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Vector Embeddings

GoingMerry supports generating high-dimensional vector representations of text. These vector embeddings are critical for RAG (Retrieval-Augmented Generation) systems, PGVector storage, semantic search, and document classification.

1. Generating Embeddings​

To generate embeddings, send a request to the /api/embeddings endpoint using a compatible embedding model (such as nomic-embed-text or standard language models).

curl Example​

curl http://localhost:11434/api/embeddings -d '{
  "model": "nomic-embed-text",
  "prompt": "The quick brown fox jumps over the lazy dog"
}'

2. Response Payload​

The endpoint returns the high-dimensional float array vector:

{
  "embedding": [
    0.01723901,
    -0.0823901,
    0.1023812,
    -0.0049102,
    ...
  ]
}

This output can be inserted directly into vector databases (like Redis, ChromaDB, or Postgres PGVector).