How to use OpenAI's embedding models trained with Matryoshka Representation Learning in a vector database like Weaviate
How to select an embedding model for your search and retrieval-augmented generation system.
How to implement a local Retrieval-Augmented Generation pipeline with Ollama language models and a self-hosted Weaviate vector database via Docker in Python.
Learn how to vectorize ~50 million objects and ingest into Weaviate using Modal!
Dive into using Weaviate for image recognition to find the "needle in a haystack"!
One prompt does not fit all language models. Learn how to optimize your prompts using DSPy compilers.
Hybrid Search for curious Web Developers with the new Weaviate TypeScript client and Next.js
Hurricane is a web application to demonstrate Generative Feedback Loops with blog posts.
Verba is an open source Retrieval Augmented Generation (RAG) application built using a modular, customizable architecture that makes it easy for anyone to use AI methods to get personalized answers on their own data.
Learn how to fine-tune Cohere's reranker and generate synthetic data using DSPy!
An end-to-end generative feedback loop demo using Weaviate, Ollama, Mistral and Snowflake’s Snowpark Container Services!
What you need to consider when using the Weaviate vector database with non-English languages, such as Hindi, Chinese, or Japanese.
