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Moss embeds text on-device with built-in models (moss-minilm, moss-mediumlm). If you already generate embeddings elsewhere - a proprietary model, a hosted embedding API, or a shared pipeline across services - use model_id="custom" to supply your own vectors. Moss indexes and searches them; it does not load a local model. This works for both cloud indexes and sessions.

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How it works

  • At index time, every document must carry its own embedding. With model_id="custom", Moss does not embed for you. (If you omit model_id and every document has an embedding, Moss infers "custom" automatically; mixed documents are rejected.)
  • At query time, you must pass the query vector via QueryOptions.embedding, because there is no local model to embed the query text.
  • All vectors must share the same dimensionality.

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On a cloud index

import asyncio
from moss import DocumentInfo, MossClient, QueryOptions

def embed(text: str) -> list[float]:
    """Your embedding function - any model, as long as dimensions are consistent."""
    ...

async def main():
    client = MossClient(MOSS_PROJECT_ID, MOSS_PROJECT_KEY)

    # Index with precomputed vectors. model_id="custom" means Moss does not embed.
    docs = [
        DocumentInfo(id="1", text="Customer asked about billing", embedding=embed("Customer asked about billing")),
        DocumentInfo(id="2", text="Refund requested for duplicate charge", embedding=embed("Refund requested for duplicate charge")),
    ]
    await client.create_index("tickets", docs, model_id="custom")
    await client.load_index("tickets")   # required before querying

    # Query with your own query vector (required for custom embeddings).
    q = embed("billing problem")
    results = await client.query("tickets", "billing problem", QueryOptions(top_k=3, embedding=q))
    for doc in results.docs:
        print(f"{doc.id} score={doc.score:.3f} {doc.text}")

asyncio.run(main())

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In a session

Sessions support custom embeddings too: open the session with model_id="custom", set .embedding on every document you add, and pass QueryOptions.embedding on every query. 
session = await client.session(index_name="conv-123", model_id="custom")
await session.add_docs([DocumentInfo(id="1", text="Customer asked about billing", embedding=embed("Customer asked about billing"))])
results = await session.query("billing problem", QueryOptions(top_k=3, embedding=embed("billing problem")))
With model_id="custom", adding a document without .embedding, or querying without QueryOptions.embedding, raises a ValueError.

Hybrid search

Blend semantic and keyword scoring.

Sessions

Use custom vectors in a live local index.