How to deploy DeepSeek big model locally to achieve network-enhanced AI applications

1|11. Preface

Deploying large language models (LLMs) locally and giving them networking capabilities is an important direction in the development of AI applications. This article will be based on the Microsoft Semantic Kernel framework, combined with DeepSeek local model and custom search skills, to show how to build an intelligent application with networking enhancement capabilities.

1|22. Environmental preparation

• Operating environment requirements:

  • .NET 6+ Running Environment
  • Ollama service running locally (the version must support the DeepSeek model)
  • Accessible search engine API endpoints

• Core NuGet Package:

1|33. Implementation principle

1. Architectural design

[User input] → [Search module] → [Result preprocessing] → [LLM integration] → [Final response]

2. Core Components

• Ollama Service: Local Inference for Hosting DeepSeek Models
• Semantic Kernel: AI service orchestration framework
• Custom SearchSkill: Internet search capability encapsulation

1|44. Code implementation analysis

1. Ollama Service Integration

var endpoint = new Uri("http://Your ollama address: 11434");var modelId = "deepseek-r1:14b";
var builder = ();
(modelId, endpoint);

2. Search skills implementation

public class SearchSkill
{
    // Perform a search and process the results    public async Task<List<SearchResult>> SearchAsync(string query)
    {
        // Build request parameters        var parameters = new Dictionary<string, string> {
            { "q", query },
            { "format", "json" },
            // ...Other parameters        };
        // Process the response and parse it        var jsonResponse = await ();
        return ProcessResults(jsonResponse);
    }
}

3. Main process orchestration

// Initialize the servicevar kernel = ();
var chatService = <IChatCompletionService>();
var searchService = <SearchSkill>();
// Perform a searchList<SearchResult> result = await (query);
// Construction prompt wordsvar chatHistory = new ChatHistory();
($"turn up{}Results：");
// ...Add search results// Get the model responseawait foreach (var item in (chatHistory))
{
    ();
}

1|55. Functional characteristics

• Hybrid intelligent architecture

  • Local models guarantee data privacy
  • Network search extends knowledge boundaries
  • Streaming response enhances interactive experience

• Search enhancements

• Result correlation sort

var sortedResults = (r => );

• Domain name filtering mechanism
• Safe search support

private List<Result> FilterResults(...)

1|66. Application scenario examples

Taking Vue-Pure-Admin template development as an example:

User input: Make a table page based on vue-pure-admin

System response:

1. Search for official documents related content

2. Integrate best practice code examples

3. Give step-by-step implementation suggestions

1|77. Summary

Through the implementation solution of this article, developers can:

• Run DeepSeek big model safely locally
• Flexible expansion of the model's real-time information acquisition capability
• Build enterprise-level AI application solutions

The complete project code has been hosted to GitHub (sample address). Developers are welcome to refer to and contribute. This local + networking hybrid architecture provides new possibilities for building safe and reliable smart applications.
/zt199510/deepseeksk

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