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update rag doc

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docs/design_pattern/rag.md
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# RAG (Retrieval Augmented Generation)
For certain LLM tasks like answering questions, providing context is essential.
Most common way to retrive text-based context is through embedding:
1. Given texts, you first [chunk](../utility_function/chunking.md) them.
2. Next, you [embed](../utility_function/embedding.md) each chunk.
3. Then you store the chunks in [vector databases](../utility_function/vector.md).
4. Finally, given a query, you embed the query and find the closest chunk in the vector databases.
For certain LLM tasks like answering questions, providing relevant context is essential. One common architecture is a **two-stage** RAG pipeline:
1. **Offline stage**: Preprocess and index documents ("building the index").
2. **Online stage**: Given a question, generate answers by retrieving the most relevant context from the index.
<div align="center">
<img src="https://github.com/the-pocket/PocketFlow/raw/main/assets/rag.png?raw=true" width="250"/>
</div>
# RAG (Retrieval Augmented Generation)
For certain LLM tasks like answering questions, providing relevant context is essential. One common architecture is a **two-stage** RAG pipeline:
1. **Offline stage**: Preprocess and index documents ("building the index").
2. **Online stage**: Given a question, generate answers by retrieving the most relevant context from the index.
---
## Stage 1: Offline Indexing