Semantic Search with PostgreSQL: Pragmatism Beats Hype - Most of the Time

Short summary

PostgreSQL with pgvector extension can handle semantic search for many applications without needing a separate vector database, reducing infrastructure complexity. The tutorial covers embedding model selection (OpenAI, local models via Ollama), schema design with document chunking, and C#/.NET implementation details, emphasizing that index and query models must match. Key insight: pragmatism over hype—use what you have before adding specialized tools.

• •pgvector lets PostgreSQL store and query embeddings alongside relational data
• •Embedding model choice determines vector dimensions and must be consistent for indexing and querying
• •Includes practical schema design and .NET code examples for production use

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