Comparison
langchain_semantic_search vs weaviate-examples
Verdict
Pick langchain_semantic_search if builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook; pick weaviate-examples if weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications.
Markdown twin · langchain_semantic_search alternatives · weaviate-examples alternatives
GraphCanon updated Sep 20, 2026
15views this month
vs
Trust & integrity
| Signal | langchain_semantic_search | weaviate-examples |
|---|---|---|
| Maintenance | Dormant (1320d since push) As of Sep 20, 2026 · github_public_v1 | Dormant (408d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization 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
- langchain_semantic_search
- Semantic search for Google Drive files using GPT3, LangChain, and Python
- weaviate-examples
- Weaviate vector database – examples
Stars
- langchain_semantic_search
- 44
- weaviate-examples
- 331
Forks
- langchain_semantic_search
- 8
- weaviate-examples
- 86
Open issues
- langchain_semantic_search
- 0
- weaviate-examples
- 12
Language
- langchain_semantic_search
- Jupyter Notebook
- weaviate-examples
- HTML
Adopt for
- langchain_semantic_search
- Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook.
- weaviate-examples
- weaviate-examples aids developers by providing practical usage scenarios for Weaviate vector database, optimizing deep learning applications.
Persona
- langchain_semantic_search
- -
- weaviate-examples
- -
Runtime
- langchain_semantic_search
- -
- weaviate-examples
- -
License
- langchain_semantic_search
- -
- weaviate-examples
- MIT
Last pushed
- langchain_semantic_search
- Feb 7, 2023
- weaviate-examples
- Aug 7, 2025
Categories
- langchain_semantic_search
- Data & Retrieval, Vector Databases
- weaviate-examples
- Data & Retrieval, Vector Databases
Trust and health
Days since push
- langchain_semantic_search
- 1320d
- weaviate-examples
- 408d
Open issues (now)
- langchain_semantic_search
- 0
- weaviate-examples
- 12
Stars delta
- langchain_semantic_search
- 0 (30d)
- weaviate-examples
- -1 (30d)
Owner type
- langchain_semantic_search
- User
- weaviate-examples
- Organization
Full report
- langchain_semantic_search
- Trust report
- weaviate-examples
- Trust report
Shared compatibility
- Python · langchain_semantic_search: Python runtime · weaviate-examples: Python runtime
Choose langchain_semantic_search if…
- langchain_semantic_search is primarily Jupyter Notebook; weaviate-examples is HTML.
- Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain.
- Need semantic search capabilities specifically for your own documents in Google Drive
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
Choose weaviate-examples if…
- weaviate-examples is primarily HTML; langchain_semantic_search is Jupyter Notebook.
- Tags unique to weaviate-examples: deep-learning, examples, vector-database, vector-search.
- You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality.
When NOT to use weaviate-examples
- Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing.
- You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (weaviate/weaviate-examples) · observed Sep 20, 2026
- GitHub forks (weaviate/weaviate-examples) · observed Sep 20, 2026
- Last push (weaviate/weaviate-examples) · observed Aug 7, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: langchain_semantic_search 44 · weaviate-examples 331 (synced Sep 20, 2026).
Common questions
- What is the difference between langchain_semantic_search and weaviate-examples?
- langchain_semantic_search: Semantic search for Google Drive files using GPT3, LangChain, and Python. weaviate-examples: Weaviate vector database – examples. See the comparison table for live GitHub stats and shared categories.
- When should I choose langchain_semantic_search over weaviate-examples?
- Choose langchain_semantic_search over weaviate-examples when langchain_semantic_search is primarily Jupyter Notebook; weaviate-examples is HTML; Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain; Need semantic search capabilities specifically for your own documents in Google Drive.
- When should I choose weaviate-examples over langchain_semantic_search?
- Choose weaviate-examples over langchain_semantic_search when weaviate-examples is primarily HTML; langchain_semantic_search is Jupyter Notebook; Tags unique to weaviate-examples: deep-learning, examples, vector-database, vector-search; You aim to integrate vector search capabilities into your deep-learning projects and need hands-on examples to understand functionality.
- 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
- When should I avoid weaviate-examples?
- Your project utilizes a different vector database that aligns better with its specific requirements, such as more customizability in indexing. You seek general tutorial material on deep learning without the context of Weaviate's implementation specifics.
- Is langchain_semantic_search or weaviate-examples more popular on GitHub?
- weaviate-examples has more GitHub stars (331 vs 44). Stars measure visibility, not whether either tool fits your constraints.
- Are langchain_semantic_search and weaviate-examples open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to langchain_semantic_search or weaviate-examples?
- GraphCanon lists graph-backed alternatives at langchain_semantic_search alternatives and weaviate-examples alternatives (langchain_semantic_search markdown twin, weaviate-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, langchain_semantic_search or weaviate-examples?
- langchain_semantic_search: Dormant. weaviate-examples: 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 langchain_semantic_search and weaviate-examples?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langchain_semantic_search trust report; weaviate-examples trust report.