Semantic Search: A Deep Dive Into Vector Databases (with Zain Hasan)
Oct 18, 2023
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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.
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.
Multimodal search technology enables the integration of models that understand various types of data, creating the potential for cross-modal searches and unlocking new search experiences.
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.
The Power of Vector Databases
This episode features the guest, Zann Hasan, who sheds light on vector databases. Vector databases store and search data based on vector representations. The concept of retrieval augmented generation is introduced, where a base model is combined with a vector database to enhance the system's ability to reason and generate contextually grounded answers. The episode explores how vector databases work, including the use of distance metrics to measure similarity between vectors. It also touches on the scalability of vector databases and the trade-off between accuracy and performance.
Advancements in Multimodal Search
The podcast episode unveils the future of search technology, specifically in the realm of multimodal search. The integration of models that understand various modalities, such as images, text, audio, and video, is discussed. The potential of conducting cross-modal searches is highlighted, showcasing the ability to search for concepts across different types of data. The episode mentions the importance of unifying vector representations across modalities and outlines current approaches, including specialized models for each modality and fusion techniques through contrastive learning. The possibilities and creativity that multimodal search can unlock are emphasized, encouraging experimentation and exploration in this field.
Implementing Vector Search with Weave
For those interested in implementing vector search, Weave is recommended as an open-source database. The episode mentions how Weave provides modules and examples to facilitate vectorizing data and building search engines. Weave's Python client is highlighted for ease of use, along with the benefits of utilizing Weave's managed instances for larger-scale deployments. The community and resources offered by Weave, including tutorials, forums, and Slack channels, are mentioned as valuable for learning, troubleshooting, and accessing support. Finally, the episode touches on the scalability and affordability of Weave, with a flexible licensing model that allows for self-hosting or managed hosting options.
The Exciting Possibilities of Multimodal Search
The episode concludes by emphasizing the exciting possibilities of multimodal search. The ability to search for and connect different types of data, such as text, images, audio, and video, is highlighted as a particularly intriguing development. Examples are given, including searching for paintings similar to a query or finding medieval poetry based on visual inspiration. The potential for creativity and exploration is underscored, with reference to ongoing hackathons and the power of combining multimodal models to unlock new search experiences. The episode encourages listeners to engage with Weave and contribute to the growing field of multimodal search.
As interesting and useful as LLMs (Large Language Models) are proving, they have a severe limitation: they only know about the information they were trained on. If you train it on a snapshot of the internet from 2023, it’ll think it’s 2023 forever. So what do you do if you want to teach it some new information, but don’t want to burn a million AWS credits to get there?
In exploring that answer, we dive deep into the world of semantic search, augmented LLMs, and exactly how vector databases bridge that gap from the old dog to the new tricks. Along the way we’ll go from an easy trick to teach ChatGPT some new information by hand, all the way down to how vector databases store documents by their meaning, and how they efficiently search through those meanings to give custom, relevant answers to your questions.
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Zain on Twitter: https://twitter.com/zainhasan6 Zain on LinkedIn: https://www.linkedin.com/in/zainhas Kris on Twitter: https://twitter.com/krisajenkins Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/ HNSW Paper: https://arxiv.org/abs/1603.09320 ImageBind - One Embedding Space To Bind Them All (pdf): https://openaccess.thecvf.com/content/CVPR2023/papers/Girdhar_ImageBind_One_Embedding_Space_To_Bind_Them_All_CVPR_2023_paper.pdf Weaviate: https://weaviate.io/ Source: https://github.com/weaviate/weaviate Examples: https://github.com/weaviate/weaviate-examples Community Links: https://forum.weaviate.io/ and https://weaviate.io/slack
