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
Last synced: 6 months ago
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This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!
- Host: GitHub
- URL: https://github.com/weaviate/recipes
- Owner: weaviate
- Created: 2023-05-16T15:19:31.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-12T20:49:39.000Z (6 months ago)
- Last Synced: 2025-05-12T21:43:38.322Z (6 months ago)
- Topics: function-calling, generative-ai, llm-frameworks, python, retrieval-augmented-generation, vector-database, vector-search
- Language: Jupyter Notebook
- Homepage:
- Size: 298 MB
- Stars: 744
- Watchers: 36
- Forks: 147
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 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 |
| -------------|---------|
| [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 |
| Data Platforms| Databricks, Confluent, Box, Spark, Unstructured, Firecrawl, Context Data, Aryn, Astronomer, Airbyte, IBM (Docling) |
| Operations | Arize, DeepEval, Langtrace, LangWatch, Nomic, Ragas, Weights & Biases |
## 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 |
| 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. |
## 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!