Semantic Indexing for Search, Clustering & Entity Resolution
VectorLink is a vector database that is part of TerminusCMS. It is a semantic indexer, that enables you to use advanced AI techniques and OpenAI with your data and content.
Using OpenAI Vector Embeddings
VectorLink provides developers and non-technical users with semantic data and content management tools using vector embeddings.
It uses OpenAI to define the embeddings, and after an indexing request is made to TerminusCMS, your documents are sent to OpenAI which returns a list of float vectors in JSON.
To obtain good semantics, VectorLink creates high-quality text embeddings by defining a GraphQL query together with a Handlebars template rendering it as text so OpenAI can understand it better.
After your data is indexed, you can ask the semantic index server questions about your data and the magic of the vector proximity produces semantic results.
{
"embedding": {
"query": "query($id: ID){ People(id : $id) { birth_year, created, desc, edited, eye_color, gender, hair_colors, height, homeworld { label }, label, mass, skin_colors, species { label }, url } }",
"template": "The person's name is {{label}}.{{#if desc}} They are described with the following synopsis: {{#each desc}} *{{this}} {{/each}}.{{/if}}{{#if gender}} Their gender is {{gender}}.{{/if}}{{#if hair_colors}} They have the following hair colours: {{hair_colors}}.{{/if}}{{#if mass}} They have a mass of {{mass}}.{{/if}}{{#if skin_colors}} Their skin colours are {{skin_colors}}.{{/if}}{{#if species}} Their species is {{species.label}}.{{/if}}{{#if homeworld}} Their homeworld is {{homeworld.label}}.{{/if}}"
}
}
Using the People class in the Star Wars demo project, this example shows the GraphQL query and Handlebars template definition.
More Details
Semantic Search
Find answers to difficult questions. What did data look like a year ago, what products are impacted by a component shortage?
Entity Resolution
See what records refer to the same real-world thing and resolve these entities into a single record.
Clustering
Cluster content and data with similar traits and segment it into groups for analysis and action.
The TerminusDB Semantic Indexer
VectorLink Semantic Uses
The ability to semantically index your data and leverage OpenAI provides the following features –
Semantic Search
Achieve precise and contextually relevant search results with vector embeddings.
Entity Resolution
Identify and eliminate duplicate content using intelligent entity resolution powered by vector embeddings
Similarity Search
Enhance content discovery and recommendation systems with advanced similarity search capabilities
Clustering
Organize and categorize content seamlessly using clustering techniques powered by vector embeddings
Incremental Indexing
Keep up-to-date with real-time content changes through incremental indexing, ideal for CI workflows
VectorLink Sectors
Biotech & Pharmaceutical
Aid research through academic paper semantic search, clustering data points and test results, and finding similarities in datasets
Corporate Intelligence
Resolve and harmonize company or investor records
Manufacturing Supply Chain
Resolve data from many databases, cluster data to identify patterns, and apply semantic search for more accurate and context-aware search capabilities
E-commerce
Personalize product recommendations and improve search accuracy for online shoppers
Media and Publishing
Manage large volumes of articles, images, and videos efficiently, delivering targeted content
Knowledge Management
Organize and retrieve information from complex document repositories with ease
Digital Marketing
Optimize content discovery, recommendation engines, and targeted campaigns for effective customer engagement
Research and Development
Rapidly access relevant scientific articles and research papers to accelerate innovation
Financial Services
Fraud detection, customer identity matching, risk assessment, and due diligence are some ways financial services organizations can utilize TerminusCMS
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.
Start For Free
Bring your own OpenAI key and unleash the power of intelligent content management with TerminusCMS and vector embeddings. Experience enhanced efficiency, relevance, and user experience in managing digital content. Start leveraging the future of content management for developers today.