Comparison
examples vs langchain_semantic_search
Verdict
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; pick langchain_semantic_search if builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook.
Markdown twin · examples alternatives · langchain_semantic_search alternatives
GraphCanon updated Sep 20, 2026
5views this month
vs
Trust & integrity
| Signal | examples | langchain_semantic_search |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (1320d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- examples
- Jupyter Notebooks to help you get hands-on with Pinecone vector databases
- langchain_semantic_search
- Semantic search for Google Drive files using GPT3, LangChain, and Python
Stars
- examples
- 3.0k
- langchain_semantic_search
- 44
Forks
- examples
- 1.1k
- langchain_semantic_search
- 8
Open issues
- examples
- 66
- langchain_semantic_search
- 0
Language
- examples
- Jupyter Notebook
- langchain_semantic_search
- Jupyter Notebook
Adopt for
- examples
- Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance.
- langchain_semantic_search
- Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook.
Persona
- examples
- -
- langchain_semantic_search
- -
Runtime
- examples
- -
- langchain_semantic_search
- -
License
- examples
- MIT
- langchain_semantic_search
- -
Last pushed
- examples
- Sep 19, 2026
- langchain_semantic_search
- Feb 7, 2023
Categories
- examples
- Data & Retrieval, Vector Databases
- langchain_semantic_search
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- examples
- Very active (96%)
- langchain_semantic_search
- Dormant (18%)
Days since push
- examples
- 1d
- langchain_semantic_search
- 1320d
Open issues (now)
- examples
- 66
- langchain_semantic_search
- 0
Stars delta
- examples
- +7 (30d)
- langchain_semantic_search
- 0 (30d)
Open issues delta
- examples
- +5 (30d)
- langchain_semantic_search
- 0 (30d)
Owner type
- examples
- Organization
- langchain_semantic_search
- User
Full report
- examples
- Trust report
- langchain_semantic_search
- Trust report
Choose examples if…
- Tags unique to examples: ai, jupyter-notebook, llm, python.
- When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks.
- More GitHub stars (3.0k vs 44) - visibility, not fit.
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.
Choose langchain_semantic_search if…
- Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain.
- Need semantic search capabilities specifically for your own documents in Google Drive
- Leaner open-issue backlog (0).
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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (pinecone-io/examples) · observed Sep 20, 2026
- GitHub forks (pinecone-io/examples) · observed Sep 20, 2026
- Last push (pinecone-io/examples) · observed Sep 19, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (venuv/langchain_semantic_search) · observed Sep 20, 2026
- GitHub forks (venuv/langchain_semantic_search) · observed Sep 20, 2026
- Last push (venuv/langchain_semantic_search) · observed Feb 7, 2023
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: examples 3.0k · langchain_semantic_search 44 (synced Sep 20, 2026).
Common questions
- What is the difference between examples and langchain_semantic_search?
- examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. langchain_semantic_search: Semantic search for Google Drive files using GPT3, LangChain, and Python. See the comparison table for live GitHub stats and shared categories.
- When should I choose examples over langchain_semantic_search?
- Choose examples over langchain_semantic_search when Tags unique to examples: ai, jupyter-notebook, llm, python; When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks; More GitHub stars (3.0k vs 44) - visibility, not fit.
- When should I choose langchain_semantic_search over examples?
- Choose langchain_semantic_search over examples when Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain; Need semantic search capabilities specifically for your own documents in Google Drive; Leaner open-issue backlog (0).
- 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.
- 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
- Is examples or langchain_semantic_search more popular on GitHub?
- examples has more GitHub stars (3,043 vs 44). Stars measure visibility, not whether either tool fits your constraints.
- Are examples and langchain_semantic_search open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to examples or langchain_semantic_search?
- GraphCanon lists graph-backed alternatives at examples alternatives and langchain_semantic_search alternatives (examples markdown twin, langchain_semantic_search 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, examples or langchain_semantic_search?
- examples: Very active. langchain_semantic_search: 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 examples and langchain_semantic_search?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: examples trust report; langchain_semantic_search trust report.