--- title: "aquila vs what_are_embeddings" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-veekaybee-what-are-embeddings" tools: ["aquila-network-aquila", "veekaybee-what-are-embeddings"] --- # aquila vs what_are_embeddings *GraphCanon updated Aug 22, 2026* ## Verdict Pick aquila if aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches; pick what_are_embeddings if focuses on educational materials for understanding embeddings in ML and NLP using Jupyter Notebooks. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [what_are_embeddings](http://vickiboykis.com/what_are_embeddings/) has 1.1k stars, 86 forks, and 0 open issues, last pushed Jan 17, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [what_are_embeddings's repository](https://github.com/veekaybee/what_are_embeddings). | | [aquila](/tools/aquila-network-aquila.md) | [what_are_embeddings](/tools/veekaybee-what-are-embeddings.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | A deep dive into embeddings starting from fundamentals | | Stars | 379 | 1,096 | | Forks | 26 | 86 | | Open issues | 13 | 0 | | Language | HTML | Jupyter Notebook | | Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | Focuses on educational materials for understanding embeddings in ML and NLP using Jupyter Notebooks. | | Persona | - | - | | Runtime | - | - | | License | - | - | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [what_are_embeddings](/tools/veekaybee-what-are-embeddings.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 817d | 217d | | Open issues (now) | 13 | 0 | | Stars delta | Unknown | +4 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/veekaybee-what-are-embeddings/trust.md) | ## Decision facts: aquila - **Adopt for:** Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. ## Decision facts: what_are_embeddings - **Adopt for:** Focuses on educational materials for understanding embeddings in ML and NLP using Jupyter Notebooks. ## Choose when ### Choose aquila if… - aquila is primarily HTML; what_are_embeddings is Jupyter Notebook. - Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors. - Also covers Vector Databases. - When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary ### Choose what_are_embeddings if… - what_are_embeddings is primarily Jupyter Notebook; aquila is HTML. - Tags unique to what_are_embeddings: embeddings, machine-learning-algorithms, nlp-machine-learning. - When you are looking to gain foundational knowledge about how embeddings work in machine learning and natural language processing tasks. ## When NOT to use aquila - If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration - In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ## When NOT to use what_are_embeddings - If you need practical, real-world application examples or code implementations not grounded in explanatory educational content. - When an advanced understanding of embeddings is required as this repository prioritizes fundamental comprehension over deep technical insights. ## Common questions ### What is the difference between aquila and what_are_embeddings? aquila: Efficient Neural Search Engine. what_are_embeddings: A deep dive into embeddings starting from fundamentals. See the comparison table for live GitHub stats and shared categories. ### When should I choose aquila over what_are_embeddings? Choose aquila over what_are_embeddings when aquila is primarily HTML; what_are_embeddings is Jupyter Notebook; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; Also covers Vector Databases; When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary. ### When should I choose what_are_embeddings over aquila? Choose what_are_embeddings over aquila when what_are_embeddings is primarily Jupyter Notebook; aquila is HTML; Tags unique to what_are_embeddings: embeddings, machine-learning-algorithms, nlp-machine-learning; When you are looking to gain foundational knowledge about how embeddings work in machine learning and natural language processing tasks. ### When should I avoid aquila? If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ### When should I avoid what_are_embeddings? If you need practical, real-world application examples or code implementations not grounded in explanatory educational content. When an advanced understanding of embeddings is required as this repository prioritizes fundamental comprehension over deep technical insights. ### Is aquila or what_are_embeddings more popular on GitHub? what_are_embeddings has more GitHub stars (1,096 vs 379). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and what_are_embeddings open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or what_are_embeddings? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [what_are_embeddings alternatives](/tools/veekaybee-what-are-embeddings/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [what_are_embeddings markdown twin](/tools/veekaybee-what-are-embeddings/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/aquila-network-aquila-vs-veekaybee-what-are-embeddings.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aquila or what_are_embeddings? aquila: Dormant. what_are_embeddings: Slowing. 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 aquila and what_are_embeddings? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [what_are_embeddings trust report](/tools/veekaybee-what-are-embeddings/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aquila-network-aquila`](/api/graphcanon/graph?tool=aquila-network-aquila) - 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/_