Setting up a Retrieval Augmented Generation (RAG) workflow with DeepSeek on AWS involves several steps, primarily using Amazon SageMaker and Amazon OpenSearch Service. Here's a detailed guide on how to implement this:
Step 1: Deploy DeepSeek-R1 on Amazon SageMaker
1. Create a SageMaker Domain: Navigate to Amazon SageMaker and create a new domain. Choose the "Single user quick setup" option and wait for the setup to complete.
2. Deploy DeepSeek-R1 Model: Once your domain is ready, open SageMaker Studio and deploy the DeepSeek-R1 model. This step involves setting up an endpoint for the model.
Step 2: Set Up Amazon OpenSearch Service
1. Create an OpenSearch Domain: Go to the AWS console and create a new OpenSearch domain. This will serve as your vector database for storing and retrieving embeddings.
2. Configure IAM Roles: Create IAM roles to manage permissions between OpenSearch and SageMaker. This includes roles for invoking the SageMaker model and for your user to create connectors.
Step 3: Configure IAM Roles and Permissions
1. Create IAM Role for SageMaker Access: This role allows OpenSearch to invoke the DeepSeek model on SageMaker. Attach necessary policies to enable model invocation.
2. Configure IAM Role in OpenSearch: Ensure that OpenSearch has the necessary permissions to interact with SageMaker models.
Step 4: Create OpenSearch Connector
1. Use Scripts to Create Connector: Utilize provided Python scripts to create an OpenSearch connector to SageMaker. This connector enables OpenSearch to call the DeepSeek model for text generation.
2. Register the Model: Use the OpenSearch API to register the DeepSeek model. This involves specifying the model name, function type, and connector ID.
Step 5: Implement RAG Workflow
1. Use Vector Embeddings for Search: Configure OpenSearch to use vector embeddings for semantic search. This allows for more accurate retrieval of relevant documents.
2. Integrate with DeepSeek for Text Generation: Once relevant documents are retrieved, use the DeepSeek model to generate text responses based on the retrieved information.
Step 6: Test and Deploy
1. Test the RAG System: Use a sample query to test the system's ability to retrieve relevant documents and generate coherent text responses.
2. Deploy the Application: Once tested, deploy the RAG application for production use, ensuring all components are properly secured and configured.
This setup leverages DeepSeek's reasoning capabilities and OpenSearch's vector database features to create a robust RAG workflow on AWS. For more detailed instructions and scripts, refer to AWS documentation and GitHub repositories related to DeepSeek and OpenSearch[1][2][6].
Citations:
[1] https://www.youtube.com/watch?v=K2BSe_hWL78
[2] https://aws.amazon.com/blogs/big-data/use-deepseek-with-amazon-opensearch-service-vector-database-and-amazon-sagemaker/
[3] https://github.com/opensearch-project/ml-commons/blob/main/docs/tutorials/aws/RAG_with_DeepSeek_R1_model_on_Bedrock.md
[4] https://github.com/Spidy20/Deepseek-RAG-App
[5] https://www.youtube.com/watch?v=_jXeIxVUVnw
[6] https://opensearch.org/docs/latest/vector-search/tutorials/rag/rag-deepseek-r1-sagemaker/
[7] https://opensearch.org/docs/latest/vector-search/tutorials/rag/rag-deepseek-r1-bedrock/
[8] https://aws-news.com/article/0194e24b-49c8-f3c1-2748-3b9c36468666