// Test data for different parser formats - realistic complex examples
constJSON_SIMPLE:&str=r#"{"name": "code_interpreter", "arguments": "{\"language\": \"python\", \"code\": \"import numpy as np\\nimport matplotlib.pyplot as plt\\n\\n# Generate sample data\\nx = np.linspace(0, 10, 100)\\ny = np.sin(x) * np.exp(-x/10)\\n\\n# Create the plot\\nplt.figure(figsize=(10, 6))\\nplt.plot(x, y, 'b-', linewidth=2)\\nplt.grid(True)\\nplt.xlabel('Time (s)')\\nplt.ylabel('Amplitude')\\nplt.title('Damped Oscillation')\\nplt.show()\"}"}"#;
constJSON_ARRAY:&str=r#"[{"name": "web_search", "arguments": "{\"query\": \"latest developments in quantum computing 2024\", \"num_results\": 10, \"search_type\": \"news\", \"date_range\": \"2024-01-01:2024-12-31\", \"exclude_domains\": [\"reddit.com\", \"facebook.com\"], \"language\": \"en\"}"}, {"name": "analyze_sentiment", "arguments": "{\"text\": \"The breakthrough in quantum error correction represents a significant milestone. Researchers are optimistic about practical applications within the next decade.\", \"granularity\": \"sentence\", \"aspects\": [\"technology\", \"timeline\", \"impact\"], \"confidence_threshold\": 0.85}"}, {"name": "create_summary", "arguments": "{\"content_ids\": [\"doc_1234\", \"doc_5678\", \"doc_9012\"], \"max_length\": 500, \"style\": \"technical\", \"include_citations\": true}"}]"#;
constJSON_WITH_PARAMS:&str=r#"{"name": "database_query", "parameters": {"connection_string": "postgresql://user:pass@localhost:5432/analytics", "query": "SELECT customer_id, COUNT(*) as order_count, SUM(total_amount) as lifetime_value, AVG(order_amount) as avg_order_value FROM orders WHERE created_at >= '2024-01-01' GROUP BY customer_id HAVING COUNT(*) > 5 ORDER BY lifetime_value DESC LIMIT 100", "timeout_ms": 30000, "read_consistency": "strong", "partition_key": "customer_id"}}"#;
constMISTRAL_FORMAT:&str=r#"I'll help you analyze the sales data and create visualizations. Let me start by querying the database and then create some charts.
[TOOL_CALLS] [{"name": "sql_query", "arguments": {"database": "sales_analytics", "query": "WITH monthly_sales AS (SELECT DATE_TRUNC('month', order_date) as month, SUM(total_amount) as revenue, COUNT(DISTINCT customer_id) as unique_customers, COUNT(*) as total_orders FROM orders WHERE order_date >= CURRENT_DATE - INTERVAL '12 months' GROUP BY DATE_TRUNC('month', order_date)) SELECT month, revenue, unique_customers, total_orders, LAG(revenue) OVER (ORDER BY month) as prev_month_revenue, (revenue - LAG(revenue) OVER (ORDER BY month)) / LAG(revenue) OVER (ORDER BY month) * 100 as growth_rate FROM monthly_sales ORDER BY month DESC", "format": "json", "timeout": 60000}}]
Based on the query results, I can see interesting trends in your sales data."#;
constMISTRAL_MULTI:&str=r#"Let me help you with a comprehensive analysis of your application's performance.
{"name": "create_dashboard", "arguments": {"title": "Customer Analytics Dashboard", "widgets": [{"type": "time_series", "title": "Customer Acquisition", "query": "SELECT DATE(timestamp) as date, COUNT(DISTINCT customer_id) as new_customers FROM analytics.customer_metrics WHERE event_type = 'signup' GROUP BY date ORDER BY date", "visualization": "line"}, {"type": "metric", "title": "Total Revenue", "query": "SELECT SUM(amount) as total FROM analytics.customer_metrics WHERE event_type = 'purchase' AND DATE(timestamp) = CURRENT_DATE()", "format": "currency"}, {"type": "table", "title": "Top Customers", "query": "SELECT customer_id, COUNT(*) as purchases, SUM(amount) as total_spent FROM analytics.customer_metrics WHERE event_type = 'purchase' GROUP BY customer_id ORDER BY total_spent DESC LIMIT 10"}], "refresh_interval": 300}}
</tool_call>
The data pipeline has been configured and the dashboard is ready."#;
