---
Title: Optimize results
URL Source: https://company-skill.com/p/es/es-optimize-results
Language: en
Description: You want to improve how well your Elasticsearch (or OpenSearch) search results match user intent—using techniques like neural reranking, synonym expansion, spelling correction, or custom ranking…
---

# Optimize results

Part of **Elasticsearch**. Route queries via `POST https://company-skill.com/api/route`.

## What You Want to Do

You want to improve how well your Elasticsearch (or OpenSearch) search results match user intent—using techniques like neural reranking, synonym expansion, spelling correction, or custom ranking models.

**Typical User Questions**:
- How can I improve the relevance of my Elasticsearch search results?
- Can I use neural reranking in OpenSearch?
- How do I configure an intervention dictionary to correct search results?
- What's the best way to fine-tune search ranking?
- How can I use A/B testing to validate relevance improvements?
- Is there a console UI for relevance tuning?

## Decision Tree

Pick the best path for your situation:

- **If** you need to embed relevance tuning into CI/CD pipelines, automate at scale, or programmatically manage models/dictionaries using REST APIs → Use ** API ** (go to `skills/es/api/es-search-relevance`)
- **If** your primary goal is to configure synonym dictionaries, spelling correction, or custom analyzers via UI without neural reranking → Use **/** (go to `skills/es/guide/es-text_analysis`)
- **If** you want a unified console experience to configure both neural reranking (via AI Search Open Platform), NL2SQL, and intervention dictionaries visually → Use **** (go to `skills/es/guide/es-search-relevance`)
- **Otherwise (default)** → Start with **** if you’re using OpenSearch Advanced Edition with HA3 engine and want rapid visual iteration; otherwise, verify your instance type first.

## Path Comparison

| Path | Best For | Complexity | Code Required | Automation | Key Fact | Detail Skill |
|------|----------|------------|---------------|------------|----------|-------------|
| API | CI/CD | high | Yes | Yes | Requires AI Search Open Platform; QPS limit = 20 | `es/api/es-search-relevance` |
| Console / Dashboard | low | No | No | Supports NL2SQL Service Configuration and Tailored Retrieval Models (HA3 only) | `es/guide/es-search-relevance` |
| / | medium | No | No | Max 20 dictionaries (spelling/synonyms); requires reindexing for Analyzer changes | `es/guide/es-text_analysis` |

## Path Details

### Path 1: API 

**Best For**: CI/CD 

**Brief Description**: This approach uses REST APIs from the **AI Search Open Platform** to programmatically manage neural reranking (with models like `ops-bge-reranker-larger` or `ops-qwen3-reranker-0.6b`), create intervention dictionaries, configure fine sort logic, and run A/B tests. Key components include **Manage Query Processors**, **Create Intervention Dictionary**, and **Create AB Test Group**.

**Key technical facts**:
- Billing: API0.002 CNY/1k tokens0.0001 CNY/call
- Cold start: 19latency
- Auth method: Bearer Token（Authorization: Bearer <your-api-key>）
- Regions available: cn-hangzhou, cn-shanghai, cn-beijing

**When to Use**:
- Need CI/CD integration or large-scale automated tuning
- Require precise control over neural reranker model selection (e.g., BGE vs Qwen3)
- Must implement A/B testing experiment management
- Need programmatic dictionary or query processor management

**When NOT to Use**:
- Prefer no-code, quick experimentation
- Only need basic synonyms/spelling correction (no neural reranking)
- Lack API key or cannot configure Bearer Token auth
- Anticipate >20 QPS (shared across all RAM users)

**Known Limitations**:
- Only supported on **AI Search Open Platform** or OpenSearch High-Performance Edition (not standard Elasticsearch)
- QPS capped at 20 for all users under an Alibaba Cloud account
- Request body ≤ 8 MB
- BGE models limited to 512 tokens (query+doc); Qwen3 supports 32k tokens
- Multi-modal reranking max 100 docs per request

### Path 2: Console / Dashboard
**Brief Description**: This path uses the OpenSearch console’s **Search Algorithm Center** to visually configure **AI Search Open Platform** integrations, manage **Dictionary Management**, define **Tailored Retrieval Models**, set up **NL2SQL Service Configuration**, and build **Query Analysis Rule Management**—all without writing code.

