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Data model

Each document has an id and text (plus optional metadata). Use moss-minilm as the default model.

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Examples

import { MossClient } from '@moss-dev/moss'
const client = new MossClient(process.env.MOSS_PROJECT_ID!, process.env.MOSS_PROJECT_KEY!)
await client.createIndex('my-index', [
  { id: 'doc-1', text: '...', metadata: { category: 'faq', tags: 'returns,shipping', lang: 'en' } },
  { id: 'doc-2', text: '...' }
], { modelId: 'moss-minilm' })

// Upsert more docs later
await client.addDocs('my-index', [
  { id: 'doc-2', text: 'updated text' }, // will be updated
  { id: 'doc-3', text: 'new text' }
], { upsert: true })

// Fetch specific docs
const subset = await client.getDocs('my-index', { docIds: ['doc-1', 'doc-3'] })

// Delete docs or the index when done
await client.deleteDocs('my-index', ['doc-3'])
await client.deleteIndex('my-index')

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Local-first indexing (sessions)

createIndex / create_index builds an index through the cloud (then keeps it usable locally). When you need to index during a live interaction - adding transcript turns mid-call or building a per-user working set - use a session instead. A session indexes documents locally in real time with no cloud round trip, embedding each document on-device, and lets you optionally push the result to the cloud when you’re done. 
import asyncio
from moss import DocumentInfo, MossClient

async def main():
    client = MossClient(project_id, project_key)

    # Create or resume a local index by name.
    session = await client.session(index_name="my-session-index")

    # Index locally in real time - embedded on-device, no network.
    await session.add_docs([
        DocumentInfo(id="turn-1", text="Customer confirmed the refund was received."),
    ])

    # Optionally push the session to the cloud when done.
    await session.push_index()

asyncio.run(main())
See Sessions for the full create-resume-query-push lifecycle.

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Notes

  • createIndex / create_index will sync indexes to cloud (if enabled) while remaining usable locally
  • Supports multiple indexes per project; pick the model per index:
    • moss-minilm: fast, lightweight (default)
    • moss-mediumlm: higher accuracy, still efficient
    • Use moss-minilm for speed-first, edge/offline use; use moss-mediumlm when you need higher recall/precision

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Chunking tips

  • Aim for ~200-500 tokens per chunk
  • Overlap 10-20% to preserve context
  • Normalize whitespace and strip boilerplate