30 Days of LLMs: Day 15 — Mastering Embedding Stores & Vector Databases in LLM Apps with Anton Troynikov
On Day 15 of our 30 Days of LLMs, where we learn embedding stores and vector databases with Anton Troynikov, co-founder of Chroma. Master LLM applications today!
Created on December 8|Last edited on December 10
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Day 15: Mastering Embedding Stores & Vector Databases in LLM Apps with Anton Troynikov
Welcome to Day 15 of the Weights & Biases 30 Days of LLMs. We're continuing with our complimentary "Building LLM-Powered Apps" course. In this segment, Chroma's co-founder Anton Troynikov imparts his knowledge on embedding stores and vector databases, integral elements for crafting advanced LLM applications.
Chapter Highlights
- Getting to Know Embedding Stores: Uncover the functionality of embedding stores and their role in enhancing operations like nearest neighbor search in LLM projects.
- Applicability of Embedding Stores: Understand the situations where embedding stores are particularly beneficial, notably in managing extensive datasets.
- The Function of Vector Databases: Delve into how vector databases efficiently process large datasets with approximate nearest neighbor algorithms.
- Enhancing Searches with Advanced Algorithms: Learn about the different algorithms employed in embedding stores, such as inverted file indexes, locality-sensitive hashing, and hierarchical navigable Small World graphs.
- Practical Implementation Tips: Acquire hands-on insights into the application of these technologies in LLM projects, informed by Chroma's co-founder.
Key Course Information
- No deep machine learning knowledge is needed, just some familiarity with Python programming.
- Strategies for continual enhancement of your LLM applications.
- Unique perspectives on the LLM tools used by Weights & Biases.
Free Enrollment
Preview of the Next Chapter
Join us tomorrow, as we dive into the complexities of evaluating LLM applications.
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