feat:(first commit)created repository and complete 0.1.0
This commit is contained in:
345
src/llm/openai.rs
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345
src/llm/openai.rs
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use super::{create_http_client, LlmProvider};
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use anyhow::{bail, Context, Result};
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use async_trait::async_trait;
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use serde::{Deserialize, Serialize};
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use std::time::Duration;
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/// OpenAI API client
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pub struct OpenAiClient {
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base_url: String,
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api_key: String,
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model: String,
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client: reqwest::Client,
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}
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#[derive(Debug, Serialize)]
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struct ChatCompletionRequest {
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model: String,
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messages: Vec<Message>,
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#[serde(skip_serializing_if = "Option::is_none")]
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max_tokens: Option<u32>,
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#[serde(skip_serializing_if = "Option::is_none")]
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temperature: Option<f32>,
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stream: bool,
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}
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#[derive(Debug, Serialize, Deserialize)]
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struct Message {
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role: String,
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content: String,
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}
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#[derive(Debug, Deserialize)]
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struct ChatCompletionResponse {
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choices: Vec<Choice>,
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}
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#[derive(Debug, Deserialize)]
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struct Choice {
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message: Message,
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}
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#[derive(Debug, Deserialize)]
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struct ErrorResponse {
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error: ApiError,
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}
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#[derive(Debug, Deserialize)]
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struct ApiError {
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message: String,
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#[serde(rename = "type")]
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error_type: String,
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}
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impl OpenAiClient {
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/// Create new OpenAI client
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pub fn new(base_url: &str, api_key: &str, model: &str) -> Result<Self> {
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let client = create_http_client(Duration::from_secs(60))?;
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Ok(Self {
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base_url: base_url.trim_end_matches('/').to_string(),
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api_key: api_key.to_string(),
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model: model.to_string(),
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client,
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})
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}
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/// Set timeout
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pub fn with_timeout(mut self, timeout: Duration) -> Result<Self> {
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self.client = create_http_client(timeout)?;
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Ok(self)
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}
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/// List available models
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pub async fn list_models(&self) -> Result<Vec<String>> {
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let url = format!("{}/models", self.base_url);
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let response = self.client
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.get(&url)
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.header("Authorization", format!("Bearer {}", self.api_key))
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.send()
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.await
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.context("Failed to list OpenAI models")?;
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if !response.status().is_success() {
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let status = response.status();
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let text = response.text().await.unwrap_or_default();
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bail!("OpenAI API error: {} - {}", status, text);
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}
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#[derive(Deserialize)]
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struct ModelsResponse {
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data: Vec<Model>,
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}
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#[derive(Deserialize)]
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struct Model {
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id: String,
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}
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let result: ModelsResponse = response
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.json()
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.await
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.context("Failed to parse OpenAI response")?;
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Ok(result.data.into_iter().map(|m| m.id).collect())
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}
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/// Validate API key
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pub async fn validate_key(&self) -> Result<bool> {
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match self.list_models().await {
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Ok(_) => Ok(true),
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Err(e) => {
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let err_str = e.to_string();
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if err_str.contains("401") || err_str.contains("Unauthorized") {
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Ok(false)
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} else {
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Err(e)
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}
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}
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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 OpenAiClient {
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async fn generate(&self, prompt: &str) -> Result<String> {
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let messages = vec![
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Message {
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role: "user".to_string(),
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content: prompt.to_string(),
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},
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];
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self.chat_completion(messages).await
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}
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async fn generate_with_system(&self, system: &str, user: &str) -> Result<String> {
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let mut messages = vec![];
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if !system.is_empty() {
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messages.push(Message {
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role: "system".to_string(),
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content: system.to_string(),
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});
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}
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messages.push(Message {
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role: "user".to_string(),
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content: user.to_string(),
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});
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self.chat_completion(messages).await
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}
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async fn is_available(&self) -> bool {
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self.validate_key().await.unwrap_or(false)
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}
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fn name(&self) -> &str {
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"openai"
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}
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}
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impl OpenAiClient {
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async fn chat_completion(&self, messages: Vec<Message>) -> Result<String> {
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let url = format!("{}/chat/completions", self.base_url);
