Hi - I answer from the OpenSmartRoute documentation: routing, the API, plans and quotas, self-hosting. Ask away, or open a support ticket if you need a person.
Grounded in the docs - follow a source before acting on it.
reranking-models - Skill - OpenSmartRoute
Skillv1.0.0
reranking-models
The mandatory tree at `content/06-decision-tree.xml` picks reranker type by traffic, self-host policy, and recall need. Each leaf references a rule id from `01-core-rules.xml`.
Imported from faionfaion/faion-network (skills/faion/knowledge/ml-engineering/reranking-models/AGENTS.md). Install upstream with npx skills add faionfaion/faion-network --skill reranking-models. Copyright stays with the author.
Reranking Models
Summary
One-sentence: Picks a reranker (cross-encoder vs Cohere/MixedBread API) for two-stage retrieval based on latency, cost, and self-host constraints.
One-paragraph: Two-stage retrieval: fast ANN retrieves top-50 candidates, then a reranker (cross-encoder or API service) rescores them to return top-5 for generation. Choice between local cross-encoders (ms-marco-MiniLM, bge-reranker) and API services (Cohere rerank-3, MixedBread) is driven by latency budget, cost, accuracy targets, and self-host requirements.
Ефективно для: інженерів, які обирають конкретну реранкер-модель для two-stage RAG: cross-encoder локально vs Cohere/MixedBread API.
Applies If (ALL must hold)
Two-stage RAG retrieval already designed; need to pick the actual reranker model.
Have constraints on cost-per-query, latency p95, or self-hosting.
Need to benchmark candidate rerankers on a labeled set.
Skip If (ANY kills it)
Single-stage retrieval is enough (recall target met without rerank).
All-in on a managed retrieval product that includes reranking — pick rules don't matter.
Prerequisites
Input artifact
Format
Source
Candidate top-N from ANN
list
rag pipeline
Labeled eval set
JSONL
rag-eval-test-set-generation
Latency + cost budgets
config
product
Assumes Loaded
Methodology
Why
geek/ai/rag-engineer/reranking-two-stage
Two-stage pattern this model plugs into.
Content (load on demand)
File
Depth
What's inside
Est. tokens
content/01-core-rules.xml
essential
5 testable rules
~900
content/02-output-contract.xml
essential
JSON schema + valid/invalid examples
~700
content/03-failure-modes.xml
essential
3 antipatterns with symptom/root-cause/fix
~700
content/06-decision-tree.xml
essential
Decision tree with rule-id refs
~500
Task Routing
Sub-task
Model
Rationale
Run rerank benchmarks
haiku
Mechanical eval loop.
Pick winner with rationale
sonnet
Multi-criteria.
Templates
File
Purpose
templates/reranker-config.yaml
Reranker config + warmup hook + fallback policy.
Scripts
File
Purpose
When to call
scripts/validate-reranking-models.py
Validates output against the 02-output-contract schema.
Pre-commit; CI.
Related
[[reranking-two-stage]]
[[reranking-pipeline-integration]]
[[reranking-diversity-mmr]]
Decision tree
The mandatory tree at content/06-decision-tree.xml picks reranker type by traffic, self-host policy, and recall need. Each leaf references a rule id from 01-core-rules.xml.
Use it
Copy one of these into your project. Installing also returns the manifest and these snippets.
# after Install: the listing is in your workspace's routing pool - a plan picks it for its slot
curl -s -X POST https://api.opensmartroute.ai/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'
Manifest
An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.
{
"ocm": "1",
"id": "faionfaion-faion-network-reranking-models",
"kind": "skill",
"name": "reranking-models",
"description": "The mandatory tree at `content/06-decision-tree.xml` picks reranker type by traffic, self-host policy, and recall need. Each leaf references a rule id from `01-core-rules.xml`.",
"publisher": "faionfaion",
"version": "1.0.0",
"capabilities": {
"domains": [
"general"
],
"tags": [
"skill-md",
"github-agents"
],
"languages": [
"en"
]
},
"quality_prior": 0.6,
"examples": [
"The mandatory tree at `content/06-decision-tree.xml` picks reranker type by traffic, self-host policy, and recall need. Each leaf references a rule id from `01-core-rules.xml`."
],
"primary": false,
"metadata": {
"source": {
"provider": "github-agents",
"repository": "https://github.com/faionfaion/faion-network",
"path": "skills/faion/knowledge/ml-engineering/reranking-models/AGENTS.md",
"ref": "8e31684537e64d45c75264b1e0a2384f9b024a34",
"url": "https://github.com/faionfaion/faion-network/blob/8e31684537e64d45c75264b1e0a2384f9b024a34/skills/faion/knowledge/ml-engineering/reranking-models/AGENTS.md",
"key": "faionfaion/faion-network/skills/faion/knowledge/ml-engineering/reranking-models/AGENTS.md"
}
},
"instructions": "# Reranking Models\n\n## Summary\n\n**One-sentence:** Picks a reranker (cross-encoder vs Cohere/MixedBread API) for two-stage retrieval based on latency, cost, and self-host constraints.\n\n**One-paragraph:** Two-stage retrieval: fast ANN retrieves top-50 candidates, then a reranker (cross-encoder or API service) rescores them to return top-5 for generation. Choice between local cross-encoders (ms-marco-MiniLM, bge-reranker) and API services (Cohere rerank-3, MixedBread) is driven by latency budget, cost, accuracy targets, and self-host requirements.\n\n**Ефективно для:** інженерів, які обирають конкр",
"cost": {
"context_tokens": 736
}
}
Fetch it by URL: GET /api/v1/registry/faionfaion-faion-network-reranking-models/manifest?version=1.0.0
Reviews
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