Building Multi-Modal Search with Vector Databases

TL;DR

Learn how to use vector databases like we8 for efficient multimodal search and retrieval of text, images, audio, and video data.

Transcript

hi everyone my name is Diana Chan Morgan and I run things Community here at deeplearning.ai today we are so excited to host an amazing Workshop about leveraging the power of vector databases like web8 in conjunction with multimodel embedding models to power at scale production ready applications capable of understanding and searching text images au... Read More

Key Insights

• 👨‍🔬 Vector databases enable real-time semantic search by encoding data into vector embeddings.
• 🏪 Multimodal data, including text, images, audio, and video, can be stored and retrieved efficiently using vector databases.
• 😥 Vector databases allow any-to-any modality search and retrieval, providing flexibility in retrieving relevant data points.
• 🧡 The frequency of database updates depends on the specific needs of the business, ranging from real-time updates to periodic updates.
• 👨‍🔬 Vector search provides more accurate and relevant results compared to keyword-based search methods.
• 🤗 The combination of vector databases and generative multimodal models opens up possibilities for multimodal retrieval and generation.
• 🤗 We8 is an open-source Vector Database that offers tools and resources for developers to build and scale multimodal search applications.

Questions & Answers

Q: How do vector databases enable real-time semantic search?

Vector databases allow for real-time semantic search by encoding data into vector embeddings, capturing the meaning and relationships between different data points. This enables efficient retrieval of semantically similar objects.

Q: Can vector databases handle different types of data, such as text, images, audio, and video?

Yes, vector databases can handle multiple data types. By using specialized models for each modality, such as image embeddings or audio embeddings, the database can store and retrieve multimodal data efficiently.

Q: How often should the vector database be updated to keep the data current?

The frequency of database updates depends on the specific needs of the business. If data changes frequently, updates can be done in real-time. However, the frequency can be adjusted based on the requirements of the application, ranging from real-time updates to periodic updates.

Q: What is the advantage of using vector search over keyword search?

Vector search offers the advantage of semantic understanding and similarity-based retrieval. It goes beyond matching keywords and considers the meaning and relationships between data points, providing more accurate and relevant results compared to keyword-based searches.

Summary & Key Takeaways

• This workshop introduces the concept of vector databases and their ability to enable real-time semantic search and retrieval of multimodal data.

• The workshop covers the process of embedding multimodal data using machine learning models, storing the embeddings in vector databases, and performing any-to-any modality search applications.

• Speakers Sebastian and Zayn from we8 demonstrate how to set up and use the we8 Vector Database, along with code implementations for querying and retrieving multimodal data.