Dive into the advanced aspects of programming language models with our new module on DSPy. This series covers cutting-edge use cases from knowledge graphs to RAG, and explores how DSPy can be effectively combined with FastAPI. New videos will be released daily post-subscription, enriching your learning experience with the latest in AI development techniques.
Course Overview
Prepare to take your DSPy skills to the next level! This course will guide you through the advanced functionalities of DSPy, enabling you to build more complex and efficient AI-driven applications. From constructing knowledge graphs to implementing retrieval-augmented generation (RAG) using different database systems, each chapter is designed to enhance your technical expertise and expand your understanding of DSPy's potential.
Chapters
- How to Build a Knowledge Graph Using DSPy: Start your journey by building a robust knowledge graph.
- Improving Our Knowledge Graph Extractor Using Multi-Relationships Recognition: Enhance your knowledge graph extractor with advanced relationship recognition techniques.
- Coreference Resolution to Improve Our Knowledge Graph: Implement coreference resolution to refine your knowledge graph's accuracy.
- Near-perfect Coreference Resolution to Improve Our Knowledge Graph: Push the boundaries of coreference resolution to near-perfection in your knowledge graph applications.
- Introduction to Spacy-llm + DSPy for NER: Explore the integration of Spacy-llm with DSPy for advanced named entity recognition.
- Should we really use Spacy-llm and DSPy for NER?: A critical analysis of using Spacy-llm in conjunction with DSPy for NER.
- Graphical representation of DSPy modules P.1: Visualize the structure and flow of DSPy modules.
- Graphical representation of DSPy modules P.2: Continue exploring the graphical representations of DSPy's modules.
- Graphical representation of DSPy modules P.3: Conclude your visual tour of DSPy's module architecture.
- Doing RAG with a Neo4j graph database (upcoming): Learn how to implement RAG using the Neo4j graph database.
- Doing RAG using a Weaviate vector database (upcoming): Discover how to use a Weaviate vector database for RAG.
- Using DSPy in a FastAPI application (upcoming): Discover how to use DSPy in a FastAPI application.
By the end of this course, you will have mastered the more complex aspects of DSPy, ready to tackle challenging projects and develop innovative solutions using this powerful framework.
