Comparison
embedbase vs examples
Verdict
Pick embedbase if embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases; 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.
Markdown twin · embedbase alternatives · examples alternatives
GraphCanon updated 6d
vs
Trust & integrity
| Signal | embedbase | examples |
|---|---|---|
| Maintenance | Dormant (601d since push) As of 1mo · github_public_v1 | Very active (0d since push) As of 6d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1mo · github_public_v1 | Not a fork · Organization account As of 6d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- embedbase
- A dead-simple API to build LLM-powered apps
- examples
- Jupyter Notebooks to help you get hands-on with Pinecone vector databases
Stars
- embedbase
- 524
- examples
- 3.0k
Forks
- embedbase
- 55
- examples
- 1.1k
Open issues
- embedbase
- 35
- examples
- 61
Language
- embedbase
- TypeScript
- examples
- Jupyter Notebook
Adopt for
- embedbase
- Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases.
- examples
- Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance.
Persona
- embedbase
- -
- examples
- -
Runtime
- embedbase
- -
- examples
- -
License
- embedbase
- MIT
- examples
- MIT
Last pushed
- embedbase
- Nov 27, 2024
- examples
- Aug 14, 2026
Categories
- embedbase
- Data & Retrieval, Vector Databases
- examples
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- embedbase
- Dormant (18%)
- examples
- Very active (96%)
Days since push
- embedbase
- 601d
- examples
- 0d
Open issues (now)
- embedbase
- 35
- examples
- 61
Stars delta
- embedbase
- Unknown
- examples
- +8 (30d)
Open issues delta
- embedbase
- Unknown
- examples
- -3 (30d)
Full report
- embedbase
- Trust report
- examples
- Trust report
Choose embedbase if…
- embedbase is primarily TypeScript; examples is Jupyter Notebook.
- Tags unique to embedbase: artificial-intelligence, chatgpt, embeddings, machine-learning.
- * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase.
When NOT to use embedbase
- * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python.
- * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers.
Choose examples if…
- examples is primarily Jupyter Notebook; embedbase is TypeScript.
- Tags unique to examples: jupyter-notebook, llm, python, semantic-search.
- When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (different-ai/embedbase) · observed Jul 22, 2026
- GitHub forks (different-ai/embedbase) · observed Jul 22, 2026
- Last push (different-ai/embedbase) · observed Nov 27, 2024
- License file (MIT) · observed Jul 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (pinecone-io/examples) · observed Aug 15, 2026
- GitHub forks (pinecone-io/examples) · observed Aug 15, 2026
- Last push (pinecone-io/examples) · observed Aug 14, 2026
- License file (MIT) · observed Aug 15, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: embedbase 524 · examples 3.0k (synced Jul 22, 2026).
Common questions
- What is the difference between embedbase and examples?
- embedbase: A dead-simple API to build LLM-powered apps. examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. See the comparison table for live GitHub stats and shared categories.
- When should I choose embedbase over examples?
- Choose embedbase over examples when embedbase is primarily TypeScript; examples is Jupyter Notebook; Tags unique to embedbase: artificial-intelligence, chatgpt, embeddings, machine-learning; * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase.
- When should I choose examples over embedbase?
- Choose examples over embedbase when examples is primarily Jupyter Notebook; embedbase is TypeScript; Tags unique to examples: jupyter-notebook, llm, python, semantic-search; When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks.
- When should I avoid embedbase?
- * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python. * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers.
- 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.
- Is embedbase or examples more popular on GitHub?
- examples has more GitHub stars (3,036 vs 524). Stars measure visibility, not whether either tool fits your constraints.
- Are embedbase and examples open source?
- Yes - both are open-source projects on GitHub (embedbase: MIT, examples: MIT).
- Where can I find alternatives to embedbase or examples?
- GraphCanon lists graph-backed alternatives at embedbase alternatives and examples alternatives (embedbase markdown twin, examples markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, embedbase or examples?
- embedbase: Dormant. examples: Very active. 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 embedbase and examples?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: embedbase trust report; examples trust report.