Embedditor is an open-source embeddable editor, similar to MS Word, that helps you fully leverage vector search. Through a user-friendly interface, enhance your embedding metadata and embedding tags. Seamlessly apply advanced NLP cleaning techniques, such as TF-IDF, normalization, and enrichment of your embedding tags, boosting the efficiency and accuracy of LLM-based applications. Optimize the relevance of content retrieved from your vector database. Intelligently split or merge content based on its structure, adding blank or hidden tags to enhance semantic consistency within blocks. You have complete control over your data, with Embedditor deploying easily on personal computers, dedicated enterprise clouds, or on-premise environments, prioritizing data security. By applying Embedditor's advanced cleaning techniques, you can filter out irrelevant tags in your embeddings, such as stop words, punctuation, and low-relevance common words, potentially saving up to 40% on embedding and vector storage costs while achieving better search results.