LLMClient

helios_engine::llm

Struct LLMClient 

Source 
pub struct LLMClient { /* private fields */ }

Implementations§

Source§

impl LLMClient

Source

pub async fn new(provider_type: LLMProviderType) -> Result<Self>

Examples found in repository?
examples/direct_llm_usage.rs (line 62)
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}
84
85/// Example 2: Multi-turn conversation with context
86async fn conversation_with_context() -> helios_engine::Result<()> {
87    let llm_config = LLMConfig {
88        model_name: "gpt-3.5-turbo".to_string(),
89        base_url: "https://api.openai.com/v1".to_string(),
90        api_key: std::env::var("OPENAI_API_KEY")
91            .unwrap_or_else(|_| "your-api-key-here".to_string()),
92        temperature: 0.7,
93        max_tokens: 2048,
94    };
95
96    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
97
98    // Use ChatSession to manage conversation
99    let mut session = ChatSession::new()
100        .with_system_prompt("You are a helpful math tutor. Give brief, clear explanations.");
101
102    // First turn
103    println!("Turn 1:");
104    session.add_user_message("What is 15 * 23?");
105    print!("  User: What is 15 * 23?\n  ");
106
107    match client.chat(session.get_messages(), None).await {
108        Ok(response) => {
109            session.add_assistant_message(&response.content);
110            println!("Assistant: {}", response.content);
111        }
112        Err(e) => {
113            println!("Error: {}", e);
114            return Ok(());
115        }
116    }
117
118    // Second turn (with context from first turn)
119    println!("\nTurn 2:");
120    session.add_user_message("Now divide that by 5.");
121    print!("  User: Now divide that by 5.\n  ");
122
123    match client.chat(session.get_messages(), None).await {
124        Ok(response) => {
125            session.add_assistant_message(&response.content);
126            println!("Assistant: {}", response.content);
127        }
128        Err(e) => {
129            println!("Error: {}", e);
130        }
131    }
132
133    println!("\n💡 Notice how the assistant remembered the result from the first calculation!");
134
135    Ok(())
136}
137
138/// Example 3: Information about using different providers
139fn different_providers_info() {
140    println!("You can use Helios with various LLM providers:\n");
141
142    println!("🔵 OpenAI:");
143    println!("   LLMConfig {{");
144    println!("       model_name: \"gpt-4\".to_string(),");
145    println!("       base_url: \"https://api.openai.com/v1\".to_string(),");
146    println!("       api_key: env::var(\"OPENAI_API_KEY\").unwrap(),");
147    println!("       temperature: 0.7,");
148    println!("       max_tokens: 2048,");
149    println!("   }}\n");
150
151    println!("🟢 Local LM Studio:");
152    println!("   LLMConfig {{");
153    println!("       model_name: \"local-model\".to_string(),");
154    println!("       base_url: \"http://localhost:1234/v1\".to_string(),");
155    println!("       api_key: \"not-needed\".to_string(),");
156    println!("       temperature: 0.7,");
157    println!("       max_tokens: 2048,");
158    println!("   }}\n");
159
160    println!("🦙 Ollama:");
161    println!("   LLMConfig {{");
162    println!("       model_name: \"llama2\".to_string(),");
163    println!("       base_url: \"http://localhost:11434/v1\".to_string(),");
164    println!("       api_key: \"not-needed\".to_string(),");
165    println!("       temperature: 0.7,");
166    println!("       max_tokens: 2048,");
167    println!("   }}\n");
168
169    println!("🔷 Azure OpenAI:");
170    println!("   LLMConfig {{");
171    println!("       model_name: \"gpt-35-turbo\".to_string(),");
172    println!("       base_url: \"https://your-resource.openai.azure.com/...\".to_string(),");
173    println!("       api_key: env::var(\"AZURE_OPENAI_KEY\").unwrap(),");
174    println!("       temperature: 0.7,");
175    println!("       max_tokens: 2048,");
176    println!("   }}\n");
177}
178
179/// Example 4: Interactive chat session
180async fn interactive_chat() -> helios_engine::Result<()> {
181    let llm_config = LLMConfig {
182        model_name: "gpt-3.5-turbo".to_string(),
183        base_url: "https://api.openai.com/v1".to_string(),
184        api_key: std::env::var("OPENAI_API_KEY")
185            .unwrap_or_else(|_| "your-api-key-here".to_string()),
186        temperature: 0.7,
187        max_tokens: 2048,
188    };
189
190    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
191    let mut session =
192        ChatSession::new().with_system_prompt("You are a friendly and helpful AI assistant.");
193
194    println!("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
195
196    loop {
197        print!("You: ");
198        io::stdout().flush()?;
199
200        let mut input = String::new();
201        io::stdin().read_line(&mut input)?;
202        let input = input.trim();
203
204        if input.is_empty() {
205            continue;
206        }
207
208        if input == "exit" || input == "quit" {
209            println!("\n👋 Goodbye!");
210            break;
211        }
212
213        // Special commands
214        if input == "clear" {
215            session.clear();
216            println!("🧹 Conversation cleared!\n");
217            continue;
218        }
219
220        if input == "history" {
221            println!("\n📜 Conversation history:");
222            for (i, msg) in session.messages.iter().enumerate() {
223                println!("  {}. {:?}: {}", i + 1, msg.role, msg.content);
224            }
225            println!();
226            continue;
227        }
228
229        session.add_user_message(input);
