{"data":{"slug":"weaviate-recipes","name":"recipes","tagline":"End-to-end notebooks for using Weaviate features and integrations.","github_url":"https://github.com/weaviate/recipes","owner":"weaviate","repo":"recipes","owner_avatar_url":"https://avatars.githubusercontent.com/u/37794290?v=4","primary_language":"Jupyter Notebook","stars":947,"forks":198,"topics":["function-calling","generative-ai","llm-frameworks","python","retrieval-augmented-generation","vector-database","vector-search"],"archived":false,"github_pushed_at":"2026-09-16T22:08:32+00:00","maintenance_label":"Very active","stars_delta_30d":6,"url":"https://www.graphcanon.com/tools/weaviate-recipes","markdown_url":"https://www.graphcanon.com/tools/weaviate-recipes.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/weaviate-recipes","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=weaviate-recipes","description":"This repository shares end-to-end notebooks on how to use various Weaviate features and integrations!","homepage_url":null,"license":null,"open_issues":4,"watchers":39,"ai_summary":"Contains Jupyter Notebook examples demonstrating the use of various functionalities and integrations with Weaviate, a vector database for building end-to-end semantic search stacks.","readme_excerpt":"# Welcome to Weaviate Recipes 💚\n\n\n\nThis repository covers end-to-end examples of the various features and integrations with [Weaviate](https://www.weaviate.io).\n\n| Category | Description |\n| -------------|---------|\n| [Datasets](/datasets/) | Ready to use datasets to ingest data into your Weaviate cluster |\n| [Integrations](/integrations)| Notebooks showing you how to use Weaviate plus another technology |\n| [Weaviate Features](/weaviate-features) | Notebooks covering vector, hybrid and generative search, reranking, multi-tenancy, and more |\n| [Query Agent](/query-agent/) | Notebooks showing you how to build with Weaviate's Query Agent |\n\n\n## Integrations 🌐\nCheck out Weaviate's [Integrations Documentation](https://weaviate.io/developers/integrations)!\n\n| Company Category | Companies |\n|------------------|-----------|\n| Cloud Hyperscalers | Google, AWS, NVIDIA |\n| Compute Infrastructure | Modal, Replicate |\n| LLM and Agent Frameworks | Agno, CrewAI, Composio, DSPy, Dynamiq, LangChain, LlamaIndex, Pydantic, Semantic Kernel, Ollama, Haystack, Modaic |\n| Data Platforms| Databricks, Confluent, Box, Boomi, Spark, Unstructured, Firecrawl, Context Data, Aryn, Astronomer, Airbyte, IBM (Docling), Cardinal, Contextual AI, Chonkie, Parallel |\n| Operations | AIMon, Arize, Cleanlab, Comet, DeepEval, Langtrace, LangWatch, Nomic, Patronus AI, Ragas, TruLens, Weights & Biases |\n\n\n## Weaviate Features 🔧\n\n| Feature | Description |\n|---------|-------------|\n| Model Providers | Use Weaviate's `nearText`, `hybrid`, and `.generate` operator with various model providers |\n| Filters | Narrow down your search results by adding filters to your queries |\n| Reranking | Add reranking to your pipeline to improve search results (broken out by model provider) |\n| Media Search | Use Weaviate's `nearImage` and `nearVideo` operator to search using images and videos |\n| Classification | Learn how to use KNN and zero-shot classification |\n| Multi-Tenancy | Store tenants on separate shards for complete data isolation |\n| Multi-Vector Embeddings | Use Weaviate with powerful ColBERT-style embeddings to improve search results |\n| Product Quantization | Compress vector embeddings and reduce the memory footprint using Weaviate's PQ feature |\n| Evaluation | Evaluate your search system |\n\n## Query Agent 🔍\n| Mode | Description |\n|---------|-------------|\n| Ask | Transforms your query into actionable searches or aggregations, and then provides a final answer to the question  |\n| Search | Transforms your query into actionable searches and returns the matching Weaviate objects directly |\n| Suggest Queries |  Suggest queries based on the data in your collection |\n\n## Adding Recipes to Weaviate Docs\n\nCheck out [this contributor guide](./.docs/README.md) to convert recipes (Jupyter Notebooks) into docs friendly markdown.\n\n## Feedback ❓\nPlease 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!","github_created_at":"2023-05-16T15:19:31+00:00","created_at":"2026-07-07T17:44:49.088282+00:00","updated_at":"2026-09-20T06:05:37.740147+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"function-calling","name":"function-calling"},{"slug":"generative-ai","name":"generative-ai"},{"slug":"llm-frameworks","name":"llm frameworks"},{"slug":"python","name":"python"},{"slug":"retrieval-augmented-generation","name":"retrieval-augmented generation"},{"slug":"vector-database","name":"vector-database"},{"slug":"vector-search","name":"vector-search"}],"trust":{"provenance":{"is_fork":false,"github_id":641490799,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-20T06:05:36.007Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":3,"last_release_at":null,"stars_delta_30d":6,"open_issues_delta_30d":-2},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:26:45.551Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-20T06:05:37.055Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-09-20T06:05:37.055Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you are specifically interested in exploring various integrations with cloud hyperscalers (Google, AWS), LLM frameworks (LangChain, LlamaIndex), and other technologies mentioned, such as Databri","If you need detailed examples on Weaviate's capabilities like multi-tenancy support or media search functionality which require nuanced configurations","When preparing to leverage specific Weaviate services like QueryAgent, TransformationAgent, and embeddings directly from a Weaviate Cloud database instance"],"when_not_to_use":["If you are looking for generalized vector database use case examples that do not specifically showcase Weaviate's unique integrations or features","When your focus is on understanding and using broad category services instead of the specific, detailed examples and configurations available in the Weaviate ecosystem","For cases where a competitor tool offers better support for other specific needs, such as more comprehensive integration with data platforms not specifically covered by Weaviate like MongoDB or Redis"],"source":"enrich:decision_facts","observed_at":"2026-07-09T09:10:59.806Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases."}]}}