hack/tools/scripts/llm-key-hunter/results/_cache/content/ailib-official_ai-lib-rust.json

{"repo": "ailib-official/ai-lib-rust", "files": {"crates/ai-lib-rust/examples/tavily_tool_calling.rs": {"022cb30c227f111b8e044e5e37b9706339ffcf57": {"t": 1785745838.325615, "n": 10600, "h": "6b539bf785958088", "c": "//! Tavily Search Tool Calling Example\n//!\n//! This example demonstrates how to use tool calling with the Tavily search API.\n//! It shows a complete workflow:\n//! 1. Define the Tavily search tool\n//! 2. Send a request with the tool to a capable model\n//! 3. Parse the tool calls from the model response\n//! 4. Execute the Tavily search\n//! 5. Send the results back to the model for final response\n//!\n//! Prerequisites:\n//! Set one of the following environment variables:\n//! - DEEPSEEK_API_KEY (recommended for tool calling)\n//! - OPENAI_API_KEY\n//! - ANTHROPIC_API_KEY\n//! - GROQ_API_KEY\n//! - TAVILY_API_KEY (for actual Tavily searches) - optional, can use mock\n//!\n//! Usage:\n//!   # Use DeepSeek (recommended)\n//!   $env:DEEPSEEK_API_KEY=\"your_key\"; cargo run --example tavily_tool_calling\n//!\n//!   # Or use OpenAI\n//!   $env:OPENAI_API_KEY=\"your_key\"; cargo run --example tavily_tool_calling -- --provider openai\n\nuse ai_lib_rust::types::message::{ContentBlock, MessageContent, MessageRole};\nuse ai_lib_rust::types::tool::{FunctionDefinition, ToolCall, ToolDefinition, ToolResult};\nuse ai_lib_rust::{AiClient, Message};\nuse serde_json::{json, Value};\nuse std::env;\n\n/// Define the Tavily search tool\nfn tavily_search_tool() -> ToolDefinition {\n    ToolDefinition {\n        tool_type: \"function\".to_string(),\n        function: FunctionDefinition {\n            name: \"tavily_search\".to_string(),\n            description: Some(\n                \"Search the web using Tavily API for real-time information\".to_string(),\n            ),\n            parameters: Some(json!({\n                \"type\": \"object\",\n                \"properties\": {\n                    \"query\": {\n                        \"type\": \"string\",\n                        \"description\": \"The search query to find information\"\n                    },\n                    \"search_depth\": {\n                        \"type\": \"string\",\n                        \"enum\": [\"basic\", \"advanced\"],\n                        \"default\": \"basic\",\n                        \"description\": \"Search depth (basic for quick results, advanced for comprehensive)\"\n                    },\n                    \"max_results\": {\n                        \"type\": \"integer\",\n                        \"minimum\": 1,\n                        \"maximum\": 10,\n                        \"default\": 5,\n                        \"description\": \"Maximum number of search results\"\n                    }\n                },\n                \"required\": [\"query\"]\n            })),\n        },\n    }\n}\n\n/// Mock Tavily search implementation\n/// In production, this would call the actual Tavily API\nasync fn mock_tavily_search(\n    query: &str,\n    _depth: &str,\n    _max_results: i32,\n) -> Result<Value, Box<dyn std::error::Error>> {\n    println!(\"\\n🔍 Executing Tavily search for: {}\", query);\n\n    // Simulated search results\n    let results = json!({\n        \"results\": [\n            {\n                \"title\": \"Latest News about Rust Programming\",\n                \"url\": \"https://example.com/rust-news\",\n                \"content\": \"Rust 1.82 released with improved performance and new features...\",\n                \"published_date\": \"2025-02-08\"\n            },\n            {\n                \"title\": \"Rust Web Framework Benchmarks 2025\",\n                \"url\": \"https://example.com/rust-benchmarks\",\n                \"content\": \"Comparison of Actix, Axum, and Rocket frameworks...\",\n                \"published_date\": \"2025-02-05\"\n            },\n            {\n                \"title\": \"Rust AI Libraries Overview\",\n                \"url\": \"https://example.com/rust-ai\",\n                \"content\": \"A comprehensive guide to AI/ML libraries in Rust ecosystem...\",\n                \"published_date\": \"2025-02-01\"\n            }\n        ],\n        \"response_time\": \"0.423s\",\n        \"query\": query\n    });\n\n    Ok(results)\n}\n\n/// Process tool calls from model response\nasync fn process_tool_calls(\n    tool_calls: &[ToolCall],\n) -> Result<Vec<ToolResult>, Box<dyn std::error::Error>> {\n    let mut results = Vec::new();\n\n    for tool_call in tool_calls {\n        println!(\"\\n📌 Tool Call: {}\", tool_call.name);\n        println!(\"   ID: {}\", tool_call.id);\n        println!(\n            \"   Arguments: {}\",\n            serde_json::to_string_pretty(&tool_call.arguments)?\n        );\n\n        let result = if tool_call.name == \"tavily_search\" {\n            let query = tool_call\n                .arguments\n                .get(\"query\")\n                .and_then(|v| v.as_str())\n                .unwrap_or(\"unknown\");\n            let depth = tool_call\n                .arguments\n                .get(\"search_depth\")\n                .and_then(|v| v.as_str())\n                .unwrap_or(\"basic\");\n            let max_results = tool_call\n                .arguments\n                .get(\"max_results\")\n                .and_then(|v| v.as_i64())\n                .unwrap_or(5) as i32;\n\n            match mock_tavily_search(query, depth, max_results).await {\n                Ok(search_results) => ToolResult {\n                    tool_use_id: tool_call.id.clone(),\n                    content: search_results,\n                    is_error: false,\n                },\n                Err(e) => ToolResult {\n                    tool_use_id: tool_call.id.clone(),\n                    content: json!({ \"error\": e.to_string() }),\n                    is_error: true,\n                },\n            }\n        } else {\n            ToolResult {\n                tool_use_id: tool_call.id.clone(),\n                content: json!({ \"error\": format!(\"Unknown tool: {}\", tool_call.name) }),\n                is_error: true,\n            }\n        };\n\n        results.push(result);\n    }\n\n    Ok(results)\n}\n\n/// Get provider from arguments or environment\nfn get_provider() -> String {\n    let args: Vec<String> = env::args().collect();\n\n    // Check for --provider argument\n    if let Some(idx) = args.iter().position(|arg| arg == \"--provider\") {\n        if let Some(provider) = args.get(idx + 1) {\n            return format!(\"{}/auto\", provider);\n        }\n    }\n\n    // Auto-detect from available environment variables\n    if env::var(\"DEEPSEEK_API_KEY\").is_ok() {\n        \"deepseek/auto\".to_string()\n    } else if env::var(\"OPENAI_API_KEY\").is_ok() {\n        \"openai/auto\".to_string()\n    } else if env::var(\"ANTHROPIC_API_KEY\").is_ok() {\n        \"anthropic/auto\".to_string()\n    } else if env::var(\"GROQ_API_KEY\").is_ok() {\n        \"groq/auto\".to_string()\n    } else {\n        eprintln!(\"❌ Error: No API key found in environment variables.\");\n        eprintln!(\"Please set one of:\");\n        eprintln!(\"  - DEEPSEEK_API_KEY\");\n        eprintln!(\"  - OPENAI_API_KEY\");\n        eprintln!(\"  - ANTHROPIC_API_KEY\");\n        eprintln!(\"  - GROQ_API_KEY\");\n        std::process::exit(1);\n    }\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    // Initialize tracing\n    tracing_subscriber::fmt()\n        .with_env_filter(tracing_subscriber::EnvFilter::from_default_env())\n        .init();\n\n    println!(\"🚀 Tavily Search Tool Calling Example\\n\");\n\n    let provider = get_provider();\n    println!(\"📦 Using provider: {}\\n\", provider);\n\n    // Create client\n    let client = AiClient::new(&provider).await?;\n\n    // First request: Ask the model to search for information\n    let messages = vec![\n        Message::system(\n            \"You are a helpful research assistant. When asked about topics, use the tavily_search tool to find current information.\"\n        ),\n        Message::user(\n            \"What are the latest developments in Rust programming? Please search for recent news and updates.\"\n        ),\n    ];\n\n    // Force tool usage\n    let tool_choice = json!({\n        \"type\": \"function\",\n        \"function\": { \"name\": \"tavily_search\" }\n    });\n\n    println!(\"📤 Sending initial request with tool definition...\\n\");\n\n    let resp = client\n        .chat()\n        .messages(messages)\n        .temperature(0.7)\n        .max_tokens(1024)\n        .tools(vec![tavily_search_tool()])\n        .tool_choice(tool_choice)\n        .execute()\n        .await?;\n\n    println!(\"✅ Initial response received\");\n    println!(\"   Content: {}\", resp.content);\n\n    // Check if model called tools\n    if resp.tool_calls.is_empty() {\n        println!(\"\\n⚠️  No tool calls in response. Some models may not support tool calling.\");\n        println!(\"   Response: {}\", resp.content);\n        return Ok(());\n    }\n\n    // Process tool calls\n    println!(\"\\n🔄 Processing {} tool call(s)...\", resp.tool_calls.len());\n    let tool_results = process_tool_calls(&resp.tool_calls).await?;\n\n    // Second request: Send tool results back to model for final response\n    println!