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User-Roles RAG recipe (#54)
* Add example user roles recipe
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README.md

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@@ -63,6 +63,7 @@ To get started with RAG, either from scratch or using a popular framework like L
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| [/RAG/04_advanced_redisvl.ipynb](python-recipes/RAG/04_advanced_redisvl.ipynb) | Advanced RAG techniques |
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| [/RAG/05_nvidia_ai_rag_redis.ipynb](python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb) | RAG using Redis and Nvidia NIMs |
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| [/RAG/06_ragas_evaluation.ipynb](python-recipes/RAG/06_ragas_evaluation.ipynb) | Utilize the RAGAS framework to evaluate RAG performance |
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| [/RAG/07_user_role_based_rag.ipynb](python-recipes/RAG/07_user_role_based_rag.ipynb) | Implement a simple RBAC policy with vector search using Redis |
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### LLM Memory
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LLMs are stateless. To maintain context within a conversation chat sessions must be stored and resent to the LLM. Redis manages the storage and retrieval of chat sessions to maintain context and conversational relevance.

assets/role-based-rag.png

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