[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-04-14(UTC)"],[[["AlloyDB can be used as a vector database by utilizing the `vector` extension, which includes `pgvector` functions and operators for storing embeddings."],["The `vector` extension, version `0.5.0.google-1` or later, is required and has Google-specific optimizations for AlloyDB to efficiently manage vector values."],["To store embeddings, add a `vector[]` column to your table with `ALTER TABLE`, specifying the number of dimensions supported by your model, and then populate it with generated embeddings."],["If using the Langchain framework with a dataset of 100k embeddings or more, consider using the `AlloyDBVectorStore` vector class from the alloydb langChain library."],["Once embeddings are stored, you can use either the `vector` extension or the `alloydb_scann` extension to create indexes, which will improve query performance."]]],[]]