moss-docs

name	moss-docs
description	Documentation and capabilities reference for Moss semantic search.

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
createIndex()	create_index()	Create index with documents
loadIndex()	load_index()	Load index from storage
getIndex()	get_index()	Get index metadata
listIndexes()	list_indexes()	List all indexes
deleteIndex()	delete_index()	Delete an index
addDocs()	add_docs()	Add/upsert documents
getDocs()	get_docs()	Retrieve documents
deleteDocs()	delete_docs()	Remove documents
query()	query()	Semantic / hybrid search

API Actions

All REST API operations go through POST /v1/manage (base URL: https://service.usemoss.dev/v1) with an action field:

Action	Purpose	Extra required fields
initUpload	Get a presigned URL to upload index data	indexName, modelId, docCount, dimension
startBuild	Trigger an index build after uploading data	jobId
getJobStatus	Check the status of an async build job	jobId
getIndex	Fetch metadata for a single index	indexName
listIndexes	Enumerate every index under the project	—
deleteIndex	Remove an index record and assets	indexName
getIndexUrl	Get download URLs for a built index	indexName
addDocs	Upsert documents into an existing index	indexName, docs
deleteDocs	Remove documents by ID	indexName, docIds
getDocs	Retrieve stored documents (without embeddings)	indexName

Workflows

Basic Semantic Search Workflow

1. Initialize MossClient with project credentials
2. Call createIndex() with documents and model options ({ modelId: 'moss-minilm' } in JS; "moss-minilm" string in Python)
3. Call loadIndex() to prepare index for queries
4. Call query() with search text and topK (JS) or QueryOptions(top_k=...) (Python)
5. Process returned documents with scores

Hybrid Search Workflow (Python)

Hybrid blending via alpha is available in the Python SDK via QueryOptions:

1. Create and load index as above
2. Call query() with a QueryOptions object specifying alpha
3. alpha=1.0 = pure semantic, alpha=0.0 = pure keyword, alpha=0.6 = 60/40 blend
4. Default is semantic-heavy for conversational use cases

Document Update Workflow

1. Initialize client and ensure index exists
2. Call addDocs() with new documents (upserts by default — existing IDs are updated)
3. Call deleteDocs() to remove outdated documents by ID

Voice Agent Context Injection Workflow

This is an opt-in integration pattern for voice agent pipelines — it is not automatic behavior of this skill.

1. Initialize MossClient and load index at agent startup
2. In your application code, call query() on each user message to retrieve relevant context
3. Inject search results into the LLM context before generating a response
4. Respond with knowledge-grounded answer (no tool-calling latency)

Offline-First Search Workflow

1. Create index with documents using local embedding model
2. Load index from local storage
3. Query runs entirely on-device with sub-10ms latency
4. Optionally sync to cloud for backup and sharing

Integration

Voice Agent Frameworks

• LiveKit: Context injection into voice agent pipeline with inferedge-moss SDK
• Pipecat: Pipeline processor via pipecat-moss package that auto-injects retrieval results

Context

Authentication

SDK requires project credentials:

• MOSS_PROJECT_ID: Project identifier from Moss Portal
• MOSS_PROJECT_KEY: Project access key from Moss Portal

export MOSS_PROJECT_ID=your_project_id
export MOSS_PROJECT_KEY=your_project_key

REST API requires the following on every request:

• x-project-key header: project access key
• x-service-version: v1 header: API version
• projectId field in the JSON body

curl -X POST "https://service.usemoss.dev/v1/manage" \
  -H "Content-Type: application/json" \
  -H "x-service-version: v1" \
  -H "x-project-key: moss_access_key_xxxxx" \
  -d '{"action": "listIndexes", "projectId": "project_123"}'

Package Installation

Language	Package	Install Command
JavaScript/TypeScript	@inferedge/moss	npm install @inferedge/moss
Python	inferedge-moss	pip install inferedge-moss
Pipecat Integration	pipecat-moss	pip install pipecat-moss

Document Schema

interface DocumentInfo {
  id: string;        // Required: unique identifier
  text: string;      // Required: content to embed and search
  metadata?: object; // Optional: key-value pairs for filtering
}

Query Parameters

Parameter	SDK	Type	Default	Description
indexName	JS + Python	string	—	Target index name (required)
query	JS + Python	string	—	Natural language search text (required)
topK	JS	number	5	Max results to return
top_k	Python	int	5	Max results to return
alpha	Python only	float	~0.8	Hybrid weighting: 0.0=keyword, 1.0=semantic
filters	JS + Python	object	—	Metadata constraints

Model Selection

Model	Use Case	Tradeoff
moss-minilm	Edge, offline, browser, speed-first	Fast, lightweight
moss-mediumlm	Precision-critical, higher accuracy	Slightly slower

Performance Expectations

• Sub-10ms local queries (hardware-dependent)
• Instant index updates without reindexing entire corpus
• Sync is optional; compute stays on-device
• No infrastructure to manage

Chunking Best Practices

• Aim for ~200–500 tokens per chunk
• Overlap 10–20% to preserve context
• Normalize whitespace and strip boilerplate

Common Errors

Error	Cause	Fix
Unauthorized	Missing credentials	Set MOSS_PROJECT_ID and MOSS_PROJECT_KEY
Index not found	Query before create	Call createIndex() first
Index not loaded	Query before load	Call loadIndex() before query()
Missing embeddings runtime	Invalid model	Use moss-minilm or moss-mediumlm

Async Pattern

All SDK methods are async — always use await:

// JavaScript
import { MossClient, DocumentInfo } from '@inferedge/moss'
const client = new MossClient(process.env.MOSS_PROJECT_ID!, process.env.MOSS_PROJECT_KEY!)
await client.createIndex('faqs', docs, { modelId: 'moss-minilm' })
await client.loadIndex('faqs')
const results = await client.query('faqs', 'search text', { topK: 5 })

# Python
import os
from inferedge_moss import MossClient, QueryOptions
client = MossClient(os.getenv('MOSS_PROJECT_ID'), os.getenv('MOSS_PROJECT_KEY'))
await client.create_index('faqs', docs, 'moss-minilm')
await client.load_index('faqs')
results = await client.query('faqs', 'search text', QueryOptions(top_k=5, alpha=0.6))

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For additional documentation and navigation, see: https://docs.moss.dev/llms.txt