Gunakan ekstensi vector, versi 0.5.0.google-1 atau yang lebih baru, yang mencakup fungsi dan operator pgvector, untuk menyimpan sematan yang dihasilkan sebagai nilai vector. Ini adalah versi pgvector yang telah diperluas oleh Google dengan pengoptimalan khusus untuk AlloyDB.
CREATEEXTENSIONIFNOTEXISTSvector;
Menyimpan embedding yang dibuat
Pastikan Anda telah membuat tabel di database AlloyDB.
Untuk menyimpan sematan vektor, ikuti langkah-langkah berikut:
Buat kolom vector[] di tabel Anda untuk menyimpan embedding:
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[[["\u003cp\u003eAlloyDB can be used as a vector database by utilizing the \u003ccode\u003evector\u003c/code\u003e extension, which includes \u003ccode\u003epgvector\u003c/code\u003e functions and operators for storing embeddings.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003evector\u003c/code\u003e extension, version \u003ccode\u003e0.5.0.google-1\u003c/code\u003e or later, is required and has Google-specific optimizations for AlloyDB to efficiently manage vector data.\u003c/p\u003e\n"],["\u003cp\u003eTo store embeddings, add a \u003ccode\u003evector[]\u003c/code\u003e column to your table with \u003ccode\u003eALTER TABLE\u003c/code\u003e, specifying the dimensions supported by your embedding model.\u003c/p\u003e\n"],["\u003cp\u003eYou can copy vectors into the designated column using a \u003ccode\u003e.csv\u003c/code\u003e file with the \u003ccode\u003eCOPY\u003c/code\u003e command.\u003c/p\u003e\n"],["\u003cp\u003eAfter storing embeddings, you can improve query performance by creating indexes with the \u003ccode\u003evector\u003c/code\u003e extension or the \u003ccode\u003ealloydb_scann\u003c/code\u003e extension.\u003c/p\u003e\n"]]],[],null,["Select a documentation version: 15.7.0keyboard_arrow_down\n\n- [Current (16.8.0)](/alloydb/omni/current/docs/ai/store-embeddings)\n- [16.8.0](/alloydb/omni/16.8.0/docs/ai/store-embeddings)\n- [16.3.0](/alloydb/omni/16.3.0/docs/ai/store-embeddings)\n- [15.12.0](/alloydb/omni/15.12.0/docs/ai/store-embeddings)\n- [15.7.1](/alloydb/omni/15.7.1/docs/ai/store-embeddings)\n- [15.7.0](/alloydb/omni/15.7.0/docs/ai/store-embeddings)\n\n\u003cbr /\u003e\n\nThis page shows you how to use AlloyDB as a vector database with the `vector` extension that includes `pgvector` functions and operators. These functions and operators let you store embeddings as vector values.\n\n\u003cbr /\u003e\n\nRequired database extension\n\nUse the `vector` extension, version `0.5.0.google-1` or later, which includes\n`pgvector` functions and operators, to store generated embeddings as `vector` values. This\nis a version of `pgvector` that Google has extended with optimizations specific\nto AlloyDB. \n\n CREATE EXTENSION IF NOT EXISTS vector;\n\nStore generated embeddings\n\nEnsure that you have already created a table in your AlloyDB database.\n| **Note:** If your application uses the LangChain framework and your dataset has `O(100k)` embeddings, we recommend that you use the `AlloyDBVectorStore` vector class included in the AlloyDB LangChain library to store your embeddings. For more information, see [Build LLM-powered applications using LangChain](/alloydb/docs/ai/langchain#vector_store_procedure_guide).\n\nTo store vector embeddings, follow these steps:\n\n1. Create a `vector[]` column in your table to store your embeddings:\n\n ALTER TABLE \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-k\"\u003eTABLE\u003c/span\u003e\u003c/var\u003e ADD COLUMN \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eEMBEDDING_COLUMN\u003c/span\u003e\u003c/var\u003e vector(\u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eDIMENSIONS\u003c/span\u003e\u003c/var\u003e);\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eTABLE\u003c/var\u003e: the table name.\n\n - \u003cvar translate=\"no\"\u003eEMBEDDING_COLUMN\u003c/var\u003e: the name of the new embedding column.\n\n - \u003cvar translate=\"no\"\u003eDIMENSIONS\u003c/var\u003e: the number of dimensions that the model\n supports.\n\n For example, if you are using one of the `text-embedding`English models---for example, `text-embedding-005` with Vertex AI, specify `768`.\n2. Copy the vectors to the vector column. The following example assumes that your\n embeddings are available in a CSV file:\n\n COPY \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-k\"\u003eTABLE\u003c/span\u003e\u003c/var\u003e (\u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eEMBEDDING_COLUMN\u003c/span\u003e\u003c/var\u003e) FROM '\u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003ePATH_TO_VECTOR_CSV\u003c/span\u003e\u003c/var\u003e (FORMAT CSV);\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003ePATH_TO_VECTOR_CSV\u003c/var\u003e: the full path of where you stored your CSV file.\n\nAfter you store the embeddings, you can use the `vector` extension or the `alloydb_scann`\nextension to create indexes for faster query performance.\n\nWhat's next\n\n- [Create indexes and query vectors](/alloydb/omni/15.7.0/docs/ai/store-index-query-vectors).\n- Learn [an example embedding workflow](/alloydb/omni/15.7.0/docs/ai/example-embeddings)."]]
