The foundational model can then use its knowledge of donuts and croissants to wax eloquently about cronuts. For example, if someone asks a baking chatbot, “What is a cronut?” and the foundational model has never heard of cronuts, you can transform the prompt into context: “A cronut resembles a doughnut and is made from croissant-like dough filled with flavored cream and fried in grapeseed oil. RAG’s solution is dead simple: provide additional context to the foundational model in the prompt. RAG addresses the problem that a foundational model (e.g., GPT-3 or GPT-4) may be missing some information needed to give a good answer because that information was not in the dataset used to train the model (for example, the information is stored in private documents or only became available recently). We will explain how to use Retrieval Augmented Generation (RAG) to create a chatbot that combines your data with the power of ChatGPT using OpenAI and pgvector. Why Create and Store OpenAI Embeddings for Your Documents? We recommend cloning the repo and following along by executing the code cells as you read through the tutorial. Jupyter Notebook and Code: You can find all the code used in this tutorial in a Jupyter Notebook, as well as sample content and embeddings on the Timescale GitHub: timescale/vector-cookbook. One could think of this tutorial as a first step to building a chatbot that can reference a company knowledge base or developer docs. Part 3: How to use embeddings retrieved from a vector database to augment LLM generation.Part 2: How to use PostgreSQL as a vector database and store OpenAI embedding vectors using pgvector.Part 1: How to create embeddings from content using the OpenAI API.We’ll use the example of creating a chatbot to answer questions about Timescale use cases, referencing content from the Timescale Developer Q&A blog posts, to illustrate the key concepts for creating, storing, and querying OpenAI embeddings with PostgreSQL and pgvector. Much more on OpenAI embeddings, pgvector and vector databases later in this post. Used to measure the similarity of text strings, OpenAI embeddings are a type of data representation (in the shape of vectors, i.e., lists of numbers) for OpenAI’s models. With a little help from the pgvector extension, you can leverage PostgreSQL, the flexible and robust SQL database, as a vector database to store and query OpenAI embeddings. Vector databases enable efficient storage and search of vector data and are essential to developing and maintaining AI applications using Large Language Models (LLMs). Looking for a “Hello world” tutorial for pgvector and OpenAI embeddings that gives you the basics of using PostgreSQL as a vector database? You’ve found it!
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