Enhance Data Exploration with VectorLink's Semantic Search
Discover deeper insights and uncover meaningful connections in your data with VectorLink’s powerful semantic search. Search with meaning.
Entity Resolution with VectorLink
VectorLink is designed to enhance your data exploration and content management with its powerful semantic search functionality.
Utilizing the power of LLMs, VectorLink employs vector embeddings. When you make an indexing request data is sent to the AI model, which returns a list of float vectors in JSON format.
To ensure high-quality semantics, VectorLink creates superior text embeddings by defining a GraphQL query alongside a Handlebars template, rendering it as text. This approach enables OpenAI to better understand the nuances of your data, resulting in more accurate and relevant search results.
Once your data is indexed, you can unleash the power of the semantic index server. By simply asking questions about your data, VectorLink’s vector proximity magic comes into play, generating semantic results that go beyond traditional keyword matching.
Discover hidden relationships, uncover patterns, and gain a deeper understanding of your data through intuitive and context-aware search queries.
"Wise old man"
"The person's name is Yoda. They are described with the following synopsis: Yoda is a fictional character in the Star Wars franchise created by George Lucas, first appearing in the 1980 film The Empire Strikes Back. In the original films, he trains Luke Skywalker to fight against the Galactic Empire. In the prequel films, he serves as the Grand Master of the Jedi Order and as a high-ranking general of Clone Troopers in the Clone Wars. Following his death in Return of the Jedi at the age of 900, Yoda was the oldest living character in the Star Wars franchise in canon, until the introduction of Maz Kanata in Star Wars: The Force Awakens. Their gender is male. They have the following hair colours: white. They have a mass of 17. Their skin colours are green."
With a Star Wars dataset, we used VectorLink’s semantic search to prompt ‘wise old man’. The answer came back as Yoda.
Key Features
VectorLink’s semantic search help you to derive value from your data and content with a quick search. It’s key features are –
Efficient Tools
Efficient semantic data and content management tools
OpenAI Integration
Integration with OpenAI for advanced vector embeddings
Customizable Rendering
Customizable GraphQL queries and Handlebars templates for superior text rendering
Context-Aware Search
Intuitive search queries leveraging vector proximity for accurate and context-aware results
Deeper Insights
Uncover hidden relationships and patterns in your data for deeper insights
4 Steps to Get Started
Add your OpenAI key to your team
Visit your profile page and copy and paste the key from OpenAI and turn indexing on.
Configure Vector Embeddings
Create the vector embeddings for the document classes you want to index.
Index Your Data
To begin indexing your data, create a change request and approve it. Indexing is versioned, so happens on a commit level.
Use VectorLink
Ask the semantic index server questions about your data and content to gain a deeper understanding of it.
Leverage AI's Potential
If you need to leverage the potential of your data and content using generative AI to solve difficult problems, our consultancy services can speed up the project. Enquire today to see about working with us.