230
231        print!("Assistant: ");
232        io::stdout().flush()?;
233
234        match client.chat(session.get_messages(), None).await {
235            Ok(response) => {
236                session.add_assistant_message(&response.content);
237                println!("{}\n", response.content);
238            }
239            Err(e) => {
240                println!("\n❌ Error: {}", e);
241                println!("   (Make sure OPENAI_API_KEY is set correctly)\n");
242                // Remove the last user message since it failed
243                session.messages.pop();
244            }
245        }
246    }
247
248    Ok(())
249}
More examples
Hide additional examples
examples/streaming_chat.rs (line 24)
10async fn main() -> helios_engine::Result<()> {
11    println!("🚀 Helios Engine - Streaming Example");
12    println!("=====================================\n");
13
14    // Setup LLM configuration
15    let llm_config = LLMConfig {
16        model_name: "gpt-3.5-turbo".to_string(),
17        base_url: "https://api.openai.com/v1".to_string(),
18        api_key: std::env::var("OPENAI_API_KEY")
19            .unwrap_or_else(|_| "your-api-key-here".to_string()),
20        temperature: 0.7,
21        max_tokens: 2048,
22    };
23
24    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
25
26    println!("Example 1: Simple Streaming Response");
27    println!("======================================\n");
28
29    let messages = vec![
30        ChatMessage::system("You are a helpful assistant."),
31        ChatMessage::user("Write a short poem about coding."),
32    ];
33
34    print!("Assistant: ");
35    io::stdout().flush()?;
36
37    let response = client
38        .chat_stream(messages, None, |chunk| {
39            print!("{}", chunk);
40            io::stdout().flush().unwrap();
41        })
42        .await?;
43
44    println!("\n\n");
45
46    println!("Example 2: Interactive Streaming Chat");
47    println!("======================================\n");
48
49    let mut session = ChatSession::new().with_system_prompt("You are a helpful coding assistant.");
50
51    let questions = vec![
52        "What is Rust?",
53        "What are its main benefits?",
54        "Show me a simple example.",
55    ];
56
57    for question in questions {
58        println!("User: {}", question);
59        session.add_user_message(question);
60
61        print!("Assistant: ");
62        io::stdout().flush()?;
63
64        let response = client
65            .chat_stream(session.get_messages(), None, |chunk| {
66                print!("{}", chunk);
67                io::stdout().flush().unwrap();
68            })
69            .await?;
70
71        session.add_assistant_message(&response.content);
72        println!("\n");
73    }
74
75    println!("\nExample 3: Streaming with Thinking Tags");
76    println!("=========================================\n");
77    println!("When using models that support thinking tags (like o1),");
78    println!("you can detect and display them during streaming.\n");
79
80    struct ThinkingTracker {
81        in_thinking: bool,
82        thinking_buffer: String,
83    }
84
85    impl ThinkingTracker {
86        fn new() -> Self {
87            Self {
88                in_thinking: false,
89                thinking_buffer: String::new(),
90            }
91        }
92
93        fn process_chunk(&mut self, chunk: &str) -> String {
94            let mut output = String::new();
95            let mut chars = chunk.chars().peekable();
96
97            while let Some(c) = chars.next() {
98                if c == '<' {
99                    let remaining: String = chars.clone().collect();
100                    if remaining.starts_with("thinking>") {
101                        self.in_thinking = true;
102                        self.thinking_buffer.clear();
103                        output.push_str("\n💭 [Thinking");
104                        for _ in 0..9 {
105                            chars.next();
106                        }
107                        continue;
108                    } else if remaining.starts_with("/thinking>") {
109                        self.in_thinking = false;
110                        output.push_str("]\n");
111                        for _ in 0..10 {
112                            chars.next();
113                        }
114                        continue;
115                    }
116                }
117
118                if self.in_thinking {
119                    self.thinking_buffer.push(c);
120                    if self.thinking_buffer.len() % 3 == 0 {
121                        output.push('.');
122                    }
123                } else {
124                    output.push(c);
125                }
126            }
127
128            output
129        }
130    }
131
132    let messages = vec![ChatMessage::user(
133        "Solve this problem: What is 15 * 234 + 89?",
134    )];
135
136    let mut tracker = ThinkingTracker::new();
137    print!("Assistant: ");
138    io::stdout().flush()?;
139
140    let _response = client
141        .chat_stream(messages, None, |chunk| {
142            let output = tracker.process_chunk(chunk);
143            print!("{}", output);
144            io::stdout().flush().unwrap();
145        })
146        .await?;
147
148    println!("\n\n✅ Streaming examples completed!");
149    println!("\nKey benefits of streaming:");
150    println!("  • Real-time response display");
151    println!("  • Better user experience for long responses");
152    println!("  • Ability to show thinking/reasoning process");
153    println!("  • Early cancellation possible (future feature)");
154
155    Ok(())
156}
Source

