Vector databases are so hot right now. WTF are they?

TL;DR

Vector databases are becoming popular for storing and querying complex objects represented by arrays of numbers called vectors, with applications in recommendation systems, search engines, and text generation.

Transcript

it is April 7 2023 and you're watching the code report one month ago Vector database weeviate landed 16 million dollars in series a funding last week Pinecone DB just got a check for 28 million at a 700 million valuation and yesterday chroma an open source project with only 1.2 GitHub Stars raised 18 million for its embeddings database and I just l... Read More

Key Insights

• 👾 Vector databases store arrays of numbers representing complex objects in a high-dimensional space called an embedding.
• 😘 These databases are gaining popularity for their ability to query data with low latency, making them ideal for AI applications.
• 🤗 Weeviate, Pinecone DB, and Chroma are among the vector database options available, with some being open source and others proprietary.
• 🍉 Integrating vector databases with language models like GPT-4 and Lambda enhances AI capabilities by providing long-term memory and personalized responses.
• 👨‍🔬 Vector databases have applications in recommendation systems, search engines, and text generation.
• 👶 Native vector database options are emerging but are still relatively new compared to established database technologies like PostgreSQL and Redis.
• 🪛 Vector databases are essential for AI-driven applications that require efficient storage and retrieval of complex objects.

Questions & Answers

Q: What is a vector database?

A vector database stores arrays of numbers called vectors, representing complex objects in a high-dimensional space. The vectors can be used to map semantic meaning or similar features in various data types for applications like recommendation systems, search engines, and text generation.

Q: How do vector databases differ from relational or document databases?

Relational databases have rows and columns, document databases have documents and collections, while vector databases store arrays of numbers clustered by similarity. These databases offer ultra-low latency querying and are particularly useful for AI-driven applications.

Q: What are some popular vector database options?

Weeviate and Milvus are open-source vector databases written in Go. Pinecone is a widely used but non-open-source option. Chroma, based on ClickHouse, is another vector database option. Redis and PostgreSQL also have vector support.

Q: How do vector databases enhance language models like OpenAI's GPT-4 and Google's Lambda?

Vector databases extend language models by providing long-term memory. Developers can customize AI responses by querying relevant documents stored in a vector database. This integration allows for a more personalized and context-aware AI experience.

Summary & Key Takeaways

• Weeviate, Pinecone DB, and Chroma have recently received significant funding for their vector databases.

• Vector databases store arrays of numbers that represent complex objects like words, sentences, images, or audio files in a high-dimensional space called an embedding.

• These databases are ideal for AI-driven applications and can be queried with low latency, offering support for recommendation systems, search engines, and text generation.