lance Vector Vector Vector

GitHub Repo https://github.com/lance-format/lance-flink

lance-format/lance-flink

Apache Flink Connector for Lance Vector Database

GitHub Repo https://github.com/lance-format/lance-cuvs

lance-format/lance-cuvs

cuVS-backed vector build backend for Lance

GitHub Repo https://github.com/csclyde/VectorLance

csclyde/VectorLance

No repository description available.

GitHub Repo https://github.com/rustic-ai/uni-db

Uni is a modern, embedded database that combines property graph (OpenCypher), vector search, and columnar storage (Lance) into a single, cohesive engine. It is designed for applications requiring local, fast, and multimodal data access, backed by object storage (S3/GCS) durability.

GitHub Repo https://github.com/scientist-labs/lancelot

scientist-labs/lancelot

Ruby bindings for the Lance columnar data format. Built on the Lance Rust crate, Lancelot brings high-performance vector search, full-text search, and hybrid retrieval to Ruby applications with a native, idiomatic API.

GitHub Repo https://github.com/lancedb/lancedb-vercel-chatbot

lancedb/lancedb-vercel-chatbot

Build an AI chatbot with website context retrieved from a vector store like LanceDB.

GitHub Repo https://github.com/gordonmurray/firnflow

gordonmurray/firnflow

The cost efficiency of S3 with the speed of local RAM. A multi-tenant vector and full-text search engine featuring a tiered RAM → NVMe → S3 architecture for microsecond latency on top of object storage

GitHub Repo https://github.com/prrao87/lancedb-study

prrao87/lancedb-study

Comparing LanceDB and Elasticsearch for full-text search and vector search performance

GitHub Repo https://github.com/CortexReach/memory-lancedb-pro

CortexReach/memory-lancedb-pro

Enhanced LanceDB memory plugin for OpenClaw — Hybrid Retrieval (Vector + BM25), Cross-Encoder Rerank, Multi-Scope Isolation, Management CLI

GitHub Repo https://github.com/lance-format/lance

lance-format/lance

Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..