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VectorLink

The AI Engineer presents VectorLink

Overview

Using cloud embeddings, VectorLink enables semantic search, entity resolution, and clustering on TerminusDB data.

Description

Making sense of relationships in data or finding precise answers to queries can be challenging, especially when dealing with complex, interconnected data.

That's why the team at TerminusDB created VectorLink - a production-ready semantic search add-on for their distributed Git-like database. It lets AI developers apply cutting-edge NLP to enhance data exploration.

💡 VectorLink Key Highlights

✅ Create custom text embeddings of your data with cloud models

✅ Index these vectors to enable semantic similarity searches

🔎 Empower precise queries that uncover hidden insights through vector proximity scoring

VectorLink with TerminusDB is attractive for use cases such as:

📈 Intelligent clustering to reveal patterns

🔍 Accurate semantic search across data silos

🤖 Entity resolution to merge duplicates

With VectorLink, we can apply modern NLP techniques to structured data to develop sophisticated knowledge graphs.

🤔 Why should The AI Engineer care about VectorLink?

  1. 👩‍💻 It enables building sophisticated semantic search, clustering, and entity resolution capabilities on top of existing data infrastructure with just a few additional configuration lines.
  2. 🚀 It indexes custom OpenAI embeddings created from data to unlock similarity queries that uncover hidden insights in data relationships.
  3. 🔭 It reveals patterns and connections in data through clustering algorithms applied to vectorized data.
  4. 🔎 It powers semantic search across data silos by scoring vector proximity to find the most relevant results.
  5. 🤖 It merges duplicate records through clever entity resolution techniques using vector space analysis.

📊 Tell me more about VectorLink!

🖇️ Where can I find out more about VectorLink?


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