警告:这些是“加入”的草稿。
可能很多下面的东西是行不通的; 我制作了这些作为给ME的操作方法, 然后做所有步骤,让样本应用起作用...你一直偷偷摸摸地看到它们!它们很可能在12月中旬就绪。
## 一. 导言 导言 导言 导言 导言 导言 一,导言 导言 导言 导言 导言 导言
欢迎来到第八部分 最后一部分RAG 区域包
- 基础写作助理从头开始。
现在让我们加上一个能使其生产就绪的抛光: 自动连接、部署、配置管理 以及现实世界的使用模式。
同一个声音,同样的务实;只是更快的手指。
namespace Mostlylucid.BlogLLM.Core.Services
{
public interface ILinkSuggestionService
{
Task<List<LinkSuggestion>> SuggestLinksAsync(string text);
string InsertLinks(string text, List<LinkSuggestion> acceptedLinks);
}
public class LinkSuggestion
{
public string Phrase { get; set; } = string.Empty;
public string TargetSlug { get; set; } = string.Empty;
public string TargetTitle { get; set; } = string.Empty;
public float RelevanceScore { get; set; }
public int Position { get; set; }
}
public class LinkSuggestionService : ILinkSuggestionService
{
private readonly BatchEmbeddingService _embedder;
private readonly QdrantVectorStore _vectorStore;
private readonly ILogger<LinkSuggestionService> _logger;
public LinkSuggestionService(
BatchEmbeddingService embedder,
QdrantVectorStore vectorStore,
ILogger<LinkSuggestionService> logger)
{
_embedder = embedder;
_vectorStore = vectorStore;
_logger = logger;
}
public async Task<List<LinkSuggestion>> SuggestLinksAsync(string text)
{
var suggestions = new List<LinkSuggestion>();
// Extract key phrases (noun phrases, technical terms)
var phrases = ExtractKeyPhrases(text);
_logger.LogInformation("Extracted {Count} key phrases for linking", phrases.Count);
foreach (var phrase in phrases)
{
// Search for related posts
var embedding = _embedder.GenerateEmbedding(phrase.Text);
var results = await _vectorStore.SearchAsync(
queryEmbedding: embedding,
limit: 3,
scoreThreshold: 0.75f // High threshold for links
);
if (results.Any())
{
var topResult = results.First();
// Don't link to current post
if (!IsCurrentPost(topResult.BlogPostSlug, text))
{
suggestions.Add(new LinkSuggestion
{
Phrase = phrase.Text,
TargetSlug = topResult.BlogPostSlug,
TargetTitle = topResult.BlogPostTitle,
RelevanceScore = topResult.Score,
Position = phrase.Position
});
_logger.LogDebug("Link suggestion: '{Phrase}' -> '{Target}' (score: {Score:F3})",
phrase.Text, topResult.BlogPostTitle, topResult.Score);
}
}
}
// Remove duplicates and low-value links
return DeduplicateAndFilter(suggestions);
}
public string InsertLinks(string text, List<LinkSuggestion> acceptedLinks)
{
// Sort by position (descending) to maintain positions as we insert
var sorted = acceptedLinks.OrderByDescending(l => l.Position).ToList();
foreach (var link in sorted)
{
var before = text.Substring(0, link.Position);
var phrase = link.Phrase;
var after = text.Substring(link.Position + phrase.Length);
// Check if already a link
if (IsAlreadyLinked(before, phrase, after))
{
continue;
}
var markdownLink = $"[{phrase}](/blog/{link.TargetSlug})";
text = before + markdownLink + after;
_logger.LogInformation("Inserted link: {Phrase} -> /blog/{Slug}",
phrase, link.TargetSlug);
}
return text;
}
private List<KeyPhrase> ExtractKeyPhrases(string text)
{
var phrases = new List<KeyPhrase>();
// Simple regex-based extraction
// In production, use NLP library like Stanford.NLP or Azure Cognitive Services
// Technical terms (CamelCase, dot notation)
var technicalTerms = Regex.Matches(text,
@"\b([A-Z][a-z]+([A-Z][a-z]+)+|[A-Z]\w+\.\w+)\b");
foreach (Match match in technicalTerms)
