embeddings · diff
git:20260130.78e49cb to git:20260202.77a3ed3
4 added, 3 removed. Audit A to A.
---
name: embeddings
description: "Vector embeddings configuration and semantic search"
emoji: "🧬"
---
# Embeddings - Complete API Reference
Configure embedding providers, manage vector storage, and perform semantic search.
---
## Chat Commands
### View Config
```
/embeddings Show current settings
/embeddings status Provider status
/embeddings stats Cache statistics
```
### Configure Provider
```
/embeddings provider openai Use OpenAI embeddings
/embeddings provider voyage Use Voyage AI
/embeddings provider local Use local model
/embeddings model text-embedding-3-small Set model
```
### Cache Management
```
/embeddings cache stats View cache stats
/embeddings cache clear Clear cache
/embeddings cache size Total cache size
```
### Testing
```
/embeddings test "sample text" Generate test embedding
/embeddings similarity "text1" "text2" Compare similarity
```
---
## TypeScript API Reference
### Create Embeddings Service
```typescript
import { createEmbeddingsService } from 'clodds/embeddings';
const embeddings = createEmbeddingsService({
// Provider
provider: 'openai', // 'openai' | 'voyage' | 'local' | 'cohere'
apiKey: process.env.OPENAI_API_KEY,
// Model
model: 'text-embedding-3-small',
dimensions: 1536,
// Caching
cache: true,
cacheBackend: 'sqlite',
cachePath: './embeddings-cache.db',
// Batching
batchSize: 100,
maxConcurrent: 5,
});
```
### Generate Embeddings
```typescript
// Single text
const embedding = await embeddings.embed('Hello world');
console.log(`Dimensions: ${embedding.length}`);
// Multiple texts (batched)
const vectors = await embeddings.embedBatch([
'First document',
'Second document',
'Third document',
]);
```
### Semantic Search
```typescript
// Search against stored vectors
const results = await embeddings.search({
query: 'trading strategies',
collection: 'documents',
limit: 10,
threshold: 0.7,
});
for (const result of results) {
console.log(`${result.text} (score: ${result.score})`);
}
```
### Similarity
```typescript
// Compare two texts
const score = await embeddings.similarity(
'The cat sat on the mat',
'A feline rested on the rug'
);
console.log(`Similarity: ${score}`); // 0.0 - 1.0
```
### Store Vectors
```typescript
// Store embedding with metadata
await embeddings.store({
collection: 'documents',
id: 'doc-1',
text: 'Original text',
embedding: vector,
metadata: {
source: 'wiki',
date: '2024-01-01',
},
});
// Store batch
await embeddings.storeBatch({
collection: 'documents',
items: [
{ id: 'doc-1', text: 'First doc' },
{ id: 'doc-2', text: 'Second doc' },
],
});
```
### Cache Management
```typescript
// Get cache stats
const stats = await embeddings.getCacheStats();
console.log(`Cached: ${stats.count} embeddings`);
console.log(`Size: ${stats.sizeMB} MB`);
console.log(`Hit rate: ${stats.hitRate}%`);
// Clear cache
await embeddings.clearCache();
// Clear specific entries
await embeddings.clearCache({ olderThan: '7d' });
```
### Provider Configuration
```typescript
// Switch provider
embeddings.setProvider('voyage', {
apiKey: process.env.VOYAGE_API_KEY,
model: 'voyage-large-2',
});
- // Use local model
+ // Use local model (Transformers.js)
+ // No API key required - runs locally via @xenova/transformers
embeddings.setProvider('local', {
- modelPath: './models/all-MiniLM-L6-v2',
+ model: 'Xenova/all-MiniLM-L6-v2', // 384 dimensions
});
```
---
## Providers
| Provider | Models | Quality | Speed | Cost |
|----------|--------|---------|-------|------|
| **OpenAI** | text-embedding-3-small/large | Excellent | Fast | $0.02/1M |
| **Voyage** | voyage-large-2 | Excellent | Fast | $0.02/1M |
| **Cohere** | embed-english-v3 | Good | Fast | $0.10/1M |
- | **Local** | all-MiniLM-L6-v2 | Good | Slow | Free |
+ | **Local (Transformers.js)** | Xenova/all-MiniLM-L6-v2 | Good | Medium | Free |
---
## Models
### OpenAI
| Model | Dimensions | Best For |
|-------|------------|----------|
| `text-embedding-3-small` | 1536 | General use |
| `text-embedding-3-large` | 3072 | High accuracy |
### Voyage
| Model | Dimensions | Best For |
|-------|------------|----------|
| `voyage-large-2` | 1024 | General use |
| `voyage-code-2` | 1536 | Code search |
---
## Use Cases
### Semantic Memory Search
```typescript
// Store user memories
await embeddings.store({
collection: 'memories',
id: 'mem-1',
text: 'User prefers conservative trading',
});
// Search memories
const relevant = await embeddings.search({
query: 'what is user risk preference',
collection: 'memories',
limit: 5,
});
```
### Document Similarity
```typescript
// Find similar documents
const similar = await embeddings.findSimilar({
text: 'How to trade options',
collection: 'docs',
limit: 5,
});
```
---
## Best Practices
1. **Use caching** — Avoid redundant API calls
2. **Batch requests** — More efficient than single calls
3. **Choose dimensions wisely** — Balance quality vs storage
4. **Monitor costs** — Embeddings can add up
5. **Local for development** — Use local model to save costs