pub fn provider_type(&self) -> &LLMProviderType

Source§

impl LLMClient

Source

pub async fn chat( &self, messages: Vec<ChatMessage>, tools: Option<Vec<ToolDefinition>>, ) -> Result<ChatMessage>

Examples found in repository?
examples/direct_llm_usage.rs (line 72)
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}
84
85/// Example 2: Multi-turn conversation with context
86async fn conversation_with_context() -> helios_engine::Result<()> {
87    let llm_config = LLMConfig {
88        model_name: "gpt-3.5-turbo".to_string(),
89        base_url: "https://api.openai.com/v1".to_string(),
90        api_key: std::env::var("OPENAI_API_KEY")
91            .unwrap_or_else(|_| "your-api-key-here".to_string()),
92        temperature: 0.7,
93        max_tokens: 2048,
94    };
95
96    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
97
98    // Use ChatSession to manage conversation
99    let mut session = ChatSession::new()
100        .with_system_prompt("You are a helpful math tutor. Give brief, clear explanations.");
101
102    // First turn
103    println!("Turn 1:");
104    session.add_user_message("What is 15 * 23?");
105    print!("  User: What is 15 * 23?\n  ");
106
107    match client.chat(session.get_messages(), None).await {
108        Ok(response) => {
109            session.add_assistant_message(&response.content);
110            println!("Assistant: {}", response.content);
111        }
112        Err(e) => {
113            println!("Error: {}", e);
114            return Ok(());
115        }
116    }
117
118    // Second turn (with context from first turn)
119    println!("\nTurn 2:");
120    session.add_user_message("Now divide that by 5.");
121    print!("  User: Now divide that by 5.\n  ");
122
123    match client.chat(session.get_messages(), None).await {
124        Ok(response) => {
125            session.add_assistant_message(&response.content);
126            println!("Assistant: {}", response.content);
127        }
128        Err(e) => {
129            println!("Error: {}", e);
130        }
131    }
132
133    println!("\n💡 Notice how the assistant remembered the result from the first calculation!");
134
135    Ok(())
136}
137
138/// Example 3: Information about using different providers
139fn different_providers_info() {
140    println!("You can use Helios with various LLM providers:\n");
141
142    println!("🔵 OpenAI:");
143    println!("   LLMConfig {{");
144    println!("       model_name: \"gpt-4\".to_string(),");
145    println!("       base_url: \"https://api.openai.com/v1\".to_string(),");
146    println!("       api_key: env::var(\"OPENAI_API_KEY\").unwrap(),");
147    println!("       temperature: 0.7,");
148    println!("       max_tokens: 2048,");
149    println!("   }}\n");
150
151    println!("🟢 Local LM Studio:");
152    println!("   LLMConfig {{");
153    println!("       model_name: \"local-model\".to_string(),");
154    println!("       base_url: \"http://localhost:1234/v1\".to_string(),");
155    println!("       api_key: \"not-needed\".to_string(),");
156    println!("       temperature: 0.7,");
157    println!("       max_tokens: 2048,");
158    println!("   }}\n");
159
160    println!("🦙 Ollama:");
161    println!("   LLMConfig {{");
