Embeddings API

Generate and manage vector embeddings for your content using LanOnasis's advanced embedding models.

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 <your-api-key>

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
text	string	Yes	Text content to generate embeddings for
model	string	No	Embedding model to use (default: text-embedding-3-large)
dimensions	number	No	Number of dimensions for the embedding (default: 1536)
metadata	object	No	Additional metadata to store with the embedding

Example Request

curl -X POST https://api.lanonasis.com/api/v1/embeddings \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "LanOnasis provides intelligent memory management for AI applications",
    "model": "text-embedding-3-large",
    "dimensions": 1536,
    "metadata": {
      "source": "documentation",
      "category": "introduction"
    }
  }'

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

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

const embedding = await client.generateEmbedding({
  text: "LanOnasis provides intelligent memory management for AI applications",
  model: "text-embedding-3-large",
  dimensions: 1536,
  metadata: {
    source: "documentation",
    category: "introduction"
  }
});

console.log(embedding.vector); // [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(
    text="LanOnasis provides intelligent memory management for AI applications",
    model="text-embedding-3-large",
    dimensions=1536,
    metadata={
        "source": "documentation",
        "category": "introduction"
    }
)

print(embedding.vector)  # [0.1, -0.2, 0.3, ...]

Response

Success Response (200 OK)

{
  "success": true,
  "data": {
    "id": "emb_1234567890abcdef",
    "vector": [0.1, -0.2, 0.3, 0.4, -0.5, ...],
    "model": "text-embedding-3-large",
    "dimensions": 1536,
    "text": "LanOnasis provides intelligent memory management for AI applications",
    "metadata": {
      "source": "documentation",
      "category": "introduction"
    },
    "created_at": "2024-01-15T10:30:00Z",
    "usage": {
      "tokens": 12,
      "cost": 0.0001
    }
  }
}

Error Responses

400 Bad Request

{
  "success": false,
  "error": {
    "code": "INVALID_REQUEST",
    "message": "Text content is required",
    "details": {
      "field": "text",
      "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
text-embedding-3-small	512, 1024, 1536	8191	Fast, cost-effective
text-embedding-3-large	1024, 1536, 3072	8191	High quality, balanced
text-embedding-ada-002	1536	8191	Legacy, stable

Rate Limits

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

Best Practices

1. Batch Processing: Use the batch endpoint for multiple texts
2. Model Selection: Choose appropriate model based on use case
3. Caching: Cache embeddings for repeated text
4. Error Handling: Implement retry logic for rate limits

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