---
title: "recipes"
type: "tool"
slug: "weaviate-recipes"
canonical_url: "https://www.graphcanon.com/tools/weaviate-recipes"
github_url: "https://github.com/weaviate/recipes"
homepage_url: null
stars: 939
forks: 194
primary_language: "Jupyter Notebook"
license: null
categories: ["vector-databases", "data-retrieval", "llm-frameworks"]
tags: ["vector-database", "llm-frameworks", "python", "generative-ai", "retrieval-augmented-generation", "function-calling", "vector-search"]
updated_at: "2026-07-07T18:48:34.069682+00:00"
---

# recipes

> End-to-end notebooks for using Weaviate features and integrations

The repository provides comprehensive Jupyter Notebook tutorials covering various Weaviate functionalities including vector search, hybrid search, generative AI, and more. It also showcases integrations with other technologies such as cloud services, LLM/agent frameworks, data platforms, operations tools.

## Facts

- Repository: https://github.com/weaviate/recipes
- Stars: 939 · Forks: 194 · Open issues: 5 · Watchers: 38
- Primary language: Jupyter Notebook
- Last pushed: 2026-06-12T09:31:07+00:00

## Categories

- [Vector Databases](/categories/vector-databases.md)
- [Data & Retrieval](/categories/data-retrieval.md)
- [LLM Frameworks](/categories/llm-frameworks.md)

## Tags

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

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- [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. (★ 116,702)
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## README (excerpt)

```text
# Welcome to Weaviate Recipes 💚



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

| Category | Description |
| -------------|---------|
| [Datasets](/datasets/) | Ready to use datasets to ingest data into your Weaviate cluster |
| [Integrations](/integrations)| Notebooks showing you how to use Weaviate plus another technology |
| [Weaviate Features](/weaviate-features) | Notebooks covering vector, hybrid and generative search, reranking, multi-tenancy, and more |
| [Weaviate Services](/weaviate-services/) | Notebooks showing you how to build with Weaviate Services |


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

| 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 |


## Weaviate Features 🔧

| 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 |

## Weaviate Services 🧰
| Service | Description |
|---------|-------------|
| Agents | Use Weaviate's inherent agents like the `QueryAgent` & `TransformationAgent` |
| 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. | 

## Adding Recipes to Weaviate Docs

Check out [this contributor guide](./.docs/README.md) to convert recipes (Jupyter Notebooks) into docs friendly markdown.

## 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!
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/weaviate-recipes`](/api/graphcanon/tools/weaviate-recipes)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_