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    #[allow(dead_code)]
18    model: String,
19}
20
21impl Service {
22    /// Create a new service with the specified backend configuration
23    pub fn new(config: &LlmBackendSettings) -> Result<Self> {
24        let client = Client::new(config)?;
25        let model = match config {
26            LlmBackendSettings::OpenAI { model, .. } => model.clone(),
27            LlmBackendSettings::Anthropic { model, .. } => model.clone(),
28            LlmBackendSettings::Ollama { model, .. } => model.clone(),
29            LlmBackendSettings::Aliyun { model, .. } => model.clone(),
30            LlmBackendSettings::Zhipu { model, .. } => model.clone(),
31            LlmBackendSettings::Volcengine { model, .. } => model.clone(),
32            LlmBackendSettings::Tencent { model, .. } => model.clone(),
33            LlmBackendSettings::Longcat { model, .. } => model.clone(),
34        };
35
36        Ok(Self { client, model })
37    }
38
39    /// Chat with a specific model (non-streaming)
40    ///
41    /// If model is None, uses the default model from configuration.
42    #[allow(dead_code)]
43    pub async fn chat(
44        &self,
45        model: Option<&str>,
46        messages: Vec<llm_connector::types::Message>,
47        tools: Option<Vec<Tool>>,
48    ) -> Result<Response> {
49        let model = model.unwrap_or(&self.model);
50        self.client.chat(model, messages, tools).await
51    }
52
53    /// Chat with streaming (Ollama format)
54    ///
55    /// If model is None, uses the default model from configuration.
56    #[allow(dead_code)]
57    pub async fn chat_stream_ollama(
58        &self,
59        model: Option<&str>,
60        messages: Vec<llm_connector::types::Message>,
61        format: StreamFormat,
62    ) -> Result<UnboundedReceiverStream<String>> {
63        let model = model.unwrap_or(&self.model);
64        self.client.chat_stream_with_format(model, messages, format).await
65    }
66
67    /// Chat with streaming (OpenAI format)
68    ///
69    /// If model is None, uses the default model from configuration.
70    #[allow(dead_code)]
71    pub async fn chat_stream_openai(
72        &self,
73        model: Option<&str>,
74        messages: Vec<llm_connector::types::Message>,
75        tools: Option<Vec<Tool>>,
76        format: StreamFormat,
77    ) -> Result<UnboundedReceiverStream<String>> {
78        let model = model.unwrap_or(&self.model);
79        self.client.chat_stream_openai(model, messages, tools, format).await
80    }
81
82    /// List available models
83    pub async fn list_models(&self) -> Result<Vec<Model>> {
84        self.client.list_models().await
85    }
86
87    /// Validate if a model is available
88    #[allow(dead_code)]
89    pub async fn validate_model(&self, model: &str) -> Result<bool> {
90        let available_models = self.client.list_models().await?;
91        Ok(available_models.iter().any(|m| m.id == model))
92    }
93}
llm_link/service.rs

llm_link/
service.rs
