This repository covers end-to-end examples of the various features and integrations with Weaviate.
| Category | Description |
|---|---|
| Datasets | Ready to use datasets to ingest data into your Weaviate cluster |
| 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 |
| Query Agent | Notebooks showing you how to build with Weaviate's Query Agent |
Check out Weaviate's Integrations Documentation!
| Company Category | Companies |
|---|---|
| Cloud Hyperscalers | Google, AWS, NVIDIA |
| Compute Infrastructure | Modal, Replicate |
| LLM and Agent Frameworks | Agno, CrewAI, Composio, DSPy, Dynamiq, LangChain, LlamaIndex, Pydantic, Semantic Kernel, Ollama, Haystack, Modaic |
| Data Platforms | Databricks, Confluent, Box, Boomi, Spark, Unstructured, Firecrawl, Context Data, Aryn, Astronomer, Airbyte, IBM (Docling), Cardinal, Contextual AI, Chonkie, Parallel |
| Operations | AIMon, Arize, Cleanlab, Comet, DeepEval, Langtrace, LangWatch, Nomic, Patronus AI, Ragas, TruLens, Weights & Biases |
| Feature | Description |
|---|---|
| Model Providers | Use Weaviate's nearText, hybrid, and .generate operator with various model providers |
| 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 |
| Multi-Vector Embeddings | Use Weaviate with powerful ColBERT-style embeddings to improve search results |
| Product Quantization | Compress vector embeddings and reduce the memory footprint using Weaviate's PQ feature |
| Evaluation | Evaluate your search system |
| Mode | Description |
|---|---|
| Ask | Transforms your query into actionable searches or aggregations, and then provides a final answer to the question |
| Search | Transforms your query into actionable searches and returns the matching Weaviate objects directly |
| Suggest Queries | Suggest queries based on the data in your collection |
Check out this contributor guide to convert recipes (Jupyter Notebooks) into docs friendly markdown.
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!
