---
Title: Build solution
URL Source: https://company-skill.com/p/opensearch/opensearch-build-solution
Language: en
Description: You want to integrate OpenSearch’s vector search capabilities with a large language model (LLM) to build a RAG system that retrieves relevant documents and generates informed answers. This involves…
---

# Build solution

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

## What You Want to Do

You want to integrate OpenSearch’s vector search capabilities with a large language model (LLM) to build a RAG system that retrieves relevant documents and generates informed answers. This involves embedding documents, storing vectors, querying semantically, and combining results with an LLM.

**Typical User Questions**:
- How to implement RAG with OpenSearch?
- Can I build a RAG pipeline using OpenSearch?

## Decision Tree

Pick the best path for your situation:

- **If** you want to use a graphical interface like **Query Test** and **Data Management** to validate RAG without writing code → Use RAG (go to *opensearch/opensearch-vector*)
- **If** you need **Agentic Search**, **Effect Evaluation**, and **Model Service** deployment in a unified AI platform → Use AIRAG (go to *opensearch/opensearch-text*)
- **If** you require programmatic control via APIs like **ListVectorQueryResult**, **text-embedding**, or **compatible-mode/v1/embeddings** for integration into custom apps → Use APIRAG (go to *opensearch/opensearch-vector*)
- **Otherwise (default)** → Start with **RAG** if you're prototyping; choose **APIRAG** if you're building production software.

## Path Comparison

| Path | Best For | Complexity | Code Required | Automation | Key Fact | Detail Skill |
|------|----------|------------|---------------|------------|----------|-------------|
| RAG | RAG | low | No | No | Billing: 0.0001/0.0002//0.002/0.003/ | `opensearch/guide/opensearch-vector` |
| AIRAG | LLMAI Search | medium | No | No | Supports **NL2SQL**, **Document Splitting Service**, and **Experience Center** in cn-shanghai/eu-central-1 | `opensearch/guide/opensearch-text` |
| APIRAG | RAG | high | Yes | Yes | Uses **Bearer Token** auth with **DASHSCOPE_API_KEY**; supports **text-sparse-embedding** and **embedding-tuning** | `opensearch/api/opensearch-vector` |

## Path Details

### Path 1: RAG

**Best For**: RAG

**Brief Description**: OpenSearch Vector Search EditionUI for **Query Test**, **Data Management**, and configuring **CUSTOMIZED index** with **HNSW** and **dimension** settings. You can manage **Primary Key Index**, insert data, and run hybrid queries for text or image vectors without code.

**Key technical facts**:
- Billing: 0.0001/0.0002/0.0001/0.0002//0.002/0.003/
- Regions: cn-hangzhou
- Prerequisites: , OpenSearch, MaxCompute

- MaxCompute+OSS+OpenSearch

- AI Search Open Platform
- LLMAI Search

- ops-text-embedding-000

### Path 2: AIRAG

**Best For**: LLMAI Search

**Brief Description**: AI Search Open Platform offers **Service Plaza**, **Manage Workspaces**, and **Model Service** deployment. You can configure **RAG Model Service Configuration**, use **Document Splitting Service**, run **Effect Evaluation**, and test via **Experience Center**. It supports **Agentic Search** and **NL2SQL** for natural language to SQL conversion.

**Key technical facts**:
- Billing: 0.0005/NL2SQL0.0001/0.0001/Agentic Search 0.002/0.004/0.002/0.002/
- Regions: cn-shanghai, eu-central-1
- Prerequisites: AI Search Open Platform, API, RAMModel Service-Service Deployment

**When to Use**:
- LLMAI Search

- Agentic Search

### Path 3: APIRAG

**Best For**: RAG

**Brief Description**: OpenSearch Vector Search API provides RESTful endpoints like **ListVectorQueryResult** and **ProximaScore** for vector similarity search. It supports **text-embedding**, **text-sparse-embedding**, **embedding-tuning**, and OpenAI-compatible **compatible-mode/v1/embeddings**. Authentication uses **Bearer Token** with **DASHSCOPE_API_KEY**.

**Key technical facts**:
- Billing: ops-text-embedding-0010.002//0.001/
- Auth method: Bearer Token via Authorization header
- Regions: cn-hangzhou, cn-shanghai, cn-beijing
- Prerequisites: API, DASHSCOPE_API_KEY, SDKalibabacloud_searchplat20240529>=2.1.0

- HNSW efQC scan_ratio
- OpenAIAPIcompatible-mode/v1/embeddings

- LLMAI Search

- ops-text-embedding-0028192
- 10,000

## FAQ

Q: Which path should I start with?
A: If you're exploring or validating a RAG concept, start with **RAG**. If you're building a production application that needs integration, choose **APIRAG**.

Q: What if I need to deploy a RAG chatbot to DingTalk but used the Vector Search Console?
A: You’ll hit a dead end — the Vector Search Console lacks **Agentic Search** and **Model Service** deployment needed for enterprise chatbot integration, which are only available in the AI Service Console.

Q: What if I try to automate daily document ingestion using the AI Service Console?
A: You’ll be blocked — the AI Service Console doesn’t support automation or CI/CD. Only the API path (**ListVectorQueryResult**, **text-embedding**) allows scripted, scheduled RAG pipelines.

Q: Can I use custom embedding models in the Vector Search Console?
A: No — the console only supports pre-defined models like ops-text-embedding-000 series. Custom models require the API path with **embedding-tuning** or external embedding generation.

Q: Why does my API request fail even when I follow the docs?
A: Common causes include exceeding 8MB request size, sending >32 inputs to **text-embedding**, or not setting **DASHSCOPE_API_KEY**. Failed requests still incur charges.

Q: Can I evaluate RAG quality in the Vector Search Console?
A: No — **Effect Evaluation** is exclusive to the AI Service Console. The Vector Search Console only supports query testing via **Query Test**, not systematic metric tracking.

Q: Is HNSW tuning possible in all paths?
A: Yes, but only programmatically in the API path (via parameters like ef and scan_ratio). In the Vector Search Console, you can configure **HNSW** through **CUSTOMIZED index**, but changes require index rebuild.

## Related queries

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