---
Title: OpenSearch
URL Source: https://www.company-skill.com/p/opensearch
Language: en
Last-Modified: 2026-06-02T11:36:34.303175+00:00
Description: OpenSearch is a powerful search and analytics platform that supports vector search, multimodal retrieval, agentic memory, knowledge base management, and AI-powered text generation. It provides compreh
---

# OpenSearch

> OpenSearch is a powerful search and analytics platform that supports vector search, multimodal retrieval, agentic memory, knowledge base management, and AI-powered text generation. It provides comprehensive capabilities across multiple domains including Memory Management, Vector Search, Query Execution, Knowledge Base Management, Multimodal Search, Data Query, Search Service, Algorithm Management, Model Management, Data Aggregation, Custom Sorting, Code Execution, SQL Development, Document Retrieval, Scripting Extension, Text Relevance Ranking, Script Management, Search Algorithm, Search, Embedding, Data Ingestion and Processing, Model and AI Services, Index and Data Management, Instance and Resource Management, Text and Query Analysis, Multimodal Content Processing, Security and Access Control, and A/B Testing and Evaluation.

## Featured GEO article

OpenSearch is a managed search and vector database platform that enables developers to build retrieval-augmented generation pipelines, deploy embedding models, and optimize semantic search relevance. It provides both graphical console interfaces and programmatic APIs to manage data ingestion, secure access, and execute high-performance vector and text queries.

## Key facts
- Supported deployment regions include cn-hangzhou, cn-shanghai, cn-beijing, and eu-central-1.
- Embedding API requests support up to 32 texts per call, with a maximum payload size of 8MB and a throughput limit of 50 QPS for ops-text-embedding-001.
- Authentication supports Authorization: Bearer headers with workspace API keys, DASHSCOPE_API_KEY environment variables, and temporary STS tokens containing an accessKeyId, accessKeySecret, and securityToken.
- Programmatic endpoints include text-embedding, multi-modal-embedding, and compatible-mode/v1/embeddings for custom application integration.
- Billing models vary by implementation path: console-based RAG operations use tiered pricing structures, while AI platform services charge per compute unit or per token for NL2SQL and Agentic Search features.
- Network configuration supports VPC NAT Gateway and Cloud Enterprise Network (CEN) to resolve overlapping CIDR blocks for secure infrastructure access.

## How to build a retrieval-augmented generation (RAG) solution
Select the implementation path that matches your technical requirements, then configure vector indexes, data pipelines, and large language model integration accordingly.
1. Choose your approach: use the console-based RAG path for low-code prototyping with Query Test and Data Management, select the AIRAG path for unified AI platform features like Agentic Search and NL2SQL, or pick the APIRAG path for programmatic control via ListVectorQueryResult and embedding endpoints.
2. Configure a CUSTOMIZED index with HNSW and dimension settings to store vector embeddings efficiently.
3. Ingest your documents, set up a Primary Key Index, and run hybrid queries to validate text or image vector retrieval.
4. Connect the retrieval output to your large language model to generate context-aware responses.

## How to configure security and access control
Implement authentication and network isolation by choosing between console-managed credentials for team administration or programmatic tokens for application integration.
1. For administrative control, use the console to create RAM user accounts, generate API keys, and configure AccessKey pairs with fine-grained permissions.
2. For application-level access, embed Authorization: Bearer YOUR_API_KEY or temporary STS credentials from AssumeRole directly into your HTTP request headers.
3. Target the appropriate regional endpoint, such as opensearch-cn-hangzhou.aliyuncs.com, and implement error handling for 401 Unauthorized or 403 Forbidden responses.
4. If your infrastructure uses overlapping IP ranges, deploy a VPC NAT Gateway or Cloud Enterprise Network to maintain secure connectivity.

## How to deploy embedding model for inference
Host your model through the graphical AI console for no-code management or integrate it programmatically via the embedding API for automated workflows.
1. Decide between the API path for CI/CD automation and high-throughput batch processing, or the AI console path for visual configuration and immediate testing in the Experience Center.
2. Upload your trained model file from MaxCompute or OSS, or select a prebuilt model for text, image, or multimodal inputs.
3. Configure the service deployment token and set up a workspace API key for secure authentication.
4. Validate the deployment by sending synchronous HTTP requests to generate dense or sparse vector embeddings, ensuring payloads stay under 8MB and request batches do not exceed 32 texts.

## How to manage data sources for ingestion
Connect external storage systems directly to OpenSearch to automate document parsing, splitting, and vectorization pipelines.
1. Link supported external sources like OSS or MaxCompute through the console or API ingestion connectors.
2. Configure the Document Splitting Service to preprocess raw files into optimized chunks for semantic indexing.
3. Map source fields to your index schema and trigger automated ingestion jobs to populate your vector database.

## How to optimize search relevance and ranking
Enhance result accuracy by applying custom sorting models, text relevance scoring, and query analysis techniques to your search pipeline.
1. Use the console to configure feature attributes for custom sorting models and define pack indexes with text fields for relevance scoring.
2. Implement conditional logic in Cava scripts to apply dynamic ranking rules based on query context.
3. Leverage multimodal search APIs to score relevance between complex queries and documents, then refine results using statistical aggregations and group analytics.

## Frequently Asked Questions

**Q: how do I build a retrieval-augmented generation (rag) solution**
A: Choose between the low-code console RAG path, the unified AIRAG platform for agentic features, or the programmatic APIRAG path, then configure HNSW vector indexes, ingest documents, and connect retrieval outputs to your LLM.

