Structured Output CLI

# Basic structured output with inline schema
praisonai-ts llm json "Extract person info from: John is 30 years old" \
  --schema '{"type":"object","properties":{"name":{"type":"string"},"age":{"type":"number"}}}'

# Using a schema file
praisonai-ts llm json "Analyze this text" --schema-file ./schemas/analysis.json

# With specific model
praisonai-ts llm json "Extract data" --model openai/gpt-4o -m ./schema.json

{
  "type": "object",
  "properties": {
    "title": { "type": "string" },
    "summary": { "type": "string" },
    "tags": {
      "type": "array",
      "items": { "type": "string" }
    },
    "sentiment": {
      "type": "string",
      "enum": ["positive", "negative", "neutral"]
    }
  },
  "required": ["title", "summary"]
}

$ praisonai-ts llm json "Extract: Apple Inc CEO Tim Cook announced new products" \
  --schema '{"type":"object","properties":{"company":{"type":"string"},"person":{"type":"string"},"role":{"type":"string"},"action":{"type":"string"}}}'

{
  "company": "Apple Inc",
  "person": "Tim Cook",
  "role": "CEO",
  "action": "announced new products"
}

$ praisonai-ts llm json "Classify: You won a million dollars! Click here!" \
  --schema '{"type":"object","properties":{"category":{"type":"string","enum":["spam","ham"]},"confidence":{"type":"number"},"reason":{"type":"string"}}}'

{
  "category": "spam",
  "confidence": 0.95,
  "reason": "Contains typical spam indicators: prize claim, urgency, call to action"
}

$ praisonai-ts llm json "Analyze sentiment: I love this product! Best purchase ever." \
  --schema-file ./schemas/sentiment.json

{
  "type": "object",
  "properties": {
    "sentiment": { "type": "string", "enum": ["positive", "negative", "neutral"] },
    "score": { "type": "number", "minimum": -1, "maximum": 1 },
    "keywords": { "type": "array", "items": { "type": "string" } }
  }
}

$ praisonai-ts llm json "Parse this order: 2 pizzas ($15 each), 1 salad ($8), delivery to 123 Main St" \
  --schema-file ./schemas/order.json --json

{
  "type": "object",
  "properties": {
    "items": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "quantity": { "type": "number" },
          "price": { "type": "number" }
        }
      }
    },
    "total": { "type": "number" },
    "delivery": {
      "type": "object",
      "properties": {
        "address": { "type": "string" }
      }
    }
  }
}

{
  "items": [
    { "name": "pizza", "quantity": 2, "price": 15 },
    { "name": "salad", "quantity": 1, "price": 8 }
  ],
  "total": 38,
  "delivery": {
    "address": "123 Main St"
  }
}

# OpenAI
praisonai-ts llm json "Extract data" --model openai/gpt-4o --schema '...'

# Anthropic
praisonai-ts llm json "Extract data" --model anthropic/claude-3-sonnet --schema '...'

# Google
praisonai-ts llm json "Extract data" --model google/gemini-pro --schema '...'

# Pipe text content
cat document.txt | praisonai-ts llm json --schema-file ./schema.json

# Process multiple files
for f in *.txt; do
  praisonai-ts llm json "$(cat $f)" --schema-file ./schema.json --json >> results.jsonl
done

#!/bin/bash
# extract-entities.sh

SCHEMA='{"type":"object","properties":{"entities":{"type":"array","items":{"type":"object","properties":{"name":{"type":"string"},"type":{"type":"string"}}}}}}'

result=$(praisonai-ts llm json "$1" --schema "$SCHEMA" --json)
echo "$result" | jq '.entities'

# Process batch and output JSONL
while read -r line; do
  praisonai-ts llm json "$line" --schema-file ./schema.json --json
done < inputs.txt > outputs.jsonl

# Check exit code
praisonai-ts llm json "Extract data" --schema '...'
if [ $? -ne 0 ]; then
  echo "Extraction failed"
fi

# Capture errors
result=$(praisonai-ts llm json "Extract data" --schema '...' --json 2>&1)
if echo "$result" | jq -e '.success == false' > /dev/null; then
  echo "Error: $(echo "$result" | jq -r '.error.message')"
fi

# Required: API key for provider
export OPENAI_API_KEY=sk-...

# Optional: Default model
export PRAISONAI_MODEL=openai/gpt-4o-mini

# Optional: Default temperature for structured output
export PRAISONAI_STRUCTURED_TEMP=0.1