Moss client library for Python

inferedge-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.

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Features

• On-device vector search — Sub-millisecond retrieval with zero network latency
• Semantic, keyword, and 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 inferedge-moss

Quick start

import asyncio
from inferedge_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 with cloud fallback

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Available models

Model	Description
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 Moss project credentials from Moss Portal

Environment setup

Install the package:

pip install inferedge-moss

Set up environment variables (optional):

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

Basic usage

import asyncio
from inferedge_moss import MossClient, DocumentInfo, QueryOptions

async def main():
    client = MossClient("your-project-id", "your-project-key")

    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")

    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 controls the balance between semantic similarity and keyword relevance:

Value	Behavior
0.0	Pure keyword search
0.8	Semantic-heavy blend (default)
1.0	Pure embedding search

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

# Default blend
await client.query("my-docs", "programming language")

# Pure embedding search
await client.query("my-docs", "programming language", QueryOptions(alpha=1.0))

Metadata filtering

Pass a metadata filter directly to query() after loading an index:

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"}},
        ]
    },
)

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Providing custom embeddings

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

import asyncio
from inferedge_moss import DocumentInfo, MossClient, QueryOptions


def my_embedding_model(text: str) -> list[float]:
    """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)
    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.

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License

BSD 2-Clause License