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RAG (Retrieval-Augmented Generation) is a powerful approach for enhancing a model's knowledge by leveraging your own dataset.
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-[Inference Deployment](/ai-data/managed-inference/how-to/create-deployment/): Set up an inference deployment using [sentence-transformers/sentence-t5-xxl](/ai-data/managed-inference/reference-content/sentence-t5-xxl/) on an L4 instance to efficiently process embeddings.
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-[Inference Deployment](/ai-data/managed-inference/how-to/create-deployment/) with the model of your choice.
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-[Object Storage Bucket](/storage/object/how-to/create-a-bucket/) to store all the data you want to inject into your LLM model.
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-[Managed Database](/managed-databases/postgresql-and-mysql/how-to/create-a-database/) to securely store all your embeddings.
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-[Managed Database](/managed-databases/postgresql-and-mysql/how-to/create-a-database/) to securely store all your embeddings.
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## Configure your developement environnement
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1. Install necessary packages: run the following command to install the required packages:
2. Configure your environnement variables: create a .env file and add the following variables. These will store your API keys, database connection details, and other configuration values.
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