162    println!("       model_name: \"llama2\".to_string(),");
163    println!("       base_url: \"http://localhost:11434/v1\".to_string(),");
164    println!("       api_key: \"not-needed\".to_string(),");
165    println!("       temperature: 0.7,");
166    println!("       max_tokens: 2048,");
167    println!("   }}\n");
168
169    println!("🔷 Azure OpenAI:");
170    println!("   LLMConfig {{");
171    println!("       model_name: \"gpt-35-turbo\".to_string(),");
172    println!("       base_url: \"https://your-resource.openai.azure.com/...\".to_string(),");
173    println!("       api_key: env::var(\"AZURE_OPENAI_KEY\").unwrap(),");
174    println!("       temperature: 0.7,");
175    println!("       max_tokens: 2048,");
176    println!("   }}\n");
177}
178
179/// Example 4: Interactive chat session
180async fn interactive_chat() -> helios_engine::Result<()> {
181    let llm_config = LLMConfig {
182        model_name: "gpt-3.5-turbo".to_string(),
183        base_url: "https://api.openai.com/v1".to_string(),
184        api_key: std::env::var("OPENAI_API_KEY")
185            .unwrap_or_else(|_| "your-api-key-here".to_string()),
186        temperature: 0.7,
187        max_tokens: 2048,
188    };
189
190    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
191    let mut session =
192        ChatSession::new().with_system_prompt("You are a friendly and helpful AI assistant.");
193
194    println!("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
195
196    loop {
197        print!("You: ");
198        io::stdout().flush()?;
199
200        let mut input = String::new();
201        io::stdin().read_line(&mut input)?;
202        let input = input.trim();
203
204        if input.is_empty() {
205            continue;
206        }
207
208        if input == "exit" || input == "quit" {
209            println!("\n👋 Goodbye!");
210            break;
211        }
212
213        // Special commands
214        if input == "clear" {
215            session.clear();
216            println!("🧹 Conversation cleared!\n");
217            continue;
218        }
219
220        if input == "history" {
221            println!("\n📜 Conversation history:");
222            for (i, msg) in session.messages.iter().enumerate() {
223                println!("  {}. {:?}: {}", i + 1, msg.role, msg.content);
224            }
225            println!();
226            continue;
227        }
228
229        session.add_user_message(input);
230
231        print!("Assistant: ");
232        io::stdout().flush()?;
233
234        match client.chat(session.get_messages(), None).await {
235            Ok(response) => {
236                session.add_assistant_message(&response.content);
237                println!("{}\n", response.content);
238            }
239            Err(e) => {
240                println!("\n❌ Error: {}", e);
241                println!("   (Make sure OPENAI_API_KEY is set correctly)\n");
242                // Remove the last user message since it failed
243                session.messages.pop();
244            }
245        }
246    }
247
248    Ok(())
249}
Source

pub async fn chat_stream<F>( &self, messages: Vec<ChatMessage>, tools: Option<Vec<ToolDefinition>>, on_chunk: F, ) -> Result<ChatMessage>
where F: FnMut(&str) + Send,

