---
Title: Deploy application
URL Source: https://company-skill.com/p/es/es-deploy-application
Language: en
Description: You want to build a RAG system that answers questions using your private documents—either as a standalone service, an enterprise chatbot (e.g., in DingTalk), or as part of a larger AI pipeline. This…
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# Deploy application

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

## What You Want to Do

You want to build a RAG system that answers questions using your private documents—either as a standalone service, an enterprise chatbot (e.g., in DingTalk), or as part of a larger AI pipeline. This involves document ingestion, text splitting, vector/embedding generation, retrieval, and LLM-based answer synthesis.

**Typical User Questions**:
- How do I build a RAG chatbot with my documents?
- Can I create an enterprise chatbot in DingTalk?

## Decision Tree

Pick the best path for your situation:

- **If** you want to deploy a zero-code enterprise chatbot in DingTalk/Feishu using a UI wizard in **China (Shanghai)** → Use (go to *es/es-text-generation*)
- **If** you already have a document processing pipeline and only need to generate vectors via **Create Text Embedding**, **Create Sparse Embedding**, or **Create Multimodal Embedding** → Use Embedding API (go to *es/es-text-embedding*)
- **If** you need full control over retrieval, re-ranking, and generation—including real-time web search via **deepseek-r1** with **enable_search** or OpenAI-compatible calls to **ops-qwen-turbo** → Use API RAG (go to *es/es-text-generation*)
- **Otherwise (default)** → Start with **** if you’re non-technical and in **China (Shanghai)**; otherwise, use ** API RAG ** for maximum flexibility.

## Path Comparison

| Path | Best For | Complexity | Code Required | Automation | Key Fact | Detail Skill |
|------|----------|------------|---------------|------------|----------|-------------|
| RAG | low | No | No | Requires adding IP 47.100.254.67 to Elasticsearch instance whitelist | `es/guide/es-text-generation` |
| API RAG | high | Yes | Yes | Supports **deepseek-r1** with **enable_search** and **ops-qwen-turbo** via **compatible-mode/v1** | `es/api/es-text-generation` |
| Embedding API | medium | Yes | Yes | Supports **ops-text-embedding-001**, **ops-text-sparse-embedding-001**, and **ops-m2-encoder** with **dimension** control | `es/api/es-text-embedding` |

## Path Details

### Path 1: Console / Dashboard
**Best For**: RAG 

**Brief Description**: This path uses the **AI Search Open Platform** console to activate services, create a **Knowledge Base**, configure the **Document Splitting Service**, and **Create RAG Pipeline** for **LLM-Based Conversational Search**. It also supports deploying enterprise chatbots to DingTalk or Feishu and includes built-in evaluation tasks.

**Key technical facts**:
- Billing: 
- Regions: China (Shanghai), Germany (Frankfurt)

- RAG Faithfulness Context Recall

- cn-hangzhou

- IP47.100.254.67 Elasticsearch 

- 7 20 MB

### Path 2: API RAG 

**Brief Description**: This path uses the **Elasticsearch AI and RAG API** to call **Generate Text**, **Document Split**, **Analyze Query**, and **Perform Web Search**. It supports models like **qwen3-235b-a22b**, **ops-qwen-turbo**, and **deepseek-r1**, with **enable_search** for live web results. Authentication uses **Authorization: Bearer**, and **compatible-mode/v1** enables OpenAI SDK compatibility.

**Key technical facts**:
- Billing: 
- Regions: cn-hangzhou, cn-shanghai, cn-beijing
- Auth method: Authorization: Bearer

**When to Use**:
- deepseek-r1 
- OpenAI SDK ops-qwen-turbo 

- temperaturetop_pmax_tokens

### Path 3: Embedding API 

**Brief Description**: This path uses the **Elasticsearch Embedding API** to call **Create Text Embedding**, **Create Sparse Embedding**, or **Create Multimodal Embedding** with models like **ops-text-embedding-001**, **ops-text-sparse-embedding-001**, and **ops-m2-encoder**. It supports **input_type** and **dimension** parameters and uses **Authorization: Bearer** for auth.

**Key technical facts**:
- Billing: 
- Regions: cn-hangzhou, cn-shanghai, cn-beijing
- Auth method: Authorization: Bearer

**When to Use**:
- OpenAI API

## FAQ

Q: Which path should I start with?
A: If you’re a non-technical user in **China (Shanghai)** and want a DingTalk bot, start with the console. Otherwise, if you’re an engineer needing integration or live web search, use the **Generate Text** API path.

Q: What if I need to process files larger than 20 MB but used the console path?
A: You’ll hit the 20 MB file upload limit in the **AI Search Open Platform** experience center, and files are auto-deleted after 7 days—making it unsuitable for production document pipelines.

Q: What if I chose the Embedding API path but actually need a full RAG chatbot?
A: You’ll get vector generation (**Create Text Embedding**) but miss **LLM-Based Conversational Search**, **Document Splitting Service**, and chatbot deployment—you’d still need to build retrieval and generation yourself.

Q: Can I use **deepseek-r1** with **enable_search** in the console path?
A: No—**enable_search** and **deepseek-r1** are only available via the **Generate Text** API in the custom RAG flow. The console uses fixed models without live web search.

Q: Does the Embedding API support controlling output vector size?
A: Yes—you can use the **dimension** parameter with **ops-text-embedding-001** to customize vector length, which is useful for indexing efficiency.

Q: What happens if I’m in **cn-hangzhou** and try to use the console path?
A: The console path only works directly in **China (Shanghai)** and Frankfurt—you’d need VPC peering or proxy setups, making the API paths (which support **cn-hangzhou**) a better choice.

Q: Can I automate the console-based RAG pipeline in CI/CD?
A: No—the console path is **non-automation friendly** and relies on manual UI steps like **Create RAG Pipeline**, so it cannot be scripted or integrated into deployment pipelines.

## Related queries

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