OpenSearch AI
OpenSearch AI is a powerful open-source search and analytics suite that integrates generative AI capabilities, large language models, and semantic search to deliver intelligent search experiences and data insights.
https://kaisouai.com/
Product Information
Updated:Feb 16, 2025
OpenSearch AI Monthly Traffic Trends
OpenSearch AI experienced a 30.5% decline in traffic, reaching 38.7K visits. The significant drop may be influenced by the broader market focus on generative AI and the launch of major updates by competitors such as Google's Gemini 2.0 Flash and Microsoft's $80 billion investment in AI-enabled datacenters.
What is OpenSearch AI
OpenSearch AI represents the AI-enhanced evolution of the OpenSearch project, which is a community-driven, Apache 2.0-licensed search and analytics suite built on Apache Lucene. Starting from version 2.9, OpenSearch introduced neural search capabilities and AI/ML connectors that allow seamless integration with large language models and AI services like Amazon Bedrock and SageMaker. It provides developers with flexible tools for building generative AI experiences while maintaining its core strengths in search, analytics, and data visualization.
Key Features of OpenSearch AI
OpenSearch AI is a community-driven, open-source search and analytics suite that integrates advanced AI/ML capabilities including vector search, neural search, and generative AI features. It extends the core OpenSearch functionality with machine learning capabilities to enable semantic understanding, vector database operations, and AI-powered search applications while maintaining compatibility with popular AI frameworks like LangChain.
Vector Search and Database Capabilities: Supports k-NN search and vector database operations with efficient filtering through FAISS engine, enabling scalable similarity search for AI applications
ML Commons Integration: Built-in plugin for managing ML models, allowing users to use pre-trained models, upload custom models, or connect to external foundation models
Neural Search Framework: Integrated neural search capability that simplifies the process of converting documents and queries into vector embeddings for semantic search
RAG Support: Native support for Retrieval Augmented Generation, including templates and vector store integration with LangChain for building AI-powered chatbots
Use Cases of OpenSearch AI
AI-Powered Search Applications: Build intelligent search systems with semantic understanding and contextual awareness for improved search relevance
Log Analytics: Apply AI and ML techniques to analyze large volumes of log data for IT operations and security insights
Conversational AI: Create generative chatbots and interactive search experiences using RAG and LLM integration
Document Intelligence: Process and analyze complex datasets with AI-powered features to extract actionable insights
Pros
100% open-source with Apache 2.0 license allowing full customization and commercial use
Strong community support and regular feature updates
Seamless integration with popular AI frameworks and services
Cons
Requires technical expertise to set up and configure
Managing ML models and infrastructure can be complex
How to Use OpenSearch AI
Set up OpenSearch Environment: Ensure you have a running OpenSearch instance. You can either set it up locally or use a managed service like Amazon OpenSearch Service.
Configure ML Model Integration: Set up AI/ML connectors to services like Amazon SageMaker or Amazon Bedrock, or use OpenSearch's pre-trained models. Navigate to OpenSearch Dashboards Security section to configure the ml_full_access role.
Create Vector Database: Set up vector database capabilities to store and manage AI-generated embeddings. Configure indexes to support vector search operations with k-NN functionality.
Enable Neural Search: Configure neural search features to transform document text into vector embeddings during ingestion. This allows for semantic understanding and similarity searches.
Set up RAG Pipeline: Create a Retrieval-Augmented Generation (RAG) pipeline by creating a connector to a model, setting up a search pipeline, and ingesting RAG data into an index.
Configure Conversation Memory: Enable conversation memory and RAG pipeline features by creating a memory ID and setting up appropriate security permissions for user interactions.
Implement Search Features: Choose and implement the desired search type: semantic search, hybrid search, or sparse search based on your use case requirements.
Test and Deploy: Test the implementation with sample queries and deploy to production once satisfied with the results. Monitor performance metrics through OpenSearch Dashboards.
OpenSearch AI FAQs
OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite built on Apache Lucene that makes it easy to ingest, search, visualize, and analyze data.
Analytics of OpenSearch AI Website
OpenSearch AI Traffic & Rankings
38.7K
Monthly Visits
#905456
Global Rank
#8177
Category Rank
Traffic Trends: Jun 2024-Jan 2025
OpenSearch AI User Insights
00:01:59
Avg. Visit Duration
1.79
Pages Per Visit
64.01%
User Bounce Rate
Top Regions of OpenSearch AI
CN: 95.19%
TW: 2.82%
HK: 1.99%
Others: NAN%
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