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Optimizing filtered vector queries from tens of seconds to single-digit milliseconds in PostgreSQL
I built 50+ RAGs in 2 years. Here are the architectures that get products out the door!
[R] The Massive Legal Embedding Benchmark (MLEB) and Voyage/Cohere/Jina training on user data?
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weaviate
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
qdrant
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
milvus
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
meilisearch
A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.
lance
Modern columnar data format for ML and LLMs implemented in Rust. 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..
llama_index
LlamaIndex is the leading framework for building LLM-powered agents over your data.
kuzu
Embedded property graph database built for speed. Vector search and full-text search built in. Implements Cypher.