This repository covers end-to-end examples of the various features and integrations with Weaviate.
Category | Description |
---|---|
Integrations | Notebooks showing you how to use Weaviate plus another technology |
Weaviate Features | Notebooks covering vector, hybrid and generative search, reranking, multi-tenancy and more |
Company Category | Companies |
---|---|
Cloud Hyperscalers | Google, AWS, NVIDIA |
Compute Infrastructure | Modal, Replicate |
Data Platforms | Confluent, Spark, Unstructured, Firecrawl |
LLM Frameworks | DSPy, LangChain, LlamaIndex, Semantic Kernel, Ollama |
Observability and Evaluation | Arize, Langtrace, LangWatch, Nomic, Ragas, Weights & Biases |
Feature | Description |
---|---|
Similarity Search | Use Weaviate's nearText operator to run semantic search queries (broken out by model provider) |
Hybrid Search | Use Weaviate's hybrid operator to run hybrid search queries (broken out by model provider) |
Generative Search | Build a simple RAG workflow using Weaviate's .generate (broken out by model provider) |
Filters | Narrow down your search results by adding filters to your queries |
Reranking | Add reranking to your pipeline to improve search results (broken out by model provider) |
Media Search | Use Weaviate's nearImage and nearVideo operator to search using images and videos |
Classification | Learn how to use KNN and zero-shot classification |
Multi-Tenancy | Store tenants on separate shards for complete data isolation |
Product Quantization | Compress vector embeddings and reduce the memory footprint using Weaviate's PQ feature |
Evaluation | Evaluate your search system |
CRUD APIs | Learn how to use Weaviate's Create, Read, Update, and Delete APIs |
Generative Feedback Loops | Write back to your database by storing the language model outputs |
Please note this is an ongoing project, and updates will be made frequently. If you have a feature you would like to see, please create a GitHub issue or feel free to contribute one yourself!