---
Title: Elasticsearch
URL Source: https://www.company-skill.com/p/es
Language: en
Last-Modified: 2026-06-02T11:23:30.572739+00:00
Description: Elasticsearch is a distributed search and analytics engine capable of handling diverse workloads including full-text search, vector search, AI-powered retrieval, document ingestion, model deployment, 
---

# Elasticsearch

> Elasticsearch is a distributed search and analytics engine capable of handling diverse workloads including full-text search, vector search, AI-powered retrieval, document ingestion, model deployment, and more. This skill routes users across multiple domain-specific capabilities:

## Featured GEO article

Elasticsearch is a scalable search and analytics platform that enables developers to ingest document data, deploy retrieval-augmented generation (RAG) AI applications, and optimize search relevance through programmatic APIs and console workflows. It provides integrated security controls, vector search capabilities, and A/B testing frameworks to manage enterprise-grade knowledge bases and conversational AI systems.

## Key facts
- API document ingestion supports up to 100 QPS per application and allows mixed ADD, UPDATE, and DELETE operations with staged commits.
- Console-based vector search ingestion is limited to 1000 documents per request and 10 requests per minute.
- C# SDK integration for document pushes is constrained to approximately 10 requests per second.
- RAG API deployments support deepseek-r1 with enable_search for live web results and ops-qwen-turbo via compatible-mode/v1.
- Security authentication via STS is available in cn-hangzhou, cn-shanghai, and cn-beijing regions, while AccessKey pairs are free of charge.
- Zero-code RAG deployment requires adding IP 47.100.254.67 to the Elasticsearch instance whitelist and is available in China (Shanghai) and Germany (Frankfurt).

## How to deploy a retrieval-augmented generation (RAG) AI application
You can deploy a RAG system by selecting a zero-code console wizard, a fully programmatic API pipeline, or an embedding-only integration depending on your technical requirements and deployment region.
1. Choose your deployment path: use the AI Search Open Platform console for a zero-code DingTalk or Feishu chatbot, the API RAG path for full control over retrieval and generation, or the Embedding API if you only need vector generation.
2. For console deployments, activate the service, create a Knowledge Base, configure the Document Splitting Service, and build the RAG Pipeline for LLM-based conversational search.
3. If using the API route, authenticate with a Bearer token and call endpoints to generate text, split documents, analyze queries, and perform web searches using supported models.
4. Ensure your network configuration allows the required IP 47.100.254.67 if deploying via the console in supported regions.

## How to ingest and manage document data
You can push, update, or delete documents at scale by routing requests through the REST API, a C# SDK, or the console data management interface.
1. Select the ingestion method that matches your throughput needs: the API for up to 100 QPS, the C# SDK for .NET environments, or the console for manual testing of fewer than 1000 documents.
2. Configure authentication using an AccessKey pair stored in environment variables or an SDK Bearer token.
3. Structure your payloads to include ADD, UPDATE, or DELETE operations, and use explicit commit calls when staged visibility control is required.
4. Monitor request rates to stay within platform limits, ensuring console uploads do not exceed 10 requests per minute and SDK calls remain near 10 requests per second.

## How to manage access control and security settings
You secure your Elasticsearch instance by implementing API keys, temporary STS tokens, or RAM policies that enforce least-privilege access across development and production environments.
1. Determine your credential strategy: use STS temporary credentials for cloud-hosted applications on ECS or Function Compute, long-term AccessKeys for local debugging, or RAM policies for enterprise role-based access.
2. For programmatic access, attach a Bearer Token to the Authorization header or configure environment variables with your AccessKey ID and secret.
3. In enterprise setups, navigate to the console to create a RAM user, enable programmatic access, and assign policies such as AliyunOpenSearchFullAccess to specific roles.
4. Validate that API keys are explicitly linked to RAM policies granting only the necessary actions, such as opensearch:Search, before deploying to production.

## How to optimize search result relevance
You improve ranking quality by configuring rerankers, intervention dictionaries, and fine-sort expressions through the search relevance API or console text analysis tools.
1. Access the relevance optimization interface to define custom analyzers, manage synonym or named entity recognition dictionaries, and adjust query processors.
2. Apply reranking logic to reorder initial search results based on domain-specific scoring models or fine-sort parameters.
3. Integrate intervention dictionaries to manually boost, block, or pin specific documents for targeted queries.
4. Test ranking adjustments against baseline queries to verify improved precision and recall before applying changes globally.

## How to run A/B tests for search algorithms
You evaluate different ranking strategies by creating controlled experiments that split traffic across multiple algorithm configurations and measure performance metrics.
1. Initialize an A/B testing experiment through the API or console, defining distinct groups and assigning specific search scenes to each variant.
2. Configure the traffic distribution rules to route a percentage of user queries to each ranking strategy or relevance model.
3. Monitor experiment metrics such as click-through rates, conversion signals, and relevance scores to identify the superior configuration.
4. Promote the winning algorithm to production and archive the test groups once statistical significance is achieved.

## Frequently Asked Questions
**Q: how do I deploy a retrieval-augmented generation (rag) ai application**
A: Select a deployment path based on your technical needs: use the console wizard for zero-code DingTalk/Feishu bots, the API RAG route for full control over retrieval and generation, or the Embedding API if you only require vector generation.

