This blog post provides a thorough tutorial on how to integrate vector search and embeddings with large language models, explaining how to use these technologies for semantic search applications. The post begins with an explanation of vector embeddings and how they capture a deeper understanding of data beyond keyword matches. It explains how to build these embeddings and store them in MongoDB Atlas, demonstrating the process with Python code snippets and step-by-step instructions. The post then expands into practical use cases like building a semantic search application, including the preparation of data and performing semantic searches. Finally, it concludes by examining the limitations and potential of large language models and the new opportunities opened up by integrating vector search.
