weaviate / recipes

This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!
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dspy generative-ai llm-frameworks python retrieval-augmented-generation vector-database vector-search

Welcome to Weaviate Recipes 💚

Weaviate logo

This repository covers end-to-end examples of the various features and integrations with Weaviate.

Category Description
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

Integrations 🌐

Check out Weaviate's Integrations Documentation!

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
LLM Frameworks DSPy, LangChain, LlamaIndex, Semantic Kernel, Ollama, Composio, Haystack
Operations Arize, 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
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!