Vector Database 101
Deploying Vector Databases in Multi-Cloud Environments
Multi-cloud deployment has become increasingly popular for services looking for as much uptime as possible, with organizations leveraging multiple cloud providers to optimize performance, reliability, and cost-efficiency.
Engineering
Hybrid Search: Combining Text and Image for Enhanced Search Capabilities
Milvus enables hybrid sparse and dense vector search and multi-vector search capabilities, simplifying the vectorization and search process.
Engineering
Falcon 180B: Advancing Language Models in the AI Frontier
This report condenses and elaborates on the Falcon series of LLMs model architecture, dataset considerations, training strategy, and performance metrics.
Engineering
Using Your Vector Database as a JSON (or Relational) Datastore
This simple guide walks you through using your vector database to store and search your structured data.
Vector Database 101
What is a Vector Database?
A vector database is a fully managed, no-frills solution for storing, indexing, and searching unstructured data through vector embeddings from ML models.
Evaluating Your Embedding Model
In this blog, we'll review some key considerations for selecting a model. We'll also review a practical example of using Arize Pheonix and RAGAS to evaluate different text embedding models.
Product
The Best Vector Database Just Got Better
Zilliz Cloud's new features drive huge business impact for real-world use cases like autonomous agents, recommenders, and AI-powered drug discovery.
Engineering
Training Your Own Text Embedding Model
Explore how to train your text embedding model using the `sentence-transformers` library and generate our training data by leveraging a pre-trained LLM.
Engineering
Sentence Transformers for Long-Form Text
Learn about sentence transformers for long-form text, Sentence-BERT architecture and use the IMDB dataset for evaluating different embedding models.