backend/mcp_servers/embeddings.py

from sentence_transformers import SentenceTransformer

# Load MiniLM model (384-dimensional embeddings)
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

def embed_text(text: str):
    """
    Generate sentence embedding for use with pgvector.

    Args:
        text (str): Input text

    Returns:
        List[float]: 384-dimensional embedding vector
    """
    vector = model.encode(text)
    return vector.tolist()