Moss client library for Python

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

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

✨ Features

• ⚡ On-Device Vector Search - Sub-millisecond retrieval with zero network latency
• 🔍 Semantic, Keyword & Hybrid Search - Embedding search blended with Keyword matching
• ☁️ Cloud Storage Integration - Automatic index synchronization with cloud storage
• 📦 Multi-Index Support - Manage multiple isolated search spaces
• 🛡️ Privacy-First by Design - Computation happens locally, only indexes sync to cloud
• 🚀 High-Performance Rust Core - Built on optimized Rust bindings for maximum speed
• 🧠 Custom Embedding Overrides - Provide your own document and query vectors when you need full control

📦 Installation

pip install moss

🚀 Quick Start

import asyncio
from moss import MossClient, DocumentInfo, QueryOptions

async def main():
    # Initialize search client with project credentials
    client = MossClient("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)
    index_name = "faqs"
    await client.create_index(index_name, documents)  # Defaults to moss-minilm
    print("Index created and synced to cloud!")

    # Load the index (from cloud or local cache)
    await client.load_index(index_name)

    # Search the index
    result = await client.query(
        index_name,
        "How do I return a damaged product?",
        QueryOptions(top_k=3, alpha=0.6),
    )

    # Display results
    print(f"Query: {result.query}")
    for doc in result.docs:
        print(f"Score: {doc.score:.4f}")
        print(f"ID: {doc.id}")
        print(f"Text: {doc.text}")
        print("---")

asyncio.run(main())

🔥 Example Use Cases

• Smart knowledge base search with cloud backup
• Realtime Voice AI agents with persistent indexes
• Personal note-taking search with sync across devices
• Private in-app AI features with cloud storage
• Local semantic search in edge devices, fully on-device

Available Models

• moss-minilm: Lightweight model optimized for speed and efficiency
• moss-mediumlm: Balanced model offering higher accuracy with reasonable performance

🔧 Getting Started

Prerequisites

• Python 3.8 or higher
• Valid InferEdge project credentials

Environment Setup

1. Install the package:

pip install moss

2. Get your credentials:

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

3. Set up environment variables (optional):

export MOSS_PROJECT_ID="your-project-id"
export MOSS_PROJECT_KEY="your-project-key"

Basic Usage

import asyncio
from moss import MossClient, DocumentInfo, QueryOptions

async def main():
    # Initialize client
    client = MossClient("your-project-id", "your-project-key")
    
    # Create and populate an index
    documents = [
        DocumentInfo(id="1", text="Python is a programming language"),
        DocumentInfo(id="2", text="Machine learning with Python is popular"),
    ]
    
    await client.create_index("my-docs", documents)
    await client.load_index("my-docs")
    
    # Search
    results = await client.query(
        "my-docs",
        "programming language",
        QueryOptions(alpha=1.0),
    )
    for doc in results.docs:
        print(f"{doc.id}: {doc.text} (score: {doc.score:.3f})")

asyncio.run(main())

Hybrid Search Controls

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

# Pure keyword search
await client.query("my-docs", "programming language", QueryOptions(alpha=0.0))

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

# Pure embedding search
await client.query("my-docs", "programming language", QueryOptions(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.

loaded = await client.load_indexes(["products", "reviews"])
if not loaded.loaded:
    raise RuntimeError(f"no indexes loaded: {loaded.failed}")

results = await client.query_multi_index(
    loaded.loaded,
    "noise cancelling headphones",
    QueryOptions(top_k=5, alpha=0.5),
)
for doc in results.docs:
    print(f"[{doc.index_name}] {doc.id}: {doc.text} (score: {doc.score:.3f})")

await client.unload_indexes(loaded.loaded)

alpha works exactly as in query() (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; because BM25 statistics stay per-corpus, cross-index keyword ranking is approximate. top_k caps the merged result, not each index, and filter applies to every index. load_indexes 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:

results = await client.query(
    "my-docs",
    "running shoes",
    QueryOptions(top_k=5, alpha=0.6),
    filter={
        "$and": [
            {"field": "category", "condition": {"$eq": "shoes"}},
            {"field": "price", "condition": {"$lt": "100"}},
        ]
    },
)

For a complete runnable example, see python/user-facing-sdk/samples/metadata_filtering.py.

🧠 Providing custom embeddings

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

import asyncio

from moss import DocumentInfo, MossClient, QueryOptions


def my_embedding_model(text: str) -> list[float]:
    """Placeholder for your custom embedding generator."""
    ...


async def main() -> None:
    client = MossClient("your-project-id", "your-project-key")

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

    await client.create_index("custom-embeddings", documents)  # Defaults to moss-minilm
    await client.load_index("custom-embeddings")

    results = await client.query(
        "custom-embeddings",
        "<query text>",
        QueryOptions(embedding=my_embedding_model("<query text>"), top_k=10),
    )

    print(results.docs[0].id, results.docs[0].score)


asyncio.run(main())

Leaving the model argument undefined defaults to moss-minilm. Pass QueryOptions to reuse your own embeddings or to override top_k on a per-query basis.

📄 License

This package is licensed under the PolyForm Shield License 1.0.0.

• ✅ Free for testing, evaluation, internal use, and modifications.
• ❌ Not permitted for production or competing commercial use.
• 📩 For commercial licenses, contact: contact@usemoss.dev

📬 Contact

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