Unlock the power of semantic search with our comprehensive course, where we dive deep into the practicalities of generative AI in real-world data processing projects. Building on the foundational knowledge from the course The Hidden Foundation of GenAI, we embark on a journey to apply embeddings in practice. You will master the entire process of creating a semantic search pipeline—from generating embeddings and storing them in a vector database to executing natural language queries.
Course Overview
This course is structured around an impactful data observability project. You will construct a pipeline that aggregates logs, processes them with FastAPI, and secures the embeddings in qdrant—a high-performance vector storage solution. Furthermore, you'll craft an intuitive dashboard on Streamlit, enabling semantic log searches instead of traditional keyword searches, and evaluate the outputs against conventional SQL queries in DuckDB.
Key Course Steps
- From Embeddings to Search: Revisit the basics of embeddings and delve into how they enable semantic search functionality.
- Building a Pipeline: Implement an API with FastAPI for processing logs and generating embeddings.
- Working with qdrant: Explore collections, points, cosine similarity search, and optimize the embedding structure.
- Streamlit Interface: Develop a user-friendly search interface and compare the semantic search approach with traditional SQL.
- Improving Accuracy: Discover methods for optimizing embeddings, refining query formulations, and configuring searches.
- Launching in Docker: Deploy the entire stack (FastAPI, qdrant, Streamlit, DuckDB) using Docker Compose.
- Bonus: Utilize DuckDB for analytics by implementing WAL, handling data in Docker, and contrasting SQL capabilities with vector search.
Course Outcomes
By the end of the course, you will not only comprehend the mechanics of semantic search but also possess a ready-to-use project that can be tailored for your personal AI-driven solutions. This hands-on experience will prepare you to apply semantic search capabilities effectively and innovate within the realm of AI.