(\"\\n📤 Sending tool results back to model...\\n\");\n\n    let mut follow_up_messages = vec![\n        Message::system(\"You are a helpful research assistant.\"),\n        Message::user(\n            \"What are the latest developments in Rust programming? Please search for recent news and updates.\"\n        ),\n    ];\n\n    // Add assistant's tool call as a content block\n    let mut assistant_blocks = vec![ContentBlock::text(&resp.content)];\n    for tool_call in &resp.tool_calls {\n        assistant_blocks.push(ContentBlock::ToolUse {\n            id: tool_call.id.clone(),\n            name: tool_call.name.clone(),\n            input: tool_call.arguments.clone(),\n        });\n    }\n    follow_up_messages.push(Message::with_content(\n        MessageRole::Assistant,\n        MessageContent::blocks(assistant_blocks),\n    ));\n\n    // Add tool results\n    for result in tool_results {\n        follow_up_messages.push(Message::with_content(\n            MessageRole::User,\n            MessageContent::blocks(vec![ContentBlock::ToolResult {\n                tool_use_id: result.tool_use_id,\n                content: result.content,\n            }]),\n        ));\n    }\n\n    // Get final response\n    let final_resp = client\n        .chat()\n        .messages(follow_up_messages)\n        .temperature(0.7)\n        .max_tokens(2048)\n        .execute()\n        .await?;\n\n    println!(\"✅ Final response received\\n\");\n    println!(\"📝 Assistant Response:\\n{}\", final_resp.content);\n\n    if let Some(usage) = final_resp.usage {\n        println!(\"\\n📊 Token Usage:\");\n        if let Some(prompt_tokens) = usage.get(\"prompt_tokens\").and_then(|v| v.as_u64()) {\n            println!(\"   Prompt tokens: {}\", prompt_tokens);\n        }\n        if let Some(completion_tokens) = usage.get(\"completion_tokens\").and_then(|v| v.as_u64()) {\n            println!(\"   Completion tokens: {}\", completion_tokens);\n        }\n    }\n\n    println!(\"\\n✨ Example completed successfully!\");\n\n    Ok(())\n}\n"}, "d32d4bc6a5c18cddebc9050cc5746a3496d8f58d": {"t": 1785757088.238675, "n": 10600, "h": "6b539bf785958088", "c": "//! Tavily Search Tool Calling Example\n//!\n//! This example demonstrates how to use tool calling with the Tavily search API.\n//! It shows a complete workflow:\n//! 1. Define the Tavily search tool\n//! 2. Send a request with the tool to a capable model\n//! 3. Parse the tool calls from the model response\n//! 4. Execute the Tavily search\n//! 5. Send the results back to the model for final response\n//!\n//! Prerequisites:\n//! Set one of the following environment variables:\n//! - DEEPSEEK_API_KEY (recommended for tool calling)\n//! - OPENAI_API_KEY\n//! - ANTHROPIC_API_KEY\n//! - GROQ_API_KEY\n//! - TAVILY_API_KEY (for actual Tavily searches) - optional, can use mock\n//!\n//! Usage:\n//!   # Use DeepSeek (recommended)\n//!   $env:DEEPSEEK_API_KEY=\"your_key\"; cargo run --example tavily_tool_calling\n//!\n//!   # Or use OpenAI\n//!   $env:OPENAI_API_KEY=\"your_key\"; cargo run --example tavily_tool_calling -- --provider openai\n\nuse ai_lib_rust::types::message::{ContentBlock, MessageContent, MessageRole};\nuse ai_lib_rust::types::tool::{FunctionDefinition, ToolCall, ToolDefinition, ToolResult};\nuse ai_lib_rust::{AiClient, Message};\nuse serde_json::{json, Value};\nuse std::env;\n\n/// Define the Tavily search tool\nfn tavily_search_tool() -> ToolDefinition {\n    ToolDefinition {\n        tool_type: \"function\".to_string(),\n        function: FunctionDefinition {\n            name: \"tavily_search\".to_string(),\n            description: Some(\n                \"Search the web using Tavily API for real-time information\".to_string(),\n            ),\n            parameters: Some(json!({\n                \"type\": \"object\",\n                \"properties\": {\n                    \"query\": {\n                        \"type\": \"string\",\n                        \"description\": \"The search query to find information\"\n                    },\n                    \"search_depth\": {\n                        \"type\": \"string\",\n                        \"enum\": [\"basic\", \"advanced\"],\n                        \"default\": \"basic\",\n                        \"description\": \"Search depth (basic for quick results, advanced for comprehensive)\"\n                    },\n                    \"max_results\": {\n                        \"type\": \"integer\",\n                        \"minimum\": 1,\n                        \"maximum\": 10,\n                        \"default\": 5,\n                        \"description\": \"Maximum number of search results\"\n                    }\n                },\n                \"required\": [\"query\"]\n            })),\n        },\n    }\n}\n\n/// Mock Tavily search implementation\n/// In production, this would call the actual Tavily API\nasync fn mock_tavily_search(\n    query: &str,\n    _depth: &str,\n    _max_results: i32,\n) -> Result<Value, Box<dyn std::error::Error>> {\n    println!(\"\\n🔍 Executing Tavily search for: {}\", query);\n\n    // Simulated search results\n    let results = json!({\n        \"results\": [\n            {\n                \"title\": \"Latest News about Rust Programming\",\n                \"url\": \"https://example.com/rust-news\",\n                \"content\": \"Rust 1.82 released with improved performance and new features...\",\n                \"published_date\": \"2025-02-08\"\n            },\n            {\n                \"title\": \"Rust Web Framework Benchmarks 2025\",\n                \"url\": \"https://example.com/rust-benchmarks\",\n                \"content\": \"Comparison of Actix, Axum, and Rocket frameworks...\",\n                \"published_date\": \"2025-02-05\"\n            },\n            {\n                \"title\": \"Rust AI Libraries Overview\",\n                \"url\": \"https://example.com/rust-ai\",\n                \"content\": \"A comprehensive guide to AI/ML libraries in Rust ecosystem...\",\n                \"published_date\": \"2025-02-01\"\n            }\n        ],\n        \"response_time\": \"0.423s\",\n        \"query\": query\n    });\n\n    Ok(results)\n}\n\n/// Process tool calls from model response\nasync fn process_tool_calls(\n    tool_calls: &[ToolCall],\n) -> Result<Vec<ToolResult>, Box<dyn std::error::Error>> {\n    let mut results = Vec::new();\n\n    for tool_call in tool_calls {\n        println!(\"\\n📌 Tool Call: {}\", tool_call.name);\n        println!(\"   ID: {}\", tool_call.id);\n        println!(\n            \"   Arguments: {}\",\n            serde_json::to_string_pretty(&tool_call.arguments)?\n        );\n\n        let result = if tool_call.name == \"tavily_search\" {\n            let query = tool_call\n                .arguments\n                .get(\"query\")\n                .and_then(|v| v.as_str())\n                .unwrap_or(\"unknown\");\n            let depth = tool_call\n                .arguments\n                .get(\"search_depth\")\n                .and_then(|v| v.as_str())\n                .unwrap_or(\"basic\");\n            let max_results = tool_call\n                .arguments\n                .get(\"max_results\")\n                .and_then(|v| v.as_i64())\n                .unwrap_or(5) as i32;\n\n            match mock_tavily_search(query, depth, max_results).await {\n                Ok(search_results) => ToolResult {\n                    tool_use_id: tool_call.id.clone(),\n                    content: search_results,\n                    is_error: false,\n                },\n                Err(e) => ToolResult {\n                    tool_use_id: tool_call.id.clone(),\n                    content: json!({ \"error\": e.to_string() }),\n                    is_error: true,\n                },\n            }\n        } else {\n            ToolResult {\n                tool_use_id: tool_call.id.clone(),\n                content: json!({ \"error\": format!(\"Unknown tool: {}\", tool_call.name) }),\n                is_error: true,\n            }\n        };\n\n        results.push(result);\n    }\n\n    Ok(results)\n}\n\n/// Get provider from arguments or environment\nfn get_provider() -> String {\n    let args: Vec<String> = env::args().collect();\n\n    // Check for --provider argument\n    if let Some(idx) = args.iter().position(|arg| arg == \"--provider\") {\n        if let Some(provider) = args.get(idx + 1) {\n            return format!(\"{}/auto\", provider);\n        }\n    }\n\n    // Auto-detect from available environment variables\n    if env::var(\"DEEPSEEK_API_KEY\").is_ok() {\n        \"deepseek/auto\".to_string()\n    } else if env::var(\"OPENAI_API_KEY\").is_ok() {\n        \"openai/auto\".to_string()\n    } else if env::var(\"ANTHROPIC_API_KEY\").is_ok() {\n        \"anthropic/auto\".to_string()\n    } else if env::var(\"GROQ_API_KEY\").is_ok() {\n        \"groq/auto\".to_string()\n    } else {\n        eprintln!(\"❌ Error: No API key found in environment variables.\");\n        eprintln!(\"Please set one of:\");\n        eprintln!(\"  - DEEPSEEK_API_KEY\");\n        eprintln!(\"  - OPENAI_API_KEY\");\n        eprintln!(\"  - ANTHROPIC_API_KEY\");\n        eprintln!(\"  - GROQ_API_KEY\");\n        std::process::exit(1);\n    }\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    // Initialize tracing\n    tracing_subscriber::fmt()\n        .with_env_filter(tracing_subscriber::EnvFilter::from_default_env())\n        .init();\n\n    println!