This course explores managed vector search and semantic retrieval using Pinecone, a cloud-based vector database. It focuses on moving from locally managed vector storage to scalable vector search, covering index configuration, namespace management, vector operations, and the development of Pinecone-backed retrieval pipelines.

Through structured lessons and practical demonstrations, you will learn how k-nearest-neighbour search and dense retrieval support semantic matching, how managed vector databases differ from local solutions, and how Pinecone organises vectors through indexes, namespaces, records, and metadata. You will also create a Pinecone serverless index, organise records using namespaces, upsert and fetch product vectors, and configure index dimensions and similarity metrics for effective retrieval. The course progresses from retrieval fundamentals to building complete Pinecone-backed retrieval workflows, emphasising knowledge-base preparation, document chunking, vector indexing, and semantic search. Rather than treating vector databases as standalone storage systems, it focuses on designing retrieval pipelines that connect embedding models, managed vector storage, and retrieval frameworks. You will also integrate Pinecone with LangChain and explore migrating vector collections from ChromaDB to Pinecone. By the end of this course, you will be able to: - Explain vector search fundamentals and the role of managed vector databases - Configure and create Pinecone serverless indexes with appropriate dimensions and similarity metrics - Organise vector records using Pinecone namespaces and metadata - Perform vector upsert and fetch operations in Pinecone - Prepare, chunk, and index knowledge-base documents for semantic retrieval - Build Pinecone-backed retrieval pipelines and integrate them with LangChain - Migrate vector collections from ChromaDB to Pinecone This course is ideal for AI engineers, machine learning practitioners, developers, and professionals building scalable semantic search and retrieval applications. A foundational understanding of Python, vector embeddings, similarity measures, and basic retrieval concepts is recommended; prior experience with Pinecone or managed vector databases is not required. Join us to learn how to build and manage scalable semantic retrieval systems using Pinecone, from configuring vector indexes and organising records to developing complete retrieval pipelines and integrating managed vector search into practical applications.