Comparison
swiss_army_llama vs cherche
Verdict
Pick swiss_army_llama if swiss_army_llama offers a versatile semantic text search FastAPI service with precomputed embeddings, similarity measures, and support for various file types via textract; pick cherche if cherche is a Python library for implementing neural search capabilities.
Markdown twin · swiss_army_llama alternatives · cherche alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | swiss_army_llama | cherche |
|---|---|---|
| Maintenance | Dormant (526d since push) As of 2w · github_public_v1 | Dormant (812d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2d · github_public_v1 |
| OSV dependency advisories | No published findings from this source as of 2026-07-11 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
- swiss_army_llama
- A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures
- cherche
- Neural Search
Stars
- swiss_army_llama
- 1.1k
- cherche
- 332
Forks
- swiss_army_llama
- 66
- cherche
- 14
Open issues
- swiss_army_llama
- 0
- cherche
- 4
Language
- swiss_army_llama
- Python
- cherche
- Python
Adopt for
- swiss_army_llama
- Swiss_army_llama offers a versatile semantic text search FastAPI service with precomputed embeddings, similarity measures, and support for various file types via textract.
- cherche
- Cherche is a Python library for implementing neural search capabilities.
Persona
- swiss_army_llama
- -
- cherche
- -
Runtime
- swiss_army_llama
- -
- cherche
- -
License
- swiss_army_llama
- -
- cherche
- MIT
Last pushed
- swiss_army_llama
- Feb 27, 2025
- cherche
- Jun 1, 2024
Categories
- swiss_army_llama
- Data & Retrieval, Vector Databases
- cherche
- Data & Retrieval, Evaluation & Observability, Vector Databases
Trust and health
Days since push
- swiss_army_llama
- 526d
- cherche
- 812d
Open issues (now)
- swiss_army_llama
- 0
- cherche
- 4
Stars delta
- swiss_army_llama
- Unknown
- cherche
- 0 (30d)
Open issues delta
- swiss_army_llama
- Unknown
- cherche
- 0 (30d)
OSV dependency advisories
- swiss_army_llama
- No published findings from this source as of 2026-07-11
- cherche
- No lockfile (source not queried)
Full report
- swiss_army_llama
- Trust report
- cherche
- Trust report
Shared compatibility
- Python · swiss_army_llama: Python runtime · cherche: Python runtime
Choose swiss_army_llama if…
- Tags unique to swiss_army_llama: embedding-similarity, embedding-vectors, embeddings, llama2.
- swiss_army_llama ships Docker support for self-hosted deployment.
- For projects requiring a comprehensive API solution that includes built-in support for diverse file formats like PDF, image, audio and more through textract
When NOT to use swiss_army_llama
- Avoid if your project is strictly focused on real-time embeddings calculation without leveraging precomputed data
- Not suitable for developers looking to avoid extensive system dependencies listed in its requirements
Choose cherche if…
- Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning.
- Also covers Evaluation & Observability.
- Cherche is a Python library for implementing neural search capabilities.
When NOT to use cherche
- Last GitHub push was 815 days ago (dormant maintenance, Jun 1, 2024). Validate activity before betting a new project on cherche.
- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Dicklesworthstone/swiss_army_llama) · observed Aug 8, 2026
- GitHub forks (Dicklesworthstone/swiss_army_llama) · observed Aug 8, 2026
- Last push (Dicklesworthstone/swiss_army_llama) · observed Feb 27, 2025
- License file (unknown) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (raphaelsty/cherche) · observed Aug 23, 2026
- GitHub forks (raphaelsty/cherche) · observed Aug 23, 2026
- Last push (raphaelsty/cherche) · observed Jun 1, 2024
- License file (MIT) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: swiss_army_llama 1.1k · cherche 332 (synced Aug 8, 2026).
Common questions
- What is the difference between swiss_army_llama and cherche?
- swiss_army_llama: A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures. cherche: Neural Search. See the comparison table for live GitHub stats and shared categories.
- When should I choose swiss_army_llama over cherche?
- Choose swiss_army_llama over cherche when Tags unique to swiss_army_llama: embedding-similarity, embedding-vectors, embeddings, llama2; swiss_army_llama ships Docker support for self-hosted deployment; For projects requiring a comprehensive API solution that includes built-in support for diverse file formats like PDF, image, audio and more through textract.
- When should I choose cherche over swiss_army_llama?
- Choose cherche over swiss_army_llama when Tags unique to cherche: bm25, flashtext, information-retrieval, machine-learning; Also covers Evaluation & Observability; Cherche is a Python library for implementing neural search capabilities.
- When should I avoid swiss_army_llama?
- Avoid if your project is strictly focused on real-time embeddings calculation without leveraging precomputed data Not suitable for developers looking to avoid extensive system dependencies listed in its requirements
- When should I avoid cherche?
- Last GitHub push was 815 days ago (dormant maintenance, Jun 1, 2024). Validate activity before betting a new project on cherche. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Is swiss_army_llama or cherche more popular on GitHub?
- swiss_army_llama has more GitHub stars (1,056 vs 332). Stars measure visibility, not whether either tool fits your constraints.
- Are swiss_army_llama and cherche open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to swiss_army_llama or cherche?
- GraphCanon lists graph-backed alternatives at swiss_army_llama alternatives and cherche alternatives (swiss_army_llama markdown twin, cherche 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, swiss_army_llama or cherche?
- swiss_army_llama: Dormant. cherche: 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 swiss_army_llama and cherche?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: swiss_army_llama trust report; cherche trust report.