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VectorLink - The TerminusDB Semantic Indexer - Logo White

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.


Cluster content and data with similar traits and segment it into groups for analysis and action.

"What products are impacted by a 4 week shipping delay for component ID SRL23891"
"Let me see all people similar to Joe Doe"
"What are the vulnerabilities for version 8 of the hosted product"
VectorLink - The TerminusDB Semantic Indexer - Logo White

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


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


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.