moss-docs

# Moss Agent Skills

## Capabilities

Moss is the real-time semantic search runtime for conversational AI. It delivers sub-10ms lookups and instant index updates that run in the browser, on-device, or in the cloud - wherever your agent lives. Agents can create indexes, embed documents, perform semantic/hybrid searches, and manage document lifecycles without managing infrastructure. The platform handles embedding generation, index persistence, and optional cloud sync - allowing agents to focus on retrieval logic rather than infrastructure.

## Skills

### Index Management

- **Create Index**: Build a new semantic index with documents and embedding model selection
- **Load Index**: Load an existing index from persistent storage for querying
- **Get Index**: Retrieve metadata about a specific index (document count, model, etc.)
- **List Indexes**: Enumerate all indexes under a project
- **Delete Index**: Remove an index and all associated data

### Document Operations

- **Add Documents**: Insert or upsert documents into an existing index with optional metadata
- **Get Documents**: Retrieve stored documents by ID or fetch all documents
- **Delete Documents**: Remove specific documents from an index by their IDs

### Search & Retrieval

- **Semantic Search**: Query using natural language with vector similarity matching
- **Keyword Search**: Use BM25-based keyword matching for exact term lookups
- **Hybrid Search**: Blend semantic and keyword search with configurable alpha weighting (Python SDK)
- **Metadata Filtering**: Constrain results by document metadata (category, language, tags)
- **Top-K Results**: Return configurable number of best-matching documents with scores

### Embedding Models

- **moss-minilm**: Fast, lightweight model optimized for edge/offline use (default)
- **moss-mediumlm**: Higher accuracy model with reasonable performance for precision-critical use cases

### SDK Methods

| JavaScript        | Python             | Description                    |...