Moss - Real-time Semantic Search for Conversational AI

Moss loads an index into memory and runs embedding and search in-process, so a query is a local call with no network round trip per operation. The pattern is the same in every SDK: load the index once, then query it.

await client.loadIndex(indexName)
const results = await client.query(indexName, 'How do I return a damaged product?', { topK: 3 })

Queries run against the in-memory index and embed the query text with a local model.

The query pipeline

Query text is embedded locally, retrieved from an in-process loaded index, optionally reranked, and returned as results

Load the index once, then query it in-process.
Optional background sync to the cloud.
Query pipeline: text -> embed -> retrieve -> rerank (optional).

Prerequisites

Node.js 18+ or Python 3.10+
Moss credentials: MOSS_PROJECT_ID, MOSS_PROJECT_KEY
An index to query (create faqs via the Quickstart if you don’t have one yet)

Steps

Export credentials (and index if different)

export MOSS_PROJECT_ID=your_project_id
export MOSS_PROJECT_KEY=your_project_key
export MOSS_INDEX_NAME=faqs   # or your index

Ensure the index exists

Ensure the index exists (see Quickstart to create faqs).

Run one of the snippets below

Run one of the snippets below.

Run the sample (JavaScript or Python)

import { MossClient } from '@moss-dev/moss'

const client = new MossClient(
  process.env.MOSS_PROJECT_ID!,
  process.env.MOSS_PROJECT_KEY!
)

const indexName = process.env.MOSS_INDEX_NAME || 'faqs' // ensure this exists (see Quickstart)

async function main() {
  await client.loadIndex(indexName)
  const results = await client.query(indexName, 'How do I return a damaged product?', { topK: 3 })

  console.log(`Found ${results.docs.length} docs in ${results.timeTakenInMs}ms`)
  results.docs.forEach((doc, i) => {
    console.log(`${i + 1}. [${doc.id}] ${doc.text} (score: ${doc.score.toFixed(3)})`)
  })
}

main().catch(console.error)

import os
import asyncio
from moss import MossClient, QueryOptions

project_id = os.getenv("MOSS_PROJECT_ID")
project_key = os.getenv("MOSS_PROJECT_KEY")
index_name = os.getenv("MOSS_INDEX_NAME", "faqs")  # ensure this exists (see Quickstart)

async def main():
    client = MossClient(project_id, project_key)
    await client.load_index(index_name)
    results = await client.query(index_name, "How do I return a damaged product?", QueryOptions(top_k=3))

    print(f"Found {len(results.docs)} docs")
    for i, doc in enumerate(results.docs, 1):
        print(f"{i}. [{doc.id}] {doc.text} (score: {doc.score:.3f})")

asyncio.run(main())

The sample loads your index into memory and returns the top matches. An index must be loaded with loadIndex / load_index (or opened as a session) before you can query it.

Getting Started

Capabilities

Use Cases

How it works

Pricing

Sub-10ms Knowledge Retrieval

The query pipeline

Prerequisites

Steps

Run the sample (JavaScript or Python)

​The query pipeline

​Prerequisites

​Steps

​Run the sample (JavaScript or Python)

The query pipeline

Prerequisites

Steps

Run the sample (JavaScript or Python)