Embeddings

Generate vector embeddings from input text.

Live model (2026-09-04): Qwen/Qwen3-Embedding-0.6B, 1024 dimensions, 32768 max input tokens. Confirmed by GET /v1/models on the embeddings service and by GET /v1/stats on the data layer (embedding_dimension: 1024).

Endpoint

POST /v1/embeddings

Request Body

Parameter	Type	Required	Description
model	string	Yes	Qwen/Qwen3-Embedding-0.6B
input	string/array	Yes	Text to embed (string or array of strings)
encoding_format	string	No	float (default) or base64

Example Request

Single Input

curl https://api.solidrust.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "Qwen/Qwen3-Embedding-0.6B",
    "input": "What is semantic search?"
  }'

Batch Input

curl https://api.solidrust.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "Qwen/Qwen3-Embedding-0.6B",
    "input": [
      "First document to embed",
      "Second document to embed",
      "Third document to embed"
    ]
  }'

Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0047, 0.0112]
    }
  ],
  "model": "Qwen/Qwen3-Embedding-0.6B",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Embedding Dimensions

Model	Dimensions	Max input
Qwen/Qwen3-Embedding-0.6B	1024	32768 tokens

bge-m3 is not deployed. Do not send that id.

Use Cases

• Semantic Search - Find similar documents by meaning
• RAG Applications - Retrieve relevant context for LLM prompts
• Clustering - Group related content together
• Classification - Use embeddings as features for ML models

Best Practices

Optimization Tips

• Batch multiple inputs in a single request for efficiency
• Precompute and cache embeddings for static content
• Use the same model for queries and documents

Related

• RAG Guide - Building retrieval-augmented generation systems
• Document Q&A Example - Complete RAG implementation