Technical Specifications of text-embedding-3-small
| Item | Details |
|---|---|
| Model ID | text-embedding-3-small |
| Model type | Text embedding model |
| Description | A small text embedding model for efficient processing. |
| Primary use | Converting text into dense vector embeddings for semantic search, retrieval, clustering, classification, and similarity tasks |
| Input modality | Text |
| Output modality | Embedding vectors |
| Context suitability | Optimized for efficient text embedding workloads |
| Typical integration pattern | API-based embedding generation for downstream NLP and retrieval systems |
What is text-embedding-3-small?
text-embedding-3-small is a text embedding model designed to transform text into numerical vector representations that capture semantic meaning. These embeddings make it easier for applications to compare pieces of text by similarity rather than exact keyword matching.
Because it is a small embedding model, text-embedding-3-small is well suited for efficient processing in production systems that need fast turnaround and scalable embedding generation. It can be used in workflows such as semantic search, recommendation pipelines, document retrieval, deduplication, intent matching, and knowledge base indexing.
Main features of text-embedding-3-small
- Efficient embedding generation: Designed for fast and lightweight text-to-vector conversion in applications that need responsive performance.
- Semantic understanding: Encodes text into embeddings that help capture meaning and contextual similarity beyond simple lexical overlap.
- Scalable deployment: Suitable for high-volume pipelines such as indexing documents, search corpora, FAQs, product catalogs, or support content.
- Versatile downstream usage: Supports use cases including retrieval, reranking preparation, clustering, classification, recommendation, and duplicate detection.
- API-friendly integration: Works well in modern application stacks that rely on programmatic embedding generation through hosted APIs.
How to access and integrate text-embedding-3-small
Step 1: Sign Up for API Key
To get started, sign up on the CometAPI platform and generate your API key from the dashboard. After obtaining the key, store it securely and use it to authenticate all requests to the text-embedding-3-small API.
Step 2: Send Requests to text-embedding-3-small API
Once you have your API key, send HTTPS requests to the CometAPI endpoint and specify text-embedding-3-small as the model. Include your input text in the request body and ensure your authorization header is properly configured.
curl https://api.cometapi.com/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_API_KEY" \
-d '{
"model": "text-embedding-3-small",
"input": "Your text goes here"
}'
Step 3: Retrieve and Verify Results
After the request is processed, the API returns embedding data for the supplied input. Verify that the response includes the expected vector output, confirm the request completed successfully, and then store or pass the embeddings into your search, retrieval, ranking, or analytics pipeline.