OpenSearch：基於DeepSeek-R1模型搭建RAG系統

Java

步驟 1：配置Java環境

java -version

<dependency>
    <groupId>com.aliyun.opensearch</groupId>
    <artifactId>aliyun-sdk-opensearch</artifactId>
    <version>6.0.0</version>
</dependency>

步驟 2：擷取調用參數

SDK調用依賴以下關鍵參數：

• AppName：應用程式名稱。

• host：應用的API訪問地址。

步驟 3：調用介面

運行以下代碼調用樣本：

package com.aliyun.opensearch;

import com.aliyun.opensearch.OpenSearchClient;
import com.aliyun.opensearch.sdk.generated.OpenSearch;
import com.aliyun.opensearch.sdk.generated.commons.OpenSearchClientException;
import com.aliyun.opensearch.sdk.generated.commons.OpenSearchException;
import com.aliyun.opensearch.sdk.generated.commons.OpenSearchResult;

import java.util.HashMap;
import java.util.Map;

public class LLMSearch {

    private static String appName = "替換為應用程式名稱";
    private static String host = "替換應用的API訪問地址";
    private static String accesskey = "替換accesskey";
    private static String secret = "替換secret";
    private static String path = "/apps/%s/actions/knowledge-search";

    public static void main(String[] args) {
      
        String appPath = String.format(path, appName);

        //ApiReadTimeOut
        OpenSearch openSearch = new OpenSearch(accesskey, secret, host);
        openSearch.setTimeout(90000);

        OpenSearchClient openSearchClient = new OpenSearchClient(openSearch);

        Map<String, String> params = new HashMap<String, String>() {{
            put("format", "full_json");
            put("_POST_BODY", "{\"question\":{\"text\":\"怎麼充電\",\"type\":\"TEXT\",\"session\":\"\"},\"options\":{\"retrieve\":{\"doc\":{\"filter\":\"\",\"top_n\":5,\"sf\":\"\",\"dense_weight\":\"0.7\",\"formula\":\"\",\"operator\":\"AND\"},\"entry\":{\"sf\":\"\"},\"image\":{\"sf\":\"\",\"dense_weight\":\"0.7\"},\"qp\":{\"query_extend\":false,\"query_extend_num\":5},\"return_hits\":false,\"rerank\":{\"enable\":true,\"model\":\"ops-bge-reranker-larger\"}},\"chat\":{\"stream\":true,\"prompt_config\":{\"attitude\":\"normal\",\"rule\":\"detailed\",\"noanswer\":\"sorry\",\"language\":\"Chinese\",\"role\":false,\"role_name\":\"AI小助手\",\"out_format\":\"text\"},\"agent\":{\"tools\":[]},\"csi_level\":\"strict\",\"history_max\":\"\",\"link\":\"false\",\"model\":\"deepseek-r1\",\"model_generation\":\"\"}}}");

        }};
          try {
            OpenSearchResult openSearchResult = openSearchClient
            .callAndDecodeResult(appPath, params, "POST");
            System.out.println("RequestID=" + openSearchResult.getTraceInfo().getRequestId());
            System.out.println(openSearchResult.getResult());
        } catch (
            OpenSearchException e) {
            System.out.println("RequestID=" + e.getRequestId());
            System.out.println("ErrorCode=" + e.getCode());
            System.out.println("ErrorMessage=" + e.getMessage());
        } catch (
            OpenSearchClientException e) {
            System.out.println("ErrorMessage=" + e.getMessage());
        }
    }
}

Python

步驟 1：配置 Python 環境

# 如果運行失敗，您可以將python替換成python3再運行
python -V

pip install alibabacloud_tea_util 
pip install alibabacloud_opensearch_util
pip install alibabacloud_credentials

步驟 2：擷取參數

SDK調用依賴以下關鍵參數：

• app_name：應用程式名稱。

• endpoint：應用的API訪問地址。

步驟 3：調用介面

BaseRequest參考：Python client 樣本。

# -*- coding: utf-8 -*-

import time, os
from typing import Dict, Any

from Tea.exceptions import TeaException
from Tea.request import TeaRequest
from alibabacloud_tea_util import models as util_models
from BaseRequest import Config, Client


class LLMSearch:
    def __init__(self, config: Config):
        self.Clients = Client(config=config)
        self.runtime = util_models.RuntimeOptions(
            connect_timeout=10000,
            read_timeout=90000,
            autoretry=False,
            ignore_ssl=False,
            max_idle_conns=50,
            max_attempts=3
        )
        self.header = {}


    def searchDoc(self, app_name: str,body:Dict, query_params: dict={}) -> Dict[str, Any]:
        try:
            response = self.Clients._request(method="POST", pathname=f'/v3/openapi/apps/{app_name}/actions/knowledge-search',
                                             query=query_params, headers=self.header, body=body, runtime=self.runtime)
            return response
        except TeaException as e:
            print(e)


if __name__ == "__main__":
    # 配置統一的請求入口和  需要去掉http://
    endpoint = "<endpoint>"

    # 支援 protocol 配置 HTTPS/HTTP
    endpoint_protocol = "HTTP"

    # 使用者識別資訊
    # 從環境變數讀取配置的AccessKey ID和AccessKey Secret，
    # 運行程式碼範例前必須先配置環境變數，參考文檔上面“配置環境變數”步驟
    access_key_id = os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID")
    access_key_secret = os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET")

    # 支援 type 配置 sts/access_key 鑒權. 其中 type 預設為 access_key 鑒權. 使用 sts 可配置 RAM-STS 鑒權.
    # 備選參數為:  sts 或者 access_key
    auth_type = "access_key"

    # 如果使用 RAM-STS 鑒權, 請配置 security_token, 可使用 阿里雲 AssumeRole 擷取 相關 STS 鑒權結構.
    security_token =  "<security_token>"

    # 配置請求使用的通用資訊.
    # type和security_token 參數如果不是子帳號，需要省略
    Configs = Config(endpoint=endpoint, access_key_id=access_key_id, access_key_secret=access_key_secret,
                     security_token=security_token, type=auth_type, protocol=endpoint_protocol)

    # 建立 opensearch 執行個體
    # 請將<應用程式名稱>替換為您建立的智能問答版執行個體名稱
    ops = LLMSearch(Configs)
    app_name = "<應用程式名稱>"

    # --------------- 文檔搜尋 ---------------

    docQuery = {"question": {"text": "搜尋", "type": "TEXT"}, "options": {"chat": {"model": "deepseek-r1"}}}

    res1 = ops.searchDoc(app_name=app_name, body=docQuery)
    print(res1)