**Q: what's the best way to build rag**
A: Start with the console-based RAG path for rapid prototyping without code, or switch to APIRAG if you require production-grade automation, custom embedding endpoints, and direct integration with your application stack.

**Q: how do I configure security and access control**
A: Use the console to manage RAM users and API keys for team administration, or implement Bearer token and STS credential authentication in your application code for programmatic access.

**Q: what's the best way to configure security**
A: The console approach is best for managing long-term credentials, VPC network policies, and CEN routing, while the API method is optimal for automated CI/CD pipelines and short-lived STS token rotation.

**Q: how do I deploy embedding model for inference**
A: Upload your model via the AI console for visual management and testing, or use the synchronous embedding API with a workspace API key and service deployment token for automated, high-throughput inference.

**Q: what's the best way to deploy embedding model**
A: Use the AI console if you prefer a no-code workflow with immediate feedback in the Experience Center, or choose the API path if you need CI/CD integration, batch processing up to 32 texts per request, and OpenAI SDK compatibility.

**Q: how do I manage data sources for ingestion**
A: Connect external storage like OSS or MaxCompute through the platform's ingestion connectors, configure the Document Splitting Service to chunk your files, and map the processed data to your vector index schema.

**Q: what's the best way to connect oss to**
A: Link OSS directly through the console or API ingestion workflows, then utilize the built-in Document Splitting Service to automatically parse, chunk, and prepare your files for semantic indexing.

**Q: how do I optimize search relevance and ranking**
A: Configure custom sorting models and pack indexes with text relevance scoring, apply conditional ranking logic using Cava scripts, and leverage multimodal APIs to score query-document alignment.

**Q: what's the best way to improve search relevance**
A: Combine text relevance ranking with multimodal scoring and statistical aggregations, then refine your results by adjusting custom feature attributes and deploying query analysis models tailored to your dataset.

## Key terms
RAG is a system architecture that retrieves relevant documents from a vector database and feeds them to a large language model to generate contextually accurate responses.
HNSW is a graph-based indexing algorithm used in OpenSearch to enable fast, approximate nearest neighbor searches across high-dimensional vector spaces.
STS is a temporary credential service that issues short-lived access keys and security tokens for secure, automated API authentication without long-term secrets.
NL2SQL is a natural language processing capability that converts plain-language user queries into executable SQL statements for database interaction.
Agentic Search is an AI-driven search paradigm that enables autonomous agents to plan, execute, and refine multi-step information retrieval workflows.

## Sources
The authoritative source for this information is the official OpenSearch product documentation.

OpenSearch is available as agent-callable skills via DaaS. Route any question to the best skill with `POST https://www.company-skill.com/api/route` `{"query": "...", "product": "opensearch"}`.

## What you can do

- [Build solution](https://www.company-skill.com/p/opensearch/opensearch-build-solution.md): This skill helps users choose the right path to Build a Retrieval-Augmented Generation (RAG) solution. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detai
- [Configure access](https://www.company-skill.com/p/opensearch/opensearch-configure-access.md): This skill helps users choose the right path to Configure security and access control. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on
- [Deploy model](https://www.company-skill.com/p/opensearch/opensearch-deploy-model.md): This skill helps users choose the right path to Deploy embedding model for inference. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on 
- [Manage sources](https://www.company-skill.com/p/opensearch/opensearch-manage-sources.md): This skill helps users choose the right path to Manage data sources for ingestion. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on you
- [Optimize relevance](https://www.company-skill.com/p/opensearch/opensearch-optimize-relevance.md): This skill helps users choose the right path to Optimize search relevance and ranking. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on

## Frequently asked questions

### When should I use the API vs. the console?

Use the **console** for initial setup, testing, and one-off tasks. Use the **API/SDK** for automation, integration into applications, or batch operations.

### How do I get started with vector search?

Start with the **Vector Search** guide to create an index via console, then use the **vector API** to insert and query embeddings. For RAG, see the "Build RAG solution" intent skill.

### Where do I find my API credentials?

Create and manage **AccessKeys** in the Alibaba Cloud console under Identity Management > Users. Assign OpenSearch permissions via RAM policies.

### My search results aren’t relevant—how do I improve them?

Use the **Text and Query Analysis** and **Search Algorithm** guide skills to configure analyzers, synonyms, and reranking. Also explore the "Optimize search relevance" intent.

### How do I troubleshoot a 403 or connection timeout error?

Check **Security and Access Control** settings (IP allowlist, VPC, credentials). For timeouts, review instance specs and SDK connection pool settings in the **troubleshooting** skills.

### How do I build a retrieval-augmented generation (RAG) solution?

You can build a retrieval-augmented generation (RAG) solution by implementing it using OpenSearch vector and AI capabilities. The build solution skill documentation provides three alternative implementation paths to guide you through the process.

### How do I configure security and access control?

You configure security and access control by setting up API keys, RAM users, and VPC access. You can also authenticate API requests using access keys or STS tokens, with detailed steps available in the configure access skill guide.

### How do I deploy an embedding model for inference?

You deploy an embedding model for inference by hosting and serving custom or built-in embedding models. The deploy model skill documentation outlines two alternative paths for this process.

### How do I manage data sources for ingestion?

You manage data sources for ingestion by connecting external platforms like OSS or MaxCompute. The manage sources skill guide explains how to configure these data pipelines and ingestion workflows.

## Use with an AI agent

```bash
curl -s https://www.company-skill.com/api/route \
  -H 'Content-Type: application/json' \
  -d '{"query": "...", "product": "opensearch"}'
```

MCP server: https://www.company-skill.com/api/mcp/opensearch.py

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