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When was the last time you tried finding logs that “felt” similar, but weren’t exact keyword matches? Classic string search just isn’t enough for today’s semantic-heavy, high-volume workloads. Enter vector search—an approach where data is embedded as high-dimensional vectors, enabling fast, intelligent similarity lookups at any scale. GreptimeDB 0.10+ makes this state-of-the-art feature available for real observability and data intelligence scenarios.

Why Vector Search Changes the Observability Game

• Move beyond simple keyword queries—find related logs, documents, or traces based on meaning, not just words.

• Combined with time stamp-based indexing, you can filter, sort, and retrieve forensics data more accurately than ever.

How Does It Work Under the Hood?

Data (e.g., log messages or document descriptions) gets transformed into numeric vectors—think of them as digital “fingerprints.” GreptimeDB integrates the VSAG library for ultra-fast, high-dimensional matching.

• Text (or even images) are embedded via popular models like sentence-transformers.

• Vectors are stored efficiently in GreptimeDB—batch insert supports easy entry of millions of log embeddings.

• Similarity search becomes as simple as running vec_dot_product() over stored rows.

Case Study: Finding Related Articles in Seconds

Using the AG News dataset, running a similarity search for "China Sports" pulled up the Yao Ming headline instantly—something a keyword-only query didn’t achieve nearly as well.

Here’s a quick sample:

SELECT title, description, genre, vec_dot_product(embedding, :embedding) AS score
FROM news_articles
ORDER BY score DESC
LIMIT 10;

Now try the same with keyword matching—the results are much less precise, missing subtle context.

Comparing Keyword vs. Vector Search

• Keyword Matching: Fast and easy, but poor at surfacing semantic connections.

• Vector Search: Finds what you “mean,” not just what you type. Essential for fuzzy troubleshooting, similar log detection, or document clustering.

Not in the Docs? Advanced Roadmap

• Continued expansion to multimodal data (images, structured telemetry).

• Tighter integration with AI/ML pipelines for detection and alerting.

• Open-source community plug-and-play for custom vector indexes.

Make Your Data Work Harder, Not Your Team

GreptimeDB with vector search offers a smarter layer of log and trace intelligence for cloud-native ops, AIOps, and next-gen analytics. Curious? Fork or try the vector demo today.