llm_link/
service.rs

1use crate::llm::{Client, Model, Response};
2use crate::settings::LlmBackendSettings;
3use anyhow::Result;
4use llm_connector::types::Tool;
5use llm_connector::StreamFormat;
6use tokio_stream::wrappers::UnboundedReceiverStream;
7
8/// Service layer - Business logic for LLM operations
9///
10/// This layer sits between handlers (HTTP) and client (LLM communication).
11/// It handles:
12/// - Model selection and validation
13/// - Delegating to the appropriate client methods
14/// - Business-level error handling
15pub struct Service {
16    client: Client,
17    model: String,
18}
19
20impl Service {
21    /// Create a new service with the specified backend configuration
22    pub fn new(config: &LlmBackendSettings) -> Result<Self> {
23        let client = Client::new(config)?;
24        let model = match config {
25            LlmBackendSettings::OpenAI { model, .. } => model.clone(),
26            LlmBackendSettings::Anthropic { model, .. } => model.clone(),
27            LlmBackendSettings::Ollama { model, .. } => model.clone(),
28            LlmBackendSettings::Aliyun { model, .. } => model.clone(),
29            LlmBackendSettings::Zhipu { model, .. } => model.clone(),
30            LlmBackendSettings::Volcengine { model, .. } => model.clone(),
31            LlmBackendSettings::Tencent { model, .. } => model.clone(),
32            LlmBackendSettings::Longcat { model, .. } => model.clone(),
33        };
34
35        Ok(Self { client, model })
36    }
37
38    /// Chat with a specific model (non-streaming)
39    ///
40    /// If model is None, uses the default model from configuration.
41    pub async fn chat(
42        &self,
43        model: Option<&str>,
44        messages: Vec<llm_connector::types::Message>,
45        tools: Option<Vec<Tool>>,
46    ) -> Result<Response> {
47        let model = model.unwrap_or(&self.model);
48        self.client.chat(model, messages, tools).await
49    }
50
51    /// Chat with streaming (Ollama format)
52    ///
53    /// If model is None, uses the default model from configuration.
54    pub async fn chat_stream_ollama(
55        &self,
56        model: Option<&str>,
57        messages: Vec<llm_connector::types::Message>,
58        format: StreamFormat,
59    ) -> Result<UnboundedReceiverStream<String>> {
60        let model = model.unwrap_or(&self.model);
61        self.client.chat_stream_with_format(model, messages, format).await
62    }
63
64    /// Chat with streaming (OpenAI format)
65    ///
66    /// If model is None, uses the default model from configuration.
67    pub async fn chat_stream_openai(
68        &self,
69        model: Option<&str>,
70        messages: Vec<llm_connector::types::Message>,
71        tools: Option<Vec<Tool>>,
72        format: StreamFormat,
73    ) -> Result<UnboundedReceiverStream<String>> {
74        let model = model.unwrap_or(&self.model);
75        self.client.chat_stream_openai(model, messages, tools, format).await
76    }
77
78    /// List available models
79    pub async fn list_models(&self) -> Result<Vec<Model>> {
80        self.client.list_models().await
81    }
82
83    /// Validate if a model is available
84    pub async fn validate_model(&self, model: &str) -> Result<bool> {
85        let available_models = self.client.list_models().await?;
86        Ok(available_models.iter().any(|m| m.id == model))
87    }
88}
llm_link/service.rs

llm_link/
service.rs