(\"🚀 Tavily Search Tool Calling Example\\n\");\n\n    let provider = get_provider();\n    println!(\"📦 Using provider: {}\\n\", provider);\n\n    // Create client\n    let client = AiClient::new(&provider).await?;\n\n    // First request: Ask the model to search for information\n    let messages = vec![\n        Message::system(\n            \"You are a helpful research assistant. When asked about topics, use the tavily_search tool to find current information.\"\n        ),\n        Message::user(\n            \"What are the latest developments in Rust programming? Please search for recent news and updates.\"\n        ),\n    ];\n\n    // Force tool usage\n    let tool_choice = json!({\n        \"type\": \"function\",\n        \"function\": { \"name\": \"tavily_search\" }\n    });\n\n    println!(\"📤 Sending initial request with tool definition...\\n\");\n\n    let resp = client\n        .chat()\n        .messages(messages)\n        .temperature(0.7)\n        .max_tokens(1024)\n        .tools(vec![tavily_search_tool()])\n        .tool_choice(tool_choice)\n        .execute()\n        .await?;\n\n    println!(\"✅ Initial response received\");\n    println!(\"   Content: {}\", resp.content);\n\n    // Check if model called tools\n    if resp.tool_calls.is_empty() {\n        println!(\"\\n⚠️  No tool calls in response. Some models may not support tool calling.\");\n        println!(\"   Response: {}\", resp.content);\n        return Ok(());\n    }\n\n    // Process tool calls\n    println!(\"\\n🔄 Processing {} tool call(s)...\", resp.tool_calls.len());\n    let tool_results = process_tool_calls(&resp.tool_calls).await?;\n\n    // Second request: Send tool results back to model for final response\n    println!(\"\\n📤 Sending tool results back to model...\\n\");\n\n    let mut follow_up_messages = vec![\n        Message::system(\"You are a helpful research assistant.\"),\n        Message::user(\n            \"What are the latest developments in Rust programming? Please search for recent news and updates.\"\n        ),\n    ];\n\n    // Add assistant's tool call as a content block\n    let mut assistant_blocks = vec![ContentBlock::text(&resp.content)];\n    for tool_call in &resp.tool_calls {\n        assistant_blocks.push(ContentBlock::ToolUse {\n            id: tool_call.id.clone(),\n            name: tool_call.name.clone(),\n            input: tool_call.arguments.clone(),\n        });\n    }\n    follow_up_messages.push(Message::with_content(\n        MessageRole::Assistant,\n        MessageContent::blocks(assistant_blocks),\n    ));\n\n    // Add tool results\n    for result in tool_results {\n        follow_up_messages.push(Message::with_content(\n            MessageRole::User,\n            MessageContent::blocks(vec![ContentBlock::ToolResult {\n                tool_use_id: result.tool_use_id,\n                content: result.content,\n            }]),\n        ));\n    }\n\n    // Get final response\n    let final_resp = client\n        .chat()\n        .messages(follow_up_messages)\n        .temperature(0.7)\n        .max_tokens(2048)\n        .execute()\n        .await?;\n\n    println!(\"✅ Final response received\\n\");\n    println!(\"📝 Assistant Response:\\n{}\", final_resp.content);\n\n    if let Some(usage) = final_resp.usage {\n        println!(\"\\n📊 Token Usage:\");\n        if let Some(prompt_tokens) = usage.get(\"prompt_tokens\").and_then(|v| v.as_u64()) {\n            println!(\"   Prompt tokens: {}\", prompt_tokens);\n        }\n        if let Some(completion_tokens) = usage.get(\"completion_tokens\").and_then(|v| v.as_u64()) {\n            println!(\"   Completion tokens: {}\", completion_tokens);\n        }\n    }\n\n    println!(\"\\n✨ Example completed successfully!\");\n\n    Ok(())\n}\n"}, "3bc56f0edaab54ac146898dba17ef31b659f3bf8": {"t": 1786068327.883242, "n": 10600, "h": "6b539bf785958088", "c": "//! Tavily Search Tool Calling Example\n//!\n//! This example demonstrates how to use tool calling with the Tavily search API.\n//! It shows a complete workflow:\n//! 1. Define the Tavily search tool\n//! 2. Send a request with the tool to a capable model\n//! 3. Parse the tool calls from the model response\n//! 4. Execute the Tavily search\n//! 5. Send the results back to the model for final response\n//!\n//! Prerequisites:\n//! Set one of the following environment variables:\n//! - DEEPSEEK_API_KEY (recommended for tool calling)\n//! - OPENAI_API_KEY\n//! - ANTHROPIC_API_KEY\n//! - GROQ_API_KEY\n//! - TAVILY_API_KEY (for actual Tavily searches) - optional, can use mock\n//!\n//! Usage:\n//!   # Use DeepSeek (recommended)\n//!   $env:DEEPSEEK_API_KEY=\"your_key\"; cargo run --example tavily_tool_calling\n//!\n//!   # Or use OpenAI\n//!   $env:OPENAI_API_KEY=\"your_key\"; cargo run --example tavily_tool_calling -- --provider openai\n\nuse ai_lib_rust::types::message::{ContentBlock, MessageContent, MessageRole};\nuse ai_lib_rust::types::tool::{FunctionDefinition, ToolCall, ToolDefinition, ToolResult};\nuse ai_lib_rust::{AiClient, Message};\nuse serde_json::{json, Value};\nuse std::env;\n\n/// Define the Tavily search tool\nfn tavily_search_tool() -> ToolDefinition {\n    ToolDefinition {\n        tool_type: \"function\".to_string(),\n        function: FunctionDefinition {\n            name: \"tavily_search\".to_string(),\n            description: Some(\n                \"Search the web using Tavily API for real-time information\".to_string(),\n            ),\n            parameters: Some(json!({\n                \"type\": \"object\",\n                \"properties\": {\n                    \"query\": {\n                        \"type\": \"string\",\n                        \"description\": \"The search query to find information\"\n                    },\n                    \"search_depth\": {\n                        \"type\": \"string\",\n                        \"enum\": [\"basic\", \"advanced\"],\n                        \"default\": \"basic\",\n                        \"description\": \"Search depth (basic for quick results, advanced for comprehensive)\"\n                    },\n                    \"max_results\": {\n                        \"type\": \"integer\",\n                        \"minimum\": 1,\n                        \"maximum\": 10,\n                        \"default\": 5,\n                        \"description\": \"Maximum number of search results\"\n                    }\n                },\n                \"required\": [\"query\"]\n            })),\n        },\n    }\n}\n\n/// Mock Tavily search implementation\n/// In production, this would call the actual Tavily API\nasync fn mock_tavily_search(\n    query: &str,\n    _depth: &str,\n    _max_results: i32,\n) -> Result<Value, Box<dyn std::error::Error>> {\n    println!(\"\\n🔍 Executing Tavily search for: {}\", query);\n\n    // Simulated search results\n    let results = json!({\n        \"results\": [\n            {\n                \"title\": \"Latest News about Rust Programming\",\n                \"url\": \"https://example.com/rust-news\",\n                \"content\": \"Rust 1.82 released with improved performance and new features...\",\n                \"published_date\": \"2025-02-08\"\n            },\n            {\n                \"title\": \"Rust Web Framework Benchmarks 2025\",\n                \"url\": \"https://example.com/rust-benchmarks\",\n                \"content\": \"Comparison of Actix, Axum, and Rocket frameworks...\",\n                \"published_date\": \"2025-02-05\"\n            },\n            {\n                \"title\": \"Rust AI Libraries Overview\",\n                \"url\": \"https://example.com/rust-ai\",\n                \"content\": \"A comprehensive guide to AI/ML libraries in Rust ecosystem...\",\n                \"published_date\": \"2025-02-01\"\n            }\n        ],\n        \"response_time\": \"0.423s\",\n        \"query\": query\n    });\n\n    Ok(results)\n}\n\n/// Process tool calls from model response\nasync fn process_tool_calls(\n    tool_calls: &[ToolCall],\n) -> Result<Vec<ToolResult>, Box<dyn std::error::Error>> {\n    let mut results = Vec::new();\n\n    for tool_call in tool_calls {\n        println!(\"\\n📌 Tool Call: {}\", tool_call.name);\n        println!(\"   ID: {}\", tool_call.id);\n        println!(\n            \"   Arguments: {}\",\n            serde_json::to_string_pretty(&tool_call.arguments)?\n        );\n\n        let result = if tool_call.name == \"tavily_search\" {\n            let query = tool_call\n                .arguments\n                .get(\"query\")\n                .and_then(|v| v.as_str())\n                .unwrap_or(\"unknown\");\n            let depth = tool_call\n                .arguments\n                .get(\"search_depth\")\n                .and_then(|v| v.as_str())\n                .unwrap_or(\"basic\");\n            let max_results = tool_call\n                .arguments\n                .get(\"max_results\")\n                .and_then(|v| v.as_i64())\n                .unwrap_or(5) as i32;\n\n            match mock_tavily_search(query, depth, max_results).await {\n                Ok(search_results) => ToolResult {\n                    tool_use_id: tool_call.id.clone(),\n                    content: search_results,\n                    is_error: false,\n                },\n                Err(e) => ToolResult {\n                    tool_use_id: tool_call.id.clone(),\n                    content: json!