AI Models API
Embeddings API
Generate vector embeddings from text for semantic search and similarity operations
POST
Embeddings API
The Embeddings API allows you to convert text into numerical vector representations that capture semantic meaning. These embeddings enable powerful operations like:
- Semantic text search
- Text clustering and classification
- Content recommendation
- Similarity comparisons
Available Models
Taam Cloud offers several embedding models with different dimensions and performance characteristics:
Model | Dimensions | Context Window | Best For |
---|---|---|---|
jina-embeddings-v3 | 1024 | 8192 tokens | General purpose, multilingual |
text-embedding-3-large | 3072 | 8191 tokens | High accuracy applications |
text-embedding-3-small | 1536 | 8191 tokens | Efficient, general purpose |
jina-embeddings-v2-base-en | 768 | 8192 tokens | English text optimization |
Example Usage
Response Format
The API returns a list of embedding vectors corresponding to each input text, along with usage information:
Best Practices
- Use consistent embedding models for related datasets
- For long texts, consider chunking into smaller segments
- Normalize embeddings if performing cosine similarity
- Cache embeddings for frequently used content
Authorizations
Enter your API key prefixed with 'Bearer '
Response
200 - application/json
Successful response
The response is of type object
.
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