ChatMessage

helios_engine::chat

Struct ChatMessage 

Source 
pub struct ChatMessage {
    pub role: Role,
    pub content: String,
    pub name: Option<String>,
    pub tool_calls: Option<Vec<ToolCall>>,
    pub tool_call_id: Option<String>,
}

Fields§

§role: Role§content: String§name: Option<String>§tool_calls: Option<Vec<ToolCall>>§tool_call_id: Option<String>

Implementations§

Source§

impl ChatMessage

Source

pub fn system(content: impl Into<String>) -> Self

Examples found in repository?
examples/direct_llm_usage.rs (line 66)
50async fn simple_call() -> helios_engine::Result<()> {
51    // Create configuration
52    let llm_config = LLMConfig {
53        model_name: "gpt-3.5-turbo".to_string(),
54        base_url: "https://api.openai.com/v1".to_string(),
55        api_key: std::env::var("OPENAI_API_KEY")
56            .unwrap_or_else(|_| "your-api-key-here".to_string()),
57        temperature: 0.7,
58        max_tokens: 2048,
59    };
60
61    // Create client
62    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
63
64    // Prepare messages
65    let messages = vec![
66        ChatMessage::system("You are a helpful assistant that gives concise answers."),
67        ChatMessage::user("What is the capital of France? Answer in one sentence."),
68    ];
69
70    // Make the call
71    println!("Sending request...");
72    match client.chat(messages, None).await {
73        Ok(response) => {
74            println!("✓ Response: {}", response.content);
75        }
76        Err(e) => {
77            println!("✗ Error: {}", e);
78            println!("  (Make sure to set OPENAI_API_KEY environment variable)");
79        }
80    }
81
82    Ok(())
83}
More examples
Hide additional examples
examples/local_streaming.rs (line 38)
12async fn main() -> helios_engine::Result<()> {
13    println!("🚀 Helios Engine - Local Model Streaming Example");
14    println!("=================================================\n");
15
16    // Configure local model
17    let local_config = LocalConfig {
18        huggingface_repo: "unsloth/Qwen2.5-0.5B-Instruct-GGUF".to_string(),
19        model_file: "Qwen2.5-0.5B-Instruct-Q4_K_M.gguf".to_string(),
20        context_size: 2048,
21        temperature: 0.7,
22        max_tokens: 512,
23    };
24
25    println!("📥 Loading local model...");
26    println!("   Repository: {}", local_config.huggingface_repo);
27    println!("   Model: {}\n", local_config.model_file);
28
29    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Local(local_config)).await?;
30
31    println!("✓ Model loaded successfully!\n");
32
33    // Example 1: Simple streaming
34    println!("Example 1: Simple Streaming Response");
35    println!("======================================\n");
36
37    let messages = vec![
38        ChatMessage::system("You are a helpful coding assistant."),
39        ChatMessage::user("Write a short explanation of what Rust is."),
40    ];
41
42    print!("Assistant: ");
43    io::stdout().flush()?;
44
45    let _response = client
46        .chat_stream(messages, None, |chunk| {
47            print!("{}", chunk);
48            io::stdout().flush().unwrap();
49        })
50        .await?;
51
52    println!("\n");
53
54    // Example 2: Multiple questions with streaming
55    println!("Example 2: Interactive Streaming");
56    println!("==================================\n");
57
58    let questions = vec![
59        "What are the main benefits of Rust?",
60        "Give me a simple code example.",
61    ];
62
63    let mut session = helios_engine::ChatSession::new()
64        .with_system_prompt("You are a helpful programming assistant.");
65
66    for question in questions {
67        println!("User: {}", question);
68        session.add_user_message(question);
69
70        print!("Assistant: ");
71        io::stdout().flush()?;
72
73        let response = client
74            .chat_stream(session.get_messages(), None, |chunk| {
75                print!("{}", chunk);
76                io::stdout().flush().unwrap();
77            })
78            .await?;
79
80        session.add_assistant_message(&response.content);
81        println!("\n");
82    }
83
84    println!("✅ Local model streaming completed successfully!");
85    println!("\n💡 Features:");
86    println!("  • Token-by-token streaming for local models");
87    println!("  • Real-time response display (no more instant full responses)");
88    println!("  • Same streaming API for both local and remote models");
89    println!("  • Improved user experience with progressive output");
90
91    Ok(())
92}
examples/streaming_chat.rs (line 32)
12async fn main() -> helios_engine::Result<()> {
13    println!("🚀 Helios Engine - Streaming Example");
14    println!("=====================================\n");
15
16    // Setup LLM configuration
