FAQ from Embedditor.ai
What is Embedditor.ai?
Embedditor.ai is an advanced, open-source tool for embedding, designed to enhance the efficiency of vector searches. It offers a user-friendly interface to improve embedding metadata and tokens, utilizing NLP techniques like TF-IDF normalization. The tool also optimizes content relevance and can be deployed locally, in the cloud, or on-premises, offering significant cost savings.
How to use Embedditor.ai?
1. Download the Docker image from Embedditor's GitHub.
2. Run the Docker image.
3. Access the UI through a web browser.
4. Improve embedding metadata and tokens using the interface.
5. Apply NLP techniques for better token quality.
6. Optimize content relevance.
7. Use features for content splitting or merging.
8. Add void or hidden tokens.
9. Deploy locally or in the cloud.
10. Save costs by filtering irrelevant tokens.
Can Embedditor be deployed locally or on a cloud platform?
Yes, Embedditor.ai supports deployment on local PCs, dedicated enterprise clouds, or on-premises environments.
What benefits does Embedditor offer for vector search?
Embedditor.ai enhances vector search relevance by managing content structure and introducing void or hidden tokens for better semantic coherence.
How does Embedditor reduce costs?
By filtering out irrelevant tokens, Embedditor.ai reduces embedding and vector storage costs by up to 40%, while improving search results.
What languages does Embedditor support?
The supported languages depend on the NLP models used. Refer to the documentation or contact support for details.