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The Cloudcast
Intro to Vector Databases
Nov 15, 2023
Jeff Huber, Co-Founder and CEO @ Chroma, discusses vector databases and their importance to AI. Topics include the basics of vector databases, integration into models, trade-offs between fine-tuning and retrieval, and creating an AI stack. They also touch on Chroma's OSS project.
25:51
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Quick takeaways
- Chroma and vector databases have diverse use cases in chat-your-data, agent-based systems, and hybrid models, providing developers with versatile tools for AI applications.
- Vector databases, like Chroma, augment foundational models by enabling fuzzy searching, bridging the gap between general knowledge and contextual information, and enhancing the performance of AI systems.
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Use Cases for Chroma and Vector Databases
The use cases for Chroma and vector databases are diverse, but they can be categorized into three main areas: chat-your-data, agent-based systems, and hybrid models. Chat-your-data applications involve a back-and-forth conversation between humans and AI, with the AI providing responses based on the data or documents it has been trained on. This use case is popular and widely used, accounting for a significant portion of applications built with vector databases. Agent-based systems, on the other hand, focus on AI operating autonomously and performing tasks on behalf of the user. This area of application is still in its early stages, with experimentation and development ongoing. The hybrid approach combines elements of both chat-your-data and agent-based systems, enabling bidirectional communication between humans and AI, where the AI can ask questions and seek input from users. This embedded intelligence in products and services is a promising frontier, though still emerging. Overall, the use cases for Chroma and vector databases vary across these three categories, offering developers versatile tools for creating AI-powered applications.
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