{
phrases.Add(new KeyPhrase
{
Text = match.Value,
Position = match.Index
});
}
// Multi-word phrases in quotes or code backticks
var quotedPhrases = Regex.Matches(text, @"[`""]([^`""]{10,50})[`""]");
foreach (Match match in quotedPhrases)
{
phrases.Add(new KeyPhrase
{
Text = match.Groups[1].Value,
Position = match.Index
});
}
return phrases;
}
private List<LinkSuggestion> DeduplicateAndFilter(List<LinkSuggestion> suggestions)
{
// Remove duplicate phrases (keep highest score)
var deduped = suggestions
.GroupBy(s => s.Phrase.ToLowerInvariant())
.Select(g => g.OrderByDescending(s => s.RelevanceScore).First())
.ToList();
// Limit links per post
return deduped
.OrderByDescending(s => s.RelevanceScore)
.Take(5) // Max 5 auto-links per draft
.ToList();
}
private bool IsCurrentPost(string slug, string text)
{
// Simple heuristic - check if slug appears in text
// In production, track actual post being edited
return text.ToLowerInvariant().Contains(slug.ToLowerInvariant());
}
private bool IsAlreadyLinked(string before, string phrase, string after)
{
// Check if phrase is already inside markdown link
var lookback = before.TakeLast(20).ToString() ?? "";
var lookahead = new string(after.Take(20).ToArray());
return lookback.Contains("[") || lookahead.StartsWith("]");
}
}
public class KeyPhrase
{
public string Text { get; set; } = string.Empty;
public int Position { get; set; }
}
}
[RelayCommand]
private async Task SuggestLinks()
{
IsProcessing = true;
StatusMessage = "Analyzing text for link opportunities...";
try
{
var suggestions = await _linkService.SuggestLinksAsync(EditorText);
// Show suggestions in UI
LinkSuggestions.Clear();
foreach (var suggestion in suggestions)
{
LinkSuggestions.Add(new LinkSuggestionViewModel(suggestion));
}
StatusMessage = $"Found {suggestions.Count} link opportunities";
}
finally
{
IsProcessing = false;
}
}
[RelayCommand]
private void AcceptAllLinks()
{
var acceptedLinks = LinkSuggestions
.Where(vm => vm.IsAccepted)
.Select(vm => vm.Suggestion)
.ToList();
EditorText = _linkService.InsertLinks(EditorText, acceptedLinks);
StatusMessage = $"Inserted {acceptedLinks.Count} links";
}
最宝贵的特点之一是自动建议与相关员额挂钩。
namespace Mostlylucid.BlogLLM.Configuration
{
public class BlogLLMConfig
{
public EmbeddingConfig Embedding { get; set; } = new();
public VectorStoreConfig VectorStore { get; set; } = new();
public LLMConfig LLM { get; set; } = new();
public UIConfig UI { get; set; } = new();
}
public class EmbeddingConfig
{
public string ModelPath { get; set; } = string.Empty;
public string TokenizerPath { get; set; } = string.Empty;
public bool UseGpu { get; set; } = true;
public int BatchSize { get; set; } = 32;
}
public class VectorStoreConfig
{
public string Type { get; set; } = "Qdrant"; // or "pgvector"
public string Host { get; set; } = "localhost";
public int Port { get; set; } = 6334;
public string CollectionName { get; set; } = "blog_embeddings";
}
public class LLMConfig
{
public string ModelPath { get; set; } = string.Empty;
public int ContextSize { get; set; } = 4096;
public int GpuLayers { get; set; } = 35;
public float DefaultTemperature { get; set; } = 0.7f;
public int MaxTokens { get; set; } = 500;
public bool EnableStreaming { get; set; } = true;
}
public class UIConfig
{
public int AutoSaveIntervalSeconds { get; set; } = 60;
