Multimodal RAG: Chat with VideosBuild an interactive system for querying video content using multimodal AIIntel
Quality and Safety for LLM ApplicationsLearn how to evaluate the safety and security of your LLM applications and protect against risks. Monitor and enhance security measures to safeguard your apps.WhyLabs
LangChain Chat with Your DataCreate a chatbot with LangChain to interface with your private data and documents. Learn from LangChain creator, Harrison Chase.LangChain
Knowledge Graphs for RAGLearn how to build and use knowledge graph systems to improve your retrieval augmented generation applications. Use Neo4j's query language Cypher to manage and retrieve data.Neo4j
Embedding Models: from Architecture to ImplementationLearn how to build embedding models and how to create effective semantic retrieval systems.Vectara
Large Language Models with Semantic SearchLearn to use LLMs to enhance search and summarize results, using Cohere Rerank and embeddings for dense retrieval.Cohere
Vector Databases: from Embeddings to ApplicationsDesign and execute real-world applications of vector databases. Build efficient, practical applications, including hybrid and multilingual searches.Weaviate
Building Applications with Vector DatabasesLearn to build six applications powered by vector databases, including semantic search, retrieval augmented generation (RAG), and anomaly detection.Pinecone
Preprocessing Unstructured Data for LLM ApplicationsImprove your RAG system to retrieve diverse data types. Learn to extract and normalize content from a wide variety of document types, such as PDFs, PowerPoints, and HTML files.Unstructured
Building Multimodal Search and RAGBuild smarter search and RAG applications for multimodal retrieval and generation.Weaviate
