--- title: "langchain_semantic_search vs weaviate-examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/venuv-langchain-semantic-search-vs-weaviate-weaviate-examples" tools: ["venuv-langchain-semantic-search", "weaviate-weaviate-examples"] --- # langchain_semantic_search vs weaviate-examples *GraphCanon updated Sep 20, 2026* ## Verdict Pick langchain_semantic_search if builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook; pick weaviate-examples if weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. [langchain_semantic_search](https://github.com/venuv/langchain_semantic_search) reports 44 GitHub stars, 8 forks, and 0 open issues, last pushed Feb 7, 2023. [weaviate-examples](https://github.com/weaviate/weaviate-examples) has 331 stars, 86 forks, and 12 open issues, last pushed Aug 7, 2025. Figures are from public GitHub metadata via [langchain_semantic_search's repository](https://github.com/venuv/langchain_semantic_search) and [weaviate-examples's repository](https://github.com/weaviate/weaviate-examples). | | [langchain_semantic_search](/tools/venuv-langchain-semantic-search.md) | [weaviate-examples](/tools/weaviate-weaviate-examples.md) | | --- | --- | --- | | Tagline | Semantic search for Google Drive files using GPT3, LangChain, and Python | Weaviate vector database – examples | | Stars | 44 | 331 | | Forks | 8 | 86 | | Open issues | 0 | 12 | | Language | Jupyter Notebook | HTML | | Adopt for | Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook. | weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. | | 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._ | | [langchain_semantic_search](/tools/venuv-langchain-semantic-search.md) | [weaviate-examples](/tools/weaviate-weaviate-examples.md) | | --- | --- | --- | | Days since push | 1320d | 408d | | Open issues (now) | 0 | 12 | | Stars delta | 0 (30d) | -1 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/venuv-langchain-semantic-search/trust.md) | [trust report](/tools/weaviate-weaviate-examples/trust.md) | ## Shared compatibility - **Python**: [langchain_semantic_search](/tools/venuv-langchain-semantic-search.md) - Python runtime; [weaviate-examples](/tools/weaviate-weaviate-examples.md) - Python runtime ## Decision facts: langchain_semantic_search - **Adopt for:** Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook. ## Decision facts: weaviate-examples - **Adopt for:** weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications. ## Choose when ### Choose langchain_semantic_search if… - langchain_semantic_search is primarily Jupyter Notebook; weaviate-examples is HTML. - Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain. - Need semantic search capabilities specifically for your own documents in Google Drive ### Choose weaviate-examples if… - weaviate-examples is primarily HTML; langchain_semantic_search is Jupyter Notebook. - Tags unique to weaviate-examples: deep-learning, examples, vector-database, vector-search. - You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality. ## 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 ## When NOT to use weaviate-examples - Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. - You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics. ## Common questions ### What is the difference between langchain_semantic_search and weaviate-examples? langchain_semantic_search: Semantic search for Google Drive files using GPT3, LangChain, and Python. weaviate-examples: Weaviate vector database – examples. See the comparison table for live GitHub stats and shared categories. ### When should I choose langchain_semantic_search over weaviate-examples? Choose langchain_semantic_search over weaviate-examples when langchain_semantic_search is primarily Jupyter Notebook; weaviate-examples is HTML; Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain; Need semantic search capabilities specifically for your own documents in Google Drive. ### When should I choose weaviate-examples over langchain_semantic_search? Choose weaviate-examples over langchain_semantic_search when weaviate-examples is primarily HTML; langchain_semantic_search is Jupyter Notebook; Tags unique to weaviate-examples: deep-learning, examples, vector-database, vector-search; You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality. ### 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 ### When should I avoid weaviate-examples? Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics. ### Is langchain_semantic_search or weaviate-examples more popular on GitHub? weaviate-examples has more GitHub stars (331 vs 44). Stars measure visibility, not whether either tool fits your constraints. ### Are langchain_semantic_search and weaviate-examples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to langchain_semantic_search or weaviate-examples? GraphCanon lists graph-backed alternatives at [langchain_semantic_search alternatives](/tools/venuv-langchain-semantic-search/alternatives) and [weaviate-examples alternatives](/tools/weaviate-weaviate-examples/alternatives) ([langchain_semantic_search markdown twin](/tools/venuv-langchain-semantic-search/alternatives.md), [weaviate-examples markdown twin](/tools/weaviate-weaviate-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 [this comparison](/compare/venuv-langchain-semantic-search-vs-weaviate-weaviate-examples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, langchain_semantic_search or weaviate-examples? langchain_semantic_search: Dormant. weaviate-examples: 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 langchain_semantic_search and weaviate-examples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [langchain_semantic_search trust report](/tools/venuv-langchain-semantic-search/trust); [weaviate-examples trust report](/tools/weaviate-weaviate-examples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=venuv-langchain-semantic-search`](/api/graphcanon/graph?tool=venuv-langchain-semantic-search) - 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/_