Overview

Why Vector Search Engine
In this video, we explain the core technologies and use cases. We provide a detailed explanation of how LLM and embedding technologies are leveraged to extract the user's true intent.
Why Vector Search Engine

Product video
Why VectorEngine dynamically extracts the "WHY" (motives/emotions) from global text inputs using LLMs and 3072-dimensional embeddings. It transcends traditional keyword limitations by applying cosine similarity to deliver deeply relevant, intent-based search results. Furthermore, it utilizes XAI (Explainable AI) to generate transparent, natural-language evidence for all automated selections. Ideal for SNS moderation, customer support, and HR, it bridges the human-machine communication gap effectively. Please refer to the specifications stored at the following link. https://docs.google.com/spreadsheets/d/19BJYMK6__RRokLtswzTZLgkhwLrmxlrA/edit?usp=drive_link&ouid=102842108128709956183&rtpof=true&sd=true
Highlights
- Enhances search efficiency by extracting true user intent and emotional context from text using LLMs and 3072-dimensional vectors.
- Transcends keyword search limits by retrieving intent-based information via cosine similarity.
- Provides transparent, natural-language evidence for results using Explainable AI (XAI).
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Cost/hour |
|---|---|
t3.medium Recommended | $1.00 |
c5.large | $1.00 |
t3.large | $1.00 |
m5.large | $1.00 |
Vendor refund policy
Due to the nature of the AMI product, we do not offer refunds.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Initial release of the Why Vector Search Engine
Additional details
Usage instructions
Connect: ssh -i <your-key.pem> ec2-user@<instance-ip> Setup: Create .env (set OPENAI_API_KEY) and input.txt Run & Query: /usr/local/bin/trinity_rust --why output.json input.txt /usr/local/bin/trinity_rust --vec output.json vector.json /usr/local/bin/trinity_rust --query "Your question?" vector.json
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