Tutorial on using SageMaker and RAG with Pinecone for retrieval augmented generation with open source models.
Learn how to use SageMaker and RAG with Pinecone for retrieval augmented generation with open source models in this tutorial.
00:00:00 Learn how to use SageMaker and RAG with Pinecone for retrieval augmented generation with open source models in this tutorial.
🔍 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
00:04:43 Learn how to configure and deploy Hugging Face language models using SageMaker and RAG with Pinecone. Retrieve the model ID and image URI for the desired model, initialize and deploy the image on the chosen instance, and provide relevant context for better model performance.
💡 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.
00:09:24 This video showcases how to use Hugging Face LLMs with SageMaker and RAG with Pinecone to improve text generation and retrieval tasks efficiently.
👉 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.
00:14:04 Creating embeddings using Hugging Face LLMs with SageMaker and RAG with Pinecone for query and context vectors.
🔍 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.
00:18:48 Creating vector embeddings of documents using Hugging Face LLMs and SageMaker and storing them in Pinecone.
📚 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.
00:23:30 This video demonstrates how to use Hugging Face LLMs with SageMaker and RAG with Pinecone to create a database, query it, and generate answers based on the context provided.
📚 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.
00:28:13 This video demonstrates how to use SageMaker and Pinecone for retrieval augmented generation without mentioning specific brand names or subscriptions.
🔍 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.
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