内 第一部分 第一部分 和 第二部分 第二部分 在此系列中, 我们覆盖了RAG的起源、 基本原理和技术结构。 您了解RAG是什么, 为何重要, 以及它是如何在引擎盖下运行的。 现在是时候将知识付诸实践了。 文章展示了您如何建立真正的RAG系统, 使用工作 C# 代码, 解决共同的挑战, 并使用最新研究的先进技术 。
系列导航: 这是RAG系列第3部分:
如果你还没读完第一和第二部分 我建议从那里开始理解
本条假定你理解这些基本要点,并着重阐述 实施、优化和现实世界模式.
我在这个博客上建立了几个RAG动力功能。让我给你们展示具体的例子。
每一篇文章都使用语义相似性显示“相关文章”。
如何运作:
为什么它比标签更好:
代码片断 :
public async Task<List<SearchResult>> GetRelatedPostsAsync(
string currentPostSlug,
string language,
int limit = 5)
{
// Get the current post's embedding
var currentPost = await _vectorStore.GetByIdAsync(currentPostSlug);
if (currentPost == null)
return new List<SearchResult>();
// Find similar posts
var similarPosts = await _vectorStore.SearchAsync(
currentPost.Embedding,
limit: limit + 1, // +1 because result includes the current post
filter: new Filter
{
Must =
{
new Condition
{
Field = "language",
Match = new Match { Keyword = language }
}
},
MustNot =
{
new Condition
{
Field = "slug",
Match = new Match { Keyword = currentPostSlug }
}
}
}
);
return similarPosts.Take(limit).ToList();
}
博客上的搜索框使用RAG式语义搜索(虽然没有下一代部分,
用户经验:
执行: 我会在即将到来的关于矢量数据库的文章里 写上这个
我正在建立一个完整的RAG系统 来帮助我写新的博客文章
使用实例 : 当我开始写“将认证添加到 ASP. NET Core” 时,
全部RAG输油管:
public async Task<WritingAssistanceResponse> GetSuggestionsAsync(
string currentDraft,
string topic)
{
// 1. Embed the current draft
var draftEmbedding = await _embeddingService.GenerateEmbeddingAsync(
currentDraft
);
// 2. Retrieve related past content
var relatedPosts = await _vectorStore.SearchAsync(
draftEmbedding,
limit: 5
);
// 3. Build context for LLM
var prompt = BuildWritingAssistancePrompt(
currentDraft,
topic,
relatedPosts
);
// 4. Generate suggestions using local LLM
var suggestions = await _llmService.GenerateAsync(prompt);
// 5. Extract and format citations
var response = ExtractCitations(suggestions, relatedPosts);
return response;
}
这是操作中的RAG - 检索(语义搜索)+增强(附加上下文)+生成(LLM建议)。
建筑生产RAG系统并不是微不足道的。我遇到的挑战是如何解决的。
问题: 您如何分割文档 ? 太小 = 失去上下文 。 太大 = 不相干的信息 。
解决方案 : 基于文档结构的混合区块 。
public class SmartChunker
{
public List<Chunk> ChunkDocument(string markdown, string sourceId)
{
var chunks = new List<Chunk>();
// Parse markdown into sections
var document = Markdown.Parse(markdown);
var sections = ExtractSections(document);
foreach (var section in sections)
{
var wordCount = CountWords(section.Content);
if (wordCount < MinChunkSize)
{
// Merge small sections
MergeWithPrevious(chunks, section);
}
else if (wordCount > MaxChunkSize)
{
// Split large sections
var subChunks = SplitSection(section);
chunks.AddRange(subChunks);
}
else
{
// Just right
chunks.Add(CreateChunk(section, sourceId));
}
}
return chunks;
}
}
最佳做法:
问题: 通用嵌入模型可能无法捕捉特定域的语义。
解决办法:
备选方案1:精细嵌入 (已改进)
# Using sentence-transformers in Python
from sentence_transformers import SentenceTransformer, InputExample, losses
model = SentenceTransformer('all-MiniLM-L6-v2')
# Create training examples from your domain
train_examples = [
InputExample(texts=['Docker Compose', 'container orchestration'], label=0.9),
InputExample(texts=['Entity Framework', 'ORM database'], label=0.9),
InputExample(texts=['Docker', 'apple fruit'], label=0.1)
]
# Fine-tune
train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)
train_loss = losses.CosineSimilarityLoss(model)
model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=1)
备选方案2:混合嵌入 (复合多模型)
public async Task<float[]> GenerateHybridEmbeddingAsync(string text)
{
var semantic = await _semanticModel.GenerateEmbeddingAsync(text);
var keyword = await _keywordModel.GenerateEmbeddingAsync(text);
// Concatenate or weighted average
return CombineEmbeddings(semantic, keyword);
}
备选办法3:增加元数据过滤
var results = await _vectorStore.SearchAsync(
queryEmbedding,
limit: 10,
filter: new Filter
{
Must =
{
new Condition { Field = "category", Match = new Match { Keyword = "ASP.NET" } },
new Condition { Field = "date", Range = new Range { Gte = "2024-01-01" } }
}
}
);
问题: LLM 具有象征性限制。 您如何在窗口中匹配查询 + 上下文 + 提示 ?
