Vector Databases and Embeddings With ChromaDB: Vectors & Word Embeddings

Description

Download your free Python Cheat Sheet here: https://realpython.com/cheatsheet Free Python Skill Test with instant level + learning plan: https://realpython.com/skill-test Want to learn faster? Become a Python Expert with unlimited access to 5,000+ tutorials, videos, and exercises: https://realpython.com/start This is a preview of the video course, "Vector Databases and Embeddings With ChromaDB". The era of large language models (LLMs) is here, bringing with it rapidly evolving libraries like ChromaDB that help augment LLM applications. You’ve most likely heard of chatbots like OpenAI’s ChatGPT, and perhaps you’ve even experienced their remarkable ability to reason about natural language processing (NLP) problems. Modern LLMs, while imperfect, can accurately solve a wide range of problems and provide correct answers to many questions. However, due to the limits of their training and the number of text tokens they can process, LLMs aren’t a silver bullet for all tasks. You wouldn’t expect an LLM to deliver relevant responses about topics that don’t appear in its training data. For example, if you asked ChatGPT to summarize information in confidential company documents, you’d be out of luck. You could show some of these documents to ChatGPT, but there’s a limit to how many documents you can upload before you exceed ChatGPT’s maximum token count. How would you select which documents to show ChatGPT? To address these limitations and scale your LLM applications, a great option is to use a vector database like ChromaDB. A vector database allows you to store encoded unstructured objects, like text, as lists of numbers that can be compared to one another. For instance, you can find a collection of documents relevant to a question you’d like an LLM to answer. This is a portion of the complete course, which you can find here: https://realpython.com/courses/vector-databases-embeddings-chromadb/ The rest of the course covers: - Tackling Text Embeddings - Exercise: Find Similar Texts With Embeddings - Introducing Vector Databases - Querying Vector Databases - Exercise: Query a ChromaDB Collection - Adding Context for a Large Language Model - Setting Up the Project - Preparing and Loading the Data - Connecting to an LLM - Exercise: Build a RAG Context Prompt - Vector Databases and Embeddings With ChromaDB (Quiz) 🐍 Become a Python expert with real-world tutorials, on-demand courses, interactive quizzes, and 24/7 access to a community of experts at https://realpython.com ▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰ 🐍 Start Here → https://realpython.com/start 🗺️ Guided Learning Paths → https://realpython.com/learning-paths 🎧 Real Python Podcast → https://realpython.com/podcast 📚 Python Books → https://realpython.com/books 📖 Python Reference → https://realpython.com/ref 🧑‍💻 Quizzes & Exercises → https://realpython.com/quizzes 🎓 Live Courses: https://realpython.com/live ⭐️ Reviews & Learner Stories: https://realpython.com/learner-stories ▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