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Demo 06 - RAG Part 2
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===============================================
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Retrieval Augmented Generation (RAG) is a way to extend the knowledge of the LLM used in the AI service.
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The RAG pattern is composed of two parts:
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* Ingestion: This is the part that stores data in the knowledge base.
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* Augmentation: This is the part that adds the retrieved information to the input of the LLM.
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# Embedding model
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One of the core components of the RAG pattern is the embedding model. The embedding model is used to transform the
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text into numerical vectors. These vectors are used to compare the text and find the most relevant segments.
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# Vector store
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In the previous step, we used an in memory store. Now we will use a persistent store to keep the embeddings between restarts.
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# Ingesting documents into the vector store
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While you are editing the `src/main/resources/application.properties` file, add the following configuration:
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```
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rag.location=src/main/resources/rag
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```
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