17    let llm_config = LLMConfig {
18        model_name: "gpt-3.5-turbo".to_string(),
19        base_url: "https://api.openai.com/v1".to_string(),
20        api_key: std::env::var("OPENAI_API_KEY")
21            .unwrap_or_else(|_| "your-api-key-here".to_string()),
22        temperature: 0.7,
23        max_tokens: 2048,
24    };
25
26    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
27
28    println!("Example 1: Simple Streaming Response");
29    println!("======================================\n");
30
31    let messages = vec![
32        ChatMessage::system("You are a helpful assistant."),
33        ChatMessage::user("Write a short poem about coding."),
34    ];
35
36    print!("Assistant: ");
37    io::stdout().flush()?;
38
39    let response = client
40        .chat_stream(messages, None, |chunk| {
41            print!("{}", chunk);
42            io::stdout().flush().unwrap();
43        })
44        .await?;
45
46    println!("\n\n");
47
48    println!("Example 2: Interactive Streaming Chat");
49    println!("======================================\n");
50
51    let mut session = ChatSession::new().with_system_prompt("You are a helpful coding assistant.");
52
53    let questions = vec![
54        "What is Rust?",
55        "What are its main benefits?",
56        "Show me a simple example.",
57    ];
58
59    for question in questions {
60        println!("User: {}", question);
61        session.add_user_message(question);
62
63        print!("Assistant: ");
64        io::stdout().flush()?;
65
66        let response = client
67            .chat_stream(session.get_messages(), None, |chunk| {
68                print!("{}", chunk);
69                io::stdout().flush().unwrap();
70            })
71            .await?;
72
73        session.add_assistant_message(&response.content);
74        println!("\n");
75    }
76
77    println!("\nExample 3: Streaming with Thinking Tags");
78    println!("=========================================\n");
79    println!("When using models that support thinking tags (like o1),");
80    println!("you can detect and display them during streaming.\n");
81
82    struct ThinkingTracker {
83        in_thinking: bool,
84        thinking_buffer: String,
85    }
86
87    impl ThinkingTracker {
88        fn new() -> Self {
89            Self {
90                in_thinking: false,
91                thinking_buffer: String::new(),
92            }
93        }
94
95        fn process_chunk(&mut self, chunk: &str) -> String {
96            let mut output = String::new();
97            let mut chars = chunk.chars().peekable();
98
99            while let Some(c) = chars.next() {
100                if c == '<' {
101                    let remaining: String = chars.clone().collect();
102                    if remaining.starts_with("thinking>") {
103                        self.in_thinking = true;
104                        self.thinking_buffer.clear();
105                        output.push_str("\n💭 [Thinking");
106                        for _ in 0..9 {
107                            chars.next();
108                        }
109                        continue;
110                    } else if remaining.starts_with("/thinking>") {
111                        self.in_thinking = false;
112                        output.push_str("]\n");
113                        for _ in 0..10 {
114                            chars.next();
115                        }
116                        continue;
117                    }
118                }
119
120                if self.in_thinking {
121                    self.thinking_buffer.push(c);
122                    if self.thinking_buffer.len() % 3 == 0 {
123                        output.push('.');
124                    }
125                } else {
126                    output.push(c);
127                }
128            }
129
130            output
131        }
132    }
133
134    let messages = vec![ChatMessage::user(
135        "Solve this problem: What is 15 * 234 + 89?",
136    )];
137
138    let mut tracker = ThinkingTracker::new();
139    print!("Assistant: ");
140    io::stdout().flush()?;
141
142    let _response = client
143        .chat_stream(messages, None, |chunk| {
144            let output = tracker.process_chunk(chunk);
145            print!("{}", output);
146            io::stdout().flush().unwrap();
147        })
148        .await?;
149
150    println!("\n\n✅ Streaming examples completed!");
151    println!("\nKey benefits of streaming:");
152    println!("  • Real-time response display");
153    println!("  • Better user experience for long responses");
154    println!("  • Ability to show thinking/reasoning process");
155    println!("  • Early cancellation possible (future feature)");
156
157    Ok(())
158}
Source

