--- title: "examples vs langchain_semantic_search" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pinecone-io-examples-vs-venuv-langchain-semantic-search" tools: ["pinecone-io-examples", "venuv-langchain-semantic-search"] --- # examples vs langchain_semantic_search *GraphCanon updated Sep 20, 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 langchain_semantic_search if builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook. [examples](https://docs.pinecone.io) reports 3.0k GitHub stars, 1.1k forks, and 66 open issues, last pushed Sep 19, 2026. [langchain_semantic_search](https://github.com/venuv/langchain_semantic_search) has 44 stars, 8 forks, and 0 open issues, last pushed Feb 7, 2023. Figures are from public GitHub metadata via [examples's repository](https://github.com/pinecone-io/examples) and [langchain_semantic_search's repository](https://github.com/venuv/langchain_semantic_search). | | [examples](/tools/pinecone-io-examples.md) | [langchain_semantic_search](/tools/venuv-langchain-semantic-search.md) | | --- | --- | --- | | Tagline | Jupyter Notebooks to help you get hands-on with Pinecone vector databases | Semantic search for Google Drive files using GPT3, LangChain, and Python | | Stars | 3,043 | 44 | | Forks | 1,073 | 8 | | Open issues | 66 | 0 | | 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. | Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook. | | 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) | [langchain_semantic_search](/tools/venuv-langchain-semantic-search.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 1320d | | Open issues (now) | 66 | 0 | | Stars delta | +7 (30d) | 0 (30d) | | Open issues delta | +5 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/pinecone-io-examples/trust.md) | [trust report](/tools/venuv-langchain-semantic-search/trust.md) | ## 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: langchain_semantic_search - **Adopt for:** Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook. ## Choose when ### Choose examples if… - Tags unique to examples: ai, jupyter-notebook, llm, python. - When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. - More GitHub stars (3.0k vs 44) - visibility, not fit. ### Choose langchain_semantic_search if… - Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain. - Need semantic search capabilities specifically for your own documents in Google Drive - Leaner open-issue backlog (0). ## 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 langchain_semantic_search - Seeking a solution that supports large-scale, real-time or non-Google Drive document collections - Require a fully integrated end-to-end service without configuration for drive paths ## Common questions ### What is the difference between examples and langchain_semantic_search? examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. langchain_semantic_search: Semantic search for Google Drive files using GPT3, LangChain, and Python. See the comparison table for live GitHub stats and shared categories. ### When should I choose examples over langchain_semantic_search? Choose examples over langchain_semantic_search when Tags unique to examples: ai, jupyter-notebook, llm, python; When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks; More GitHub stars (3.0k vs 44) - visibility, not fit. ### When should I choose langchain_semantic_search over examples? Choose langchain_semantic_search over examples when Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain; Need semantic search capabilities specifically for your own documents in Google Drive; Leaner open-issue backlog (0). ### 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 langchain_semantic_search? Seeking a solution that supports large-scale, real-time or non-Google Drive document collections Require a fully integrated end-to-end service without configuration for drive paths ### Is examples or langchain_semantic_search more popular on GitHub? examples has more GitHub stars (3,043 vs 44). Stars measure visibility, not whether either tool fits your constraints. ### Are examples and langchain_semantic_search open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to examples or langchain_semantic_search? GraphCanon lists graph-backed alternatives at [examples alternatives](/tools/pinecone-io-examples/alternatives) and [langchain_semantic_search alternatives](/tools/venuv-langchain-semantic-search/alternatives) ([examples markdown twin](/tools/pinecone-io-examples/alternatives.md), [langchain_semantic_search markdown twin](/tools/venuv-langchain-semantic-search/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-venuv-langchain-semantic-search.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, examples or langchain_semantic_search? examples: Very active. langchain_semantic_search: Dormant. 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 langchain_semantic_search? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [examples trust report](/tools/pinecone-io-examples/trust); [langchain_semantic_search trust report](/tools/venuv-langchain-semantic-search/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/_