This video explores ChromaDB, an open-source vector database designed for storing and retrieving vector embeddings. You'll learn how vector databases differ from traditional databases and how ChromaDB plays a crucial role in semantic search, enabling AI systems to find information based on meaning and context, not just keywords.
The video dives into:
Understanding Vector Embeddings: We'll break down how data is transformed into vectors for efficient comparison and retrieval.
ChromaDB in Action: See how ChromaDB simplifies storing and searching vector data, making it accessible for developers.
Unlocking Semantic Search: Discover how ChromaDB empowers AI applications to perform more human-like searches, retrieving relevant information regardless of exact wording.
This video is perfect for anyone interested in:
Building AI-powered search engines
Enhancing large language models (LLMs) with contextual information
Leveraging semantic search for various applications
Advanced RAG: Supercharge Your AI with Richer Information Retrieval
This video delves into advanced Retrieval-Augmented Generation (RAG) techniques, taking your AI systems to the next level. RAG combines information retrieval with generative AI models to produce comprehensive and informative responses.
The video explores:
RAG Fundamentals: We'll revisit the core concepts of RAG, including its two-stage process: retrieval and generation.
Advanced Features: Discover how to expand RAG's capabilities beyond text, incorporating multimodal data like images and audio for richer context.
Real-World Applications: See how advanced RAG can be applied in various fields, from video processing to generating personalized healthcare summaries.
This video is ideal for:
Developers working with large language models
Researchers exploring the frontiers of AI
Anyone interested in building smarter and more versatile AI systems