Description
🖼️ Tool Name:
Embedditor
🔖 Tool Category:
Open-source embeddings editor; it falls under developer tools for vector search/RAG preprocessing and prompt-engineering workflows.
✏️ What does this tool offer?
Embedditor is an open-source, MS-Word-style GUI for preparing and editing GPT/LLM embeddings to improve vector search relevance and reduce embedding/storage costs. It lets you join/split chunks, edit metadata & tokens, and apply NLP cleansing (e.g., TF-IDF, normalization, stop-word removal) through a friendly UI.
⭐ What does the tool actually deliver based on user experience?
• GUI to upload content, split/merge chunks, and edit embedding tokens/metadata.
• Advanced preprocessing options (TF-IDF/normalize/enrich) to boost search accuracy.
• Workflow aimed at better RAG/vector search relevance and lower token/vector costs.
• Self-host options (GitHub/Docker) for local or private deployments.
🤖 Does it include automation?
Yes — automated text cleansing, token enrichment/normalization, and structure-aware chunk management to produce semantically coherent chunks for retrieval.
💰 Pricing Model:
Free/open-source. Public code and images are available via GitHub and Docker Hub; no official paid tiers are listed on the site/repo.
🆓 Free Plan Details:
• Open-source repo (MIT/OSS posture) with install/run instructions
• Docker image for quick self-hosting.
💳 Paid Plan Details:
• Not publicly specified by the publisher; some directories mention “try for free,” but official pricing pages are absent. Verify in-product or with the maintainers.
🧭 Access Method:
• Web app (host it yourself) via Docker or from source
• Product page & docs from IngestAI; code on GitHub.
🔗 Experience Link:
https://github.com/IngestAI/embedditor