({ \"error\": e.to_string() }),\n                    is_error: true,\n                },\n            }\n        } else {\n            ToolResult {\n                tool_use_id: tool_call.id.clone(),\n                content: json!({ \"error\": format!(\"Unknown tool: {}\", tool_call.name) }),\n                is_error: true,\n            }\n        };\n\n        results.push(result);\n    }\n\n    Ok(results)\n}\n\n/// Get provider from arguments or environment\nfn get_provider() -> String {\n    let args: Vec<String> = env::args().collect();\n\n    // Check for --provider argument\n    if let Some(idx) = args.iter().position(|arg| arg == \"--provider\") {\n        if let Some(provider) = args.get(idx + 1) {\n            return format!(\"{}/auto\", provider);\n        }\n    }\n\n    // Auto-detect from available environment variables\n    if env::var(\"DEEPSEEK_API_KEY\").is_ok() {\n        \"deepseek/auto\".to_string()\n    } else if env::var(\"OPENAI_API_KEY\").is_ok() {\n        \"openai/auto\".to_string()\n    } else if env::var(\"ANTHROPIC_API_KEY\").is_ok() {\n        \"anthropic/auto\".to_string()\n    } else if env::var(\"GROQ_API_KEY\").is_ok() {\n        \"groq/auto\".to_string()\n    } else {\n        eprintln!(\"❌ Error: No API key found in environment variables.\");\n        eprintln!(\"Please set one of:\");\n        eprintln!(\"  - DEEPSEEK_API_KEY\");\n        eprintln!(\"  - OPENAI_API_KEY\");\n        eprintln!(\"  - ANTHROPIC_API_KEY\");\n        eprintln!(\"  - GROQ_API_KEY\");\n        std::process::exit(1);\n    }\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    // Initialize tracing\n    tracing_subscriber::fmt()\n        .with_env_filter(tracing_subscriber::EnvFilter::from_default_env())\n        .init();\n\n    println!(\"🚀 Tavily Search Tool Calling Example\\n\");\n\n    let provider = get_provider();\n    println!(\"📦 Using provider: {}\\n\", provider);\n\n    // Create client\n    let client = AiClient::new(&provider).await?;\n\n    // First request: Ask the model to search for information\n    let messages = vec![\n        Message::system(\n            \"You are a helpful research assistant. When asked about topics, use the tavily_search tool to find current information.\"\n        ),\n        Message::user(\n            \"What are the latest developments in Rust programming? Please search for recent news and updates.\"\n        ),\n    ];\n\n    // Force tool usage\n    let tool_choice = json!({\n        \"type\": \"function\",\n        \"function\": { \"name\": \"tavily_search\" }\n    });\n\n    println!(\"📤 Sending initial request with tool definition...\\n\");\n\n    let resp = client\n        .chat()\n        .messages(messages)\n        .temperature(0.7)\n        .max_tokens(1024)\n        .tools(vec![tavily_search_tool()])\n        .tool_choice(tool_choice)\n        .execute()\n        .await?;\n\n    println!(\"✅ Initial response received\");\n    println!(\"   Content: {}\", resp.content);\n\n    // Check if model called tools\n    if resp.tool_calls.is_empty() {\n        println!(\"\\n⚠️  No tool calls in response. Some models may not support tool calling.\");\n        println!(\"   Response: {}\", resp.content);\n        return Ok(());\n    }\n\n    // Process tool calls\n    println!(\"\\n🔄 Processing {} tool call(s)...\", resp.tool_calls.len());\n    let tool_results = process_tool_calls(&resp.tool_calls).await?;\n\n    // Second request: Send tool results back to model for final response\n    println!(\"\\n📤 Sending tool results back to model...\\n\");\n\n    let mut follow_up_messages = vec![\n        Message::system(\"You are a helpful research assistant.\"),\n        Message::user(\n            \"What are the latest developments in Rust programming? Please search for recent news and updates.\"\n        ),\n    ];\n\n    // Add assistant's tool call as a content block\n    let mut assistant_blocks = vec![ContentBlock::text(&resp.content)];\n    for tool_call in &resp.tool_calls {\n        assistant_blocks.push(ContentBlock::ToolUse {\n            id: tool_call.id.clone(),\n            name: tool_call.name.clone(),\n            input: tool_call.arguments.clone(),\n        });\n    }\n    follow_up_messages.push(Message::with_content(\n        MessageRole::Assistant,\n        MessageContent::blocks(assistant_blocks),\n    ));\n\n    // Add tool results\n    for result in tool_results {\n        follow_up_messages.push(Message::with_content(\n            MessageRole::User,\n            MessageContent::blocks(vec![ContentBlock::ToolResult {\n                tool_use_id: result.tool_use_id,\n                content: result.content,\n            }]),\n        ));\n    }\n\n    // Get final response\n    let final_resp = client\n        .chat()\n        .messages(follow_up_messages)\n        .temperature(0.7)\n        .max_tokens(2048)\n        .execute()\n        .await?;\n\n    println!(\"✅ Final response received\\n\");\n    println!(\"📝 Assistant Response:\\n{}\", final_resp.content);\n\n    if let Some(usage) = final_resp.usage {\n        println!(\"\\n📊 Token Usage:\");\n        if let Some(prompt_tokens) = usage.get(\"prompt_tokens\").and_then(|v| v.as_u64()) {\n            println!(\"   Prompt tokens: {}\", prompt_tokens);\n        }\n        if let Some(completion_tokens) = usage.get(\"completion_tokens\").and_then(|v| v.as_u64()) {\n            println!(\"   Completion tokens: {}\", completion_tokens);\n        }\n    }\n\n    println!(\"\\n✨ Example completed successfully!\");\n\n    Ok(())\n}\n"}}, "crates/ai-lib-rust/examples/deepseek_chat_stream.rs": {"022cb30c227f111b8e044e5e37b9706339ffcf57": {"t": 1785745854.9920666, "n": 1402, "h": "7c731174e4469464", "c": "//! Real DeepSeek streaming example (no mock).\n//!\n//! Prerequisites:\n//! - Set `DEEPSEEK_API_KEY`\n//! - Ensure `ai-protocol` provider manifests are available (see README paths)\n//!\n//! Run:\n//!   DEEPSEEK_API_KEY=your_key cargo run --example deepseek_chat_stream\n\nuse ai_lib_rust::{AiClient, Message};\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    tracing_subscriber::fmt::init();\n\n    if std::env::var(\"DEEPSEEK_API_KEY\").is_err() {\n        eprintln!(\"Error: DEEPSEEK_API_KEY environment variable is not set.\");\n        eprintln!(\"Run with: DEEPSEEK_API_KEY=your_key cargo run --example deepseek_chat_stream\");\n        std::process::exit(1);\n    }\n\n    let messages = vec![\n        Message::system(\"You are a helpful assistant.\"),\n        Message::user(\"Say hello in one short sentence, then list two numbers.\"),\n    ];\n\n    // Create client directly using provider/model string (protocol-driven)\n    let client = AiClient::new(\"deepseek/deepseek-chat\").await?;\n\n    // Stream then collect into UnifiedResponse\n    let resp = client\n        .chat()\n        .messages(messages)\n        .temperature(0.2)\n        .max_tokens(128)\n        .stream()\n        .execute()\n        .await?;\n\n    println!(\"\\n\\n--- Content ---\\n{}\", resp.content);\n    println!(\"\\n--- Tool calls ---\\n{:#?}\", resp.tool_calls);\n    println!(\"\\n--- Usage ---\\n{:#?}\", resp.usage);\n\n    Ok(())\n}\n"}, "d32d4bc6a5c18cddebc9050cc5746a3496d8f58d": {"t": 1785757086.6150508, "n": 1402, "h": "7c731174e4469464", "c": "//! Real DeepSeek streaming example (no mock).\n//!\n//! Prerequisites:\n//! - Set `DEEPSEEK_API_KEY`\n//! - Ensure `ai-protocol` provider manifests are available (see README paths)\n//!\n//! Run:\n//!   DEEPSEEK_API_KEY=your_key cargo run --example deepseek_chat_stream\n\nuse ai_lib_rust::{AiClient, Message};\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    tracing_subscriber::fmt::init();\n\n    if std::env::var(\"DEEPSEEK_API_KEY\").is_err() {\n        eprintln!(\"Error: DEEPSEEK_API_KEY environment variable is not set.\");\n        eprintln!(\"Run with: DEEPSEEK_API_KEY=your_key cargo run --example deepseek_chat_stream\");\n        std::process::exit(1);\n    }\n\n    let messages = vec![\n        Message::system(\"You are a helpful assistant.\"),\n        Message::user(\"Say hello in one short sentence, then list two numbers.\"),\n    ];\n\n    // Create client directly using provider/model string (protocol-driven)\n    let client = AiClient::new(\"deepseek/deepseek-chat\").await?;\n\n    // Stream then collect into UnifiedResponse\n    let resp = client\n        .chat()\n        .messages(messages)\n        .temperature(0.2)\n        .max_tokens(128)\n        .stream()\n        .execute()\n        .await?;\n\n    println!(\"\\n\\n--- Content ---\\n{}\", resp.content);\n    println!(\"\\n--- Tool calls ---\\n{:#?}\", resp.tool_calls);\n    println!(\"\\n--- Usage ---\\n{:#?}\", resp.usage);\n\n    Ok(())\n}\n"}, "3bc56f0edaab54ac146898dba17ef31b659f3bf8": {"t": 1786068328.431178, "n": 1402, "h": "7c731174e4469464", "c": "//! Real DeepSeek streaming example (no mock).\n//!\n//! Prerequisites:\n//! - Set `DEEPSEEK_API_KEY`\n//! - Ensure `ai-protocol` provider manifests are available (see README paths)\n//!\n//! Run:\n//!   DEEPSEEK_API_KEY=your_key cargo run --example deepseek_chat_stream\n\nuse ai_lib_rust::{AiClient, Message};\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    tracing_subscriber::fmt::init();\n\n    if std::env::var(\"DEEPSEEK_API_KEY\").is_err() {\n        eprintln!