public bool EnableAutoLinking { get; set; } = true;
public int SuggestionDebounceMs { get; set; } = 500;
public int MaxRecentFiles { get; set; } = 10;
}
}
{
"BlogLLM": {
"Embedding": {
"ModelPath": "C:\\models\\bge-base-en-onnx\\model.onnx",
"TokenizerPath": "C:\\models\\bge-base-en-onnx\\tokenizer.json",
"UseGpu": true,
"BatchSize": 32
},
"VectorStore": {
"Type": "Qdrant",
"Host": "localhost",
"Port": 6334,
"CollectionName": "blog_embeddings"
},
"LLM": {
"ModelPath": "C:\\models\\mistral-7b\\mistral-7b-instruct-v0.2.Q5_K_M.gguf",
"ContextSize": 4096,
"GpuLayers": 35,
"DefaultTemperature": 0.7,
"MaxTokens": 500,
"EnableStreaming": true
},
"UI": {
"AutoSaveIntervalSeconds": 60,
"EnableAutoLinking": true,
"SuggestionDebounceMs": 500,
"MaxRecentFiles": 10
}
},
"Logging": {
"LogLevel": {
"Default": "Information",
"Mostlylucid.BlogLLM": "Debug"
}
}
}
public class App : Application
{
public override void OnFrameworkInitializationCompleted()
{
var services = new ServiceCollection();
// Load configuration
var configuration = new ConfigurationBuilder()
.SetBasePath(Directory.GetCurrentDirectory())
.AddJsonFile("appsettings.json", optional: false)
.AddJsonFile($"appsettings.{Environment.GetEnvironmentVariable("ENVIRONMENT")}.json", optional: true)
.AddEnvironmentVariables()
.Build();
// Bind configuration
var config = new BlogLLMConfig();
configuration.GetSection("BlogLLM").Bind(config);
// Register as singleton
services.AddSingleton(config);
// Register services using config
services.AddSingleton(sp => new BatchEmbeddingService(
config.Embedding.ModelPath,
config.Embedding.TokenizerPath,
config.Embedding.UseGpu
));
// ... rest of service registration
}
}
# Publish as single-file executable
dotnet publish Mostlylucid.BlogLLM.Client/Mostlylucid.BlogLLM.Client.csproj \
-c Release \
-r win-x64 \
--self-contained true \
-p:PublishSingleFile=true \
-p:IncludeNativeLibrariesForSelfExtract=true \
-o ./publish/win-x64
# Result: Single .exe file with all dependencies
<?xml version="1.0" encoding="UTF-8"?>
<!-- Using WiX Toolset: https://wixtoolset.org/ -->
<Wix xmlns="http://schemas.microsoft.com/wix/2006/wi">
<Product Id="*" Name="Blog Writing Assistant" Language="1033"
Version="1.0.0.0" Manufacturer="YourName" UpgradeCode="PUT-GUID-HERE">
<Package InstallerVersion="200" Compressed="yes" InstallScope="perMachine" />
<MediaTemplate EmbedCab="yes" />
<Directory Id="TARGETDIR" Name="SourceDir">
<Directory Id="ProgramFiles64Folder">
<Directory Id="INSTALLFOLDER" Name="BlogLLM" />
</Directory>
<Directory Id="ProgramMenuFolder">
<Directory Id="ApplicationProgramsFolder" Name="Blog Writing Assistant"/>
</Directory>
</Directory>
<DirectoryRef Id="INSTALLFOLDER">
<Component Id="MainExecutable" Guid="PUT-GUID-HERE">
<File Id="BlogLLMExe" Source="$(var.PublishDir)\BlogLLM.exe" KeyPath="yes" />
</Component>
<Component Id="ConfigFile" Guid="PUT-GUID-HERE">
<File Id="AppSettings" Source="$(var.PublishDir)\appsettings.json" />
</Component>
</DirectoryRef>
<DirectoryRef Id="ApplicationProgramsFolder">
<Component Id="ApplicationShortcut" Guid="PUT-GUID-HERE">
<Shortcut Id="ApplicationStartMenuShortcut"
Name="Blog Writing Assistant"
Target="[INSTALLFOLDER]BlogLLM.exe"
WorkingDirectory="INSTALLFOLDER"/>
<RemoveFolder Id="ApplicationProgramsFolder" On="uninstall"/>
