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---
title: FFEmbeds
emoji: 😻
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 5.49.1
license: apache-2.0
short_description: Qwen 4b Embeddings for Games
---
# 😻 FF Embeddings API
**Powered by Qwen3-Embedding-4B** - Advanced 4B-parameter multilingual embedding model with matryoshka truncation
## 🚀 Quick Start
### Python
```python
import requests
import numpy as np
url = "https://YOUR_USERNAME-fairfate-embeddings.hf.space/api/predict"
# Generate embeddings
texts = [
"Storm King's Thunder - Epic D&D 5E giant-themed adventure",
"Curse of Strahd - Gothic horror campaign in Ravenloft"
]
response = requests.post(
url,
json={
"data": [texts, True, 1536], # [texts, use_instruction, dimensions]
"fn_index": 0
}
)
result = response.json()
embeddings = np.array(result["data"][0])
# Calculate similarity
def cosine_similarity(a, b):
return np.dot(a, b) # Already normalized
similarity = cosine_similarity(embeddings[0], embeddings[1])
print(f"Similarity: {similarity:.3f}") # ~0.65 (related but different)
```
### TypeScript/JavaScript
```typescript
interface EmbeddingResponse {
data: number[][][];
}
async function getEmbeddings(
texts: string[],
useInstruction: boolean = true,
dimensions: number = 1536
): Promise<number[][]> {
const response = await fetch(
'https://YOUR_USERNAME-fairfate-embeddings.hf.space/api/predict',
{
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
data: [texts, useInstruction, dimensions],
fn_index: 0
})
}
);
const result: EmbeddingResponse = await response.json();
return result.data[0];
}
// Usage
const embeddings = await getEmbeddings([
"Pathfinder 2E Core Rulebook",
"D&D 5E Player's Handbook"
]);
// Calculate cosine similarity (already normalized)
function cosineSimilarity(a: number[], b: number[]): number {
return a.reduce((sum, val, i) => sum + val * b[i], 0);
}
const similarity = cosineSimilarity(embeddings[0], embeddings[1]);
console.log(`Similarity: ${similarity.toFixed(3)}`); // ~0.78 (both RPG rulebooks)
```
### cURL
```bash
curl -X POST \
https://YOUR_USERNAME-fairfate-embeddings.hf.space/api/predict \
-H "Content-Type: application/json" \
-d '{
"data": [
["Cyberpunk detective noir campaign for Shadowrun"],
true,
1536
],
"fn_index": 0
}'
```
## 📖 API Reference
### Endpoint
```
POST /api/predict
```
### Request Format
```json
{
"data": [
["text1", "text2", ...], // Array of texts to embed
true, // use_instruction (boolean)
1536 // output_dimensions (32-3584, production: 1536)
],
"fn_index": 0
}
```
### Response Format
```json
{
"data": [
[
[0.123, -0.456, 0.789, ...], // First embedding (1536 dims)
[-0.234, 0.567, -0.890, ...] // Second embedding (1536 dims)
]
]
}
```
### Parameters
| Parameter | Type | Default | Range | Description |
|-----------|------|---------|-------|-------------|
| `texts` | string[] | required | - | Texts to embed |
| `use_instruction` | boolean | true | - | Add instruction prefix (recommended) |
| `output_dimensions` | number | 1536 | 32-2560 | Output embedding size (production: 1536) | |