(\"Error: DEEPSEEK_API_KEY environment variable is not set.\");\n        eprintln!(\"Run with: DEEPSEEK_API_KEY=your_key cargo run --example deepseek_chat_stream\");\n        std::process::exit(1);\n    }\n\n    let messages = vec![\n        Message::system(\"You are a helpful assistant.\"),\n        Message::user(\"Say hello in one short sentence, then list two numbers.\"),\n    ];\n\n    // Create client directly using provider/model string (protocol-driven)\n    let client = AiClient::new(\"deepseek/deepseek-chat\").await?;\n\n    // Stream then collect into UnifiedResponse\n    let resp = client\n        .chat()\n        .messages(messages)\n        .temperature(0.2)\n        .max_tokens(128)\n        .stream()\n        .execute()\n        .await?;\n\n    println!(\"\\n\\n--- Content ---\\n{}\", resp.content);\n    println!(\"\\n--- Tool calls ---\\n{:#?}\", resp.tool_calls);\n    println!(\"\\n--- Usage ---\\n{:#?}\", resp.usage);\n\n    Ok(())\n}\n"}}, "crates/ai-lib-rust/examples/connectivity_report.rs": {"022cb30c227f111b8e044e5e37b9706339ffcf57": {"t": 1785745871.9222388, "n": 11348, "h": "019dc9a61f1f043e", "c": "//! AI-Protocol connectivity report\n//!\n//! For each provider that has a local API key (env var `{PROVIDER_ID}_API_KEY`),\n//! runs a basic_usage-style chat request and records success or error.\n//! Outputs a report and error analysis with links to provider API references.\n//!\n//! Usage:\n//!   AI_PROTOCOL_DIR=/path/to/ai-protocol cargo run --example connectivity_report\n//! Or with keys set:\n//!   DEEPSEEK_API_KEY=\"...\" GROQ_API_KEY=\"...\" cargo run --example connectivity_report\n\nuse ai_lib_rust::{AiClient, Message};\nuse std::env;\nuse std::time::Instant;\n\n/// One (provider_id, model_id) to test. model_id format: \"provider/model-name\"\n/// (model-name is the key in ai-protocol v1/models/{provider}.yaml or cross-provider key).\n/// Aligned with ai-protocol v1/providers and v1/models.\nconst PROVIDER_MODELS: &[(&str, &str)] = &[\n    (\"deepseek\", \"deepseek/deepseek-chat\"),\n    (\"groq\", \"groq/llama-3.1-8b-instant\"),\n    (\"nvidia\", \"nvidia/meta/llama3-70b\"),\n    (\"minimax\", \"minimax/abab6.5s-chat\"),\n    (\"zhipu\", \"zhipu/glm-4-plus\"),\n    (\"openai\", \"openai/gpt-4o-mini\"),\n    (\"anthropic\", \"anthropic/claude-3-5-sonnet\"),\n    (\n        \"together\",\n        \"together/together/meta-llama/Llama-3-70b-chat-hf\",\n    ),\n    (\"qwen\", \"qwen/qwen-turbo\"),\n    (\"moonshot\", \"moonshot/moonshot-v1-8k\"),\n    (\"mistral\", \"mistral/mistral-small-latest\"),\n    (\"cohere\", \"cohere/command-r-plus-08-2024\"),\n    (\n        \"fireworks\",\n        \"fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\",\n    ),\n    (\n        \"deepinfra\",\n        \"deepinfra/meta-llama/Meta-Llama-3.1-70B-Instruct\",\n    ),\n    (\"lepton\", \"lepton/meta-llama/Meta-Llama-3.1-70B-Instruct\"),\n    (\"sensenova\", \"sensenova/sensenova-v1\"),\n    (\"spark\", \"spark/general\"),\n    (\"yi\", \"yi/yi-large\"),\n    (\"baichuan\", \"baichuan/Baichuan2-Turbo\"),\n    (\"doubao\", \"doubao/doubao-1-5-pro-32k\"),\n    (\"hunyuan\", \"hunyuan/hunyuan-lite\"),\n    (\"tiangong\", \"tiangong/sky-pro\"),\n    (\"siliconflow\", \"siliconflow/SF-Llama3.1-8B-Instruct\"),\n    (\"perplexity\", \"perplexity/llama-3.1-sonar-small-128k-online\"),\n    (\"replicate\", \"replicate/meta/meta-llama-3-70b-instruct\"),\n    (\"cerebras\", \"cerebras/llama3.1-8b\"),\n    (\"ai21\", \"ai21/j3-mini\"),\n    (\"baidu\", \"baidu/ernie-bot-4\"),\n];\n\nfn env_key_for_provider(provider_id: &str) -> String {\n    format!(\"{}_API_KEY\", provider_id.to_uppercase())\n}\n\nfn has_api_key(provider_id: &str) -> bool {\n    env::var(env_key_for_provider(provider_id)).is_ok()\n}\n\n#[derive(Debug)]\nstruct Row {\n    provider_id: String,\n    model_id: String,\n    status: String,\n    duration_ms: u64,\n    error_message: Option<String>,\n}\n\n/// Provider API reference URLs for error analysis.\nfn api_reference_url(provider_id: &str) -> &'static str {\n    match provider_id {\n        \"deepseek\" => \"https://platform.deepseek.com/docs\",\n        \"groq\" => \"https://console.groq.com/docs\",\n        \"nvidia\" => \"https://docs.api.nvidia.com/nim/reference/llm-apis\",\n        \"minimax\" => \"https://platform.minimaxi.com/document/ChatCompletion%20v2\",\n        \"zhipu\" => \"https://open.bigmodel.cn/dev/api\",\n        \"openai\" => \"https://platform.openai.com/docs/api-reference\",\n        \"anthropic\" => \"https://docs.anthropic.com/en/api\",\n        \"openrouter\" => \"https://openrouter.ai/docs\",\n        \"together\" => \"https://docs.together.ai/reference/chat-completions\",\n        \"qwen\" => \"https://help.aliyun.com/zh/model-studio/developer-reference/api-details\",\n        \"moonshot\" => \"https://platform.moonshot.cn/docs\",\n        \"mistral\" => \"https://docs.mistral.ai/api/\",\n        \"cohere\" => \"https://docs.cohere.com/reference/chat\",\n        \"fireworks\" => \"https://docs.fireworks.ai/api-reference\",\n        \"deepinfra\" => \"https://deepinfra.com/docs\",\n        \"lepton\" => \"https://www.lepton.ai/docs\",\n        \"sensenova\" => \"https://platform.sensenova.cn/doc\",\n        \"spark\" => \"https://www.xfyun.cn/doc/spark/Web.html\",\n        \"yi\" => \"https://platform.lingyiwanwu.com/docs\",\n        \"baichuan\" => \"https://platform.baichuan-ai.com/docs\",\n        \"doubao\" => \"https://www.volcengine.com/docs/82379\",\n        \"hunyuan\" => \"https://cloud.tencent.com/document/product/1729\",\n        \"tiangong\" => \"https://help.aliyun.com/zh/model-studio/developer-reference/tongyi-qianwen\",\n        \"siliconflow\" => \"https://docs.siliconflow.cn\",\n        \"perplexity\" => \"https://docs.perplexity.ai/api-docs\",\n        \"replicate\" => \"https://replicate.com/docs/reference/http\",\n        \"cerebras\" => \"https://docs.cerebras.ai/inference-api\",\n        \"ai21\" => \"https://docs.ai21.com/reference/complete\",\n        \"baidu\" => \"https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html\",\n        _ => \"https://github.com/ailib-official/ai-protocol\",\n    }\n}\n\nfn classify_error(err: &str) -> (&'static str, &'static str) {\n    let e = err.to_lowercase();\n    // Check 429 / quota before 401 so \"429 + insufficient_quota\" is not misclassified as auth\n    if e.contains(\"429\")\n        || e.contains(\"rate_limited\")\n        || e.contains(\"insufficient_quota\")\n        || e.contains(\"quota\") && (e.contains(\"exceeded\") || e.contains(\"billing\"))\n    {\n        (\n            \"429 / 限流或配额\",\n            \"请求过于频繁或配额用尽；可重试或检查账单/升级配额。\",\n        )\n    } else if e.contains(\"401\")\n        || e.contains(\"authentication\")\n        || e.contains(\"invalid api key\")\n        || e.contains(\"authorized_error\")\n    {\n        (\"401 / 认证失败\", \"检查 API Key 是否正确、是否过期；确认环境变量名与 manifest 中 token_env 一致（如 DEEPSEEK_API_KEY）。\")\n    } else if e.contains(\"404\") || e.contains(\"not found\") || e.contains(\"not_found\") {\n        (\n            \"404 / 资源不存在\",\n            \"确认 manifest 中 endpoint path 与厂商文档一致；模型 ID 是否在厂商当前可用列表中。\",\n        )\n    } else if e.contains(\"500\")\n        || e.contains(\"503\")\n        || e.contains(\"server_error\")\n        || e.contains(\"overloaded\")\n    {\n        (\"5xx / 服务端错误\", \"厂商服务暂时不可用；稍后重试。\")\n    } else if e.contains(\"timeout\") || e.contains(\"timed out\") {\n        (\"超时\", \"增大 AI_HTTP_TIMEOUT_SECS 或检查网络。\")\n    } else if e.contains(\"protocol\") || (e.contains(\"not found\") && e.contains(\"provider\")) {\n        (\n            \"协议/Manifest 未找到\",\n            \"设置 AI_PROTOCOL_DIR 指向 ai-protocol 目录；或确认该 provider 在 ai-protocol 中存在。\",\n        )\n    } else {\n        (\"其他\", \"对照厂商 API 文档核对请求格式与参数。\")\n    }\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let _ = tracing_subscriber::fmt()\n        .with_env_filter(\n            tracing_subscriber::EnvFilter::from_default_env().add_directive(\"info\".parse()?),\n        )\n        .with_target(false)\n        .try_init();\n\n    // Auto-detect ai-protocol in common relative paths (cross-platform)\n    if env::var(\"AI_PROTOCOL_DIR\").is_err() && env::var(\"AI_PROTOCOL_PATH\").is_err() {\n        let candidates = [\"ai-protocol\", \"../ai-protocol\", \"../../ai-protocol\"];\n        for p in &candidates {\n            if std::path::Path::new(p).join(\"v1/providers\").exists() {\n                env::set_var(\"AI_PROTOCOL_DIR\", *p);\n                println!(\"Using AI_PROTOCOL_DIR={}\\n\", p);\n                break;\n            }\n        }\n    }\n\n    let to_test: Vec<(&str, &str)> = PROVIDER_MODELS\n        .iter()\n        .copied()\n        .filter(|(pid, _)| has_api_key(pid))\n        .collect();\n\n    if to_test.is_empty() {\n        println!(\"No API keys found. Set env vars such as:\");\n        println!(\"  DEEPSEEK_API_KEY, GROQ_API_KEY, NVIDIA_API_KEY, MINIMAX_API_KEY, ZHIPU_API_KEY, OPENAI_API_KEY, ...\");\n        println!(\"(Convention: {{PROVIDER_ID}}_API_KEY, e.g. deepseek -> DEEPSEEK_API_KEY)\\n\");\n        println!(\"Skipped providers (no key):\");\n        for (pid, mid) in PROVIDER_MODELS {\n            println!