Related Products
|
||||||
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
Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed.
Usually the graph requires locking during indexing, which cause stop-the-world. But Vald uses distributed index graph so it continues to work during indexing. Vald implements its own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery.
|
|||||
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
|||||
Audience
Anyone searching for an open-source platform that helps them get the most out of your vector search
|
Audience
Developers interested in a powerful vector search engine and database
|
|||||
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Not Supported
|
|||||
API
Offers API
Not Supported
|
API
Offers API
Not Supported
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
Pricing
Free
Open source
Free Version
Supported
Free Trial
Not Supported
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
|
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
|
|||||
Company InformationEmbedditor
embedditor.ai/
|
Company InformationVald
vald.vdaas.org
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
|
|
|
|||||
Categories |
Categories |
|||||
Integrations
Docker
Supported
GitHub
Supported
Go
Not Supported
IBM watsonx.data
Not Supported
IngestAI
Supported
Java
Not Supported
Kubernetes
Not Supported
Node.js
Not Supported
Python
Not Supported
|
Integrations
Docker
Supported
GitHub
Not Supported
Go
Supported
IBM watsonx.data
Supported
IngestAI
Not Supported
Java
Supported
Kubernetes
Supported
Node.js
Supported
Python
Supported
|
|||||
|
|
|