis: Redis is an in-memory data store that can be used to store and retrieve vectors efficiently. With modules like RedisAI, it can handle machine learning model inference on vectors.
Amazon Neptune: Amazon Neptune is a managed graph database service provided by AWS. While it's designed for graph data, it can be used to store vector data for various applications.
DynamoDB: Amazon's DynamoDB, a managed NoSQL database, can be used for vector storage and retrieval in applications that require high availability and scalability.
HNSW (Hierarchical Navigable Small World) Index: HNSW is an indexing method used to efficiently search for nearest neighbors in high-dimensional vector spaces. While not a database per se, it is often used in conjunction with databases to optimize vector search.
OpenSearch (formerly Open Distro for Elasticsearch): OpenSearch is a fork of Elasticsearch that provides open-source search and analytics. It's often used for vector data applications and offers a high degree of customization.
Pinecone:
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