Embeddings API

Generate OpenAI-compatible vector embeddings for your content using the LanOnasis embeddings endpoint backed by Voyage AI.

Endpoint

POST /api/v1/embeddings

Description

The Embeddings API allows you to convert text into high-dimensional vector representations that can be used for semantic search, similarity matching, and other AI-powered applications.

Authentication

Authorization: Bearer lano_your_api_key_here

Request

Headers

Header	Type	Required	Description
Authorization	string	Yes	Bearer token for API authentication
Content-Type	string	Yes	Must be application/json
X-Client-Version	string	No	Client SDK version for analytics

Body Parameters

Parameter	Type	Required	Description
input	string or string[]	Yes	Text content or list of texts to embed
model	string	No	Embedding model to use (default: voyage-4)

Example Request

curl -X POST https://api.lanonasis.com/api/v1/embeddings \
  -H "Authorization: Bearer lano_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "LanOnasis provides intelligent memory management for AI applications",
    "model": "voyage-4"
  }'

import { MemoryClient } from '@lanonasis/memory-client';

const client = new MemoryClient({
  apiKey: 'your-api-key',
  baseUrl: 'https://api.lanonasis.com'
});

const embedding = await client.generateEmbedding({
  input: "LanOnasis provides intelligent memory management for AI applications",
  model: "voyage-4"
});

console.log(embedding.data[0].embedding); // [0.1, -0.2, 0.3, ...]

from lanonasis import MemoryClient

client = MemoryClient(
    api_key="your-api-key",
    base_url="https://api.lanonasis.com"
)

embedding = client.generate_embedding(
    input="LanOnasis provides intelligent memory management for AI applications",
    model="voyage-4",
)

print(embedding["data"][0]["embedding"])  # [0.1, -0.2, 0.3, ...]

Response

Success Response (200 OK)

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [0.1, -0.2, 0.3, 0.4, -0.5],
      "index": 0
    }
  ],
  "model": "voyage-4",
  "usage": {
    "prompt_tokens": 12,
    "total_tokens": 12
  }
}

Error Responses

400 Bad Request

{
  "success": false,
  "error": {
    "code": "INVALID_REQUEST",
      "message": "Input is required",
      "details": {
      "field": "input",
      "reason": "missing_required_field"
    }
  }
}

401 Unauthorized

{
  "success": false,
  "error": {
    "code": "UNAUTHORIZED",
    "message": "Invalid or missing API key",
    "details": {
      "reason": "invalid_api_key"
    }
  }
}

429 Rate Limited

{
  "success": false,
  "error": {
    "code": "RATE_LIMITED",
    "message": "Rate limit exceeded",
    "details": {
      "limit": 1000,
      "remaining": 0,
      "reset_at": "2024-01-15T11:00:00Z"
    }
  }
}

Supported Models

Model	Dimensions	Max Tokens	Use Case
voyage-4	1024	Provider managed	Default production model

Rate Limits

• Free Tier: 1,000 requests/hour
• Pro Tier: 10,000 requests/hour
• Enterprise: Custom limits

Best Practices

1. Batch Processing: Use input: string[] to embed multiple texts in one request
2. Model Selection: Omit model unless you have an explicit provider override requirement
3. Caching: Cache embeddings for repeated text
4. Error Handling: Implement retry logic for rate limits and upstream provider failures

Use Cases

• Semantic Search: Find similar content based on meaning
• Recommendation Systems: Suggest related items
• Content Classification: Categorize text automatically
• Similarity Matching: Find duplicate or similar content