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Decision brief
Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases.
Good fit when
- 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
Avoid when
- 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
Observed Jul 9, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (39d since push)
- As of 4w
- Provenance
- Not a fork · Organization account
- As of 4w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Weaviate. Display-only - separate from trust and ranking.
- Company
- Weaviate·GitHub org profile·1mo
- Funding
- $50,000,000 (2023-04)·GraphCanon curated seed (public press)·1mo
- Commercial model
- Open core·GraphCanon curated seed·1mo
Install
git clone https://github.com/weaviate/recipesHow it fits your stack(8)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Integrates
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
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.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Jul 22, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 22, 2026)
| LLM and Agent Frameworks | Agno, CrewAI, Composio, DSPy, Dynamiq, LangChain, LlamaIndex, Pydantic, Semantic Kernel, Ollama, Haystack, Modaic |Source link
Tags
README
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 |
| Weaviate Services | Notebooks showing you how to build with Weaviate Services |
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 |
| 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 enables you to generate embeddings directly from a Weaviate Cloud database instance. |
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!
For agents
This page has a .md twin and JSON over the API.