Use Cases

from praisonaiagents import Agent

agent = Agent(name="Use Case Mapper", instructions="Map scenarios to recipe patterns.")
agent.start("Automate draft replies for SaaS support tickets.")

praisonai recipe run support-reply-drafter \
  --input '{"ticket_id": "T-123", "customer_message": "I need help"}' \
  --json

# support-reply/TEMPLATE.yaml
schema_version: "1.0"
name: support-reply-drafter
description: Generate support reply drafts

requires:
  env: [OPENAI_API_KEY]

config:
  input:
    ticket_id:
      type: string
      required: true
    customer_message:
      type: string
      required: true
    context:
      type: string
      required: false

praisonai serve recipe --port 8765 --auth api-key

import requests

def generate_reply_draft(ticket_id, customer_message):
    response = requests.post(
        "http://localhost:8765/v1/recipes/run",
        headers={"X-API-Key": "your-key"},
        json={
            "recipe": "support-reply-drafter",
            "input": {
                "ticket_id": ticket_id,
                "customer_message": customer_message
            }
        }
    )
    return response.json()["output"]["draft"]

praisonai recipe run meeting-action-items \
  --input '{"transcript": "...meeting notes..."}' \
  --json

# Triggered when new transcript arrives
from praisonai import recipe

def process_meeting(transcript_path):
    with open(transcript_path) as f:
        transcript = f.read()
    
    result = recipe.run(
        "meeting-action-items",
        input={"transcript": transcript}
    )
    
    return result.output["action_items"]

from praisonai import recipe

result = recipe.run(
    "sql-analyst",
    input={
        "question": "Show me top 10 customers by revenue",
        "schema": {
            "customers": ["id", "name", "email"],
            "orders": ["id", "customer_id", "amount", "date"]
        }
    }
)

sql_query = result.output["sql"]
explanation = result.output["explanation"]

# In GitHub Actions
- name: AI Code Review
  run: |
    git diff origin/main...HEAD > changes.diff
    praisonai recipe run code-review \
      --input "{\"diff\": \"$(cat changes.diff)\"}" \
      --json > review.json

// VS Code extension
vscode.commands.registerCommand('praisonai.review', async () => {
  const code = vscode.window.activeTextEditor.document.getText();
  const result = await invokeRecipe('code-review', { code });
  showReviewPanel(result.output.feedback);
});

praisonai endpoints invoke marketing-content \
  --input-json '{
    "product": "AI Assistant",
    "tone": "professional",
    "channels": ["email", "linkedin", "twitter"]
  }' \
  --json

from praisonai import recipe

for event in recipe.run_stream(
    "onboarding-concierge",
    input={"user_id": "U-123", "step": "initial"},
    session_id="session-abc"
):
    if event.event_type == "progress":
        print(event.data["message"])

def handle_transaction(transaction):
    result = recipe.run(
        "fraud-analyzer",
        input={
            "transaction_id": transaction["id"],
            "amount": transaction["amount"],
            "patterns": transaction["patterns"]
        }
    )
    
    if result.output["risk_score"] > 0.8:
        send_alert(result.output["summary"])

result = recipe.run(
    "doc-ingestion",
    input={"documents": ["/path/to/doc1.pdf", "/path/to/doc2.pdf"]}
)
knowledge_base_id = result.output["kb_id"]

result = recipe.run(
    "doc-qa",
    input={
        "question": "What is the refund policy?",
        "kb_id": knowledge_base_id
    }
)
print(result.output["answer"])
print(result.output["citations"])

praisonai recipe run sales-coach \
  --input '{"transcript": "...", "rep_id": "R-123"}' \
  --json

praisonai recipe run data-mapper \
  --input '{
    "source_schema": {...},
    "target_schema": {...},
    "sample_data": [...]
  }' \
  --json > mapping.json

result = recipe.run(
    "agent-router",
    input={
        "user_message": "I need help with billing",
        "available_agents": ["billing", "technical", "sales"]
    }
)

target_agent = result.output["selected_agent"]
# Route to target agent

praisonai recipe run localization \
  --input '{
    "content": "Welcome to our platform!",
    "source_lang": "en",
    "target_langs": ["es", "fr", "de", "ja"],
    "context": "marketing_homepage"
  }' \
  --json