解决方案 : 动态环境选择和概述。
public string BuildContextAwarePrompt(
string query,
List<SearchResult> retrievedDocs,
int maxTokens = 4096)
{
var promptTemplate = GetPromptTemplate();
var queryTokens = CountTokens(query);
var templateTokens = CountTokens(promptTemplate);
// Reserve tokens for: prompt + query + response
var availableForContext = maxTokens - queryTokens - templateTokens - 500; // 500 for response
// Add context until we hit limit
var selectedContext = new List<SearchResult>();
var currentTokens = 0;
foreach (var doc in retrievedDocs.OrderByDescending(d => d.Score))
{
var docTokens = CountTokens(doc.Text);
if (currentTokens + docTokens <= availableForContext)
{
selectedContext.Add(doc);
currentTokens += docTokens;
}
else
{
// Try summarizing the doc if it's important
if (doc.Score > 0.85)
{
var summary = await SummarizeAsync(doc.Text, maxTokens: 200);
var summaryTokens = CountTokens(summary);
if (currentTokens + summaryTokens <= availableForContext)
{
selectedContext.Add(new SearchResult
{
Text = summary,
Title = doc.Title,
Score = doc.Score
});
currentTokens += summaryTokens;
}
}
}
}
return FormatPrompt(query, selectedContext);
}
问题: 即便在某种情况下,LLMs有时也忽略了它,产生幻觉。
解决办法:
1. 即时工程:
var systemPrompt = @"
You are a technical assistant.
CRITICAL RULES:
1. ONLY use information from the provided CONTEXT sections
2. If the context doesn't contain the answer, say 'I don't have enough information in the provided context to answer that'
3. DO NOT use your training data to supplement answers
4. Always cite the source using [1], [2] notation
5. If you're unsure, say so
CONTEXT:
{context}
QUESTION: {query}
ANSWER (following all rules above):
";
2. 发电后验证:
public async Task<bool> ValidateResponseAgainstContext(
string response,
List<SearchResult> context)
{
// Check if response contains claims not in context
var responseSentences = SplitIntoSentences(response);
foreach (var sentence in responseSentences)
{
var isSupported = await IsClaimSupportedByContext(sentence, context);
if (!isSupported)
{
_logger.LogWarning("Hallucination detected: {Sentence}", sentence);
return false;
}
}
return true;
}
3. 迭代改进:
public async Task<string> GenerateWithValidationAsync(
string query,
List<SearchResult> context,
int maxAttempts = 3)
{
for (int attempt = 0; attempt < maxAttempts; attempt++)
{
var response = await _llm.GenerateAsync(
BuildPrompt(query, context)
);
var isValid = await ValidateResponseAgainstContext(response, context);
if (isValid)
return response;
// Refine prompt for next attempt
query = $"{query}\n\nPrevious attempt hallucinated. Stick strictly to the context.";
}
return "I couldn't generate a reliable answer. Please rephrase your question.";
}
问题: 当您添加新文档时,矢量数据库需要保持当前状态。
解决方案 : 自动索引输油管。
public class BlogIndexingBackgroundService : BackgroundService
{
private readonly IVectorStoreService _vectorStore;
private readonly IMarkdownService _markdownService;
private readonly ILogger<BlogIndexingBackgroundService> _logger;
protected override async Task ExecuteAsync(CancellationToken stoppingToken)
{
while (!stoppingToken.IsCancellationRequested)
{
try
{
await IndexNewPostsAsync(stoppingToken);
// Check for updates every hour
await Task.Delay(TimeSpan.FromHours(1), stoppingToken);
}
catch (Exception ex)
{
_logger.LogError(ex, "Error in indexing service");
}
}
}
private async Task IndexNewPostsAsync(CancellationToken ct)
{
var allPosts = await _markdownService.GetAllPostsAsync();
foreach (var post in allPosts)
{
var existingDoc = await _vectorStore.GetByIdAsync(post.Slug);
// Check if content changed
var currentHash = ComputeHash(post.Content);
