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Our semantic search solutions use vector similarity search to discover deeper insights and meaningful connections in your data.
VectorLink enhances your data exploration 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. The vector database features an index based on Hierarchical Navigable Small World graphs which is amongst the top-performing indexes for vector similarity search.
To enable the LLM to understand your data, we utilize GraphQL queries and Handlebars templates to render your data and its context as text. This ensures high-quality semantics 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, VectorLinkâs 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.
The semantic index server provides –
Tried and tested technology across data, text and unstructured documents.
Integration with OpenAI for advanced vector embeddings.
Customizable GraphQL queries and Handlebars templates for superior text rendering.
Intuitive search queries leveraging vector proximity for accurate and context-aware results.
Empower domain teams to uncover hidden relationships and patterns in your data for deeper insights
If you need to leverage the potential of combining your data with generative AI to solve difficult problems, our AI consultancy services can speed up the project. Enquire today to see about working with us.
We are AI consultants who combine our vast data knowledge with VectorLink to incorporate your structured and unstructured data with LLMs to solve challenging problems quickly.
