Welcome to an exciting journey into the world of vector search engines with Weaviate! This blog aims to provide you with step-by-step guidance on how to utilize Weaviate for various cool machine-learning tasks. Whether you’re building a semantic search application or developing a text-image search functionality, Weaviate has a rich set of features to explore. Let’s dive in!
Getting Started with Weaviate
Before we embark on creating any cool applications, it’s important to get Weaviate running smoothly on your machine. Most examples will assume that you have Weaviate up and running. To set it up locally, follow the installation guide provided in the documentation.
Essential Links
List of Examples and Tutorials
Below is a curated list of various examples that demonstrate the potential of Weaviate:
- Semantic search through Wikipedia using Weaviate – GraphQL
- PyTorch-BigGraph Wikidata search with Weaviate – GraphQL
- Multi-Modal Text-Image search using CLIP – Bash, Javascript, React
- Getting started with the Python Client – Google Colab
- Demo dataset News Publications with Contextionary – YAML
- Demo dataset with Transformers, NER, Spellcheck, and QA – YAML
- Weaviate simple schema example – Python
- Semantic search through wine dataset – Python
- Unmask Superheroes in 5 steps using Weaviate NLP – Python
- Information Retrieval with BERT – Python (Jupyter Notebook)
Understanding the Code with an Analogy
Using Weaviate can be compared to baking a cake. Each example is akin to a different cake recipe. The ingredients (like vectorizers or ML modules) you may want can be selected based on the specific type of cake (task) you want to bake (solve). For instance, if you pick a recipe for a multi-modal search (text-image search), you’ll gather the necessary items such as CLIP and React as your components. As you progress with each recipe, you’ll realize that the fundamentals of mixing, baking, and decorating remain consistent. With practice, you’ll become an expert cake baker (Weaviate user), able to tweak recipes (code) to perfection.
Troubleshooting
Even the best bakers encounter issues! If you run into trouble while using Weaviate, here are a few troubleshooting ideas:
- Ensure that your Weaviate is properly installed and running.
- Check if all required modules are included in your configuration for the task you’re trying to achieve.
- Refer back to the documentation for specific examples and code snippets that may guide you.
- Connect with the community via Slack for shared experiences and solutions.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.