OntoPrune – Pruning 85% LLM context tokens and 6.7x TTFT on CPU
Traducir un archivo a contexto compacto podado (formato stubs) # Resuelve automáticamente imports relativos y absolutos entre módulos del proyecto context = ontoprune.translate( "services/order_service.py", target="procesar_orden", fmt="stubs", multi_module=True, ) print(context) # 2. Verificar respuestas del modelo frente al contrato violations = ontoprune.check(llm_code_response, against=context) if not violations: print("Código 100% válido") 3. Desde la Línea de Comandos (CLI & Pipes) # Traducir función en proyectos multi-módulo: ontoprune translate services/order_service.py procesar_orden --format stubs # Pipeline directo con Ollama en proyectos modulares: ontoprune translate services/order_service.py procesar_orden | ollama run qwen2.5-coder:3b 4.
- ▪Traducir un archivo a contexto compacto podado (formato stubs) # Resuelve automáticamente imports relativos y absolutos entre módulos del proyecto context = ontoprune.translate( "services/order_service.py", target="procesar_orden", fmt="stu
- ▪Verificar respuestas del modelo frente al contrato violations = ontoprune.check(llm_code_response, against=context) if not violations: print("Código 100% válido") 3.
- ▪Desde la Línea de Comandos (CLI & Pipes) # Traducir función en proyectos multi-módulo: ontoprune translate services/order_service.py procesar_orden --format stubs # Pipeline directo con Ollama en proyectos modulares: ontoprune translate ser
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| Original publisher | GitHub |
| Canonical URL | https://github.com/vigmarcarlo/OntoPrune |
| Publication time | Mon, 05 Oct 2026 12:15:48 +0000 |
| Retrieval time | 2026-10-05T12:20:50.309Z |
| Last seen | 2026-10-05T12:20:50.309Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | mpPmON4fZ6u1 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
OntoPrune Neuro-Symbolic Context Pruning Middleware for Local SLMs OntoPrune es un middleware traductor ligero que transforma código fuente en contratos de contexto mínimos utilizando representación ontológica (RDF/SPARQL), reduciendo drásticamente los tokens de entrada y la latencia TTFT (Time to First Token) para modelos de lenguaje pequeños (SLMs) y evitando alucinaciones de API. Resultados Empíricos (Benchmark en CPU) Evaluación real con streaming sobre sample_service.py (300+ LOC) en CPU local (12 cores): Métrica Naive (Archivo Completo) OntoPrune (stubs) Ganancia Real Sobrecarga CPU 0.02 ms 9.9 ms $\le 10\text{ ms}$ (Meta: $\le 15\text{ ms}$) Tokens Entrada 2,390 tokens 406 tokens -83.0% ($\approx 6\text{x}$ menos) TTFT (qwen2.5-coder:3b) 22.4 s 3.3 s 6.7x más rápido (ahorra 19.1 s)…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.