New Year's opening: deployment of DeepSeek big models locally to realize the AI ​​application of connected enhancement

The deployment of the DEEPSEEK model in the local area to realize the AI ​​application of the network enhancement

First, preface

It is an important direction for the current AI application development to deploy large language models (LLM) and give it a network. This article will show how to build an intelligent application with connected enhancement capabilities based on the Microsoft Semantic Kernel framework, combined with Deepseek local models and custom search skills.

Second, environmental preparation

1. Operating environment requirements:

   • .NET 6+ operating environment
   • Local running Ollama service (version needs to support the DeepSeek model)
   • Accessable search engine API endpoint

2. Core Nuget package:

Third, the principle of implementation

1. architecture design

[User input] → [search module] → [results pre -processing] → [LLM integration] → [final response]

2. Core component

• Ollama service: local reasoning of hosting Deepseek model
• Semantic Kernel: AI service arrangement framework
• Custom SEARCHSKILL: Unioned search capacity packaging

4. Analysis of code implementation

1. Ollama service integration

var endpoint = new Uri(":11434");
var modelId = "deepseek-r1:14b";

var builder = ();
(modelId, endpoint);

2. Search skills implementation

Public Class Searchskill
 {{
     // Execute the search and process the results
     Public async Task <searchResult >> Searchasync (String Query)
     {{
         // Construct a request parameter
         var parameters = new dictionary <string, string> {{
             {"q", query},
             {"format", "json"},
             // ... other parameters
         };
        
         // Treatment response and analysis
         var jsonresponse = await ();
         Return ProcessResults (JSONRSPONSE);
     }
 }

3. Mainstream arrangement

// Initialization service
 var kernel = ();
 var ChatService = <IChatCompleTionService> ();
 var searchService = <searchskill> ();

 // Execute the search
 List <searchResult> Result = AWAIT (Query);

 // Build a prompt
 var Chathistory = New Chathistory ();
 ($ "Find {} note:");
 // ... Add search results

 // Get model response
 AWAIT FOREACH (Var Item in (Chathistory))
 {{
     ();
 }

Functional characteristics

1. Mixed intelligent architecture

   • Local model guarantee data privacy
   • Network search expansion knowledge boundary
   • Streaming response improvement interactive experience

2. Search enhancement function

   • Resistance sorting

   var sortedResults = (r => );
   

   • Domain name filtering mechanism

   private List<Result> FilterResults(...)
   

   • Safety search support

6. Example of application scenarios

Take the Vue-Pure-Admin template development as an example:

User input: Make a form page based on Vue-Pure-Admin

 System response:
 1. Search for the relevant content of the official documentation
 2. Integrate the best practice code example
 3. Give Stepshop Reality Suggestions

Seven, optimization suggestions

1. Performance optimization

   • Implement the search cache mechanism
   • Support parallel search request
   • Add the result pagination loading

2. Function extension

   // Add multiple search engine support
    <Googlesearchskill> ();
    <Bingsearchskill> ();

3. Enhance

   • Add API access certification
   • Implement the request frequency limit
   • Enhance input verification

8. Summary

Through the implementation plan of this article, developers can:

1. Running DeepSeek big models in the local area
2. The real -time information acquisition ability of flexible extension model
3. Establish an enterprise -level AI application solution

The complete project code has been hosted to GitHub (example address), and developers are welcome to refer to and contribute. This local+connected hybrid architecture provides new possibilities for building safe and reliable intelligent applications.