Rodrigo Rodriguez (Pragmatismo)
fea1574518
- Updated `BootstrapManager` to use `AppConfig::from_env().expect(...)` and `AppConfig::from_database(...).expect(...)` ensuring failures are explicit rather than silently ignored. - Refactored error propagation in bootstrap flow to use `?` where appropriate, improving reliability of configuration loading. - Added import of `llm_models` in `bot` module and introduced `ConfigManager` usage to fetch the LLM model identifier at runtime. - Integrated dynamic LLM model handler selection via `llm_models::get_handler(&model)`. - Replaced static environment variable retrieval for embedding configuration with runtime
103 lines
2.9 KiB
Rust
103 lines
2.9 KiB
Rust
use async_trait::async_trait;
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use reqwest::Client;
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use serde_json::Value;
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use std::sync::Arc;
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use crate::tools::ToolManager;
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use super::LLMProvider;
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pub struct AzureOpenAIClient {
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endpoint: String,
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api_key: String,
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api_version: String,
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deployment: String,
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client: Client,
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}
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impl AzureOpenAIClient {
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pub fn new(endpoint: String, api_key: String, api_version: String, deployment: String) -> Self {
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Self {
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endpoint,
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api_key,
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api_version,
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deployment,
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client: Client::new(),
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}
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}
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}
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#[async_trait]
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impl LLMProvider for AzureOpenAIClient {
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async fn generate(
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&self,
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prompt: &str,
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_config: &Value,
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) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
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let url = format!(
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"{}/openai/deployments/{}/chat/completions?api-version={}",
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self.endpoint, self.deployment, self.api_version
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);
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let body = serde_json::json!({
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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],
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"temperature": 0.7,
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"max_tokens": 1000,
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"top_p": 1.0,
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"frequency_penalty": 0.0,
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"presence_penalty": 0.0
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});
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let response = self.client
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.post(&url)
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.header("api-key", &self.api_key)
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.header("Content-Type", "application/json")
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.json(&body)
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.send()
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.await?;
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let result: Value = response.json().await?;
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if let Some(choice) = result["choices"].get(0) {
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Ok(choice["message"]["content"].as_str().unwrap_or("").to_string())
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} else {
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Err("No response from Azure OpenAI".into())
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}
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}
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async fn generate_stream(
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&self,
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prompt: &str,
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_config: &Value,
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tx: tokio::sync::mpsc::Sender<String>,
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) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
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let content = self.generate(prompt, _config).await?;
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let _ = tx.send(content).await;
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Ok(())
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}
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async fn generate_with_tools(
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&self,
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prompt: &str,
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_config: &Value,
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available_tools: &[String],
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_tool_manager: Arc<ToolManager>,
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_session_id: &str,
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_user_id: &str,
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) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
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let tools_info = if available_tools.is_empty() {
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String::new()
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} else {
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format!("\n\nAvailable tools: {}.", available_tools.join(", "))
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};
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let enhanced_prompt = format!("{}{}", prompt, tools_info);
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self.generate(&enhanced_prompt, _config).await
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}
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async fn cancel_job(
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&self,
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_session_id: &str,
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) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
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Ok(())
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}
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}
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