Weaviate高级教程：用C++解锁代码辅助潜力

引言

Weaviate是一个开源的向量搜索引擎，它能够存储和检索对象及其向量表示。本教程将向您展示如何使用C++与Weaviate交互，构建一个强大的代码辅助系统。通过将代码片段转换为向量表示，我们可以实现智能的代码搜索、推荐和补全功能。

准备工作

在开始之前，请确保您已满足以下要求：

安装Weaviate服务（本地或云实例）
C++17或更高版本的编译器
libcurl开发库（用于HTTP请求）
nlohmann/json库（用于JSON处理）

安装依赖

在Ubuntu/Debian系统上：

代码片段

sudo apt-get install libcurl4-openssl-dev

nlohmann/json可以通过以下方式安装：

代码片段

sudo apt-get install nlohmann-json3-dev

项目设置

首先创建一个新的C++项目并配置编译环境：

代码片段

mkdir weaviate-cpp-demo && cd weaviate-cpp-demo
touch main.cpp CMakeLists.txt

CMakeLists.txt配置

代码片段

cmake_minimum_required(VERSION 3.10)
project(weaviate_cpp_demo)

set(CMAKE_CXX_STANDARD 17)

find_package(CURL REQUIRED)
find_package(nlohmann_json REQUIRED)

add_executable(weaviate_demo main.cpp)
target_link_libraries(weaviate_demo PRIVATE ${CURL_LIBRARIES} nlohmann_json::nlohmann_json)

Weaviate C++客户端实现

我们将创建一个简单的Weaviate客户端类来处理与服务器的交互。

main.cpp完整实现

代码片段

#include <iostream>
#include <string>
#include <vector>
#include <curl/curl.h>
#include <nlohmann/json.hpp>

using json = nlohmann::json;

class WeaviateClient {
private:
    std::string server_url;
    static size_t WriteCallback(void* contents, size_t size, size_t nmemb, std::string* s) {
        size_t newLength = size * nmemb;
        try {
            s->append((char*)contents, newLength);
            return newLength;
        } catch(std::bad_alloc &e) {
            return 0;
        }
    }

public:
    WeaviateClient(const std::string& url) : server_url(url) {}

    json query(const std::string& query_path, const json& payload = {}) {
        CURL* curl = curl_easy_init();
        std::string readBuffer;

        if(curl) {
            std::string full_url = server_url + query_path;
            struct curl_slist* headers = NULL;
            headers = curl_slist_append(headers, "Content-Type: application/json");

            curl_easy_setopt(curl, CURLOPT_URL, full_url.c_str());
            curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);

            if (!payload.empty()) {
                std::string postData = payload.dump();
                curl_easy_setopt(curl, CURLOPT_POSTFIELDS, postData.c_str());
            }

            curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
            curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);

            CURLcode res = curl_easy_perform(curl);
            if(res != CURLE_OK) {
                std::cerr << "curl_easy_perform() failed: " << curl_easy_strerror(res) << std::endl;
                return json();
            }

            curl_slist_free_all(headers);
            curl_easy_cleanup(curl);

            return json::parse(readBuffer.empty() ? "{}" : readBuffer);
        }
        return json();
    }

    bool createClass(const json& class_schema) {
        json response = query("/v1/schema", class_schema);
        return !response.contains("error");
    }

    bool insertObject(const std::string& class_name, const json& object_data) {
        json response = query("/v1/objects?consistency_level=ALL", {
            {"class", class_name},
            {"properties", object_data}
        });
        return response.contains("id");
    }

    json semanticSearch(const std::string& class_name, const std::vector<float>& vector, int limit = 5) {
        json payload = {
            {"class", class_name},
            {"vector", vector},
            {"limit", limit}
        };
        return query("/v1/graphql/search", payload);
    }
};

构建代码辅助系统

1. 创建代码片段类

首先我们需要在Weaviate中定义一个类来存储代码片段：

代码片段

int main() {
    WeaviateClient client("http://localhost:8080");

    // 定义CodeSnippet类的schema
    json class_schema = {
        {"class", "CodeSnippet"},
        {"description", "A collection of code snippets with vector embeddings"},
        {"properties", {
            {{"name", "language"}, {"dataType", {"string"}}},
            {{"name", "content"}, {"dataType", {"text"}}},
            {{"name", "description"}, {"dataType", {"text"}}},
            {{"name", "tags"}, {"dataType", {"string[]"}}}
        }},
        {"vectorizer", "none"} // 我们将自己计算向量
    };

    if (client.createClass(class_schema)) {
        std::cout << "成功创建CodeSnippet类" << std::endl;
    } else {
        std::cerr << "创建类失败" << std::endl;
        return -1;
    }

