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This commit implements two major features: 1. Integration Search as Primary AI Data Source - Confluence, ServiceNow, and Azure DevOps searches execute before AI queries - Search results injected as system context for AI providers - Parallel search execution for performance - Webview-based fetch for HttpOnly cookie support - Persistent browser windows maintain authenticated sessions 2. AI Tool-Calling (Function Calling) - Allows AI to automatically execute functions during conversation - Implemented for OpenAI-compatible providers and MSI GenAI - Created add_ado_comment tool for updating Azure DevOps tickets - Iterative tool-calling loop supports multi-step workflows - Extensible architecture for adding new tools Key Files: - src-tauri/src/ai/tools.rs (NEW) - Tool definitions - src-tauri/src/integrations/*_search.rs (NEW) - Integration search modules - src-tauri/src/integrations/webview_fetch.rs (NEW) - HttpOnly cookie workaround - src-tauri/src/commands/ai.rs - Tool execution and integration search - src-tauri/src/ai/openai.rs - Tool-calling for OpenAI and MSI GenAI - All providers updated with tools parameter support Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
91 lines
2.7 KiB
Rust
91 lines
2.7 KiB
Rust
use async_trait::async_trait;
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use std::time::Duration;
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use crate::ai::provider::Provider;
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use crate::ai::{ChatResponse, Message, ProviderInfo, TokenUsage};
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use crate::state::ProviderConfig;
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pub struct MistralProvider;
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#[async_trait]
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impl Provider for MistralProvider {
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fn name(&self) -> &str {
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"mistral"
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}
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fn info(&self) -> ProviderInfo {
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ProviderInfo {
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name: "Mistral AI".to_string(),
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supports_streaming: true,
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models: vec![
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"mistral-large-latest".to_string(),
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"mistral-medium-latest".to_string(),
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"mistral-small-latest".to_string(),
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"open-mistral-nemo".to_string(),
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],
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}
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}
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async fn chat(
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&self,
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messages: Vec<Message>,
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config: &ProviderConfig,
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_tools: Option<Vec<crate::ai::Tool>>,
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) -> anyhow::Result<ChatResponse> {
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// Mistral uses OpenAI-compatible format
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let client = reqwest::Client::builder()
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.timeout(Duration::from_secs(60))
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.build()?;
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let base_url = if config.api_url.is_empty() {
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"https://api.mistral.ai/v1".to_string()
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} else {
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config.api_url.trim_end_matches('/').to_string()
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};
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let url = format!("{base_url}/chat/completions");
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let body = serde_json::json!({
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"model": config.model,
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"messages": messages,
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"max_tokens": 4096,
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});
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let resp = client
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.post(&url)
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.header(
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"Authorization",
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format!("Bearer {api_key}", api_key = config.api_key),
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)
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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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if !resp.status().is_success() {
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let status = resp.status();
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let text = resp.text().await?;
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anyhow::bail!("Mistral API error {status}: {text}");
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}
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let json: serde_json::Value = resp.json().await?;
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let content = json["choices"][0]["message"]["content"]
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.as_str()
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.ok_or_else(|| anyhow::anyhow!("No content in Mistral response"))?
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.to_string();
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let usage = json.get("usage").and_then(|u| {
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Some(TokenUsage {
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prompt_tokens: u["prompt_tokens"].as_u64()? as u32,
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completion_tokens: u["completion_tokens"].as_u64()? as u32,
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total_tokens: u["total_tokens"].as_u64()? as u32,
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})
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});
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Ok(ChatResponse {
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content,
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model: config.model.clone(),
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usage,
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tool_calls: None,
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})
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}
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}
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