Multi Index Federated Search With Result Merging

via “multi-index federated search with result merging”

Lightning-fast search engine with vector search.

Unique: Implements federated search by executing queries in parallel across multiple indexes and merging results using configurable weighting, enabling cross-collection search without requiring index consolidation. Results are ranked by combined relevance scores from all indexes.

vs others: Simpler than Elasticsearch cross-cluster search because it operates on local indexes without network overhead; more flexible than Solr collection aliasing because it supports per-index weighting and dynamic index selection.

via “fusion-retrieval-with-multi-strategy-ranking”

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

Unique: Implements Reciprocal Rank Fusion and weighted scoring to combine dense semantic retrieval with sparse keyword retrieval, allowing developers to balance semantic understanding with exact-match precision without choosing one strategy — a hybrid approach that's more robust than single-strategy retrieval

vs others: More comprehensive than pure semantic search because it captures both meaning and keywords, and more practical than pure BM25 because it includes semantic understanding; fusion is more maintainable than building a custom unified ranking function

via “multi-index search and cross-index query federation”

A modular graph-based Retrieval-Augmented Generation (RAG) system

Unique: Enables querying multiple independent GraphRAG indexes with result aggregation and deduplication, supporting federated search scenarios without requiring index merging. Supports cost-optimized search by routing queries to appropriate indexes.

vs others: More flexible than single-index search, and more efficient than merging multiple indexes into one. Enables independent index management while supporting unified query interface.

via “multi-index federated search with result merging”

A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.

Unique: Executes queries in parallel across multiple indexes and merges results using configurable weighting strategies, enabling unified search across logically separate indexes without requiring client-side aggregation or separate API calls

vs others: Simpler than Elasticsearch's cross-cluster search because Meilisearch's federated search is built into the core API and doesn't require separate cluster configuration, though less flexible for complex multi-cluster topologies

via “hybrid-search-with-configurable-fusion”

The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text.

Unique: Implements hybrid search as a first-class SQL query primitive with query planner support, executing vector and BM25 searches in parallel and fusing results inside the database engine; unlike external fusion (e.g., LangChain), maintains transaction semantics and enables index-aware optimization.

vs others: More integrated than Elasticsearch + Pinecone because both search types share query planning and metadata; faster than sequential searches because vector and BM25 indices are queried in parallel within single transaction.

via “hybrid search combining full-text and semantic ranking”

** - Interact & query with Meilisearch (Full-text & semantic search API)

Unique: Orchestrates parallel full-text and semantic search execution through MCP, with configurable fusion algorithms that blend BM25 and vector similarity scores. Abstracts ranking complexity from agents while exposing tuning parameters.

vs others: More flexible than Elasticsearch's hybrid search (which requires custom scoring scripts), simpler than implementing custom fusion logic, and faster than sequential full-text-then-semantic search due to parallel execution

via “federated search across multiple knowledge bases with result ranking”

Unique: Implements federated semantic search with result deduplication and cross-source ranking, enabling unified search across isolated knowledge bases while maintaining data governance boundaries. Supports both synchronous and asynchronous search modes.

vs others: More powerful than searching individual knowledge bases separately, but adds latency and complexity compared to centralized search. Enables data isolation that centralized search cannot provide.

via “parallel multi-source result aggregation and ranking”

Unique: Aggregates and re-ranks results from multiple heterogeneous data sources using a unified neural ranking model rather than returning source-specific results separately, enabling cross-source relevance comparison and unified result ordering.

vs others: Faster and more comprehensive than manually querying multiple search engines or databases separately, though with less control over source selection and weighting than enterprise search platforms like Elasticsearch or Solr.

via “multi-platform unified search”