High Agency: The Podcast for AI Builders Jeff Huber of Chroma: Building the open-source toolkit for AI Engineering
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Oct 24, 2024 In this discussion, Jeff Huber, founder of Chroma, shares insights on vector databases and their critical role in AI engineering. He dives into the issues surrounding retrieval-augmented generation (RAG) terminology, advocating for clearer language in the field. Jeff details the evolution of Chroma, focusing on developer experiences and real-world applications, while also debating the timelines for achieving super AI. Listeners will learn about embedding processes, the significance of context in AI, and the challenges of AI deployment in production systems.
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Vector Database Importance
- Vector databases act as a memory layer, augmenting LLMs with private data.
- Retrieval is crucial for productionizing LLM applications, improving reliability by handling diverse inputs.
Embedding Model Selection
- Start with a simple embedding model, like Sentence Transformers V6, for initial development.
- Upgrade to more sophisticated models as your application's needs evolve.
Understanding Embeddings
- Embeddings, arrays of numbers from embedding models, capture the "vibe" of text.
- Trained on internet-scale data, models associate tokens based on webpage proximity.