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        };
33
34        Ok(Self { client, model })
35    }
36
37    /// Chat with a specific model (non-streaming)
38    ///
39    /// If model is None, uses the default model from configuration.
40    pub async fn chat(
41        &self,
42        model: Option<&str>,
43        messages: Vec<llm_connector::types::Message>,
44        tools: Option<Vec<Tool>>,
45    ) -> Result<Response> {
46        let model = model.unwrap_or(&self.model);
47        self.client.chat(model, messages, tools).await
48    }
49
50    /// Chat with streaming (Ollama format)
51    ///
52    /// If model is None, uses the default model from configuration.
53    pub async fn chat_stream_ollama(
54        &self,
55        model: Option<&str>,
56        messages: Vec<llm_connector::types::Message>,
57        format: StreamFormat,
58    ) -> Result<UnboundedReceiverStream<String>> {
59        let model = model.unwrap_or(&self.model);
60        self.client.chat_stream_with_format(model, messages, format).await
61    }
62
63    /// Chat with streaming (OpenAI format)
64    ///
65    /// If model is None, uses the default model from configuration.
66    pub async fn chat_stream_openai(
67        &self,
68        model: Option<&str>,
69        messages: Vec<llm_connector::types::Message>,
70        tools: Option<Vec<Tool>>,
71        format: StreamFormat,
72    ) -> Result<UnboundedReceiverStream<String>> {
73        let model = model.unwrap_or(&self.model);
74        self.client.chat_stream_openai(model, messages, tools, format).await
75    }
76
77    /// List available models
78    pub async fn list_models(&self) -> Result<Vec<Model>> {
79        self.client.list_models().await
80    }
81
82    /// Validate if a model is available
83    pub async fn validate_model(&self, model: &str) -> Result<bool> {
84        let available_models = self.client.list_models().await?;
85        Ok(available_models.iter().any(|m| m.id == model))
86    }
87}
llm_link/service.rs

llm_link/
service.rs