pub fn user(content: impl Into<String>) -> Self

Examples found in repository?
examples/direct_llm_usage.rs (line 67)
50async fn simple_call() -> helios_engine::Result<()> {
51    // Create configuration
52    let llm_config = LLMConfig {
53        model_name: "gpt-3.5-turbo".to_string(),
54        base_url: "https://api.openai.com/v1".to_string(),
55        api_key: std::env::var("OPENAI_API_KEY")
56            .unwrap_or_else(|_| "your-api-key-here".to_string()),
57        temperature: 0.7,
58        max_tokens: 2048,
59    };
60
61    // Create client
62    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
63
64    // Prepare messages
65    let messages = vec![
66        ChatMessage::system("You are a helpful assistant that gives concise answers."),
67        ChatMessage::user("What is the capital of France? Answer in one sentence."),
68    ];
69
70    // Make the call
71    println!("Sending request...");
72    match client.chat(messages, None).await {
73        Ok(response) => {
74            println!("✓ Response: {}", response.content);
75        }
76        Err(e) => {
77            println!("✗ Error: {}", e);
78            println!("  (Make sure to set OPENAI_API_KEY environment variable)");
79        }
80    }
81
82    Ok(())
83}
More examples
Hide additional examples
examples/local_streaming.rs (line 39)
12async fn main() -> helios_engine::Result<()> {
13    println!("🚀 Helios Engine - Local Model Streaming Example");
14    println!("=================================================\n");
15
16    // Configure local model
17    let local_config = LocalConfig {
18        huggingface_repo: "unsloth/Qwen2.5-0.5B-Instruct-GGUF".to_string(),
19        model_file: "Qwen2.5-0.5B-Instruct-Q4_K_M.gguf".to_string(),
20        context_size: 2048,
21        temperature: 0.7,
22        max_tokens: 512,
23    };
24
25    println!("📥 Loading local model...");
26    println!("   Repository: {}", local_config.huggingface_repo);
27    println!("   Model: {}\n", local_config.model_file);
28
29    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Local(local_config)).await?;
30
31    println!("✓ Model loaded successfully!\n");
32
33    // Example 1: Simple streaming
34    println!("Example 1: Simple Streaming Response");
35    println!("======================================\n");
36
37    let messages = vec![
38        ChatMessage::system("You are a helpful coding assistant."),
39        ChatMessage::user("Write a short explanation of what Rust is."),
40    ];
41
42    print!("Assistant: ");
43    io::stdout().flush()?;
44
45    let _response = client
46        .chat_stream(messages, None, |chunk| {
47            print!("{}", chunk);
48            io::stdout().flush().unwrap();
49        })
50        .await?;
51
52    println!("\n");
53
54    // Example 2: Multiple questions with streaming
55    println!("Example 2: Interactive Streaming");
56    println!("==================================\n");
57
58    let questions = vec![
59        "What are the main benefits of Rust?",
60        "Give me a simple code example.",
61    ];
62
63    let mut session = helios_engine::ChatSession::new()
64        .with_system_prompt("You are a helpful programming assistant.");
65
66    for question in questions {
67        println!("User: {}", question);
68        session.add_user_message(question);
69
70        print!("Assistant: ");
71        io::stdout().flush()?;
72
73        let response = client
74            .chat_stream(session.get_messages(), None, |chunk| {
75                print!("{}", chunk);
76                io::stdout().flush().unwrap();
77            })
78            .await?;
79
80        session.add_assistant_message(&response.content);
81        println!("\n");
82    }
83
84    println!("✅ Local model streaming completed successfully!");
85    println!("\n💡 Features:");
86    println!("  • Token-by-token streaming for local models");
87    println!("  • Real-time response display (no more instant full responses)");
88    println!("  • Same streaming API for both local and remote models");
89    println!("  • Improved user experience with progressive output");
90
91    Ok(())
92}
examples/streaming_chat.rs (line 33)
12async fn main() -> helios_engine::Result<()> {
13    println!("🚀 Helios Engine - Streaming Example");
14    println!("=====================================\n");
15
16    // Setup LLM configuration
17    let llm_config = LLMConfig {
18        model_name: "gpt-3.5-turbo".to_string(),
19        base_url: "https://api.openai.com/v1".to_string(),
20        api_key: std::env::var("OPENAI_API_KEY")
21            .unwrap_or_else(|_| "your-api-key-here".to_string()),
22        temperature: 0.7,
23        max_tokens: 2048,
24    };
25
26    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
27
28    println!("Example 1: Simple Streaming Response");
29    println!("======================================\n");
30
31    let messages = vec![
32        ChatMessage::system("You are a helpful assistant."),
33        ChatMessage::user("Write a short poem about coding."),
34    ];
35
36    print!("Assistant: ");
37    io::stdout().flush()?;
38
39    let response = client
