OpenSearch Vector Search

OpenSearch Vector Search is the vector similarity and AI search capability within the OpenSearch engine. It provides a complete vector database solution that lets you store and search vector embeddings alongside traditional indexed data to power semantic search, RAG, recommendations, and other AI applications.

Key Facts

• Product type: Vector search / vector database capability within OpenSearch
• Category: Multi-model & hybrid databases
• Vendor / Project: OpenSearch
• Docs: https://docs.opensearch.org/latest/vector-search/

Features

Core Vector Database Capabilities

• Store and search vector embeddings together with existing OpenSearch documents.
• Support for vector indices using k-NN / ANN style search.
• Raw vector similarity search over embedding fields.
• Integration with OpenSearch’s existing indexing, mappings, and query DSL.

AI & Semantic Search

• Semantic search using dense vector embeddings.
• Retrieval-Augmented Generation (RAG) support by using vector search to retrieve context for LLMs.
• Conversational search scenarios using embeddings and OpenSearch queries.
• Recommendation systems and similar AI-powered applications built on top of embeddings.

Hybrid & Multimodal Search

• Hybrid search combining vector similarity with traditional keyword / sparse search.
• Multimodal search support (e.g., combining different data modalities via embeddings).
• Neural sparse search capabilities for improved lexical relevance using neural models.

Embedding & Model Integration

• Use user-provided embeddings (generated externally and indexed into OpenSearch).
• Optionally use embeddings generated automatically by OpenSearch’s ML Commons plugin.
• Model registration & deployment:
  • Register ML models (e.g., Hugging Face sentence-transformers models) via the ML Commons APIs.
  • Asynchronous model registration with task IDs and status tracking via the Get ML Task API (/_plugins/_ml/tasks/).
• On-cluster model inference on OpenSearch data nodes (configurable via cluster settings).
  • Example: enabling plugins.ml_commons.only_run_on_ml_node = false for running models on data nodes in non-production environments.

Ingest Pipelines & Automatic Embedding Generation

• Ingest pipelines for automatic embedding generation during indexing.
• text_embedding ingest processor to:
  • Call a registered ML model by model_id.
  • Map input text fields to output vector fields via field_map (e.g., question_text → question_vector).
• Ability to test pipelines and models using prediction endpoints (e.g., /_plugins/_ml/_predict/text_embedding/).
• Support for returning embedding vectors and specifying target_response fields (e.g., sentence_embedding).

Indexing & Search Configuration

• Creation of vector indices with k-NN enabled via settings like "index.knn": true.
• Ability to associate a default ingest pipeline with an index (e.g., "default_pipeline": "nlp-index-pipeline") so that embeddings are generated automatically on document ingestion.
• Use of OpenSearch mappings to define vector fields and other document fields.

Tooling & Ecosystem

• Works within the broader OpenSearch platform, including:
  • OpenSearch as the core search & analytics engine.
  • OpenSearch Dashboards for visualization and exploration (implied from images / platform context).
• Integration with ML Commons plugin APIs for model lifecycle and inference.

Typical Use Cases

• Building semantic search over text corpora.
• Implementing RAG pipelines where OpenSearch retrieves relevant context for LLMs.
• Conversational search and chat-style interfaces over enterprise or product data.
• Product and content recommendations using similarity search.
• Hybrid search experiences combining keyword relevance with vector semantics.

Pricing

• No pricing information is provided in the referenced documentation. OpenSearch is an open-source project; deployment costs will depend on the chosen hosting / infrastructure.