Moss client library for Elixir

moss enables private, on-device semantic search in your Elixir applications with cloud storage capabilities.

Built for developers who want instant, memory-efficient, privacy-first AI features with seamless cloud integration.

Features

Installation

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

Quick Start

alias Moss.{Client, DocumentInfo}

# Initialize search client with 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? You can track your order by logging into your account.",
    metadata: %{"category" => "shipping"}
  },
  %DocumentInfo{
    id: "doc2",
    text: "What is your return policy? We offer a 30-day return policy for most items.",
    metadata: %{"category" => "returns"}
  },
  %DocumentInfo{
    id: "doc3",
    text: "How can I change my shipping address? Contact our customer service team.",
    metadata: %{"category" => "support"}
  }
]

# Create an index with documents (syncs to cloud)
{:ok, _} = Client.create_index(client, "faqs", documents)  # Defaults to moss-minilm

# Load the index (from cloud or local cache)
{:ok, _} = Client.load_index(client, "faqs")

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

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

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

Example Use Cases

Available Models

Getting Started

Prerequisites

Environment Setup

  1. Install the package:
# mix.exs
defp deps do
  [{:moss, "~> 1.0"}]
end
  1. Get your credentials:

Sign up at Moss Platform to get your project_id and project_key.

  1. Set up environment variables (optional):
export MOSS_PROJECT_ID="your-project-id"
export MOSS_PROJECT_KEY="your-project-key"

Basic Usage

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

# Create and populate an index
documents = [
  %Moss.DocumentInfo{id: "1", text: "Python is a programming language"},
  %Moss.DocumentInfo{id: "2", text: "Machine learning with Python is popular"}
]

{:ok, _} = Moss.Client.create_index(client, "my-docs", documents)
{:ok, _} = Moss.Client.load_index(client, "my-docs")

# Search
{:ok, results} = Moss.Client.query(client, "my-docs", "programming language", alpha: 1.0)

for doc <- results.docs do
  IO.puts("#{doc.id}: #{doc.text} (score: #{Float.round(doc.score, 3)})")
end

Hybrid Search Controls

alpha lets you decide how much weight to give semantic similarity versus keyword relevance when running query:

# Pure keyword search
Client.query(client, "my-docs", "programming language", alpha: 0.0)

# Mixed results (default 0.8, semantic heavy)
Client.query(client, "my-docs", "programming language")

# Pure embedding search
Client.query(client, "my-docs", "programming language", alpha: 1.0)

Pick any value between 0.0 and 1.0 to tune the blend for your use case.

Multi-Index Search

Search several loaded indexes in one call and get the global top-k back, with each result tagged by its source index_name. All indexes must be loaded locally and share the same embedding model.

{:ok, %Moss.LoadIndexesResult{loaded: loaded, failed: failed}} =
  Moss.Client.load_indexes(client, ["products", "reviews"])

if loaded == [], do: raise("No indexes loaded: #{inspect(failed)}")

{:ok, results} =
  Moss.Client.query_multi_index(client, loaded, "noise cancelling headphones", top_k: 5, alpha: 0.5)

for doc <- results.docs do
  IO.puts("[#{doc.index_name}] #{doc.id}: #{doc.text} (score: #{Float.round(doc.score, 3)})")
end

:ok = Moss.Client.unload_indexes(client, loaded)

:alpha works exactly as in query/4 (default 0.8): 1.0 is embedding-only, 0.0 is keyword-only, and anything in between blends both with Reciprocal Rank Fusion. Keyword scoring runs each index's own BM25 and merges each index's raw hits before fusion, so cross-index keyword ranking is approximate. :top_k (default 10) caps the merged result, not each index, and :filter applies to every index. load_indexes/3 is best-effort: names that fail are reported in failed without rolling back the ones that loaded.

Metadata Filtering

You can pass a metadata filter directly to query after loading an index locally:

{:ok, result} = Moss.Client.query(
  client,
  "my-docs",
  "running shoes",
  top_k: 5,
  alpha: 0.6,
  filter: %{
    "$and" => [
      %{"field" => "category", "condition" => %{"$eq" => "shoes"}},
      %{"field" => "price", "condition" => %{"$lt" => "100"}}
    ]
  }
)

Supported operators: $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $near

Logical combinators: $and, $or

Custom Embeddings

Already using your own embedding model? Supply vectors directly when managing indexes and queries:

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

documents = [
  %Moss.DocumentInfo{
    id: "doc-1",
    text: "Attach a caller-provided embedding.",
    embedding: my_embedding_model("doc-1")
  },
  %Moss.DocumentInfo{
    id: "doc-2",
    text: "Fallback to the built-in model when the field is omitted.",
    embedding: my_embedding_model("doc-2")
  }
]

{:ok, _} = Moss.Client.create_index(client, "custom-embeddings", documents)
{:ok, _} = Moss.Client.load_index(client, "custom-embeddings")

{:ok, results} = Moss.Client.query(
  client,
  "custom-embeddings",
  "query text",
  embedding: my_embedding_model("query text"),
  top_k: 10
)