(\"  {} ({})\", pid, mid);\n        }\n        return Ok(());\n    }\n\n    println!(\"=== AI-Protocol connectivity test ===\\n\");\n    println!(\"Testing {} provider(s) with API key set.\\n\", to_test.len());\n\n    let mut rows: Vec<Row> = Vec::new();\n    for (provider_id, model_id) in &to_test {\n        let start = Instant::now();\n        let (status, error_message) = match run_one(model_id).await {\n            Ok(()) => (\"OK\".to_string(), None),\n            Err(e) => {\n                let msg = e.to_string();\n                (\"FAIL\".to_string(), Some(msg))\n            }\n        };\n        let duration_ms = start.elapsed().as_millis() as u64;\n        rows.push(Row {\n            provider_id: (*provider_id).to_string(),\n            model_id: (*model_id).to_string(),\n            status,\n            duration_ms,\n            error_message,\n        });\n    }\n\n    // Print table\n    println!(\"| Provider   | Model ID (abbr)           | Status | Duration(ms) |\");\n    println!(\"|------------|----------------------------|--------|--------------|\");\n    for r in &rows {\n        let mid_short = if r.model_id.len() > 26 {\n            format!(\"{}...\", &r.model_id[..23])\n        } else {\n            r.model_id.clone()\n        };\n        let err_short = r\n            .error_message\n            .as_ref()\n            .map(|s| {\n                if s.len() > 60 {\n                    format!(\"{}...\", &s[..57])\n                } else {\n                    s.clone()\n                }\n            })\n            .unwrap_or_default();\n        println!(\n            \"| {:<10} | {:<26} | {:<6} | {:<12} |\",\n            r.provider_id, mid_short, r.status, r.duration_ms\n        );\n        if !err_short.is_empty() {\n            println!(\"|            | {} |\", err_short);\n        }\n    }\n\n    // Error analysis\n    let failures: Vec<_> = rows.iter().filter(|r| r.status == \"FAIL\").collect();\n    if !failures.is_empty() {\n        println!(\"\\n=== Error analysis (fact-check against provider API reference) ===\\n\");\n        for r in &failures {\n            let err = r.error_message.as_deref().unwrap_or(\"\");\n            let (kind, suggestion) = classify_error(err);\n            let url = api_reference_url(&r.provider_id);\n            println!(\"[{}] {} — {}\", r.provider_id, kind, suggestion);\n            println!(\"  API reference: {}\", url);\n            println!(\"  Raw error: {}\\n\", err);\n        }\n    }\n\n    let ok_count = rows.iter().filter(|r| r.status == \"OK\").count();\n    println!(\n        \"=== Summary: {} OK, {} FAIL ===\\n\",\n        ok_count,\n        failures.len()\n    );\n\n    if let Ok(path) = env::var(\"CONNECTIVITY_REPORT_OUT\") {\n        let mut out = String::new();\n        out.push_str(\"=== AI-Protocol connectivity report ===\\n\\n\");\n        for r in &rows {\n            out.push_str(&format!(\n                \"{} | {} | {} | {} ms\\n\",\n                r.provider_id, r.model_id, r.status, r.duration_ms\n            ));\n            if let Some(e) = &r.error_message {\n                out.push_str(&format!(\"  Error: {}\\n\", e));\n            }\n        }\n        out.push_str(&format!(\n            \"\\nSummary: {} OK, {} FAIL\\n\",\n            ok_count,\n            failures.len()\n        ));\n        std::fs::write(&path, out).ok();\n        println!(\"Report written to {}\", path);\n    }\n    Ok(())\n}\n\nasync fn run_one(model_id: &str) -> Result<(), Box<dyn std::error::Error>> {\n    let client = AiClient::new(model_id).await?;\n    let messages = vec![\n        Message::system(\"You are a helpful assistant. Reply in one short sentence.\"),\n        Message::user(\"Say hello.\"),\n    ];\n    let _ = client\n        .chat()\n        .messages(messages)\n        .temperature(0.1)\n        .max_tokens(100)\n        .execute()\n        .await?;\n    Ok(())\n}\n"}, "d32d4bc6a5c18cddebc9050cc5746a3496d8f58d": {"t": 1785757086.5315628, "n": 11348, "h": "019dc9a61f1f043e", "c": "//! AI-Protocol connectivity report\n//!\n//! For each provider that has a local API key (env var `{PROVIDER_ID}_API_KEY`),\n//! runs a basic_usage-style chat request and records success or error.\n//! Outputs a report and error analysis with links to provider API references.\n//!\n//! Usage:\n//!   AI_PROTOCOL_DIR=/path/to/ai-protocol cargo run --example connectivity_report\n//! Or with keys set:\n//!   DEEPSEEK_API_KEY=\"...\" GROQ_API_KEY=\"...\" cargo run --example connectivity_report\n\nuse ai_lib_rust::{AiClient, Message};\nuse std::env;\nuse std::time::Instant;\n\n/// One (provider_id, model_id) to test. model_id format: \"provider/model-name\"\n/// (model-name is the key in ai-protocol v1/models/{provider}.yaml or cross-provider key).\n/// Aligned with ai-protocol v1/providers and v1/models.\nconst PROVIDER_MODELS: &[(&str, &str)] = &[\n    (\"deepseek\", \"deepseek/deepseek-chat\"),\n    (\"groq\", \"groq/llama-3.1-8b-instant\"),\n    (\"nvidia\", \"nvidia/meta/llama3-70b\"),\n    (\"minimax\", \"minimax/abab6.5s-chat\"),\n    (\"zhipu\", \"zhipu/glm-4-plus\"),\n    (\"openai\", \"openai/gpt-4o-mini\"),\n    (\"anthropic\", \"anthropic/claude-3-5-sonnet\"),\n    (\n        \"together\",\n        \"together/together/meta-llama/Llama-3-70b-chat-hf\",\n    ),\n    (\"qwen\", \"qwen/qwen-turbo\"),\n    (\"moonshot\", \"moonshot/moonshot-v1-8k\"),\n    (\"mistral\", \"mistral/mistral-small-latest\"),\n    (\"cohere\", \"cohere/command-r-plus-08-2024\"),\n    (\n        \"fireworks\",\n        \"fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\",\n    ),\n    (\n        \"deepinfra\",\n        \"deepinfra/meta-llama/Meta-Llama-3.1-70B-Instruct\",\n    ),\n    (\"lepton\", \"lepton/meta-llama/Meta-Llama-3.1-70B-Instruct\"),\n    (\"sensenova\", \"sensenova/sensenova-v1\"),\n    (\"spark\", \"spark/general\"),\n    (\"yi\", \"yi/yi-large\"),\n    (\"baichuan\", \"baichuan/Baichuan2-Turbo\"),\n    (\"doubao\", \"doubao/doubao-1-5-pro-32k\"),\n    (\"hunyuan\", \"hunyuan/hunyuan-lite\"),\n    (\"tiangong\", \"tiangong/sky-pro\"),\n    (\"siliconflow\", \"siliconflow/SF-Llama3.1-8B-Instruct\"),\n    (\"perplexity\", \"perplexity/llama-3.1-sonar-small-128k-online\"),\n    (\"replicate\", \"replicate/meta/meta-llama-3-70b-instruct\"),\n    (\"cerebras\", \"cerebras/llama3.1-8b\"),\n    (\"ai21\", \"ai21/j3-mini\"),\n    (\"baidu\", \"baidu/ernie-bot-4\"),\n];\n\nfn env_key_for_provider(provider_id: &str) -> String {\n    format!(\"{}_API_KEY\", provider_id.to_uppercase())\n}\n\nfn has_api_key(provider_id: &str) -> bool {\n    env::var(env_key_for_provider(provider_id)).is_ok()\n}\n\n#[derive(Debug)]\nstruct Row {\n    provider_id: String,\n    model_id: String,\n    status: String,\n    duration_ms: u64,\n    error_message: Option<String>,\n}\n\n/// Provider API reference URLs for error analysis.\nfn api_reference_url(provider_id: &str) -> &'static str {\n    match provider_id {\n        \"deepseek\" => \"https://platform.deepseek.com/docs\",\n        \"groq\" => \"https://console.groq.com/docs\",\n        \"nvidia\" => \"https://docs.api.nvidia.com/nim/reference/llm-apis\",\n        \"minimax\" => \"https://platform.minimaxi.com/document/ChatCompletion%20v2\",\n        \"zhipu\" => \"https://open.bigmodel.cn/dev/api\",\n        \"openai\" => \"https://platform.openai.com/docs/api-reference\",\n        \"anthropic\" => \"https://docs.anthropic.com/en/api\",\n        \"openrouter\" => \"https://openrouter.ai/docs\",\n        \"together\" => \"https://docs.together.ai/reference/chat-completions\",\n        \"qwen\" => \"https://help.aliyun.com/zh/model-studio/developer-reference/api-details\",\n        \"moonshot\" => \"https://platform.moonshot.cn/docs\",\n        \"mistral\" => \"https://docs.mistral.ai/api/\",\n        \"cohere\" => \"https://docs.cohere.com/reference/chat\",\n        \"fireworks\" => \"https://docs.fireworks.ai/api-reference\",\n        \"deepinfra\" => \"https://deepinfra.com/docs\",\n        \"lepton\" => \"https://www.lepton.ai/docs\",\n        \"sensenova\" => \"https://platform.sensenova.cn/doc\",\n        \"spark\" => \"https://www.xfyun.cn/doc/spark/Web.html\",\n        \"yi\" => \"https://platform.lingyiwanwu.com/docs\",\n        \"baichuan\" => \"https://platform.baichuan-ai.com/docs\",\n        \"doubao\" => \"https://www.volcengine.com/docs/82379\",\n        \"hunyuan\" => \"https://cloud.tencent.com/document/product/1729\",\n        \"tiangong\" => \"https://help.aliyun.com/zh/model-studio/developer-reference/tongyi-qianwen\",\n        \"siliconflow\" => \"https://docs.siliconflow.cn\",\n        \"perplexity\" => \"https://docs.perplexity.ai/api-docs\",\n        \"replicate\" => \"https://replicate.com/docs/reference/http\",\n        \"cerebras\" => \"https://docs.cerebras.ai/inference-api\",\n        \"ai21\" => \"https://docs.ai21.com/reference/complete\",\n        \"baidu\" => \"https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html\",\n        _ => \"https://github.com/ailib-official/ai-protocol\",\n    }\n}\n\nfn classify_error(err: &str) -> (&'static str, &'static str) {\n    let e = err.to_lowercase();\n    // Check 429 / quota before 401 so \"429 + insufficient_quota\" is not misclassified as auth\n    if e.contains(\"429\")\n        || e.contains(\"rate_limited\")\n        || e.contains(\"insufficient_quota\")\n        || e.contains(\"quota\") && (e.contains(\"exceeded\") || e.contains(\"billing\"))\n    {\n        (\n            \"429 / 限流或配额\",\n            \"请求过于频繁或配额用尽；可重试或检查账单/升级配额。