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Developer Voices

Semantic Search: A Deep Dive Into Vector Databases (with Zain Hasan)

Oct 18, 2023
Zain Hasan, a semantic search and augmented LLMs expert, joins the podcast to discuss the challenges of teaching large language models. They explore the concept of vector databases and their role in enhancing chat bots. The episode delves into optimizing search in a fictional service, the size and storage of indexes in vector databases, and the concept of multi-modality in vector search. The discussion also includes implementing semantic search at home using Weaviate and a conversation on Weaviate, an open-source database with managed instances.
01:02:00

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Large language models have a limitation of only knowing the information they were trained on, which creates a challenge when trying to teach them new information.
  • Vector databases are essential for efficient and fast search operations as they organize and quantify data as vectors.

Deep dives

Understanding Vector Search and Semantic Search

Vector search and semantic search are discussed in this episode. The speaker explores how to teach a large language model about a specific dataset and how to make it search through different types of data. The challenge lies in training the model to understand new information and how to integrate an auxiliary database for improved search capabilities. The episode delves into the inner workings of large language models, the role of an auxiliary database, and the flow of data between the model and the database. It highlights the importance of vector databases in enabling efficient and fast search operations by organizing and quantifying data as vectors.

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