    // ... 后续操作将在这里添加

    return 0;
}

2. 插入代码片段示例

让我们添加一些C++代码片段到数据库中：

代码片段

// ... (接上面的main函数)

// 示例1: Hello World程序
json snippet1 = {
    {"language", "C++"},
    {"content", "#include <iostream>\n\nint main() {\n\tstd::cout << \"Hello World!\" << std::endl;\n\treturn 0;\n}"},
    {"description", "Basic C++ hello world example"},
    {"tags", {"beginner", "hello-world"}}
};

// 示例2: Vector使用示例
json snippet2 = {
    {"language", "C++"},
    {"content", "#include <vector>\n#include <iostream>\n\nint main() {\n\tstd::vector<int> nums {1, 2, 3};\n\tfor(auto num : nums) {\n\t\tstd::cout << num << \" \";\n\t}\n\treturn 0;\n}"},
    {"description", "Demonstrating basic vector usage in C++"},
    {"tags", {"stl", "containers"}}
};

if (client.insertObject("CodeSnippet", snippet1)) {
    std::cout << "成功插入第一个代码片段" << std::endl;
}

if (client.insertObject("CodeSnippet", snippet2)) {
    std::cout << "成功插入第二个代码片段" << std::endl;
}

3. 实现语义搜索功能

为了进行语义搜索，我们需要先将查询文本转换为向量。这里我们简化处理，使用一个假设的向量生成函数：

代码片段

// ... (在WeaviateClient类中添加以下方法)

std::vector<float> generateTextVector(const std::string& text) {
    // TODO:这里应该调用实际的文本嵌入模型如BERT、Sentence-BERT等
    // 为了演示目的，我们返回一个简单的随机向量

    // Note:实际应用中应该替换为真实的嵌入模型调用
    static bool seeded = false;
    if (!seeded) {
        srand(time(NULL));
        seeded = true;
    }

    const int DIMENSIONS = 384; // typical small embedding size
    std::vector<float> vec(DIMENSIONS);

#pragma omp parallel for
for(int i=0; i<DIMENSIONS; ++i) { 
vec[i] = static_cast<float>(rand()) / RAND_MAX; 
}

return vec; 
}

现在我们可以实现语义搜索了：

代码片段

// ... (继续main函数)

// 搜索与"C++打印示例"相关的代码片段
std::string search_query = "C++ printing example";
std::vector<float> query_vector = client.generateTextVector(search_query);

json results = client.semanticSearch("CodeSnippet", query_vector);

std::cout << "\n搜索结果:\n";
for (const auto& item : results["data"]["Get"]["CodeSnippet"]) {
std :: cout << "\ n语言:"<< item [" language "] . get<std :: string>() 
<< "\ n描述:"<< item [" description "] . get<std :: string>() 
<< "\ n内容:\ n"<< item [" content "] . get<std :: string>() 
<< "\ n---\ n"; 
}

实践建议与注意事项

向量生成优化:
- 在实际应用中，应该使用专业的文本嵌入模型如BERT、Sentence-BERT或OpenAI的嵌入API来生成高质量的向量表示。
- 考虑缓存生成的向量以减少重复计算。
性能考虑:
- Weaviate支持批量插入操作，当需要插入大量代码片段时，应该使用批量接口。
- HTTP请求有开销，考虑使用连接池或异步IO来提高性能。
错误处理增强:
- Our example has minimal error handling. In production code , you should add proper error checking for:
  - Network failures
  - Invalid JSON responses
  - API rate limiting
安全建议:
- Never hardcode API keys in source code
- Consider adding TLS/SSL for production deployments
扩展可能性:
- Add user-specific filtering to personalize code recommendations
- Implement feedback loops to improve vector quality over time

Conclusion

In this tutorial , we’ve demonstrated how to:

1 . Set up a basic Weaviate client in C ++
2 . Define a schema for storing code snippets
3 . Insert and retrieve code examples
4 . Implement semantic search capabilities

This foundation can be extended to build sophisticated code assistance tools like :

Intelligent code completion
Context-aware documentation lookup
Example-based programming assistants

The complete source code for this tutorial is available on [GitHub repository link] .

To further enhance this system , consider integrating with :

Real-time IDE plugins
Code quality analysis tools
Documentation databases

Happy coding with Weaviate and C ++ !