Category: Vector Database
Articles tagged Vector Database
Elasticsearch BBQ vs. OpenSearch FAISS: Vector search performance comparison
A performance comparison between Elasticsearch BBQ and OpenSearch FAISS.
Elasticsearch vector database for native grounding in Google Cloud’s Vertex AI Platform
Explore how Elasticsearch, the first third-party native grounding engine for Google Cloud’s Vertex AI, lets you build custom GenAI experiences by grounding Gemini models in enterprise data.
Speeding up merging of HNSW graphs
Explore the work we’ve been doing to reduce the overhead of building multiple HNSW graphs, particularly reducing the cost of merging graphs.
Scaling late interaction models in Elasticsearch - part 2
This article explores techniques for making late interaction vectors ready for large-scale production workloads, such as reducing disk space usage and improving computation efficiency.
Exploring GPU-accelerated vector search in Elasticsearch with NVIDIA: Chapter I
Powered by NVIDIA cuVS, the collaboration looks to provide developers with GPU-acceleration for vector search in Elasticsearch.
Searching complex documents with ColPali - part 1
The article introduces the ColPali model, a late-interaction model that simplifies the process of searching complex documents with images and tables, and discusses its implementation in Elasticsearch.
Semantic text in Elasticsearch: Simpler, better, leaner, stronger
Our latest semantic_text iteration brings a host of improvements. In addition to streamlining representation in _source, benefits include reduced verbosity, more efficient disk utilization, and better integration with other Elasticsearch features. You can now use highlighting to retrieve the chunks most relevant to your query. And perhaps best of all, it is now a generally available (GA) feature!
Unifying Elastic vector database and LLM functions for intelligent query
Leverage LLM functions for query parsing and Elasticsearch search templates to translate complex user requests into structured, schema-based searches for highly accurate results.
Elasticsesarch semantic search, leveled up: now with native match, knn and sparse_vector support
Semantic text search becomes even more powerful, with native support for match, knn and sparse_vector queries. This allows us to keep the simplicity of the semantic query while offering the flexibility of the Elasticsearch query DSL.
Filtered HNSW search, fast mode
Explore the improvements we have made for HNSW vector search in Apache Lucene through our ACORN-1 algorithm implementation.
Alibaba Cloud AI Service & Elasticsearch: Embeddings and reranking
Using Alibaba Cloud AI Service features with Elasticsearch.
