超越相似性
当“80%问题”基础语义搜索在简单查询时表现良好，但在边缘情况却会失效。仅通过相似性进行搜索时，向量存储通常返回数值上最接近的片段。然而，如果这些片段几乎完全相同，大语言模型（LLM）将接收到冗余信息，浪费有限的上下文窗口，并错失更广阔的视角。
高级检索支柱
- 最大边际相关性（MMR）：与仅选择最相似的项目不同，MMR 在相关性与多样性之间取得平衡，以避免重复。$$MMR = \text{argmax}_{d \in R \setminus S} [\lambda \cdot \text{sim}(d, q) - (1 - \lambda) \cdot \max_{s \in S} \text{sim}(d, s)]$$
- 自查询：利用大语言模型（LLM）将自然语言转化为结构化元数据过滤器（例如按“第3讲”或“来源：PDF”筛选）。
- 上下文压缩：压缩检索到的文档，仅提取与查询相关的“高价值”片段，从而节省令牌。
冗余陷阱
向大语言模型提供同一段落的三个版本并不会让它变得更智能——只会让提示词变得更昂贵。多样性是实现“高价值”上下文的关键。
TERMINALbash — 80x24
> Ready. Click "Run" to execute.
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Knowledge Check
You want your system to answer "What did the instructor say about probability in the third lecture?" specifically. Which tool allows the LLM to automatically apply a filter for
{ "source": "lecture3.pdf" }?Challenge: The Token Limit Dilemma
Apply advanced retrieval strategies to solve a real-world constraint.
You are building a RAG system for a legal firm. The documents retrieved are 50 pages long, but only 2 sentences per page are actually relevant to the user's specific query. The standard "Stuff" chain is throwing an
OutOfTokens error because the context window is overflowing with irrelevant text.
Step 1
Identify the core problem and select the appropriate advanced retrieval tool to solve it without losing specific nuances.
Problem: The context window limit is being exceeded by "low-nutrient" text surrounding the relevant facts.
Tool Selection:
Tool Selection:
ContextualCompressionRetrieverStep 2
What specific component must you use in conjunction with this retriever to "squeeze" the documents?
Solution: Use an
LLMChainExtractor as the base for your compressor. This will process the retrieved documents and extract only the snippets relevant to the query, passing a much smaller, highly concentrated context to the final prompt.