--- title: "vectordb-recipes vs examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lancedb-vectordb-recipes-vs-pinecone-io-examples" tools: ["lancedb-vectordb-recipes", "pinecone-io-examples"] --- # vectordb-recipes vs examples Neutral, constraint-first comparison with live GitHub stats. | | [vectordb-recipes](/tools/lancedb-vectordb-recipes.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Tagline | Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs | Jupyter Notebooks to help you get hands-on with Pinecone vector databases | | Stars | 966 | 3,025 | | Forks | 172 | 1,073 | | Open issues | 4 | 63 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | Vectordb-recipes offers resources and tutorials for building GenAI applications using LanceDB. It is particularly designed to help users get started quickly with minimal setup required. | Examples from the Pinecone repository are tailored for hands-on learning and development with Pinecone's vector databases, featuring production-ready samples and educational materials. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability, Data & Retrieval, Vector Databases, Model Training, Inference & Serving, AI Agents | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vectordb-recipes](/tools/lancedb-vectordb-recipes.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 74d | 5d | | Open issues (now) | 4 | 63 | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/lancedb-vectordb-recipes/trust.md) | [trust report](/tools/pinecone-io-examples/trust.md) | **Typed relationship:** vectordb-recipes _(alternative)_ examples While both repositories provide examples and tutorials for working with vector databases, Pinecone's focus on its own vector database makes it an alternative solution to vectordb-recipes which focuses heavily on LanceDB. ## Decision facts: vectordb-recipes - **Adopt for:** Vectordb-recipes offers resources and tutorials for building GenAI applications using LanceDB. It is particularly designed to help users get started quickly with minimal setup required. ## Decision facts: examples - **Adopt for:** Examples from the Pinecone repository are tailored for hands-on learning and development with Pinecone's vector databases, featuring production-ready samples and educational materials. ## Choose when ### Choose vectordb-recipes if… - License: vectordb-recipes is Apache-2.0, examples is MIT. - While both repositories provide examples and tutorials for working with vector databases, Pinecone's focus on its own vector database makes it an alternative solution to vectordb-recipes which focuses heavily on LanceDB. - Tags unique to vectordb-recipes: llms, embeddings, deep-learning, fine-tuning. - Also covers Evaluation & Observability, Data & Retrieval, Model Training, Inference & Serving, AI Agents. - - When you need a comprehensive set of examples, starter code and tutorials specifically optimized for LanceDB, an open-source vector database that integrates seamlessly into the Python data ecosystem ### Choose examples if… - License: examples is MIT, vectordb-recipes is Apache-2.0. - While both repositories provide examples and tutorials for working with vector databases, Pinecone's focus on its own vector database makes it an alternative solution to vectordb-recipes which focuses heavily on LanceDB. - Tags unique to examples: vector-database, llm, python, jupyter-notebook. - - You're working exclusively with the Pinecone vector database ecosystem. ## When NOT to use vectordb-recipes - - When seeking support for a specific competitor's vector database (like Pinecone or Weaviate), as Vectordb-recipes focuses solely on LanceDB’s ecosystem - - If you have strict requirements for custom database tuning that only vendor-specific proprietary databases can offer, as Vectordb-recipes’ focus is on leveraging the out-of-the-box advantages of an - critical_facts_for_deployment_or_use_case_specifics: [ ## When NOT to use examples - - The primary technology focus of your project is not on vector databases powered by Pinecone. - - You seek a more generalized approach to learning about various vector database systems, as this repository is dedicated specifically to Pinecone's implementation. ## Common questions ### What is the difference between vectordb-recipes and examples? vectordb-recipes: Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs. examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. See the comparison table for live GitHub stats and shared categories. ### When should I choose vectordb-recipes over examples? Choose vectordb-recipes over examples when License: vectordb-recipes is Apache-2.0, examples is MIT; While both repositories provide examples and tutorials for working with vector databases, Pinecone's focus on its own vector database makes it an alternative solution to vectordb-recipes which focuses heavily on LanceDB; Tags unique to vectordb-recipes: llms, embeddings, deep-learning, fine-tuning; Also covers Evaluation & Observability, Data & Retrieval, Model Training, Inference & Serving, AI Agents; - When you need a comprehensive set of examples, starter code and tutorials specifically optimized for LanceDB, an open-source vector database that integrates seamlessly into the Python data ecosystem. ### When should I choose examples over vectordb-recipes? Choose examples over vectordb-recipes when License: examples is MIT, vectordb-recipes is Apache-2.0; While both repositories provide examples and tutorials for working with vector databases, Pinecone's focus on its own vector database makes it an alternative solution to vectordb-recipes which focuses heavily on LanceDB; Tags unique to examples: vector-database, llm, python, jupyter-notebook; - You're working exclusively with the Pinecone vector database ecosystem. ### When should I avoid vectordb-recipes? - When seeking support for a specific competitor's vector database (like Pinecone or Weaviate), as Vectordb-recipes focuses solely on LanceDB’s ecosystem - If you have strict requirements for custom database tuning that only vendor-specific proprietary databases can offer, as Vectordb-recipes’ focus is on leveraging the out-of-the-box advantages of an critical_facts_for_deployment_or_use_case_specifics: [ ### When should I avoid examples? - The primary technology focus of your project is not on vector databases powered by Pinecone. - You seek a more generalized approach to learning about various vector database systems, as this repository is dedicated specifically to Pinecone's implementation. ### Is vectordb-recipes or examples more popular on GitHub? examples has more GitHub stars (3,025 vs 966). Stars measure visibility, not whether either tool fits your constraints. ### Are vectordb-recipes and examples open source? Yes - both are open-source projects on GitHub (vectordb-recipes: Apache-2.0, examples: MIT). ### Where can I find alternatives to vectordb-recipes or examples? GraphCanon lists graph-backed alternatives at /tools/lancedb-vectordb-recipes/alternatives and /tools/pinecone-io-examples/alternatives (/tools/lancedb-vectordb-recipes/alternatives.md, /tools/pinecone-io-examples/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 /compare/lancedb-vectordb-recipes-vs-pinecone-io-examples.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, vectordb-recipes or examples? vectordb-recipes: Steady. examples: Very 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 vectordb-recipes and examples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: vectordb-recipes: /tools/lancedb-vectordb-recipes/trust; examples: /tools/pinecone-io-examples/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lancedb-vectordb-recipes`](/api/graphcanon/graph?tool=lancedb-vectordb-recipes) - 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/_