How OpenSearch Supercharges Vector Search for Large‑Model Applications

Vector Search Basics

Data can be divided into structured and unstructured types. Structured data is typically queried with databases, while unstructured data (images, video, audio, text) is transformed into vectors for similarity search. Text search can use either inverted indexes or vector similarity.

OpenSearch Vector Search Workflow

Data is ingested into OpenSearch via sources like MaxCompute, OSS, or custom APIs. OpenSearch builds vector indexes in real time, supporting graph, clustering, and linear (brute‑force) algorithms. The graph algorithm (based on HNSW) offers the best recall‑performance trade‑off.

Performance figures show a single 4C‑32G node handling 1 billion 128‑dimensional vectors, supporting tens of thousands of TPS writes, and building a 100 million 384‑dimensional index in about 3.5 hours.

Engineering and Algorithm Optimizations

Recent engineering refactoring simplified the vector engine pipeline and replaced mixed text‑vector indexes with sparse‑vector representations, eliminating the need for separate inverted indexes.

Algorithmic improvements include balanced graph construction and a prediction‑based node‑pruning strategy that reduces traversal during search, achieving up to 90% speedup on the Gist dataset and 20% on Sift.

Performance Benchmarks

Compared with leading open‑source engines on standard datasets, OpenSearch delivers roughly double the throughput at comparable recall levels.

Heterogeneous Computing

OpenSearch distributes vector computation across CPU and GPU, achieving about three‑fold higher QPS for top‑10 recall and up to six‑fold for top‑100 recall when using consumer‑grade GPUs.

Knowledge Memory with Dense and Sparse Vectors

OpenSearch treats vector retrieval as a knowledge memory for large models, using dense vectors for semantic generalization and sparse vectors for precise text matching, enabling two‑stage recall and improved results.

Conversational Search and Smart QA

OpenSearch has evolved toward conversational search, offering an intelligent Q&A product that integrates both proprietary and open‑source large models (pre‑fine‑tuned). The system ingests structured, unstructured, and user‑generated data, performs OCR, text extraction, chunking, vectorization, and stores results for end‑to‑end query processing.

During query time, the platform conducts intent recognition, routing, and, depending on the request, performs vector retrieval, SQL generation, or calls specialized models (e.g., multimodal, text‑to‑image). Plugin mechanisms allow integration with external services such as logistics or HR systems.

SmartArXiv Example

SmartArXiv demonstrates how OpenSearch can connect to the ArXiv database, translate natural‑language queries into SQL for paper search, or invoke a large model to summarize paper content, all configured within the OpenSearch conversational framework.

Overall, OpenSearch provides an end‑to‑end, customizable platform for enterprise knowledge Q&A and conversational search, encouraging developers to build their own dialogue flows and integrate proprietary data.