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    1. Home
    2. Vector Database Extensions
    3. Apache Solr Dense Vector Search

    Apache Solr Dense Vector Search

    Vector search capabilities in Apache Solr with HNSW indexing, early termination optimization, and integrated text-to-vector capabilities for hybrid search applications.

    Overview

    Apache Solr's Dense Vector Search adds support for indexing and searching dense numerical vectors, enabling k-nearest neighbor (kNN) searches with various similarity functions and advanced filtering capabilities.

    Recent Enhancements (2025-2026)

    Early Termination Strategy

    Apache Solr 10.0.0 introduces PatienceKnnVectorQuery that allows searches to exit early when the HNSW queue remains saturated over a saturation threshold, reducing query latency and resource usage with minimal impact on recall.

    Text-to-Vector Module

    Solr 9.8 introduced a module enabling transparent end-to-end semantic search by integrating with LLM providers (OpenAI, Cohere, HuggingFace, Mistral AI) via LangChain4j library.

    ACORN Algorithm

    ACORN is an algorithm designed to make hybrid searches consisting of a filter and a vector search more efficient, based on research from 2024.

    Core Functionality

    • HNSW Indexing: Hierarchical Navigable Small World graphs for efficient ANN search
    • Multiple Similarity Functions: Support for cosine, dot product, and euclidean distance
    • Filtered Search: Combine vector search with traditional Solr queries
    • Seeded Queries: Start vector search from specific points
    • Hybrid Search: Semantic fusion combining keyword and vector search

    Performance Optimizations

    • Early termination for faster queries
    • Configurable HNSW parameters (M, efConstruction)
    • Batch indexing support
    • Query-time optimization strategies

    Integration Capabilities

    • LangChain4j for LLM provider integration
    • Support for OpenAI, Cohere, HuggingFace, Mistral AI embeddings
    • REST API for all operations
    • Compatible with existing Solr ecosystem

    Use Cases

    • Hybrid search combining keywords and semantics
    • E-commerce product search with image similarity
    • Document retrieval with semantic understanding
    • Recommendation systems
    • Question-answering applications

    Pricing

    Free and open-source under Apache 2.0 license. No licensing costs for any use case.

    Surveys

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    Information

    Websitesolr.apache.org
    PublishedMar 25, 2026

    Categories

    1 Item
    Vector Database Extensions

    Tags

    4 Items
    #open-source#hybrid-search#java#search-engine

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