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https://github.com/weaviate/recipes

This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!
https://github.com/weaviate/recipes

function-calling generative-ai llm-frameworks python retrieval-augmented-generation vector-database vector-search

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This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!

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README 

        
# Welcome to Weaviate Recipes 💚



![Weaviate logo](.github/Weaviate.png)



This repository covers end-to-end examples of the various features and integrations with [Weaviate](https://www.weaviate.io).



| Category | Description |

| -------------|---------|

| [Integrations](/integrations)| Notebooks showing you how to use Weaviate plus another technology |

| [Weaviate Services](/weaviate-services/) | Notebooks showing you how to build with Weaviate Services |

| [Weaviate Features](/weaviate-features) | Notebooks covering vector, hybrid and generative search, reranking, multi-tenancy, and more |



## Integrations 🌐

Check out Weaviate's [Integrations Documentation](https://weaviate.io/developers/integrations)!



| Company Category | Companies |

|------------------|-----------|

| Cloud Hyperscalers | Google, AWS, NVIDIA |

| Compute Infrastructure | Modal, Replicate |

| Data Platforms| Databricks, Confluent Cloud, Spark, Unstructured, Firecrawl, Context Data, Aryn, Astronomer, Airbyte, IBM (Docling) |

| LLM Frameworks | DSPy, LangChain, LlamaIndex, Semantic Kernel, Ollama, Composio, Haystack |

| Operations | Arize, Langtrace, LangWatch, Nomic, Ragas, Weights & Biases |



## Weaviate Services 🧰

| Service | Description |

|---------|-------------|

| Weaviate Embeddings | [Weaviate Embeddings](https://weaviate.io/developers/wcs/embeddings) enables you to generate embeddings directly from a [Weaviate Cloud](https://console.weaviate.cloud/) database instance. | 



## Weaviate Features 🔧



| 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 |



## Feedback ❓

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!