lm.embed

The lm.embed tool generates a dense vector embedding for a text string via OpenRouter. Embeddings can be used for semantic search, similarity comparison, or as input to downstream ML models.

Capability Required

tool.invoke:lm.embed

Input Schema

{
  "type": "object",
  "required": ["text"],
  "properties": {
    "text": {
      "type": "string",
      "description": "The text to embed."
    },
    "model": {
      "type": "string",
      "description": "Embedding model ID (optional)."
    }
  }
}

Output Schema

{
  "type": "object",
  "properties": {
    "embedding": {
      "type": "array",
      "items": { "type": "number" },
      "description": "The dense vector embedding."
    },
    "dimensions": {
      "type": "integer",
      "description": "Number of dimensions in the embedding."
    },
    "cost": {
      "type": "number",
      "description": "Approximate USD cost."
    }
  }
}

Examples

#![allow(unused)]
fn main() {
let result = agent.invoke_tool("lm.embed", json!({
    "text": "Renewable energy sources include solar and wind power.",
    "model": "openai/text-embedding-3-small"
})).await?;

let embedding: Vec<f64> = serde_json::from_value(result["embedding"].clone())?;
println!("Dimensions: {}", result["dimensions"]); // e.g. 1536
}

Use Cases

• Semantic search: Embed documents and queries, compute cosine similarity
• Clustering: Group similar observations or memory entries
• RAG (Retrieval-Augmented Generation): Embed documents at ingestion, retrieve at query time
• Deduplication: Find near-duplicate content by embedding distance

Cost

Estimated cost: 0.1 (actual cost varies by model and text length).

Network Policy

Requires spec.network.policy: full or allowlist with openrouter.ai:443.