š Retrieval augmented generation with open source models using AWS SageMaker
š Setting up instances for storing the language model and embedding model
š Using a dataset to inform the language model
šļø Storing vector embeddings in Pinecone database
š” The retrieval augmented prompt process using query vectors
š„ļø Implementing the process using AWS SageMaker
š Accessing the notebook with installation instructions and importing the required libraries
š” The video explains how to configure the image and choose the model ID for Hugging Face language models.
š The speaker demonstrates how to search for and select the Google flan T5 XL model for text generation.
āļø The process of deploying the selected model to an AWS SageMaker instance is shown, including initialization and deployment steps.
š Manage spot training can be used with all instances supported in Amazon SageMaker.
š RAG (Retrieval Augmented Generation) allows finding chunks of text that can answer a question from a larger database.
š” Hugging Face Transformers can be used for efficient feature extraction in embedding models.
š The video explores the process of creating query vectors using embedding models and transformer models.
š” The embedding models generate token-level embeddings for each input sentence and use mean pooling to create a single sentence embedding.
š¢ The expected dimensionality of the embeddings is 384, but the actual output dimensions are 8 due to the input tokens and padding tokens.
š We create XC vectors by taking the mean across a single axis and package it into a single function.
š We apply the XC vectors to the Amazon SageMaker FAQs dataset and store them in Pinecone.
š We initialize a connection with Pinecone using a free API key and create a new index with the dimensionality of 384.
š Creating a database index and storing metadata and embeddings for documents.
š Querying the database with a question and retrieving relevant context.
āļø Answering a question based on the given context.
š Retrieval augmented generation with SageMaker and Pinecone allows for the creation of an answer based on given context and prompt.
ā The model is designed to respond with 'I don't know' if the context does not contain relevant information.
š” Using open source models and Pinecone, it is relatively easy to set up and use retrieval augmented generation with SageMaker.
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