pgvector-embeddings
pgvector Embeddings
Generate vector embeddings using Ollama and store them in PostgreSQL with pgvector. This skill covers the ingestion phase of RAG pipelines.
When to Apply
Use this skill when:
- Generating embeddings for documents or text
- Storing embeddings in PostgreSQL
- Building the ingestion pipeline for RAG
- Converting text to vectors for semantic search
- Chunking documents for better retrieval
Embedding Models
Recommended: nomic-embed-text
The nomic-embed-text model provides 768-dimensional embeddings with good quality and performance:
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