Picking a vector database: a comparison and guide for 2023

There are numerous integrations for vector storage. These include Alibaba Cloud OpenSearch, AnalyticDB for PostgreSQL, Meta AI’s Annoy library for Approximate Nearest Neighbor (ANN) search, Cassandra, Chroma, Elasticsearch, Facebook AI Similarity Search (Faiss), MongoDB Atlas Vector Search, PGVector as a vector similarity search for Postgres, Pinec

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The ideal solution for AI-native vectorDB would be something that would would be easy to set up and should integrate with existing APIs for rapid prototyping but should be able to scale without additional changes.

LanceDB is designed with this approach. Being server-less, it requires no setup — just import and start using. Persisted in HDD, allowing... See more

Utilize both full-text search and vector search (embeddings) for retrieving relevant documents. Ideally, you should use a single database system to avoid synchronization issues.

• Implement both full-text search and vector search


• Test the performance of each method on your specific use case


• Consider using a single database system to store both types of

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