**Key technical facts**:
- Billing: NL2SQL0.0001 CNY/
- Auth method: SSO
- Prerequisites: OpenSearch Advanced Edition instance with dedicated environment and HA3 engine

**When to Use**:
- Want fast, visual iteration without coding
- Need to configure **NL2SQL Service Configuration** (natural language to SQL)
- Require **Tailored Retrieval Models** trained on domain data
- Prefer centralized UI in **Search Algorithm Center**

**When NOT to Use**:
- Require programmatic control or CI/CD integration
- Using standard Elasticsearch engine (not HA3 Advanced Edition)
- Need immediate model updates (training takes 1–2 days)
- Require >5 custom retrieval models per instance

**Known Limitations**:
- Actual reranking still requires API/SDK calls; console only configures API keys
- **Tailored Retrieval Models** only work with HA3 engine Advanced Edition apps
- Max 5 custom models per instance
- Model retraining needed for any change (1–2 day delay)
- Console does not support direct A/B testing configuration

### Path 3: /

**Brief Description**: This approach focuses on text preprocessing via **Dictionary Management** (for synonyms, spelling correction, NER, stop words), **Analyzer Management** (custom tokenizers for e-commerce/IT domains), and **Query Analysis Rule Management**—all managed through the OpenSearch console.

**Key technical facts**:
- Billing: 0.0001 CNY/
- Auth method: SSO

**When to Use**:
- Focus on query understanding (spelling correction, synonym expansion)
- Need to manage multiple dictionary types (NER, stop words, synonyms)
- Require industry-specific **Analyzer Management** (e.g.,)
- Want visual testing of text analysis effects

**When NOT to Use**:
- Need neural reranking or AI-based relevance models
- Require bulk programmatic dictionary updates
- Need >20 intervention dictionaries
- Cannot tolerate reindexing delays (required for analyzer changes)

**Known Limitations**:
- Max 10 category prediction dictionaries; max 20 for spelling/synonyms/stop words
- Each dictionary supports 500–1000 entries (type-dependent)
- Dictionary type cannot be changed after creation
- **Query Analysis Rule Management** must be set as default in Index Orientation to take effect
- **Analyzer Management** changes require full reindexing

## FAQ

Q: Which path should I start with?
A: If you’re using OpenSearch Advanced Edition with HA3 engine and want quick visual tuning, start with ****. If you only need synonyms/spelling correction, use ****. For automation or A/B testing, choose the API path—but confirm you have **AI Search Open Platform** access first.

Q: What if I need neural reranking but chose /?
A: You’ll hit a hard limitation: that path only supports text analysis (synonyms, spelling) and **does not support neural reranking, BGE models, or Qwen3 rerankers**—you’ll have to switch paths later.

Q: What if I’m using a standard Elasticsearch instance but chose ?
A: You’ll find **Tailored Retrieval Models** and **NL2SQL Service Configuration** are grayed out or unavailable—these features require OpenSearch Advanced Edition with HA3 engine, not standard Elasticsearch.

Q: Can I use A/B testing with the console paths?
A: No. Only the ** API ** path supports A/B testing via the **Create AB Test Group** API. Console paths lack built-in experiment management.

Q: Do I need to reindex when updating synonyms in the console?
A: For basic dictionary updates (e.g., adding a synonym), no reindex is needed. But if you modify **Analyzer Management** (e.g., change tokenizer rules), you **must reindex all data**—this applies only to the text analysis path.

Q: What’s the token limit if I use Qwen3 reranker via API?
A: The Qwen3 model supports up to **32k tokens** for the combined query and document text—much higher than BGE’s 512-token limit. This is only available in the API path.

Q: Can I exceed 20 intervention dictionaries if I really need to?
A: No. The **** path enforces hard limits: max 20 for spelling/synonyms/stop words. If you need more, you’d have to consolidate entries or consider programmatic management (though even the API path doesn’t relax this underlying platform limit).

## Related queries

optimize search relevance, improve elasticsearch ranking, boost search results, relevance tuning, search result ranking, neural reranking, configure synonym dictionary, spelling correction setup, query understanding optimization, fine-tune search, A/B test relevance, console UI for search tuning, ma

---
Part of [Elasticsearch](https://company-skill.com/p/es.md) · https://company-skill.com/llms.txt