Retrieval Augmented Generation (RAG) enables a large language model (LLM) to access data from external knowledge sources like repositories, databases, and APIs without the need for fine-tuning. RAG allows LLMs to answer questions using the most relevant and up-to-date information, with the option to cite their data sources for verification. A typical RAG solution for knowledge retrieval involves converting data from external sources into embeddings using an embeddings model, and storing these embeddings in a vector database. When a user asks a question, the system searches the vector database to retrieve documents that are similar to the query. The retrieved documents and the user’s query are then combined in an augmented prompt, which is sent to the LLM for text generation. This implementation includes two models: the embeddings model and the LLM responsible for generating the final response.
In this post, we demonstrate how to build a RAG question answering solution using Amazon SageMaker Studio. SageMaker Studio provides managed Jupyter notebooks with GPU instances, allowing for rapid experimentation during the initial phase without the need for additional infrastructure. There are two options for using notebooks in SageMaker: fast launch notebooks available through SageMaker Studio, and SageMaker notebook instances.
To implement RAG, you typically experiment with different embedding models, vector databases, text generation models, and prompts, while debugging your code until you have a functional prototype. Once you have a prototype, you can transition from notebook experimentation to deploying your models to SageMaker endpoints for real-time inference. This post provides a step-by-step guide on how to develop and deploy a RAG solution using SageMaker Studio notebooks.
To get started, you’ll need an AWS account and an IAM role with the necessary permissions to create and access the solution resources. You’ll also need a SageMaker domain with a user profile that has permissions to launch the SageMaker Studio app. Additionally, you’ll need access to the required models and databases, such as Llama 2 7b chat and Pinecone.
The solution architecture involves two main steps: developing the solution using SageMaker Studio notebooks and deploying the models for inference. To develop the solution, you load the Llama-2 7b chat model and create prompts using a PromptTemplate with LangChain. You experiment with different prompts and assess the quality of responses. Once you have satisfactory results, you gather external documents, generate embeddings using the BGE embeddings model, and store them in a Pinecone index. When a user asks a question, you perform a similarity search in Pinecone and add the relevant content to the prompt’s context.
Once you achieve your performance goals, you can deploy the Llama-2 7b chat model and the BAAI/bge-small-en-v1.5 embeddings model to SageMaker real-time endpoints. You can then use these deployed models in your question answering generative AI applications.
To implement this solution, you need to set up your development environment, install the necessary Python libraries, and load the pre-trained model and tokenizer. You can then start asking questions that require up-to-date information and use LangChain and the PromptTemplate to create prompts based on the desired format.
Overall, this post provides a detailed guide on how to build and deploy a RAG question answering solution using SageMaker Studio notebooks.
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