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About

Improve your embedding metadata and embedding tokens with a user-friendly UI. Seamlessly apply advanced NLP cleansing techniques like TF-IDF, normalize, and enrich your embedding tokens, improving efficiency and accuracy in your LLM-related applications. Optimize the relevance of the content you get back from a vector database, intelligently splitting or merging the content based on its structure and adding void or hidden tokens, making chunks even more semantically coherent. Get full control over your data, effortlessly deploying Embedditor locally on your PC or in your dedicated enterprise cloud or on-premises environment. Applying Embedditor advanced cleansing techniques to filter out embedding irrelevant tokens like stop-words, punctuations, and low-relevant frequent words, you can save up to 40% on the cost of embedding and vector storage while getting better search results.

About

Local and secure AI on your desktop, ensuring comprehensive insights with complete data security and privacy. Experience unparalleled efficiency, privacy, and intelligence with our cutting-edge macOS-native app and advanced AI features. RAG can utilize data from a local knowledge base to supplement the large language model (LLM). This means you can keep sensitive data on-premises while leveraging it to enhance the model‘s response capabilities. To implement RAG locally, you first need to segment documents into smaller chunks and then encode these chunks into vectors, storing them in a vector database. These vectorized data will be used for subsequent retrieval processes. When a user query is received, the system retrieves the most relevant chunks from the local knowledge base and inputs these chunks along with the original query into the LLM to generate the final response. We promise lifetime free access for individual users.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Anyone searching for an open-source platform that helps them get the most out of your vector search

Audience

Enterprises and individuals requiring a tool to search, integrate, and display their local files and knowledge base

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Embedditor
embedditor.ai/

Company Information

Klee
kleedesktop.com

Alternatives

Alternatives

Azure AI Search

Azure AI Search

Microsoft
Cohere

Cohere

Cohere AI
Voyage AI

Voyage AI

MongoDB

Categories

Categories

Integrations

Codestral
Docker
GitHub
Hugging Face
Le Chat
Llama
Llama 2
Llama 3.1
Llama 3.2
Mathstral
Meta Pixel
Ministral 3B
Ministral 8B
Mistral AI
Mistral Large
Mistral NeMo
Mistral Small
OpenAI
Pixtral Large
Swift

Integrations

Codestral
Docker
GitHub
Hugging Face
Le Chat
Llama
Llama 2
Llama 3.1
Llama 3.2
Mathstral
Meta Pixel
Ministral 3B
Ministral 8B
Mistral AI
Mistral Large
Mistral NeMo
Mistral Small
OpenAI
Pixtral Large
Swift
Claim Embedditor and update features and information
Claim Embedditor and update features and information
Claim Klee and update features and information
Claim Klee and update features and information