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let request = ChatCompletionRequest {
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model: self.model.clone(),
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messages,
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max_tokens: Some(500),
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temperature: Some(0.7),
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stream: false,
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};
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let response = self.client
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.post(&url)
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.header("Authorization", format!("Bearer {}", self.api_key))
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.header("Content-Type", "application/json")
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.json(&request)
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.send()
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.await
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.context("Failed to send request to OpenAI")?;
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let status = response.status();
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if !status.is_success() {
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let text = response.text().await.unwrap_or_default();
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// Try to parse error
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if let Ok(error) = serde_json::from_str::<ErrorResponse>(&text) {
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bail!("OpenAI API error: {} ({})", error.error.message, error.error.error_type);
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}
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bail!("OpenAI API error: {} - {}", status, text);
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}
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let result: ChatCompletionResponse = response
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.json()
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.await
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.context("Failed to parse OpenAI response")?;
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result.choices
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.into_iter()
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.next()
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.map(|c| c.message.content.trim().to_string())
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.ok_or_else(|| anyhow::anyhow!("No response from OpenAI"))
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}
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}
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/// Azure OpenAI client (extends OpenAI with Azure-specific config)
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pub struct AzureOpenAiClient {
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endpoint: String,
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api_key: String,
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deployment: String,
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api_version: String,
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client: reqwest::Client,
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}
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impl AzureOpenAiClient {
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/// Create new Azure OpenAI client
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pub fn new(
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endpoint: &str,
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api_key: &str,
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deployment: &str,
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api_version: &str,
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) -> Result<Self> {
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let client = create_http_client(Duration::from_secs(60))?;
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Ok(Self {
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endpoint: endpoint.trim_end_matches('/').to_string(),
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api_key: api_key.to_string(),
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deployment: deployment.to_string(),
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api_version: api_version.to_string(),
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client,
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})
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}
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async fn chat_completion(&self, messages: Vec<Message>) -> Result<String> {
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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 request = ChatCompletionRequest {
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model: self.deployment.clone(),
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messages,
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max_tokens: Some(500),
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temperature: Some(0.7),
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stream: false,
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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(&request)
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.send()
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.await
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.context("Failed to send request to Azure OpenAI")?;
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if !response.status().is_success() {
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let status = response.status();
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let text = response.text().await.unwrap_or_default();
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bail!("Azure OpenAI API error: {} - {}", status, text);
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}
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let result: ChatCompletionResponse = response
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.json()
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.await
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.context("Failed to parse Azure OpenAI response")?;
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result.choices
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.into_iter()
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.next()
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.map(|c| c.message.content.trim().to_string())
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.ok_or_else(|| anyhow::anyhow!("No response from Azure OpenAI"))
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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(&self, prompt: &str) -> Result<String> {
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let messages = vec![
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Message {
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role: "user".to_string(),
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content: prompt.to_string(),
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},
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];
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self.chat_completion(messages).await
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}
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async fn generate_with_system(&self, system: &str, user: &str) -> Result<String> {
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let mut messages = vec![];
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if !system.is_empty() {
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messages.push(Message {
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role: "system".to_string(),
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content: system.to_string(),
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});
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}
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messages.push(Message {
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role: "user".to_string(),
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content: user.to_string(),
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});
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self.chat_completion(messages).await
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}
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async fn is_available(&self) -> bool {
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// Simple check - try to make a minimal request
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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 request = ChatCompletionRequest {
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model: self.deployment.clone(),
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messages: vec![Message {
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role: "user".to_string(),
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content: "Hi".to_string(),
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}],
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max_tokens: Some(5),
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temperature: Some(0.0),
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stream: false,
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};
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match self.client
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.post(&url)
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.header("api-key", &self.api_key)
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.json(&request)
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.send()
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.await
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{
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Ok(response) => response.status().is_success(),
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Err(_) => false,
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}
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}
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fn name(&self) -> &str {
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"azure-openai"
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}
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}
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