if (existingDoc == null || existingDoc.ContentHash != currentHash)
{
_logger.LogInformation("Indexing updated post: {Title}", post.Title);
var chunks = _chunker.ChunkDocument(post.Content, post.Slug);
foreach (var chunk in chunks)
{
var embedding = await _embeddingService.GenerateEmbeddingAsync(chunk.Text);
await _vectorStore.UpsertAsync(
id: $"{post.Slug}_{chunk.Index}",
embedding: embedding,
metadata: new Dictionary<string, object>
{
["slug"] = post.Slug,
["title"] = post.Title,
["chunk_index"] = chunk.Index,
["content_hash"] = currentHash
},
ct: ct
);
}
}
}
}
}
让我们探索一下最近的研究中最先进的RAG技术
问题: 用户查询往往很短,而且结构不完善。文档块详细,写得很好。这种不匹配会伤害检索。
解决方案 : 生成一个假想的理想文件 来回答询问,嵌入,然后搜索。
public async Task<List<SearchResult>> HyDESearchAsync(string query)
{
// Generate hypothetical answer (even if hallucinated)
var hypotheticalAnswer = await _llm.GenerateAsync($@"
Write a detailed, technical paragraph that would perfectly answer this question:
Question: {query}
Paragraph:"
);
// Embed the hypothetical answer
var embedding = await _embeddingService.GenerateEmbeddingAsync(
hypotheticalAnswer
);
// Search using this embedding
return await _vectorStore.SearchAsync(embedding);
}
为什么它起作用: 假设答案使用与实际文件相似的语言和结构,从而改进检索。
问题: 用户查询往往将语义搜索与元数据过滤器混在一起。
示例: “ 关于 Docker 的最近文章” = 语义 (“ Docker ” ) + 过滤器 (日期 > 2024-01-01-01)
解决方案 : 使用 LLM 将查询解析为语义+元数据过滤器 。
public async Task<SearchQuery> ParseSelfQueryAsync(string naturalLanguageQuery)
{
var parsingPrompt = $@"
Parse this search query into:
1. Semantic search query (what the user is looking for)
2. Metadata filters (category, date range, etc.)
User Query: {naturalLanguageQuery}
Output JSON:
{{
""semantic_query"": ""the core concept"",
""filters"": {{
""category"": ""...",
""date_after"": ""..."",
""date_before"": ""...""
}}
}}
";
var jsonResponse = await _llm.GenerateAsync(parsingPrompt);
var parsed = JsonSerializer.Deserialize<SearchQuery>(jsonResponse);
return parsed;
}
// Use parsed query
var parsedQuery = await ParseSelfQueryAsync("Recent ASP.NET posts about authentication");
// semantic_query: "authentication"
// filters: { category: "ASP.NET", date_after: "2024-01-01" }
var results = await _vectorStore.SearchAsync(
embedding: await _embeddingService.GenerateEmbeddingAsync(parsedQuery.SemanticQuery),
filter: BuildFilter(parsedQuery.Filters)
);
问题: 单一查询可能因措辞而错过相关文件。
解决方案 : 生成查询的多种变量, 全部搜索, 合并结果 。
public async Task<List<SearchResult>> MultiQuerySearchAsync(string query)
{
// Generate query variations
var variations = await _llm.GenerateAsync($@"
Generate 3 different ways to phrase this search query:
Original: {query}
Variations (one per line):
");
var queries = variations.Split('\n', StringSplitOptions.RemoveEmptyEntries)
.Prepend(query) // Include original
.ToList();
// Search with all variations
var allResults = new List<SearchResult>();
foreach (var q in queries)
{
var embedding = await _embeddingService.GenerateEmbeddingAsync(q);
var results = await _vectorStore.SearchAsync(embedding, limit: 10);
allResults.AddRange(results);
}
// Deduplicate and merge scores
var merged = allResults
.GroupBy(r => r.Id)
.Select(g => new SearchResult
{
Id = g.Key,
Text = g.First().Text,
Title = g.First().Title,
Score = g.Max(r => r.Score) // Take best score
})
.OrderByDescending(r => r.Score)
.ToList();
return merged;
}
问题: 回收的块含有不相关的信息。 发送所有的废品标记 。
解决方案 : 使用较小的 LLM 压缩只检索到相关部分的上下文。
public async Task<string> CompressContextAsync(
string query,
List<SearchResult> retrievedDocs)
{
var compressed = new List<string>();
foreach (var doc in retrievedDocs)
{
var compressionPrompt = $@"
Extract only the sentences from this document that are relevant to answering the question.