IO.puts("#{hd(results.docs).id}: #{hd(results.docs).score}")

Sessions

For real-time indexing during live workflows (voice AI agents, chat), use sessions:

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

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

Moss.Session.save_to_disk/2 writes a session under <cache_path>/<name>/, and Moss.Session.load_from_disk/3 restores it into a later session with the same name. Open that session with auto_load_on_init: false: it then skips the cloud index load that the snapshot would replace, so the restore downloads no index:

:ok = Moss.Session.save_to_disk(session, "/var/cache/moss")
:ok = Moss.Session.close(session)

{:ok, restored} = Moss.Client.session(client, "session-abc", auto_load_on_init: false)
{:ok, _doc_count} = Moss.Session.load_from_disk(restored, "/var/cache/moss")

Without cache_path, Moss.Session.save_to_disk(session) and Moss.Session.load_from_disk(session) use sessions/<project id>/ under the first of these that is set:

  1. the client's :cache_path (Moss.Client.new(id, key, cache_path: ...))
  2. the MOSS_SESSION_CACHE_PATH environment variable, as set when the VM started (System.put_env/2 does not reach the native core, so set :cache_path from inside the app). Only session snapshots read it: the index cache and the model cache ignore it.
  3. moss in the default cache directory: ~/.cache/moss on macOS and Linux, %LOCALAPPDATA%\moss on Windows

A relative cache_path resolves against the current working directory, and ~ is not expanded.

A snapshot that an older core saved for a built-in model has no model artifact identity. load_from_disk/3 rebuilds it for a session opened with the same model, re-embedding the stored documents with the current model and saving the snapshot back, which needs the cloud for the model. Pass rebuild_legacy: false to load such a snapshot as a custom-model session instead.

Inference

Moss.Inference is a client for OpenAI's chat completions call, answered by Moss inference at https://inference.moss.dev/v1. Moss searches your indexes, adds what it finds to the prompt and streams the model's reply. params is the OpenAI request body, so code written for OpenAI only adds :indexes.

alias Moss.Inference.Chat.Completions

client = Moss.Inference.new("project-id", "project-key")
messages = [%{role: "user", content: "Do you ship to Canada?"}]

# Streamed: chunks arrive as the model writes them
{:ok, stream} = Completions.create(client, %{messages: messages, stream: true}, indexes: ["faq"])

for chunk <- stream, choice <- chunk["choices"] do
  IO.write(choice["delta"]["content"] || "")
end

# Not streamed: one completion with the whole reply
{:ok, completion} = Completions.create(client, %{messages: messages}, indexes: ["faq"])
IO.puts(hd(completion["choices"])["message"]["content"])

model defaults to "moss", Moss's own model. Moss.Inference.new/3 checks the credentials and options and raises Moss.Inference.Error for a bad one. It takes :api_key, :timeout (milliseconds for a whole call, default :infinity) and :idle_timeout (milliseconds Moss may stay silent, default 120_000). A call takes :api_key, :timeout and :idle_timeout too, over the client's, plus retrieve: false to skip the search. A call's options go in its last argument, and params that hold one return an error.

:api_key is optional: your own OpenAI, Anthropic or Google key, for a vendor model such as gpt-4o-mini. moss ignores it, and a vendor model without one is refused. An empty key, or one that starts or ends with a space, is an error, and no key appears in an error or in inspect/2.

# A vendor model, with your own OpenAI key for this call
api_key = System.fetch_env!("OPENAI_API_KEY")
params = %{model: "gpt-4o-mini", messages: messages}
{:ok, completion} = Completions.create(client, params, api_key: api_key)

Chunks and completions are maps with OpenAI's field names as string keys.

A failure before the reply starts returns {:error, %Moss.Inference.Error{}} with status, type and message, plus the gateway's code, the reply's request_id and an error reply's retry_after in milliseconds, when they are known. A stream raises Moss.Inference.Error if the reply fails after it starts. Read a stream in the process that created it. Stopping early, for example with Enum.take/2, closes the connection and stops the model. Moss.Inference.ChatCompletionStream.close/1 does the same from any process, and the stream then ends without an error.

Calls reuse idle connections, from any process. A reply read to its end gives its connection back, and a stream that stops early or fails closes its own. No call is sent twice: a pooled connection found closed is replaced before the call is written to it. The pool keeps up to 100 idle connections, each for at most 4 seconds.

To call another gateway, set MOSS_INFERENCE_URL to its base URL, such as https://gateway.example.com/v1. Moss.Inference.new/3 reads it, and raises an error for a value that is not an http or https URL with a host and no user info, query or fragment.

Telemetry

moss always sends usage telemetry to the Moss cloud. See Telemetry for what it contains and when it is sent.

API Reference

Every public module and function is documented on HexDocs.

License

This package is licensed under the PolyForm Shield License 1.0.0.

Contact

For support, commercial licensing, or partnership inquiries: contact@moss.dev