Examples found in repository?
examples/streaming_chat.rs (lines 38-41)
10async fn main() -> helios_engine::Result<()> {
11    println!("🚀 Helios Engine - Streaming Example");
12    println!("=====================================\n");
13
14    // Setup LLM configuration
15    let llm_config = LLMConfig {
16        model_name: "gpt-3.5-turbo".to_string(),
17        base_url: "https://api.openai.com/v1".to_string(),
18        api_key: std::env::var("OPENAI_API_KEY")
19            .unwrap_or_else(|_| "your-api-key-here".to_string()),
20        temperature: 0.7,
21        max_tokens: 2048,
22    };
23
24    let client = LLMClient::new(helios_engine::llm::LLMProviderType::Remote(llm_config)).await?;
25
26    println!("Example 1: Simple Streaming Response");
27    println!("======================================\n");
28
29    let messages = vec![
30        ChatMessage::system("You are a helpful assistant."),
31        ChatMessage::user("Write a short poem about coding."),
32    ];
33
34    print!("Assistant: ");
35    io::stdout().flush()?;
36
37    let response = client
38        .chat_stream(messages, None, |chunk| {
39            print!("{}", chunk);
40            io::stdout().flush().unwrap();
41        })
42        .await?;
43
44    println!("\n\n");
45
46    println!("Example 2: Interactive Streaming Chat");
47    println!("======================================\n");
48
49    let mut session = ChatSession::new().with_system_prompt("You are a helpful coding assistant.");
50
51    let questions = vec![
52        "What is Rust?",
53        "What are its main benefits?",
54        "Show me a simple example.",
55    ];
56
57    for question in questions {
58        println!("User: {}", question);
59        session.add_user_message(question);
60
61        print!("Assistant: ");
62        io::stdout().flush()?;
63
64        let response = client
65            .chat_stream(session.get_messages(), None, |chunk| {
66                print!("{}", chunk);
67                io::stdout().flush().unwrap();
68            })
69            .await?;
70
71        session.add_assistant_message(&response.content);
72        println!("\n");
73    }
74
75    println!("\nExample 3: Streaming with Thinking Tags");
76    println!("=========================================\n");
77    println!("When using models that support thinking tags (like o1),");
78    println!("you can detect and display them during streaming.\n");
79
80    struct ThinkingTracker {
81        in_thinking: bool,
82        thinking_buffer: String,
83    }
84
85    impl ThinkingTracker {
86        fn new() -> Self {
87            Self {
88                in_thinking: false,
89                thinking_buffer: String::new(),
90            }
91        }
92
93        fn process_chunk(&mut self, chunk: &str) -> String {
94            let mut output = String::new();
95            let mut chars = chunk.chars().peekable();
96
97            while let Some(c) = chars.next() {
98                if c == '<' {
99                    let remaining: String = chars.clone().collect();
100                    if remaining.starts_with("thinking>") {
101                        self.in_thinking = true;
102                        self.thinking_buffer.clear();
103                        output.push_str("\n💭 [Thinking");
104                        for _ in 0..9 {
105                            chars.next();
106                        }
107                        continue;
108                    } else if remaining.starts_with("/thinking>") {
109                        self.in_thinking = false;
110                        output.push_str("]\n");
111                        for _ in 0..10 {
112                            chars.next();
113                        }
114                        continue;
115                    }
116                }
117
118                if self.in_thinking {
119                    self.thinking_buffer.push(c);
120                    if self.thinking_buffer.len() % 3 == 0 {
121                        output.push('.');
122                    }
123                } else {
124                    output.push(c);
125                }
126            }
127
128            output
129        }
130    }
131
132    let messages = vec![ChatMessage::user(
133        "Solve this problem: What is 15 * 234 + 89?",
134    )];
135
136    let mut tracker = ThinkingTracker::new();
137    print!("Assistant: ");
138    io::stdout().flush()?;
139
140    let _response = client
141        .chat_stream(messages, None, |chunk| {
142            let output = tracker.process_chunk(chunk);
143            print!("{}", output);
144            io::stdout().flush().unwrap();
145        })
146        .await?;
147
148    println!("\n\n✅ Streaming examples completed!");
149    println!("\nKey benefits of streaming:");
150    println!("  • Real-time response display");
151    println!("  • Better user experience for long responses");
152    println!("  • Ability to show thinking/reasoning process");
153    println!("  • Early cancellation possible (future feature)");
154
155    Ok(())
156}

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impl LLMProvider for LLMClient

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fn generate<'life0, 'async_trait>( &'life0 self, request: LLMRequest, ) -> Pin<Box<dyn Future<Output = Result<LLMResponse>> + Send + 'async_trait>>
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