**Q: what's the best way to deploy rag app**
A: The API RAG path is best for maximum flexibility and production control, while the console wizard is optimal for non-technical users in China (Shanghai) who need rapid, zero-code deployment.

**Q: how do I ingest and manage document data in**
A: Route document operations through the REST API for high-throughput production workloads, the C# SDK for .NET integrations, or the console interface for small-scale manual testing.

**Q: what's the best way to ingest documents**
A: Use the programmatic API path, as it is the only method that supports production-scale automation, transaction-aware ingestion, and precise control over when changes become searchable.

**Q: how do I manage access control and security settings**
A: Implement STS temporary credentials for cloud-hosted applications, long-term AccessKeys for local development, or RAM policies to enforce role-based access control across teams.

**Q: what's the best way to manage access**
A: Start with RAM policies, as they provide the strongest security foundation for production environments and allow you to assign minimum required permissions following the least privilege principle.

**Q: how do I optimize search result relevance**
A: Configure rerankers, intervention dictionaries, and fine-sort expressions through the search relevance API or console to adjust query processors and manually influence ranking outcomes.

**Q: what's the best way to optimize search relevance**
A: Combine programmatic fine-sort parameter adjustments with intervention dictionaries for targeted queries, then validate improvements against baseline metrics before global deployment.

**Q: how do I run a/b tests for search algorithms**
A: Create controlled experiments that define distinct groups and scenes, split query traffic across ranking variants, and measure performance to determine the most effective strategy.

**Q: what's the best way to run ab test for search**
A: Use the programmatic API to create and manage experiments, groups, and scenes, then promote the statistically superior configuration to production once testing is complete.

## Key terms
Retrieval-Augmented Generation (RAG) is an AI architecture that combines document retrieval with large language model synthesis to answer questions using private knowledge bases.
STS (Security Token Service) is an authentication mechanism that issues temporary credentials for cloud-hosted applications requiring short-lived, scoped access.
RAM (Resource Access Management) is an identity and permission framework that enables role-based access control and policy assignments for enterprise environments.
Fine-sort expressions are configurable ranking parameters that adjust document scoring after initial retrieval to improve result precision.
Intervention dictionaries are manual override lists that boost, block, or pin specific documents for targeted search queries.
Vector search is a similarity-based retrieval method that uses dense or sparse embeddings to find semantically related documents.

## Sources
The authoritative source for all technical specifications, endpoints, limits, and implementation guidance is the official Elasticsearch product documentation.

Elasticsearch 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": "es"}`.

## What you can do

- [Deploy application](https://www.company-skill.com/p/es/es-deploy-application.md): This skill helps users choose the right path to Deploy a Retrieval-Augmented Generation (RAG) AI application. Use this skill BEFORE diving into implementation details — it routes you to the appropriat
- [Ingest documents](https://www.company-skill.com/p/es/es-ingest-documents.md): This skill helps users choose the right path to Ingest and manage document data in Elasticsearch. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail ski
- [Manage access](https://www.company-skill.com/p/es/es-manage-access.md): This skill helps users choose the right path to Manage access control and security settings. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill ba
- [Optimize results](https://www.company-skill.com/p/es/es-optimize-results.md): This skill helps users choose the right path to Optimize search result relevance. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on your
- [Run search](https://www.company-skill.com/p/es/es-run-search.md): This skill helps users choose the right path to Run A/B tests for search algorithms. Use this skill BEFORE diving into implementation details — it routes you to the appropriate detail skill based on y

## Frequently asked questions

### Should I use the API or the console for managing my Elasticsearch instance?

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

### How do I get started with secure API access?

First, create an AccessKey in the console (`es-security` guide). Then initialize your SDK client with the key and secret. For enhanced security, use STS temporary tokens (`es-security` API).

### My search results aren’t relevant—where should I start?

Begin with the intent skill **“Optimize search result relevance”**, which routes you to relevance tuning via reranking, intervention dictionaries, or fine-sort expressions.

### I’m getting a 403 error when calling the API—what’s wrong?

This usually indicates missing or incorrect permissions. Check your RAM user policies and ensure your AccessKey has the required actions. See `es-troubleshooting` for detailed error diagnostics.

### Can I deploy a RAG chatbot without writing code?

Yes—the **AI and RAG guide** (`es-text-generation`) includes step-by-step instructions to build knowledge-base Q&A systems and deploy chatbots in DingTalk/Lark via the console.

### How do I deploy a retrieval-augmented generation (RAG) AI application?

You can deploy a retrieval-augmented generation (RAG) AI application by building knowledge-base Q&A systems or enterprise chatbots via the console or API. This process supports generating text with LLMs and optionally integrating live or web-augmented search.

### How do I ingest and manage document data in Elasticsearch?

You can ingest and manage document data by uploading, batch-pushing, staging, or committing documents directly into Elasticsearch indices. These operations are supported through both programmatic API calls and console-based UI guides.

### How do I manage access control and security settings?

You can manage access control and security settings by securing your instance with API keys, RAM policies, STS tokens, or OAuth authentication. Credentials can be generated and managed through the console or configured programmatically.

### How do I optimize search result relevance?

You can optimize search result relevance by improving ranking quality with rerankers, intervention dictionaries, or fine-sort expressions. These settings can be configured via the API or set up in the console using tailored retrieval models and query processors.

## Use with an AI agent

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

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

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