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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

The Parallel Search API is a web-search tool engineered specifically for AI agents, designed from the ground up to provide the most information-dense, token-efficient context for large-language models and automated workflows. Unlike traditional search engines optimized for human browsing, this API supports declarative semantic objectives, allowing agents to specify what they want rather than merely keywords. It returns ranked URLs and compressed excerpts tailored for model context windows, enabling higher accuracy, fewer search steps, and lower token cost per result. Its infrastructure includes a proprietary crawler, live-index updates, freshness policies, domain-filtering controls, and SOC 2 Type 2 security compliance. The API is built to fit seamlessly within agent workflows: developers can control parameters like maximum characters per result, select custom processors, adjust output size, and orchestrate retrieval directly into AI reasoning pipelines.

About

Combine semantic relevance and user feedback to reliably retrieve the optimal document chunks in your retrieval augmented generation system. Combine semantic relevance and document freshness in your search system, because more recent results tend to be more accurate. Build a real-time personalized ecommerce product feed with user vectors constructed from SKU embeddings the user interacted with. Discover behavioral clusters of your customers using a vector index in your data warehouse. Describe and load your data, use spaces to construct your indices and run queries - all in-memory within a Python notebook.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

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

Developers and AI labs interested in a solution providing web retrieval, semantic context and optimized token-use for large-language-model reasoning

Audience

Organizations wanting a data engineer solution to turn data into vector embeddings

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

$5 per 1,000 requests
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

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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

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

Training

Documentation
Webinars
Live Online
In Person

Company Information

Embedditor
embedditor.ai/

Company Information

Parallel
United States
parallel.ai/products/search

Company Information

Superlinked
Founded: 2021
United States
superlinked.com

Alternatives

Alternatives

txtai

txtai

NeuML

Alternatives

3RDi Search

3RDi Search

The Digital Group
Cohere

Cohere

Cohere AI
txtai

txtai

NeuML
Koog

Koog

JetBrains

Categories

Categories

Categories

Integrations

Amazon Web Services (AWS)
Docker
GPT-4.1
GitHub
IngestAI
Model Context Protocol (MCP)
OpenAI
Python

Integrations

Amazon Web Services (AWS)
Docker
GPT-4.1
GitHub
IngestAI
Model Context Protocol (MCP)
OpenAI
Python

Integrations

Amazon Web Services (AWS)
Docker
GPT-4.1
GitHub
IngestAI
Model Context Protocol (MCP)
OpenAI
Python
Claim Embedditor and update features and information
Claim Embedditor and update features and information
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Claim Parallel and update features and information
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Claim Superlinked and update features and information