Store and search vector embeddings in Postgres using pgvector and HNSW indexing.
npx skills add https://github.com/theslashdojo/dojo --skill vectors
Please help me install this Agent Skill. Skill: vectors Source: https://github.com/theslashdojo/dojo/tree/main/nodes/supabase/vectors Command: npx skills add https://github.com/theslashdojo/dojo --skill vectors
Persisting high-dimensional representations in a database enables fast similarity search, semantic retrieval, and retrieval-augmented generation over large corpora without leaving the data layer.
Embed a sample text, store the embedding in a vector column, and query with another embedding to retrieve similar documents.
High-intent search queries and answers about installing and using this skill.
You can store vector embeddings in Postgres using the pgvector extension, which adds a vector column type for high-dimensional data. This enables fast similarity search directly within the database layer for semantic retrieval and RAG pipelines.
Building a RAG pipeline with pgvector involves storing OpenAI embeddings in a Postgres vector column and querying them via an RPC-based match function. This setup retrieves similar documents to augment generation without leaving the data layer.
Yes, pgvector supports HNSW indexing to enable fast and scalable similarity search across large document corpora. This indexing method accelerates vector comparisons for semantic search and recommendation scenarios.
Yes, you can use the Supabase client to query vector embeddings stored in Postgres. By calling an RPC-based query function like match_documents, you can retrieve similar content for recommendation scenarios.
Setting up vector similarity search requires installing the pgvector extension, creating a vector column to store embeddings, and defining an RPC query function. These components work together to enable fast document retrieval.
lewisperez999Generate text embeddings with OpenAI and search by cosine similarity in Upstash Vector or pg-vector.
zilliztechDecompose questions, retrieve across documents, and synthesize cited answers.
kivo360Transform domain knowledge into vector embeddings for semantic retrieval.
DrLuggelsProvide patterns for similarity search with vector databases like Pinecone and Qdrant.