40        .chat_stream(messages, None, |chunk| {
41            print!("{}", chunk);
42            io::stdout().flush().unwrap();
43        })
44        .await?;
45
46    println!("\n\n");
47
48    println!("Example 2: Interactive Streaming Chat");
49    println!("======================================\n");
50
51    let mut session = ChatSession::new().with_system_prompt("You are a helpful coding assistant.");
52
53    let questions = vec![
54        "What is Rust?",
55        "What are its main benefits?",
56        "Show me a simple example.",
57    ];
58
59    for question in questions {
60        println!("User: {}", question);
61        session.add_user_message(question);
62
63        print!("Assistant: ");
64        io::stdout().flush()?;
65
66        let response = client
67            .chat_stream(session.get_messages(), None, |chunk| {
68                print!("{}", chunk);
69                io::stdout().flush().unwrap();
70            })
71            .await?;
72
73        session.add_assistant_message(&response.content);
74        println!("\n");
75    }
76
77    println!("\nExample 3: Streaming with Thinking Tags");
78    println!("=========================================\n");
79    println!("When using models that support thinking tags (like o1),");
80    println!("you can detect and display them during streaming.\n");
81
82    struct ThinkingTracker {
83        in_thinking: bool,
84        thinking_buffer: String,
85    }
86
87    impl ThinkingTracker {
88        fn new() -> Self {
89            Self {
90                in_thinking: false,
91                thinking_buffer: String::new(),
92            }
93        }
94
95        fn process_chunk(&mut self, chunk: &str) -> String {
96            let mut output = String::new();
97            let mut chars = chunk.chars().peekable();
98
99            while let Some(c) = chars.next() {
100                if c == '<' {
101                    let remaining: String = chars.clone().collect();
102                    if remaining.starts_with("thinking>") {
103                        self.in_thinking = true;
104                        self.thinking_buffer.clear();
105                        output.push_str("\n💭 [Thinking");
106                        for _ in 0..9 {
107                            chars.next();
108                        }
109                        continue;
110                    } else if remaining.starts_with("/thinking>") {
111                        self.in_thinking = false;
112                        output.push_str("]\n");
113                        for _ in 0..10 {
114                            chars.next();
115                        }
116                        continue;
117                    }
118                }
119
120                if self.in_thinking {
121                    self.thinking_buffer.push(c);
122                    if self.thinking_buffer.len() % 3 == 0 {
123                        output.push('.');
124                    }
125                } else {
126                    output.push(c);
127                }
128            }
129
130            output
131        }
132    }
133
134    let messages = vec![ChatMessage::user(
135        "Solve this problem: What is 15 * 234 + 89?",
136    )];
137
138    let mut tracker = ThinkingTracker::new();
139    print!("Assistant: ");
140    io::stdout().flush()?;
141
142    let _response = client
143        .chat_stream(messages, None, |chunk| {
144            let output = tracker.process_chunk(chunk);
145            print!("{}", output);
146            io::stdout().flush().unwrap();
147        })
148        .await?;
149
150    println!("\n\n✅ Streaming examples completed!");
151    println!("\nKey benefits of streaming:");
152    println!("  • Real-time response display");
153    println!("  • Better user experience for long responses");
154    println!("  • Ability to show thinking/reasoning process");
155    println!("  • Early cancellation possible (future feature)");
156
157    Ok(())
158}
Source

pub fn assistant(content: impl Into<String>) -> Self

Source

pub fn tool(content: impl Into<String>, tool_call_id: impl Into<String>) -> Self

Trait Implementations§

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impl Clone for ChatMessage

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fn clone(&self) -> ChatMessage

Returns a duplicate of the value. Read more
1.0.0 · Source§

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for ChatMessage

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl<'de> Deserialize<'de> for ChatMessage

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fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>
where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer. Read more
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impl Serialize for ChatMessage

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fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>
where __S: Serializer,

Serialize this value into the given Serde serializer. Read more

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unsafe fn clone_to_uninit(&self, dest: *mut u8)

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