--- title: "examples vs recipes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pinecone-io-examples-vs-weaviate-recipes" tools: ["pinecone-io-examples", "weaviate-recipes"] --- # examples vs recipes *GraphCanon updated Aug 21, 2026* ## Verdict Pick examples if examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance; pick recipes if comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. [examples](https://docs.pinecone.io) reports 3.0k GitHub stars, 1.1k forks, and 61 open issues, last pushed Aug 14, 2026. [recipes](https://github.com/weaviate/recipes) has 944 stars, 197 forks, and 4 open issues, last pushed Aug 13, 2026. Figures are from public GitHub metadata via [examples's repository](https://github.com/pinecone-io/examples) and [recipes's repository](https://github.com/weaviate/recipes). | | [examples](/tools/pinecone-io-examples.md) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Tagline | Jupyter Notebooks to help you get hands-on with Pinecone vector databases | End-to-end notebooks for using Weaviate features and integrations. | | Stars | 3,036 | 944 | | Forks | 1,073 | 197 | | Open issues | 61 | 4 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. | Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [examples](/tools/pinecone-io-examples.md) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 8d | | Open issues (now) | 61 | 4 | | Stars delta | +8 (30d) | +3 (30d) | | Open issues delta | -3 (30d) | -2 (30d) | | Full report | [trust report](/tools/pinecone-io-examples/trust.md) | [trust report](/tools/weaviate-recipes/trust.md) | **Typed relationship:** examples _(related)_ recipes These repos both offer end-to-end notebooks targeting the use of their respective database systems (Pinecone and Weaviate) in AI workloads. ## Decision facts: examples - **Adopt for:** Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. ## Decision facts: recipes - **Adopt for:** Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. ## Choose when ### Choose examples if… - These repos both offer end-to-end notebooks targeting the use of their respective database systems (Pinecone and Weaviate) in AI workloads. - Tags unique to examples: ai, jupyter-notebook, llm, semantic-search. - When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. ### Choose recipes if… - These repos both offer end-to-end notebooks targeting the use of their respective database systems (Pinecone and Weaviate) in AI workloads. - Tags unique to recipes: function-calling, generative-ai, llm frameworks, retrieval-augmented-generation. - 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 ## When NOT to use examples - Avoid if you're looking for generic tools applicable to a wide range of vector databases; this repository focuses exclusively on Pinecone. - Not ideal if you prefer starting with theoretical understanding before practical application; the provided guidance is geared toward immediate experimentation in Google Colab. ## When NOT to use recipes - 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 ## Common questions ### What is the difference between examples and recipes? examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. recipes: End-to-end notebooks for using Weaviate features and integrations.. See the comparison table for live GitHub stats and shared categories. ### When should I choose examples over recipes? Choose examples over recipes when These repos both offer end-to-end notebooks targeting the use of their respective database systems (Pinecone and Weaviate) in AI workloads; Tags unique to examples: ai, jupyter-notebook, llm, semantic-search; When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. ### When should I choose recipes over examples? Choose recipes over examples when These repos both offer end-to-end notebooks targeting the use of their respective database systems (Pinecone and Weaviate) in AI workloads; Tags unique to recipes: function-calling, generative-ai, llm frameworks, retrieval-augmented-generation; 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. ### When should I avoid examples? Avoid if you're looking for generic tools applicable to a wide range of vector databases; this repository focuses exclusively on Pinecone. Not ideal if you prefer starting with theoretical understanding before practical application; the provided guidance is geared toward immediate experimentation in Google Colab. ### When should I avoid recipes? 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 ### Is examples or recipes more popular on GitHub? examples has more GitHub stars (3,036 vs 944). Stars measure visibility, not whether either tool fits your constraints. ### Are examples and recipes open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to examples or recipes? GraphCanon lists graph-backed alternatives at [examples alternatives](/tools/pinecone-io-examples/alternatives) and [recipes alternatives](/tools/weaviate-recipes/alternatives) ([examples markdown twin](/tools/pinecone-io-examples/alternatives.md), [recipes markdown twin](/tools/weaviate-recipes/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/pinecone-io-examples-vs-weaviate-recipes.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, examples or recipes? examples: Very active. recipes: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for examples and recipes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [examples trust report](/tools/pinecone-io-examples/trust); [recipes trust report](/tools/weaviate-recipes/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pinecone-io-examples`](/api/graphcanon/graph?tool=pinecone-io-examples) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_