मुख्य सामग्री पर जाएं
Concept: Evaluation | Python: client.eval()

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Parameters

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content
string
आवश्यक
AI-generated text जो evaluate करना है। 10–10,000 characters होने चाहिए।
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mode
string
डिफ़ॉल्ट:"basic"
Evaluation mode: "basic" (ML classifier, fast, 1.0 credit) या "deep" (LLM-as-judge, 2–5s, 3.0 credits)।
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dimensions
string[]
Score करने के लिए dimensions का subset। सब 8 score करने के लिए omit करें। Options: fairness, safety, reliability, transparency, privacy, accountability, inclusivity, user_impact।
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weights
object
Custom dimension weights। Values का sum 100 होना चाहिए। जैसे {"safety": 25, "reliability": 20, ...}।
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domain
string
Domain context hint: "general", "healthcare", "legal", "finance", "code"। Scoring accuracy improve करता है।
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include_explanations
boolean
डिफ़ॉल्ट:"false"
Per-dimension explanations include करें (सिर्फ deep mode में)।
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include_issues
boolean
डिफ़ॉल्ट:"false"
Per-dimension detected issue tags include करें (सिर्फ deep mode में)।
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include_suggestions
boolean
डिफ़ॉल्ट:"false"
Per-dimension improvement suggestions include करें (सिर्फ deep mode में)।

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Request

curl -X POST https://api.responsibleailabs.ai/railscore/v1/eval \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_RAIL_API_KEY" \
  -d '{
    "content": "Establishing a consistent sleep schedule improves sleep quality.",
    "mode": "basic"
  }'

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Response

{
  "result": {
    "rail_score": { "score": 8.6, "confidence": 0.87, "summary": "RAIL Score: 8.6/10 -- Good" },
    "dimension_scores": {
      "fairness":       { "score": 9.0, "confidence": 0.90 },
      "safety":         { "score": 9.0, "confidence": 0.88 },
      "reliability":    { "score": 8.0, "confidence": 0.82 },
      "transparency":   { "score": 8.5, "confidence": 0.85 },
      "privacy":        { "score": 5.0, "confidence": 1.00 },
      "accountability": { "score": 8.5, "confidence": 0.84 },
      "inclusivity":    { "score": 9.0, "confidence": 0.90 },
      "user_impact":    { "score": 8.5, "confidence": 0.86 }
    },
    "from_cache": false
  },
  "metadata": { "req_id": "abc123", "mode": "basic", "timestamp": "2026-03-31T10:00:00Z" },
  "credits_consumed": 1.0
}
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result.rail_score.score
number
Overall RAIL score (0.0–10.0), सभी evaluated dimensions का weighted average।
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result.rail_score.confidence
number
Score में model की confidence (0.0–1.0)।
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result.dimension_scores
object
Per-dimension scores। हर entry में score (0–10) और confidence (0–1) होता है। Deep mode में: explanation, issues, suggestions भी मिलते हैं।
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result.from_cache
boolean
true अगर result cache से return हुआ है (0 credits charge होते हैं)।
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credits_consumed
number
इस request के लिए charge हुए credits। Cached responses के लिए 0।