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Typesense  is a modern, privacy-friendly, open source search engine built from the ground up using cutting-edge search algorithms, that take advantage of the latest advances in hardware capabilities. Typesense can be used to build blazing-fast search experiences, faceted navigation experiences, geo-search, vector search, semantic search, similarity search and much more. The Open Source Alternative to Algolia, The Easier To Use Alternative to Elasticsearch

• written in C++. github
• typesense/SHOWCASE.md -- who is using it
• Vector Search related to vector-databases

https://bfritscher.github.io/typesense-dashboard/ via @bigthingist

What is a Vector Database? from the pinecone.io team.

Common Use cases for vector search:

• Semantic Search
• Similarity search for images, audio, video, JSON, and other forms of unstructured data
• Ranking and Recommendation Engines
• Deduplication and record matching
• Anomaly detection (eg: IT Threat detection)

Capabilities of a vector database:

• Vector Indexes for Search and Retrieval
• Single-Stage Filtering
• Data Sharding
• Replication
• Hybrid Storage
• API

Vector databases

• Zvec | A lightweight, lightning-fast, in-process vector database Feb 2026 from Alibaba

• pinecone

• qdrant

• chroma

• FAISS

  • Uber Eats is using OpenSearch + FAISS for 400-dimensional embeddings across every restaurant/dish on Uber Eats. -- "They cut index build time from 12.5 hours down to 2.5 hours mostly through Spark tuning and running separate clusters for indexing vs serving (so they don't tank prod during builds). Query latency dropped from 250ms to under 120ms by disabling _source fields and tweaking shard counts." via

• Elasticsearch

• typesense

• Vespa.AI

• turbopuffer is a serverless vector database.

• sdan/vlite: fast vector database made in numpy -- "there is no database you need to set up, no server to run, and no complex configuration. just install vlite and start using it. take the CTX file with you wherever you go. its like a browser cookie but with embeddings."

• Vector Similarity Search in DuckDB – DuckDB

• lancedb

• yichuan-w/LEANN: RAG on Everything with LEANN.. "Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device." LEANN achieves this through graph-based selective recomputation with high-degree preserving pruning, computing embeddings on-demand instead of storing them all. See (Wang et al. 2025)

Articles

• Let's check back in on the vector databases; apr 2024
• Building vector search in 200 lines of Rust
• Posts · The Data Quarry -- a few articles on vector databases by Prashanth Rao at The Data Quarry
• VectorDB -- see explanation of "Why use vector search and embeddings with large language models?" with example. Uses FAISS and mrpt under the hood.
• Build a search engine, not a vector DB; Dec 2023.
• I'm writing a new vector search SQLite Extension; May 2024.

References

• Wang, Liu, Li, Wu, Mao, Zhao, Yan, Xu, Zhou, Stoica, Min, Zaharia, Gonzalez (2025). LEANN: A Low-Storage Vector Index.