Vector databases are crucial for GenAI applications, offering augmented knowledge bases for language models with support for fuzzy searching using text or media embeddings. The post evaluates various self-hosted vector databases like MongoDB, ChromaDB, Weaviate, Milvus, Neo4j, KDB.AI, PostgreSQL, and SQLite. Recommendations include using Docker for ease of setup and highlighting the benefits and limitations of each option. The guide emphasizes starting with self-hosted instances to control costs while prototyping and suggests evaluating multiple databases to find the optimal setup for your application.
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Why?MongoDBInstall MongoDB Community with DockerChromaDBchroma/docker-compose.yml at main · chroma-core/chromaWeaviateDocker Compose | Weaviate - Vector DatabaseMilvusInstall Milvus Standalone with Docker ComposeNeo4jGetting started with Neo4j in Docker - Operations ManualKDB.AI ServerKDB.AI Server SetupPostgreSQLGitHub - pgvector/pgvector: Open-source vector similarity search for PostgresImplementing the pgvector extension for a PostgreSQL databaseSQLiteGitHub - asg017/sqlite-vec: Work-in-progress vector search SQLite extension that runs anywhere.Running/Self-hosting in CloudReinventing The Wheel: ChromaDB with Persistence in AzureProduction CostWhat to Choose1 Comment
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