\",\n        )\n    } else if e.contains(\"401\")\n        || e.contains(\"authentication\")\n        || e.contains(\"invalid api key\")\n        || e.contains(\"authorized_error\")\n    {\n        (\"401 / 认证失败\", \"检查 API Key 是否正确、是否过期；确认环境变量名与 manifest 中 token_env 一致（如 DEEPSEEK_API_KEY）。\")\n    } else if e.contains(\"404\") || e.contains(\"not found\") || e.contains(\"not_found\") {\n        (\n            \"404 / 资源不存在\",\n            \"确认 manifest 中 endpoint path 与厂商文档一致；模型 ID 是否在厂商当前可用列表中。\",\n        )\n    } else if e.contains(\"500\")\n        || e.contains(\"503\")\n        || e.contains(\"server_error\")\n        || e.contains(\"overloaded\")\n    {\n        (\"5xx / 服务端错误\", \"厂商服务暂时不可用；稍后重试。\")\n    } else if e.contains(\"timeout\") || e.contains(\"timed out\") {\n        (\"超时\", \"增大 AI_HTTP_TIMEOUT_SECS 或检查网络。\")\n    } else if e.contains(\"protocol\") || (e.contains(\"not found\") && e.contains(\"provider\")) {\n        (\n            \"协议/Manifest 未找到\",\n            \"设置 AI_PROTOCOL_DIR 指向 ai-protocol 目录；或确认该 provider 在 ai-protocol 中存在。\",\n        )\n    } else {\n        (\"其他\", \"对照厂商 API 文档核对请求格式与参数。\")\n    }\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let _ = tracing_subscriber::fmt()\n        .with_env_filter(\n            tracing_subscriber::EnvFilter::from_default_env().add_directive(\"info\".parse()?),\n        )\n        .with_target(false)\n        .try_init();\n\n    // Auto-detect ai-protocol in common relative paths (cross-platform)\n    if env::var(\"AI_PROTOCOL_DIR\").is_err() && env::var(\"AI_PROTOCOL_PATH\").is_err() {\n        let candidates = [\"ai-protocol\", \"../ai-protocol\", \"../../ai-protocol\"];\n        for p in &candidates {\n            if std::path::Path::new(p).join(\"v1/providers\").exists() {\n                env::set_var(\"AI_PROTOCOL_DIR\", *p);\n                println!(\"Using AI_PROTOCOL_DIR={}\\n\", p);\n                break;\n            }\n        }\n    }\n\n    let to_test: Vec<(&str, &str)> = PROVIDER_MODELS\n        .iter()\n        .copied()\n        .filter(|(pid, _)| has_api_key(pid))\n        .collect();\n\n    if to_test.is_empty() {\n        println!(\"No API keys found. Set env vars such as:\");\n        println!(\"  DEEPSEEK_API_KEY, GROQ_API_KEY, NVIDIA_API_KEY, MINIMAX_API_KEY, ZHIPU_API_KEY, OPENAI_API_KEY, ...\");\n        println!(\"(Convention: {{PROVIDER_ID}}_API_KEY, e.g. deepseek -> DEEPSEEK_API_KEY)\\n\");\n        println!(\"Skipped providers (no key):\");\n        for (pid, mid) in PROVIDER_MODELS {\n            println!(\"  {} ({})\", pid, mid);\n        }\n        return Ok(());\n    }\n\n    println!(\"=== AI-Protocol connectivity test ===\\n\");\n    println!(\"Testing {} provider(s) with API key set.\\n\", to_test.len());\n\n    let mut rows: Vec<Row> = Vec::new();\n    for (provider_id, model_id) in &to_test {\n        let start = Instant::now();\n        let (status, error_message) = match run_one(model_id).await {\n            Ok(()) => (\"OK\".to_string(), None),\n            Err(e) => {\n                let msg = e.to_string();\n                (\"FAIL\".to_string(), Some(msg))\n            }\n        };\n        let duration_ms = start.elapsed().as_millis() as u64;\n        rows.push(Row {\n            provider_id: (*provider_id).to_string(),\n            model_id: (*model_id).to_string(),\n            status,\n            duration_ms,\n            error_message,\n        });\n    }\n\n    // Print table\n    println!(\"| Provider   | Model ID (abbr)           | Status | Duration(ms) |\");\n    println!(\"|------------|----------------------------|--------|--------------|\");\n    for r in &rows {\n        let mid_short = if r.model_id.len() > 26 {\n            format!(\"{}...\", &r.model_id[..23])\n        } else {\n            r.model_id.clone()\n        };\n        let err_short = r\n            .error_message\n            .as_ref()\n            .map(|s| {\n                if s.len() > 60 {\n                    format!(\"{}...\", &s[..57])\n                } else {\n                    s.clone()\n                }\n            })\n            .unwrap_or_default();\n        println!(\n            \"| {:<10} | {:<26} | {:<6} | {:<12} |\",\n            r.provider_id, mid_short, r.status, r.duration_ms\n        );\n        if !err_short.is_empty() {\n            println!(\"|            | {} |\", err_short);\n        }\n    }\n\n    // Error analysis\n    let failures: Vec<_> = rows.iter().filter(|r| r.status == \"FAIL\").collect();\n    if !failures.is_empty() {\n        println!(\"\\n=== Error analysis (fact-check against provider API reference) ===\\n\");\n        for r in &failures {\n            let err = r.error_message.as_deref().unwrap_or(\"\");\n            let (kind, suggestion) = classify_error(err);\n            let url = api_reference_url(&r.provider_id);\n            println!(\"[{}] {} — {}\", r.provider_id, kind, suggestion);\n            println!(\"  API reference: {}\", url);\n            println!(\"  Raw error: {}\\n\", err);\n        }\n    }\n\n    let ok_count = rows.iter().filter(|r| r.status == \"OK\").count();\n    println!(\n        \"=== Summary: {} OK, {} FAIL ===\\n\",\n        ok_count,\n        failures.len()\n    );\n\n    if let Ok(path) = env::var(\"CONNECTIVITY_REPORT_OUT\") {\n        let mut out = String::new();\n        out.push_str(\"=== AI-Protocol connectivity report ===\\n\\n\");\n        for r in &rows {\n            out.push_str(&format!(\n                \"{} | {} | {} | {} ms\\n\",\n                r.provider_id, r.model_id, r.status, r.duration_ms\n            ));\n            if let Some(e) = &r.error_message {\n                out.push_str(&format!(\"  Error: {}\\n\", e));\n            }\n        }\n        out.push_str(&format!(\n            \"\\nSummary: {} OK, {} FAIL\\n\",\n            ok_count,\n            failures.len()\n        ));\n        std::fs::write(&path, out).ok();\n        println!(\"Report written to {}\", path);\n    }\n    Ok(())\n}\n\nasync fn run_one(model_id: &str) -> Result<(), Box<dyn std::error::Error>> {\n    let client = AiClient::new(model_id).await?;\n    let messages = vec![\n        Message::system(\"You are a helpful assistant. Reply in one short sentence.\"),\n        Message::user(\"Say hello.\"),\n    ];\n    let _ = client\n        .chat()\n        .messages(messages)\n        .temperature(0.1)\n        .max_tokens(100)\n        .execute()\n        .await?;\n    Ok(())\n}\n"}, "3bc56f0edaab54ac146898dba17ef31b659f3bf8": {"t": 1786068332.5205314, "n": 11348, "h": "019dc9a61f1f043e", "c": "//! AI-Protocol connectivity report\n//!\n//! For each provider that has a local API key (env var `{PROVIDER_ID}_API_KEY`),\n//! runs a basic_usage-style chat request and records success or error.\n//! Outputs a report and error analysis with links to provider API references.\n//!\n//! Usage:\n//!   AI_PROTOCOL_DIR=/path/to/ai-protocol cargo run --example connectivity_report\n//! Or with keys set:\n//!   DEEPSEEK_API_KEY=\"...\" GROQ_API_KEY=\"...\" cargo run --example connectivity_report\n\nuse ai_lib_rust::{AiClient, Message};\nuse std::env;\nuse std::time::Instant;\n\n/// One (provider_id, model_id) to test. model_id format: \"provider/model-name\"\n/// (model-name is the key in ai-protocol v1/models/{provider}.yaml or cross-provider key).\n/// Aligned with ai-protocol v1/providers and v1/models.\nconst PROVIDER_MODELS: &[(&str, &str)] = &[\n    (\"deepseek\", \"deepseek/deepseek-chat\"),\n    (\"groq\", \"groq/llama-3.1-8b-instant\"),\n    (\"nvidia\", \"nvidia/meta/llama3-70b\"),\n    (\"minimax\", \"minimax/abab6.5s-chat\"),\n    (\"zhipu\", \"zhipu/glm-4-plus\"),\n    (\"openai\", \"openai/gpt-4o-mini\"),\n    (\"anthropic\", \"anthropic/claude-3-5-sonnet\"),\n    (\n        \"together\",\n        \"together/together/meta-llama/Llama-3-70b-chat-hf\",\n    ),\n    (\"qwen\", \"qwen/qwen-turbo\"),\n    (\"moonshot\", \"moonshot/moonshot-v1-8k\"),\n    (\"mistral\", \"mistral/mistral-small-latest\"),\n    (\"cohere\", \"cohere/command-r-plus-08-2024\"),\n    (\n        \"fireworks\",\n        \"fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\",\n    ),\n    (\n        \"deepinfra\",\n        \"deepinfra/meta-llama/Meta-Llama-3.1-70B-Instruct\",\n    ),\n    (\"lepton\", \"lepton/meta-llama/Meta-Llama-3.1-70B-Instruct\"),\n    (\"sensenova\", \"sensenova/sensenova-v1\"),\n    (\"spark\", \"spark/general\"),\n    (\"yi\", \"yi/yi-large\"),\n    (\"baichuan\", \"baichuan/Baichuan2-Turbo\"),\n    (\"doubao\", \"doubao/doubao-1-5-pro-32k\"),\n    (\"hunyuan\", \"hunyuan/hunyuan-lite\"),\n    (\"tiangong\", \"tiangong/sky-pro\"),\n    (\"siliconflow\", \"siliconflow/SF-Llama3.1-8B-Instruct\"),\n    (\"perplexity\", \"perplexity/llama-3.1-sonar-small-128k-online\"),\n    (\"replicate\", \"replicate/meta/meta-llama-3-70b-instruct\"),\n    (\"cerebras\", \"cerebras/llama3.1-8b\"),\n    (\"ai21\", \"ai21/j3-mini\"),\n    (\"baidu\", \"baidu/ernie-bot-4\"),\n];\n\nfn env_key_for_provider(provider_id: &str) -> String {\n    format!