RAG 的基礎
Standard Large Language Models (LLMs) are "frozen" in time, limited by their training data cut-off. They cannot answer questions about your company’s internal handbook or a private video meeting from yesterday. 檢索增強生成（RAG）透過從您自身的私有資料中提取相關上下文，彌補此差距。
多階段工作流程
為讓私有資料對語言模型更具可讀性，我們遵循特定流程：
- 載入：將各種格式（PDF、網頁、YouTube）轉換為標準文件格式。
- 分割：將長文件分割成較小且易於管理的「片段」。
- 嵌入：將文字片段轉換為數值向量（意義的數學表示）。
- 儲存：將這些向量儲存在向量資料庫（如 Chroma）中，以實現極快的相似度搜尋。
為什麼分塊至關重要？
語言模型具有「上下文窗口」（一次能處理的文字量上限）。若傳送一份百頁的 PDF，模型將無法運作。我們將資料分塊，以確保僅傳遞最相關的資訊給模型。
TERMINALbash — 80x24
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Question 1
Why is
chunk_overlap considered a critical parameter when splitting documents for RAG?Challenge: Preserving Context
Apply your knowledge to a real-world scenario.
You are loading a YouTube transcript for a technical lecture. You notice that the search results are confusing "Lecture 1" content with "Lecture 2."
Task
Which splitter would be best for keeping context like "Section Headers" intact?
Solution:
MarkdownHeaderTextSplitter or RecursiveCharacterTextSplitter. These allow you to maintain document structure in the metadata, helping the retrieval system distinguish between different chapters or lectures.