moss-client 1.0.0b9

Python SDK for semantic search with on-device AI capabilities

Moss client library for Python

moss-client 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

📦 Installation

pip install moss-client

🚀 Quick Start

import asyncio
from inferedge_moss import MossClient, DocumentInfo

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, "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?",
        top_k=3,
        alpha=0.6  # blend semantic (0.6) and keyword (0.4) scores
    )

    # 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

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-client

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 inferedge_moss import MossClient, DocumentInfo

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, "moss-minilm")
    await client.load_index("my-docs")
    
    # Search
    results = await client.query("my-docs", "programming language", 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", 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", alpha=1.0)

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

📄 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: [e-mail]

📬 Contact

For support, commercial licensing, or partnership inquiries, contact us: [e-mail]