(\"{}_API_KEY\", provider_id.to_uppercase())\n}\n\nfn has_api_key(provider_id: &str) -> bool {\n    env::var(env_key_for_provider(provider_id)).is_ok()\n}\n\n#[derive(Debug)]\nstruct Row {\n    provider_id: String,\n    model_id: String,\n    status: String,\n    duration_ms: u64,\n    error_message: Option<String>,\n}\n\n/// Provider API reference URLs for error analysis.\nfn api_reference_url(provider_id: &str) -> &'static str {\n    match provider_id {\n        \"deepseek\" => \"https://platform.deepseek.com/docs\",\n        \"groq\" => \"https://console.groq.com/docs\",\n        \"nvidia\" => \"https://docs.api.nvidia.com/nim/reference/llm-apis\",\n        \"minimax\" => \"https://platform.minimaxi.com/document/ChatCompletion%20v2\",\n        \"zhipu\" => \"https://open.bigmodel.cn/dev/api\",\n        \"openai\" => \"https://platform.openai.com/docs/api-reference\",\n        \"anthropic\" => \"https://docs.anthropic.com/en/api\",\n        \"openrouter\" => \"https://openrouter.ai/docs\",\n        \"together\" => \"https://docs.together.ai/reference/chat-completions\",\n        \"qwen\" => \"https://help.aliyun.com/zh/model-studio/developer-reference/api-details\",\n        \"moonshot\" => \"https://platform.moonshot.cn/docs\",\n        \"mistral\" => \"https://docs.mistral.ai/api/\",\n        \"cohere\" => \"https://docs.cohere.com/reference/chat\",\n        \"fireworks\" => \"https://docs.fireworks.ai/api-reference\",\n        \"deepinfra\" => \"https://deepinfra.com/docs\",\n        \"lepton\" => \"https://www.lepton.ai/docs\",\n        \"sensenova\" => \"https://platform.sensenova.cn/doc\",\n        \"spark\" => \"https://www.xfyun.cn/doc/spark/Web.html\",\n        \"yi\" => \"https://platform.lingyiwanwu.com/docs\",\n        \"baichuan\" => \"https://platform.baichuan-ai.com/docs\",\n        \"doubao\" => \"https://www.volcengine.com/docs/82379\",\n        \"hunyuan\" => \"https://cloud.tencent.com/document/product/1729\",\n        \"tiangong\" => \"https://help.aliyun.com/zh/model-studio/developer-reference/tongyi-qianwen\",\n        \"siliconflow\" => \"https://docs.siliconflow.cn\",\n        \"perplexity\" => \"https://docs.perplexity.ai/api-docs\",\n        \"replicate\" => \"https://replicate.com/docs/reference/http\",\n        \"cerebras\" => \"https://docs.cerebras.ai/inference-api\",\n        \"ai21\" => \"https://docs.ai21.com/reference/complete\",\n        \"baidu\" => \"https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html\",\n        _ => \"https://github.com/ailib-official/ai-protocol\",\n    }\n}\n\nfn classify_error(err: &str) -> (&'static str, &'static str) {\n    let e = err.to_lowercase();\n    // Check 429 / quota before 401 so \"429 + insufficient_quota\" is not misclassified as auth\n    if e.contains(\"429\")\n        || e.contains(\"rate_limited\")\n        || e.contains(\"insufficient_quota\")\n        || e.contains(\"quota\") && (e.contains(\"exceeded\") || e.contains(\"billing\"))\n    {\n        (\n            \"429 / 限流或配额\",\n            \"请求过于频繁或配额用尽；可重试或检查账单/升级配额。\",\n        )\n    } else if e.contains(\"401\")\n        || e.contains(\"authentication\")\n        || e.contains(\"invalid api key\")\n        || e.contains(\"authorized_error\")\n    {\n        (\"401 / 认证失败\", \"检查 API Key 是否正确、是否过期；确认环境变量名与 manifest 中 token_env 一致（如 DEEPSEEK_API_KEY）。\")\n    } else if e.contains(\"404\") || e.contains(\"not found\") || e.contains(\"not_found\") {\n        (\n            \"404 / 资源不存在\",\n            \"确认 manifest 中 endpoint path 与厂商文档一致；模型 ID 是否在厂商当前可用列表中。\",\n        )\n    } else if e.contains(\"500\")\n        || e.contains(\"503\")\n        || e.contains(\"server_error\")\n        || e.contains(\"overloaded\")\n    {\n        (\"5xx / 服务端错误\", \"厂商服务暂时不可用；稍后重试。\")\n    } else if e.contains(\"timeout\") || e.contains(\"timed out\") {\n        (\"超时\", \"增大 AI_HTTP_TIMEOUT_SECS 或检查网络。\")\n    } else if e.contains(\"protocol\") || (e.contains(\"not found\") && e.contains(\"provider\")) {\n        (\n            \"协议/Manifest 未找到\",\n            \"设置 AI_PROTOCOL_DIR 指向 ai-protocol 目录；或确认该 provider 在 ai-protocol 中存在。\",\n        )\n    } else {\n        (\"其他\", \"对照厂商 API 文档核对请求格式与参数。\")\n    }\n}\n\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let _ = tracing_subscriber::fmt()\n        .with_env_filter(\n            tracing_subscriber::EnvFilter::from_default_env().add_directive(\"info\".parse()?),\n        )\n        .with_target(false)\n        .try_init();\n\n    // Auto-detect ai-protocol in common relative paths (cross-platform)\n    if env::var(\"AI_PROTOCOL_DIR\").is_err() && env::var(\"AI_PROTOCOL_PATH\").is_err() {\n        let candidates = [\"ai-protocol\", \"../ai-protocol\", \"../../ai-protocol\"];\n        for p in &candidates {\n            if std::path::Path::new(p).join(\"v1/providers\").exists() {\n                env::set_var(\"AI_PROTOCOL_DIR\", *p);\n                println!(\"Using AI_PROTOCOL_DIR={}\\n\", p);\n                break;\n            }\n        }\n    }\n\n    let to_test: Vec<(&str, &str)> = PROVIDER_MODELS\n        .iter()\n        .copied()\n        .filter(|(pid, _)| has_api_key(pid))\n        .collect();\n\n    if to_test.is_empty() {\n        println!(\"No API keys found. Set env vars such as:\");\n        println!(\"  DEEPSEEK_API_KEY, GROQ_API_KEY, NVIDIA_API_KEY, MINIMAX_API_KEY, ZHIPU_API_KEY, OPENAI_API_KEY, ...\");\n        println!(\"(Convention: {{PROVIDER_ID}}_API_KEY, e.g. deepseek -> DEEPSEEK_API_KEY)\\n\");\n        println!(\"Skipped providers (no key):\");\n        for (pid, mid) in PROVIDER_MODELS {\n            println!(\"  {} ({})\", pid, mid);\n        }\n        return Ok(());\n    }\n\n    println!(\"=== AI-Protocol connectivity test ===\\n\");\n    println!(\"Testing {} provider(s) with API key set.\\n\", to_test.len());\n\n    let mut rows: Vec<Row> = Vec::new();\n    for (provider_id, model_id) in &to_test {\n        let start = Instant::now();\n        let (status, error_message) = match run_one(model_id).await {\n            Ok(()) => (\"OK\".to_string(), None),\n            Err(e) => {\n                let msg = e.to_string();\n                (\"FAIL\".to_string(), Some(msg))\n            }\n        };\n        let duration_ms = start.elapsed().as_millis() as u64;\n        rows.push(Row {\n            provider_id: (*provider_id).to_string(),\n            model_id: (*model_id).to_string(),\n            status,\n            duration_ms,\n            error_message,\n        });\n    }\n\n    // Print table\n    println!(\"| Provider   | Model ID (abbr)           | Status | Duration(ms) |\");\n    println!(\"|------------|----------------------------|--------|--------------|\");\n    for r in &rows {\n        let mid_short = if r.model_id.len() > 26 {\n            format!(\"{}...\", &r.model_id[..23])\n        } else {\n            r.model_id.clone()\n        };\n        let err_short = r\n            .error_message\n            .as_ref()\n            .map(|s| {\n                if s.len() > 60 {\n                    format!(\"{}...\", &s[..57])\n                } else {\n                    s.clone()\n                }\n            })\n            .unwrap_or_default();\n        println!(\n            \"| {:<10} | {:<26} | {:<6} | {:<12} |\",\n            r.provider_id, mid_short, r.status, r.duration_ms\n        );\n        if !err_short.is_empty() {\n            println!(\"|            | {} |\", err_short);\n        }\n    }\n\n    // Error analysis\n    let failures: Vec<_> = rows.iter().filter(|r| r.status == \"FAIL\").collect();\n    if !failures.is_empty() {\n        println!(\"\\n=== Error analysis (fact-check against provider API reference) ===\\n\");\n        for r in &failures {\n            let err = r.error_message.as_deref().unwrap_or(\"\");\n            let (kind, suggestion) = classify_error(err);\n            let url = api_reference_url(&r.provider_id);\n            println!(\"[{}] {} — {}\", r.provider_id, kind, suggestion);\n            println!(\"  API reference: {}\", url);\n            println!(\"  Raw error: {}\\n\", err);\n        }\n    }\n\n    let ok_count = rows.iter().filter(|r| r.status == \"OK\").count();\n    println!(\n        \"=== Summary: {} OK, {} FAIL ===\\n\",\n        ok_count,\n        failures.len()\n    );\n\n    if let Ok(path) = env::var(\"CONNECTIVITY_REPORT_OUT\") {\n        let mut out = String::new();\n        out.push_str(\"=== AI-Protocol connectivity report ===\\n\\n\");\n        for r in &rows {\n            out.push_str(&format!(\n                \"{} | {} | {} | {} ms\\n\",\n                r.provider_id, r.model_id, r.status, r.duration_ms\n            ));\n            if let Some(e) = &r.error_message {\n                out.push_str(&format!(\"  Error: {}\\n\", e));\n            }\n        }\n        out.push_str(&format!(\n            \"\\nSummary: {} OK, {} FAIL\\n\",\n            ok_count,\n            failures.len()\n        ));\n        std::fs::write(&path, out).ok();\n        println!(\"Report written to {}\", path);\n    }\n    Ok(())\n}\n\nasync fn run_one(model_id: &str) -> Result<(), Box<dyn std::error::Error>> {\n    let client = AiClient::new(model_id).await?;\n    let messages = vec![\n        Message::system(\"You are a helpful assistant. Reply in one short sentence.\"),\n        Message::user(\"Say hello.\"),\n    ];\n    let _ = client\n        .chat()\n        .messages(messages)\n        .temperature(0.1)\n        .max_tokens(100)\n        .execute()\n        .await?;\n    Ok(())\n}\n"}}}}