Tutorial on using SageMaker and RAG with Pinecone for retrieval augmented generation with open source models.

🔍 Retrieval augmented generation with open source models using AWS SageMaker

🔗 Setting up instances for storing the language model and embedding model

📚 Using a dataset to inform the language model

🗄️ Storing vector embeddings in Pinecone database

💡 The retrieval augmented prompt process using query vectors

🖥️ Implementing the process using AWS SageMaker

📝 Accessing the notebook with installation instructions and importing the required libraries

💡 The video explains how to configure the image and choose the model ID for Hugging Face language models.

🔍 The speaker demonstrates how to search for and select the Google flan T5 XL model for text generation.

⚙️ The process of deploying the selected model to an AWS SageMaker instance is shown, including initialization and deployment steps.

👉 Manage spot training can be used with all instances supported in Amazon SageMaker.

🔍 RAG (Retrieval Augmented Generation) allows finding chunks of text that can answer a question from a larger database.

💡 Hugging Face Transformers can be used for efficient feature extraction in embedding models.

🔍 The video explores the process of creating query vectors using embedding models and transformer models.

💡 The embedding models generate token-level embeddings for each input sentence and use mean pooling to create a single sentence embedding.

🔢 The expected dimensionality of the embeddings is 384, but the actual output dimensions are 8 due to the input tokens and padding tokens.

📚 We create XC vectors by taking the mean across a single axis and package it into a single function.

🔍 We apply the XC vectors to the Amazon SageMaker FAQs dataset and store them in Pinecone.

🔗 We initialize a connection with Pinecone using a free API key and create a new index with the dimensionality of 384.

📚 Creating a database index and storing metadata and embeddings for documents.

🔍 Querying the database with a question and retrieving relevant context.

✏️ Answering a question based on the given context.

🔍 Retrieval augmented generation with SageMaker and Pinecone allows for the creation of an answer based on given context and prompt.

❓ The model is designed to respond with 'I don't know' if the context does not contain relevant information.

💡 Using open source models and Pinecone, it is relatively easy to set up and use retrieval augmented generation with SageMaker.