OpenSearch Configurations

 Enhance document retrieval in OpenSearch for Generative AI applications by utilizing Hybrid Search, RRF, and metadata filtering. These techniques improve recall and precision. They are executed using OpenSearch's k-NN plugin, Search Pipelines, and Machine Learning (ML) connectors.1. Metadata Filtering & EnrichmentInstead of a blind vector search, constrain your semantic results with exact structural attributes.

How it helps: Reduces the search space and ensures the LLM only receives context relevant to specific dates, regions, or document categories.OpenSearch Components: Utilizes Metadata Fields in your mappings along with Post-Filtering or Boolean Query Clauses to combine structured and unstructured data.2.

 Hybrid Search (Semantic + Keyword)Vector embeddings capture context but sometimes miss exact product codes or names. Hybrid search brings the best of both worlds.How it helps: Blends context-aware semantic search with exact-match keyword search (BM25) to catch both synonyms and specific identifiers.

OpenSearch Components: Executed via Hybrid Queries inside the query DSL, which trigger parallel scoring for different search clauses.3. Reciprocal Rank Fusion (RRF)Combining scores from vectors and keyword searches is difficult because they operate on different scales. RRF bypasses score calibration by merging results based solely on their rank.How it helps: Provides robust, out-of-the-box relevance by prioritizing documents that rank highly across multiple search types.

OpenSearch Components: Handled by the Search Pipeline and configured using the score-ranker-processor (available via the Neural Search plugin).4. RerankingFirst-stage retrievals (like vectors) may pull a wide net of documents, but aren't always precise. A reranker re-orders these results.How it helps: Uses specialized cross-encoder ML models to deeply evaluate query-to-document context, bringing the most precise answers to the top before sending them to the LLM.OpenSearch Components: Executed via the Rerank Processor within a Search Pipeline, which can run models locally via OpenSearch’s ML Commons or via external integrations (like Amazon Bedrock or Cohere).