Question: {query}
Document:
{doc.Text}
Relevant excerpts (maintain original wording):
";
var relevantExcerpt = await _smallLLM.GenerateAsync(compressionPrompt);
if (!string.IsNullOrWhiteSpace(relevantExcerpt))
{
compressed.Add($"From '{doc.Title}':\n{relevantExcerpt}");
}
}
return string.Join("\n\n", compressed);
}
问题: 复杂的问题需要来自多种来源的信息,而这些信息需要连接起来。
例如:“博客使用什么数据库? 如何进行语义搜索?”
解决方案 : 循环检索和合成。
public async Task<string> MultiHopRAGAsync(string complexQuery, int maxHops = 3)
{
var currentQuery = complexQuery;
var allContext = new List<SearchResult>();
for (int hop = 0; hop < maxHops; hop++)
{
// Retrieve for current query
var results = await SearchAsync(currentQuery, limit: 5);
allContext.AddRange(results);
// Check if we have enough information
var synthesisPrompt = $@"
Original question: {complexQuery}
Context so far:
{FormatContext(allContext)}
Can you answer the original question with this context?
If yes, provide the answer.
If no, what additional information do you need? (be specific)
";
var synthesis = await _llm.GenerateAsync(synthesisPrompt);
if (synthesis.Contains("yes", StringComparison.OrdinalIgnoreCase))
{
// We have enough information
return ExtractAnswer(synthesis);
}
// Extract what we need for next hop
currentQuery = ExtractNextQuery(synthesis);
}
// Final synthesis with all gathered context
return await GenerateFinalAnswerAsync(complexQuery, allContext);
}
问题: 如何建立能记住数月或数年前对话的人工智能系统?
解决方案 : 将RAG与渐进式汇总结合起来,以创建耐久、可搜索的内存。
这就是在 DiSE(稀释合成合成进化) - 一个我正在建造的先进系统 使用基于RAG的背景内存 来永久保持共享的谈话历史
实例设想:
User (Today): "Remember George's specs?"
AI: "Yes, you discussed George's prescription requirements in our conversation
from 5 years ago (2019-03-15). He needed progressive lenses with..."
如何运作:
flowchart TB
A[User Message] --> B[Store in RAG Memory]
B --> C[Extract Key Entities & Topics]
C --> D[Link to Past Conversations]
E[Periodic Summarization] --> F[Summarize Old Conversations]
F --> G[Store Summary with High-Level Tags]
G --> H[Keep Original for Retrieval]
I[Future Query: 'George's specs'] --> J[Semantic Search in RAG]
J --> K[Find: 2019 conversation]
K --> L[Retrieve Original Context]
L --> M[LLM generates response with 5-year-old context!]
style B stroke:#f9f,stroke-width:3px
style J stroke:#bbf,stroke-width:3px
实施办法:
public class LongTermConversationalMemory
{
private readonly IVectorStoreService _vectorStore;
private readonly IEmbeddingService _embeddingService;
public async Task StoreConversationAsync(
string conversationId,
string userId,
List<ConversationTurn> turns,
DateTime timestamp)
{
// Extract key entities and topics
var entities = await ExtractEntitiesAsync(turns);
var topics = await ExtractTopicsAsync(turns);
// Create searchable representation
var conversationText = string.Join("\n", turns.Select(t =>
$"{t.Speaker}: {t.Message}"));
// Generate embedding
var embedding = await _embeddingService.GenerateEmbeddingAsync(
conversationText);
// Store in RAG with rich metadata
await _vectorStore.IndexDocumentAsync(
id: $"conv_{conversationId}",
embedding: embedding,
metadata: new Dictionary<string, object>
{
["user_id"] = userId,
["timestamp"] = timestamp.ToString("O"),
["entities"] = entities, // ["George", "specs", "prescription"]
["topics"] = topics, // ["healthcare", "eyewear"]
["full_text"] = conversationText,
["turn_count"] = turns.Count
}
);
}
public async Task<List<PastContext>> RetrieveRelevantPastAsync(
string currentQuery,
string userId,
int limit = 5)
{
// Embed the current query
var queryEmbedding = await _embeddingService.GenerateEmbeddingAsync(
