Erik Bamberg - What comes after ChatGPT? Vector Databases - the Simple and powerful future of ML?

#1about 3 minutes

Understanding the limitations of large language models

Large language models like ChatGPT face challenges with token limits and incorporating private data, which restricts their use on large documents or custom knowledge bases.

#2about 3 minutes

Why vector databases are attracting major investment

Unlike relational or NoSQL databases, vector databases are designed to store and semantically search unstructured data, filling a critical gap in the data landscape.

#3about 4 minutes

The challenge of searching unstructured data

Manually tagging unstructured data like images and documents is inconsistent and subjective, making it an inefficient way to enable search.

#4about 5 minutes

How vector embeddings capture semantic meaning

Machine learning models convert unstructured data into numerical representations called embeddings, where semantically similar items are positioned closely in a high-dimensional space.

#5about 5 minutes

Visualizing relationships in a vector space

A demonstration with Google's Projector TensorFlow shows how words like "king" and "queen" are clustered together, visually representing their semantic proximity.

#6about 6 minutes

Performing fast similarity search with vectors

Vector databases use mathematical formulas to measure the distance between embeddings and employ indexing techniques like Approximate Nearest Neighbor (ANN) for high-speed search.

#7about 4 minutes

An overview of the vector database market

A look at popular vector databases like Pinecone, Weaviate, and Milvus, including their features, hosting models, and integrations with platforms like Hugging Face.

#8about 4 minutes

Building applications like intrusion and face detection

Vector databases can power real-world applications such as intrusion detection systems and face similarity matching without needing constant model retraining.

#9about 6 minutes

Augmenting ChatGPT with a long-term memory

The Retrieval-Augmented Generation (RAG) pattern uses a vector database to find relevant data chunks, providing LLMs with the right context to answer questions accurately.

#10about 16 minutes

Exploring more applications for vector search

Vector search enables a wide range of applications including recommendation systems, document deduplication, time-series analysis, and advanced product search.

yesterday

Machine Learning Engineer