<RegistryValue Root="HKCU" Key="Software\BlogLLM" Name="installed" Type="integer" Value="1" KeyPath="yes"/>
</Component>
</DirectoryRef>
<Feature Id="ProductFeature" Title="Blog Writing Assistant" Level="1">
<ComponentRef Id="MainExecutable" />
<ComponentRef Id="ConfigFile" />
<ComponentRef Id="ApplicationShortcut" />
</Feature>
</Product>
</Wix>
# download-models.ps1
param(
[string]$ModelsPath = "C:\models"
)
Write-Host "Downloading models to $ModelsPath..." -ForegroundColor Green
# Create directories
New-Item -ItemType Directory -Force -Path "$ModelsPath\bge-base-en-onnx" | Out-Null
New-Item -ItemType Directory -Force -Path "$ModelsPath\mistral-7b" | Out-Null
# Install huggingface-cli if needed
$hfCli = Get-Command huggingface-cli -ErrorAction SilentlyContinue
if (-not $hfCli) {
Write-Host "Installing huggingface-cli..." -ForegroundColor Yellow
pip install huggingface-hub
}
# Download embedding model
Write-Host "Downloading BGE embedding model..." -ForegroundColor Green
huggingface-cli download BAAI/bge-base-en-v1.5-onnx `
--local-dir "$ModelsPath\bge-base-en-onnx" `
--local-dir-use-symlinks False
# Download LLM
Write-Host "Downloading Mistral 7B model..." -ForegroundColor Green
huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF `
mistral-7b-instruct-v0.2.Q5_K_M.gguf `
--local-dir "$ModelsPath\mistral-7b" `
--local-dir-use-symlinks False
Write-Host "Download complete!" -ForegroundColor Green
Write-Host "Update appsettings.json with these paths:"
Write-Host " Embedding: $ModelsPath\bge-base-en-onnx\model.onnx"
Write-Host " LLM: $ModelsPath\mistral-7b\mistral-7b-instruct-v0.2.Q5_K_M.gguf"
public class UsageTracker
{
private readonly string _usageFilePath;
public UsageTracker(string dataPath)
{
_usageFilePath = Path.Combine(dataPath, "usage.json");
}
public void TrackSuggestionAccepted(string suggestionType, int length)
{
var usage = LoadUsage();
usage.SuggestionsAccepted++;
usage.TotalCharactersGenerated += length;
usage.LastUsed = DateTime.UtcNow;
SaveUsage(usage);
}
public void TrackLinkInserted(string targetPost)
{
var usage = LoadUsage();
usage.LinksInserted++;
if (!usage.FrequentlyLinkedPosts.ContainsKey(targetPost))
{
usage.FrequentlyLinkedPosts[targetPost] = 0;
}
usage.FrequentlyLinkedPosts[targetPost]++;
SaveUsage(usage);
}
public UsageStats GetStats()
{
return LoadUsage();
}
private UsageStats LoadUsage()
{
if (!File.Exists(_usageFilePath))
{
return new UsageStats();
}
var json = File.ReadAllText(_usageFilePath);
return JsonSerializer.Deserialize<UsageStats>(json) ?? new UsageStats();
}
private void SaveUsage(UsageStats stats)
{
var json = JsonSerializer.Serialize(stats, new JsonSerializerOptions
{
WriteIndented = true
});
File.WriteAllText(_usageFilePath, json);
}
}
public class UsageStats
{
public int SuggestionsGenerated { get; set; }
public int SuggestionsAccepted { get; set; }
public int LinksInserted { get; set; }
public int TotalCharactersGenerated { get; set; }
public Dictionary<string, int> FrequentlyLinkedPosts { get; set; } = new();
public DateTime LastUsed { get; set; }
public DateTime FirstUsed { get; set; } = DateTime.UtcNow;
}
[RelayCommand]
private async Task ProvideFeedback(GenerationResult result, FeedbackType type)
{
var feedback = new SuggestionFeedback
{
GeneratedText = result.GeneratedText,
PromptType = result.PromptType,
ContextTokens = result.ContextTokensUsed,
