Moss - Real-time Semantic Search for Conversational AI

The Moss Elixir SDK (moss on Hex.pm) brings semantic search to Elixir applications. Documents are embedded and queried on-device through a high-performance Rust core, with optional cloud sync. The SDK ships built-in embedding models, hybrid (semantic + keyword) search, and per-session indexing for real-time workflows.

Requirements

Elixir 1.15 or higher
OTP 26 or higher
Valid Moss project credentials (project_id and project_key)

Install

Add moss to your dependencies in mix.exs:

# mix.exs
defp deps do
  [{:moss, "~> 1.0"}]
end

Then fetch it:

mix deps.get

Two ways to search

The Elixir SDK exposes two entry points:

Moss.Client - the entry point. Construct it with your credentials, manage cloud indexes, and load indexes into memory for fast local querying.
Moss.Session - a local, in-session index. Index documents in memory during a live workflow with no cloud round trips, then push to the cloud when done.

Load before query

Querying always runs against an index that has been loaded into memory. Call Moss.Client.load_index/3 first, then Moss.Client.query/4. Queries run entirely in-memory with no network round trip. There is no query path that runs without first loading the index (or opening a session).

Quick start

Create a client, create and populate a cloud index, load it, and query:

alias Moss.{Client, DocumentInfo}

# Initialize the client with your project credentials
{:ok, client} = Client.new("your-project-id", "your-project-key")

# Prepare documents to index
documents = [
  %DocumentInfo{
    id: "doc1",
    text: "How do I track my order? Log into your account to see live status.",
    metadata: %{"category" => "shipping"}
  },
  %DocumentInfo{
    id: "doc2",
    text: "What is your return policy? We offer a 30-day return policy.",
    metadata: %{"category" => "returns"}
  }
]

# Create a cloud index (defaults to the moss-minilm model)
{:ok, _} = Client.create_index(client, "faqs", documents)

# Load the index into memory before querying
{:ok, _} = Client.load_index(client, "faqs")

# Query the loaded index
{:ok, result} = Client.query(client, "faqs", "How do I return a damaged product?", top_k: 3, alpha: 0.6)

IO.puts("Query: #{result.query}")

for doc <- result.docs do
  IO.puts("#{doc.id} (#{Float.round(doc.score, 4)}): #{doc.text}")
end

Sessions

For real-time indexing during live workflows (voice AI agents, chat), open a session. Documents are indexed in memory with no cloud round trip, and you can push the index to the cloud when done:

{:ok, client} = Moss.Client.new("project-id", "project-key")
{:ok, session} = Moss.Client.session(client, "session-abc")

docs = [
  %Moss.DocumentInfo{id: "turn-1", text: "Customer: I need to cancel my subscription"},
  %Moss.DocumentInfo{id: "turn-2", text: "Agent: I can help with that. Can I ask why?"}
]

{:ok, {2, 0}} = Moss.Session.add_docs(session, docs)
{:ok, result} = Moss.Session.query(session, "subscription cancellation", top_k: 2)

# Push the session index to the cloud when done
{:ok, _} = Moss.Session.push_index(session)

Models

Two built-in embedding models run entirely in the Rust core:

moss-minilm - lightweight, optimized for speed (the default).
moss-mediumlm - higher accuracy with reasonable performance.

Pass model_id: "custom" to supply your own pre-computed embeddings via DocumentInfo.embedding. With a custom model, each document must set embedding, and queries must pass an embedding: option.

Reference

Classes - Moss.Client, Moss.Session
Types - DocumentInfo, SearchResult, QueryResultDoc, IndexInfo, and more

Python

JavaScript

Swift (iOS)

Elixir

Browser

C

Overview

Requirements

Install

Two ways to search

Load before query

Quick start

Sessions

Models

Reference

​Requirements

​Install

​Two ways to search

​Load before query

​Quick start

​Sessions

​Models

​Reference

Requirements

Install

Two ways to search

Load before query

Quick start

Sessions

Models

Reference