currentQuery);
// Search past conversations
var results = await _vectorStore.SearchAsync(
queryEmbedding,
limit: limit,
filter: new Filter
{
Must =
{
new Condition { Field = "user_id", Match = new Match { Keyword = userId } }
}
}
);
return results.Select(r => new PastContext
{
ConversationId = r.Id,
Timestamp = DateTime.Parse(r.Metadata["timestamp"].ToString()),
Entities = (List<string>)r.Metadata["entities"],
FullText = r.Metadata["full_text"].ToString(),
Relevance = r.Score
}).ToList();
}
// Periodic summarization to keep memory manageable
public async Task SummarizeOldConversationsAsync(DateTime olderThan)
{
var oldConversations = await _vectorStore.FindByDateRangeAsync(
endDate: olderThan);
foreach (var conv in oldConversations)
{
// Generate summary using LLM
var summary = await _llm.GenerateAsync($@"
Summarize this conversation, preserving key facts and entities:
{conv.FullText}
Summary:");
// Update document with summary while keeping original
await _vectorStore.UpdateAsync(
id: conv.Id,
additionalMetadata: new Dictionary<string, object>
{
["summary"] = summary,
["summarized_at"] = DateTime.UtcNow.ToString("O")
}
);
}
}
}
为何如此强大:
DISE 中真实世界的例子 :
DISE 使用此方法来记住 :
这创造了一个AI系统, 真正从每次互动中“吸取教训”, 建立机构记忆, 而不是每次会议都重新开始。
考虑以下挑战的挑战:
这个技术把RAG从"搜索我的文件" 转变为"记住我们讨论过的一切" 长期人工智能助理的游戏变换器
RAG不是总能解决问题的 是时候避免了
1. 一般性知识问题
2. 创意写作
3. 实时数据需求
4. 数学推理
5. 非常小的知识库
6. 当控制LLM培训时
想建立自己的RAG系统吗?
目标: 获得基本的检索 工作与无LLM。
// 1. Choose an embedding service (start with API for simplicity)
var openAI = new OpenAIClient(apiKey);
// 2. Embed a few test documents
var docs = new[]
{
"Docker is a containerization platform",
"Kubernetes orchestrates containers",
"Entity Framework is an ORM for .NET"
};
var embeddings = new List<float[]>();
foreach (var doc in docs)
{
var response = await openAI.GetEmbeddingsAsync(
new EmbeddingsOptions("text-embedding-3-small", new[] { doc })
);
embeddings.Add(response.Value.Data[0].Embedding.ToArray());
}
// 3. Implement basic search (in-memory for now)
var query = "container orchestration";
var queryEmbedding = await GetEmbeddingAsync(query);
var results = embeddings
.Select((emb, idx) => new
{
Text = docs[idx],
Score = CosineSimilarity(queryEmbedding, emb)
})
.OrderByDescending(r => r.Score)
.ToList();
// 4. Verify search works
foreach (var result in results)
{
Console.WriteLine($"{result.Score:F3}: {result.Text}");
}
// Expected: Kubernetes scores highest
目标: 缩放为真实的文档收藏 。
今后执行的步骤:
我将在即将发表的关于矢量数据库的文章中详细论述这一点。
目标: 完成RAG输油管
// 1. Retrieve context
var context = await SearchAsync(query, limit: 3);
// 2. Build prompt
var prompt = $@"
Answer the question using this context:
{FormatContext(context)}
Question: {query}
Answer:";
// 3. Generate (start with API)
var response = await openAI.GetChatCompletionsAsync(new ChatCompletionsOptions
{
Messages =
{
new ChatMessage(ChatRole.System, "You are a helpful assistant."),
new ChatMessage(ChatRole.User, prompt)
},
Temperature = 0.7f,
MaxTokens = 500
});
return response.Value.Choices[0].Message.Content;
一旦基本知识发挥作用,就迁移到当地推论(我将在即将发表的文章中述及这一点):
RAG(检索-提款一代)是使LLMs更加准确、更新和可信赖的有力技术,其方法是以实际文件作为答复的依据。
协助通知书的主要优点是:
何时使用 RAG :
避免 RAG 时 :
利用HYDE、多查询检索和背景压缩等先进技术,该领域正在迅速发展,但核心概念仍然简单:让LLMS在正确的时间获得正确的信息。
开始简单、测量结果和循环。 RAG是当今建立可靠的AI系统最实际的方法之一。
你们已经完成了三个部分的RAG系列:
第3部分:实践中的RCG(本条)
你现在有完整的图象: 从了解RAG的起源到建立具有先进优化的生产系统。
现在你们从理论到实践都了解RAG, 即将发表的文章将展示你们如何在 C# 建立完整、可生产、可生产的RAG系统:
即将到来:
这些文章将引领你从理论到实践, 完整的工作守则,部署策略, 和现实世界优化,
继续关注实际操作的实施指南, 将RAG知识转化为工作系统!
基础文件:
工具和框架:
进一步阅读:
本RAG系列:
快乐的建筑!
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