--- title: "search vs wikipedia2vec" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelindar-search-vs-wikipedia2vec-wikipedia2vec" tools: ["kelindar-search", "wikipedia2vec-wikipedia2vec"] --- # search vs wikipedia2vec *GraphCanon updated Sep 20, 2026* ## Verdict Pick search if search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework; pick wikipedia2vec if a Python-based tool for generating embeddings derived from Wikipedia content. [search](https://github.com/kelindar/search) reports 559 GitHub stars, 24 forks, and 5 open issues, last pushed Sep 19, 2026. [wikipedia2vec](http://wikipedia2vec.github.io/) has 972 stars, 101 forks, and 8 open issues, last pushed May 3, 2024. Figures are from public GitHub metadata via [search's repository](https://github.com/kelindar/search) and [wikipedia2vec's repository](https://github.com/wikipedia2vec/wikipedia2vec). | | [search](/tools/kelindar-search.md) | [wikipedia2vec](/tools/wikipedia2vec-wikipedia2vec.md) | | --- | --- | --- | | Tagline | Go library for embedded vector search and semantic embeddings with llamacpp | A tool for learning vector representations of words and entities from Wikipedia | | Stars | 559 | 972 | | Forks | 24 | 101 | | Open issues | 5 | 8 | | Language | Go | Python | | Adopt for | search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. | A Python-based tool for generating embeddings derived from Wikipedia content. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Data & Retrieval, Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [search](/tools/kelindar-search.md) | [wikipedia2vec](/tools/wikipedia2vec-wikipedia2vec.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 869d | | Open issues (now) | 5 | 8 | | Stars delta | +4 (30d) | +5 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/kelindar-search/trust.md) | [trust report](/tools/wikipedia2vec-wikipedia2vec/trust.md) | ## Decision facts: search - **Adopt for:** search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. ## Decision facts: wikipedia2vec - **Adopt for:** A Python-based tool for generating embeddings derived from Wikipedia content. ## Choose when ### Choose search if… - search is primarily Go; wikipedia2vec is Python. - License: search is MIT, wikipedia2vec is Other. - Tags unique to search: ai, bert, gguf, gpu. - Also covers Data & Retrieval. - Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go ### Choose wikipedia2vec if… - wikipedia2vec is primarily Python; search is Go. - License: wikipedia2vec is Other, search is MIT. - Tags unique to wikipedia2vec: natural language processing, nlp, python, text-classification. - You need to generate word and entity embeddings based on extensive Wikipedia data ## When NOT to use search - Avoid if relying on out-of-the-box support beyond Go or requiring heavy customization that is not supported directly by llama.cpp's capabilities - Not suitable when a more comprehensive database service with extensive querying and integration features is desired over an embedded solution ## When NOT to use wikipedia2vec - Your dataset doesn't intersect with or benefit from Wikipedia content - You require real-time updating capabilities that exceed static Wikipedia dumps ## Common questions ### What is the difference between search and wikipedia2vec? search: Go library for embedded vector search and semantic embeddings with llamacpp. wikipedia2vec: A tool for learning vector representations of words and entities from Wikipedia. See the comparison table for live GitHub stats and shared categories. ### When should I choose search over wikipedia2vec? Choose search over wikipedia2vec when search is primarily Go; wikipedia2vec is Python; License: search is MIT, wikipedia2vec is Other; Tags unique to search: ai, bert, gguf, gpu; Also covers Data & Retrieval; Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go. ### When should I choose wikipedia2vec over search? Choose wikipedia2vec over search when wikipedia2vec is primarily Python; search is Go; License: wikipedia2vec is Other, search is MIT; Tags unique to wikipedia2vec: natural language processing, nlp, python, text-classification; You need to generate word and entity embeddings based on extensive Wikipedia data. ### When should I avoid search? Avoid if relying on out-of-the-box support beyond Go or requiring heavy customization that is not supported directly by llama.cpp's capabilities Not suitable when a more comprehensive database service with extensive querying and integration features is desired over an embedded solution ### When should I avoid wikipedia2vec? Your dataset doesn't intersect with or benefit from Wikipedia content You require real-time updating capabilities that exceed static Wikipedia dumps ### Is search or wikipedia2vec more popular on GitHub? wikipedia2vec has more GitHub stars (972 vs 559). Stars measure visibility, not whether either tool fits your constraints. ### Are search and wikipedia2vec open source? Yes - both are open-source projects on GitHub (search: MIT, wikipedia2vec: Other). ### Where can I find alternatives to search or wikipedia2vec? GraphCanon lists graph-backed alternatives at [search alternatives](/tools/kelindar-search/alternatives) and [wikipedia2vec alternatives](/tools/wikipedia2vec-wikipedia2vec/alternatives) ([search markdown twin](/tools/kelindar-search/alternatives.md), [wikipedia2vec markdown twin](/tools/wikipedia2vec-wikipedia2vec/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/kelindar-search-vs-wikipedia2vec-wikipedia2vec.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, search or wikipedia2vec? search: Very active. wikipedia2vec: 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 search and wikipedia2vec? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [search trust report](/tools/kelindar-search/trust); [wikipedia2vec trust report](/tools/wikipedia2vec-wikipedia2vec/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelindar-search`](/api/graphcanon/graph?tool=kelindar-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/_