recipes

Welcome to Weaviate Recipes 💚

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

Integrations 🌐

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

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
Search REST API	Call bm25, near-text, near-vector, near-object, hybrid and aggregate over plain HTTP
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
Drop Vector Index	Drop the index of a named vector you no longer use and reclaim its disk space
Evaluation	Evaluate your search system
Rotational Quantization	Compress vectors up to ~8x with 4-bit RQ and centering, measured against exact search

Query Agent 🔍

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

Adding Recipes to Weaviate Docs

Check out this contributor guide 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!