Comparison
langchain_semantic_search vs recipes
Verdict
Pick langchain_semantic_search if builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook; pick recipes if comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases.
Markdown twin · langchain_semantic_search alternatives · recipes alternatives
GraphCanon updated 6d
vs
Trust & integrity
| Signal | langchain_semantic_search | recipes |
|---|---|---|
| Maintenance | Dormant (1285d since push) As of 6d · github_public_v1 | Steady (39d since push) As of 1mo · github_public_v1 |
| Provenance | Not a fork · Personal account As of 6d · github_public_v1 | Not a fork · Organization account As of 1mo · 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
- langchain_semantic_search
- Semantic search for Google Drive files using GPT3, LangChain, and Python
- recipes
- End-to-end notebooks for using Weaviate features and integrations.
Stars
- langchain_semantic_search
- 44
- recipes
- 941
Forks
- langchain_semantic_search
- 8
- recipes
- 195
Open issues
- langchain_semantic_search
- 0
- recipes
- 6
Language
- langchain_semantic_search
- Jupyter Notebook
- recipes
- Jupyter Notebook
Adopt for
- langchain_semantic_search
- Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook.
- recipes
- Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases.
Persona
- langchain_semantic_search
- -
- recipes
- -
Runtime
- langchain_semantic_search
- -
- recipes
- -
License
- langchain_semantic_search
- -
- recipes
- -
Last pushed
- langchain_semantic_search
- Feb 7, 2023
- recipes
- Jun 12, 2026
Categories
- langchain_semantic_search
- Data & Retrieval, Vector Databases
- recipes
- Data & Retrieval, Vector Databases
Trust and health
Maintenance
- langchain_semantic_search
- Dormant (18%)
- recipes
- Steady (60%)
Days since push
- langchain_semantic_search
- 1285d
- recipes
- 39d
Open issues (now)
- langchain_semantic_search
- 0
- recipes
- 6
Stars delta
- langchain_semantic_search
- 0 (30d)
- recipes
- Unknown
Open issues delta
- langchain_semantic_search
- 0 (30d)
- recipes
- Unknown
Owner type
- langchain_semantic_search
- User
- recipes
- Organization
Full report
- langchain_semantic_search
- Trust report
- recipes
- Trust report
Shared compatibility
- LangChain · langchain_semantic_search: LangChain integration · recipes: LangChain integration
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
Choose recipes if…
- Tags unique to recipes: function-calling, generative-ai, llm frameworks, python.
- When you are specifically interested in exploring various integrations with cloud hyperscalers (Google, AWS), LLM frameworks (LangChain, LlamaIndex), and other technologies mentioned, such as Databri
- More GitHub stars (941 vs 44) - visibility, not fit.
When NOT to use recipes
- If you are looking for generalized vector database use case examples that do not specifically showcase Weaviate's unique integrations or features
- When your focus is on understanding and using broad category services instead of the specific, detailed examples and configurations available in the Weaviate ecosystem
- For cases where a competitor tool offers better support for other specific needs, such as more comprehensive integration with data platforms not specifically covered by Weaviate like MongoDB or Redis
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 Aug 15, 2026
- GitHub forks (venuv/langchain_semantic_search) · observed Aug 15, 2026
- Last push (venuv/langchain_semantic_search) · observed Feb 7, 2023
- License file (unknown) · observed Aug 15, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (weaviate/recipes) · observed Jul 22, 2026
- GitHub forks (weaviate/recipes) · observed Jul 22, 2026
- Last push (weaviate/recipes) · observed Jun 12, 2026
- License file (unknown) · observed Jul 22, 2026
- Decision facts (enrichment) · observed Jul 9, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: langchain_semantic_search 44 · recipes 941 (synced Aug 15, 2026).
Common questions
- What is the difference between langchain_semantic_search and recipes?
- langchain_semantic_search: Semantic search for Google Drive files using GPT3, LangChain, and Python. recipes: End-to-end notebooks for using Weaviate features and integrations.. See the comparison table for live GitHub stats and shared categories.
- When should I choose langchain_semantic_search over recipes?
- Choose langchain_semantic_search over recipes 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 choose recipes over langchain_semantic_search?
- Choose recipes over langchain_semantic_search when Tags unique to recipes: function-calling, generative-ai, llm frameworks, python; When you are specifically interested in exploring various integrations with cloud hyperscalers (Google, AWS), LLM frameworks (LangChain, LlamaIndex), and other technologies mentioned, such as Databri; More GitHub stars (941 vs 44) - visibility, not fit.
- 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 recipes?
- If you are looking for generalized vector database use case examples that do not specifically showcase Weaviate's unique integrations or features When your focus is on understanding and using broad category services instead of the specific, detailed examples and configurations available in the Weaviate ecosystem For cases where a competitor tool offers better support for other specific needs, such as more comprehensive integration with data platforms not specifically covered by Weaviate like MongoDB or Redis
- Is langchain_semantic_search or recipes more popular on GitHub?
- recipes has more GitHub stars (941 vs 44). Stars measure visibility, not whether either tool fits your constraints.
- Are langchain_semantic_search and recipes open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to langchain_semantic_search or recipes?
- GraphCanon lists graph-backed alternatives at langchain_semantic_search alternatives and recipes alternatives (langchain_semantic_search markdown twin, recipes 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 recipes?
- langchain_semantic_search: Dormant. recipes: Steady. 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 recipes?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langchain_semantic_search trust report; recipes trust report.