FeedbackType = type,
Timestamp = DateTime.UtcNow
};
await _feedbackService.RecordAsync(feedback);
// Adjust parameters based on feedback
if (type == FeedbackType.TooGeneric)
{
// Increase temperature for more creativity
_config.LLM.DefaultTemperature = Math.Min(1.0f, _config.LLM.DefaultTemperature + 0.1f);
}
else if (type == FeedbackType.TooRambling)
{
// Decrease temperature for more focus
_config.LLM.DefaultTemperature = Math.Max(0.3f, _config.LLM.DefaultTemperature - 0.1f);
}
}
public enum FeedbackType
{
Helpful,
TooGeneric,
TooRambling,
WrongStyle,
Perfect
}
// User opens app
// Loads yesterday's draft
[RelayCommand]
private async Task ContinueFromYesterday()
{
// Load last saved draft
var draft = await LoadLastDraft();
EditorText = draft.Content;
// Generate fresh suggestions based on overnight ingestion
await RefreshSuggestions();
}
[RelayCommand]
private async Task ProcessAllDrafts()
{
var drafts = GetAllDraftFiles();
foreach (var draft in drafts)
{
var content = await File.ReadAllTextAsync(draft);
// Suggest links for each draft
var links = await _linkService.SuggestLinksAsync(content);
// Auto-accept high-confidence links
var autoAccept = links.Where(l => l.RelevanceScore > 0.9f).ToList();
var updated = _linkService.InsertLinks(content, autoAccept);
await File.WriteAllTextAsync(draft, updated);
_logger.LogInformation("Processed {Draft}: inserted {Count} links",
Path.GetFileName(draft), autoAccept.Count);
}
}
:在较弱部分使用“ 整合部分” 。
Solution: Reduce GpuLayers or ContextSize in config:
{
"LLM": {
"GpuLayers": 20, // Lower from 35
"ContextSize": 2048 // Lower from 4096
}
}
批次处理
Solution: Increase context tokens and adjust temperature:
{
"LLM": {
"DefaultTemperature": 0.8 // Higher = more creative
}
}
And in code: request.MaxContextTokens = 3000 // More context
排除麻烦指南
Check:
1. Is model fully on GPU? (Check GpuLayers = 35)
2. Using Q5 or Q4 quantization? (Faster than Q8)
3. Is something else using GPU? (Check nvidia-smi)
共同问题 共同问题
Solution: Increase scoreThreshold:
scoreThreshold: 0.85f // Higher threshold = only very relevant links
将检索分数用作培训信号
graph TB
subgraph "Part 1: Architecture"
A1[System Design]
A2[Technology Choices]
end
subgraph "Part 2: GPU Setup"
B1[CUDA Installation]
B2[cuDNN Setup]
B3[Testing]
end
subgraph "Part 3: Embeddings"
C1[Embedding Models]
C2[Vector Databases]
C3[Semantic Search]
end
subgraph "Part 4: Ingestion"
D1[Markdown Parsing]
D2[Chunking]
D3[Embedding Generation]
D4[Vector Storage]
end
subgraph "Part 5: UI"
E1[Avalonia Client]
E2[Editor Component]
E3[Suggestions Panel]
end
subgraph "Part 6: LLM"
F1[LLamaSharp]
F2[Model Loading]
F3[Inference]
end
subgraph "Part 7: Generation"
G1[Context Building]
G2[Prompt Engineering]
G3[Content Generation]
end
subgraph "Part 8: Production"
H1[Auto-linking]
H2[Configuration]
H3[Deployment]
H4[Monitoring]
end
A1 --> B1
B3 --> C1
C3 --> D1
D4 --> E1
E3 --> F1
F3 --> G1
G3 --> H1
class A1,F3 architecture
class D4,E1 data
class G3,H3 production
classDef architecture stroke:#333
classDef data stroke:#333
classDef production stroke:#333,stroke-width:4px
实施多式RAG(代码+图表+文本)
:当地LLM综合推理:
无APPI费用:
快速博客写作
一贯的风格和声音
旧内容的再使用
我希望你已经学到了::
Happy writing with your new AI assistant! 🚀
© 2026 Scott Galloway — Unlicense — All content and source code on this site is free to use, copy, modify, and sell.