--- title: "fastembed vs wikipedia2vec" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/qdrant-fastembed-vs-wikipedia2vec-wikipedia2vec" tools: ["qdrant-fastembed", "wikipedia2vec-wikipedia2vec"] --- # fastembed vs wikipedia2vec *GraphCanon updated Aug 22, 2026* ## Verdict Pick fastembed if fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings; pick wikipedia2vec if a Python-based tool for generating embeddings derived from Wikipedia content. [fastembed](https://qdrant.github.io/fastembed/) reports 3.2k GitHub stars, 231 forks, and 111 open issues, last pushed Aug 19, 2026. [wikipedia2vec](http://wikipedia2vec.github.io/) has 971 stars, 100 forks, and 8 open issues, last pushed May 3, 2024. Figures are from public GitHub metadata via [fastembed's repository](https://github.com/qdrant/fastembed) and [wikipedia2vec's repository](https://github.com/wikipedia2vec/wikipedia2vec). | | [fastembed](/tools/qdrant-fastembed.md) | [wikipedia2vec](/tools/wikipedia2vec-wikipedia2vec.md) | | --- | --- | --- | | Tagline | Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings | A tool for learning vector representations of words and entities from Wikipedia | | Stars | 3,158 | 971 | | Forks | 231 | 100 | | Open issues | 111 | 8 | | Language | Python | Python | | Adopt for | Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. | A Python-based tool for generating embeddings derived from Wikipedia content. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | Other | | Categories | Data & Retrieval, Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [fastembed](/tools/qdrant-fastembed.md) | [wikipedia2vec](/tools/wikipedia2vec-wikipedia2vec.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 2d | 840d | | Open issues (now) | 111 | 8 | | Stars delta | +55 (30d) | +4 (30d) | | Open issues delta | -26 (30d) | 0 (30d) | | Full report | [trust report](/tools/qdrant-fastembed/trust.md) | [trust report](/tools/wikipedia2vec-wikipedia2vec/trust.md) | ## Decision facts: fastembed - **Requirements:** Does not require Docker, making the setup straightforward for Python environments. - **Adopt for:** Fastembed is a lightweight and efficient Python library for creating state-of-the-art embeddings. - **License detail:** Apache-2.0 License ## Decision facts: wikipedia2vec - **Adopt for:** A Python-based tool for generating embeddings derived from Wikipedia content. ## Choose when ### Choose fastembed if… - License: fastembed is Apache-2.0, wikipedia2vec is Other. - Requirements: Does not require Docker, making the setup straightforward for Python environments.. - Tags unique to fastembed: openai, rag, retrieval-augmented-generation, vector-search. - Also covers Data & Retrieval. - When you need to generate high-quality embeddings quickly in Python. ### Choose wikipedia2vec if… - License: wikipedia2vec is Other, fastembed is Apache-2.0. - 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 fastembed - If your project is not using Python, as Fastembed does not offer support for other programming languages directly. - In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility. ## 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 fastembed and wikipedia2vec? fastembed: Fast, Accurate, Lightweight Python library for creating state-of-the-art embeddings. 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 fastembed over wikipedia2vec? Choose fastembed over wikipedia2vec when License: fastembed is Apache-2.0, wikipedia2vec is Other; Requirements: Does not require Docker, making the setup straightforward for Python environments.; Tags unique to fastembed: openai, rag, retrieval-augmented-generation, vector-search; Also covers Data & Retrieval; When you need to generate high-quality embeddings quickly in Python. ### When should I choose wikipedia2vec over fastembed? Choose wikipedia2vec over fastembed when License: wikipedia2vec is Other, fastembed is Apache-2.0; 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 fastembed? If your project is not using Python, as Fastembed does not offer support for other programming languages directly. In scenarios demanding heavy customization or fine-tuning at a lower level than what Fastembed provides out-of-the-box. Consider alternatives that may offer more flexibility. ### 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 fastembed or wikipedia2vec more popular on GitHub? fastembed has more GitHub stars (3,158 vs 971). Stars measure visibility, not whether either tool fits your constraints. ### Are fastembed and wikipedia2vec open source? Yes - both are open-source projects on GitHub (fastembed: Apache-2.0, wikipedia2vec: Other). ### Where can I find alternatives to fastembed or wikipedia2vec? GraphCanon lists graph-backed alternatives at [fastembed alternatives](/tools/qdrant-fastembed/alternatives) and [wikipedia2vec alternatives](/tools/wikipedia2vec-wikipedia2vec/alternatives) ([fastembed markdown twin](/tools/qdrant-fastembed/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/qdrant-fastembed-vs-wikipedia2vec-wikipedia2vec.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, fastembed or wikipedia2vec? fastembed: 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 fastembed and wikipedia2vec? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [fastembed trust report](/tools/qdrant-fastembed/trust); [wikipedia2vec trust report](/tools/wikipedia2vec-wikipedia2vec/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=qdrant-fastembed`](/api/graphcanon/graph?tool=qdrant-fastembed) - 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/_