Generate and store vector embeddings with Ollama and pgvector. Use when asked to "generate embeddings", "embed documents", "store vectors", "create document embeddings", or when implementing the ingestion phase of RAG pipelines.
Quickly install pgvector-embeddings AI skill to your development environment via command line
Source: constructive-io/constructive-skills.
Generate vector embeddings using Ollama and store them in PostgreSQL with pgvector. This skill covers the ingestion phase of RAG pipelines.
The nomic-embed-text model provides 768-dimensional embeddings with good quality and performance:
| nomic-embed-text | 768 | Fast | Good | | mxbai-embed-large | 1024 | Medium | Better | | all-minilm | 384 | Very Fast | Acceptable |
Generate and store vector embeddings with Ollama and pgvector. Use when asked to "generate embeddings", "embed documents", "store vectors", "create document embeddings", or when implementing the ingestion phase of RAG pipelines. Source: constructive-io/constructive-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/constructive-io/constructive-skills --skill pgvector-embeddingsGenerate and store vector embeddings with Ollama and pgvector. Use when asked to "generate embeddings", "embed documents", "store vectors", "create document embeddings", or when implementing the ingestion phase of RAG pipelines. Source: constructive-io/constructive-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/constructive-io/constructive-skills --skill pgvector-embeddings Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/constructive-io/constructive-skills