feat: add LiteLLM provider and cover letter generation

- Add LiteLLM provider implementing AiProvider trait
- Support both Ollama and LiteLLM via LLM_PROVIDER env var
- Add cover letter generation endpoint
- Improve chat orchestrator with better intent detection
- Add search_kb, explain_plan_limits, check_ai_pack_balance intents
- Add LiteLLM config env vars

src/chat/models.rs

@@ -12,4 +12,6 @@ pub struct ChatMessageResponse {
     pub intent: String,
     pub reply: String,
     pub data: serde_json::Value,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub conversation_id: Option<String>,
 }

src/chat/orchestrator.rs

@@ -42,6 +42,7 @@ impl ChatOrchestrator {
         request: ChatMessageRequest,
     ) -> Result<ChatMessageResponse, AppError> {
         let intent = classify_intent(&request.message);
+        let conversation_id = request.conversation_id.clone();
 
         match intent.as_str() {
             "job_description_generation" => {
@@ -60,9 +61,10 @@ impl ChatOrchestrator {
                     .await?;
 
                 Ok(ChatMessageResponse {
-                    intent,
+                    intent: intent.clone(),
                     reply: "Generated a draft job description.".to_string(),
                     data: serde_json::to_value(jd).unwrap_or(serde_json::Value::Null),
+                    conversation_id: conversation_id.clone(),
                 })
             }
             "form_filling_assistance" => {
@@ -75,23 +77,32 @@ impl ChatOrchestrator {
                     .await?;
 
                 Ok(ChatMessageResponse {
-                    intent,
+                    intent: intent.clone(),
                     reply: extracted.suggested_next_step.clone(),
                     data: serde_json::to_value(extracted).unwrap_or(serde_json::Value::Null),
+                    conversation_id: conversation_id.clone(),
                 })
             }
-            "help_article_retrieval" => {
+            "search_kb" => {
                 let matches = self.help_center.search(&request.message).await?;
                 let reply = if matches.is_empty() {
-                    "No help article match found yet.".to_string()
+                    "No help article match found. Try rephrasing your question.".to_string()
                 } else {
-                    format!("Found {} help articles.", matches.len())
+                    let mut response = format!("Found {} help article(s):\n\n", matches.len());
+                    for (i, article) in matches.iter().take(5).enumerate() {
+                        response.push_str(&format!("{}. **{}**\n{}\n\n", i + 1, article.title, article.summary));
+                    }
+                    if matches.len() > 5 {
+                        response.push_str(&format!("...and {} more articles.", matches.len() - 5));
+                    }
+                    response
                 };
 
                 Ok(ChatMessageResponse {
-                    intent,
+                    intent: intent.clone(),
                     reply,
                     data: serde_json::json!({ "matches": matches }),
+                    conversation_id: conversation_id.clone(),
                 })
             }
             "support_ticket_creation" => {
@@ -103,7 +114,7 @@ impl ChatOrchestrator {
                         priority: "medium".to_string(),
                         category: "general".to_string(),
                         user_id: request.user_id.unwrap_or_else(|| "anonymous".to_string()),
-                        conversation_id: request.conversation_id,
+                        conversation_id: conversation_id.clone(),
                         source: Some("chatbot".to_string()),
                         tags: Some(vec!["chat".to_string()]),
                         metadata: None,
@@ -111,16 +122,75 @@ impl ChatOrchestrator {
                     .await?;
 
                 Ok(ChatMessageResponse {
-                    intent,
-                    reply: format!("Support ticket created: {}", created.ticket_id),
+                    intent: intent.clone(),
+                    reply: format!("Support ticket created: {}. Our team will respond shortly.", created.ticket_id),
                     data: serde_json::to_value(created).unwrap_or(serde_json::Value::Null),
+                    conversation_id: conversation_id.clone(),
+                })
+            }
+            "explain_plan_limits" => {
+                let reply = "Your AI plan determines how many AI actions you can use per month.\n\n\
+                    - **Free AI**: 10 actions/month\n\
+                    - **Starter AI**: 100 actions/month\n\
+                    - **Growth AI**: 500 actions/month\n\
+                    - **Pro AI**: 2000 actions/month\n\n\
+                    You can purchase add-on packs for more usage. Would you like to upgrade your plan?";
+
+                Ok(ChatMessageResponse {
+                    intent: intent.clone(),
+                    reply: reply.to_string(),
+                    data: serde_json::json!({}),
+                    conversation_id: conversation_id.clone(),
+                })
+            }
+            "check_ai_pack_balance" => {
+                let reply = "To check your AI balance, please visit the AI Usage section in your dashboard.\n\n\
+                    You can view:\n\
+                    - Monthly usage vs limit\n\
+                    - Add-on balance remaining\n\
+                    - Renewal date\n\n\
+                    Would you like me to help with anything else?";
+
+                Ok(ChatMessageResponse {
+                    intent: intent.clone(),
+                    reply: reply.to_string(),
+                    data: serde_json::json!({}),
+                    conversation_id: conversation_id.clone(),
+                })
+            }
+            "generate_cover_letter" => {
+                let reply = "I can help you generate a cover letter. Please provide:\n\
+                    - The job title or position\n\
+                    - Your key skills and experience\n\
+                    - Any specific company or role details (optional)\n\n\
+                    Or you can use the 'Generate Cover Letter' button on the job application page.";
+
+                Ok(ChatMessageResponse {
+                    intent: intent.clone(),
+                    reply: reply.to_string(),
+                    data: serde_json::json!({}),
+                    conversation_id: conversation_id.clone(),
+                })
+            }
+            "improve_resume_summary" => {
+                let reply = "I can help improve your resume summary. Please share:\n\
+                    - Your current resume summary (or paste it here)\n\
+                    - The type of role you're targeting\n\
+                    - Your key skills and experience\n\n\
+                    Or you can use the 'Improve Resume' feature in your profile page.";
+
+                Ok(ChatMessageResponse {
+                    intent: intent.clone(),
+                    reply: reply.to_string(),
+                    data: serde_json::json!({}),
+                    conversation_id: conversation_id.clone(),
                 })
             }
             _ => {
                 let generic = self
                     .ai_provider
                     .complete(
-                        "You are Nxtgauge workflow assistant. Keep answers concise and actionable.",
+                        "You are Nxtgauge workflow assistant. Keep answers concise and actionable. If users ask about features, guide them to use the appropriate buttons or pages.",
                         &request.message,
                     )
                     .await?;
@@ -129,6 +199,7 @@ impl ChatOrchestrator {
                     intent,
                     reply: generic,
                     data: serde_json::json!({}),
+                    conversation_id: conversation_id.clone(),
                 })
             }
         }
@@ -137,25 +208,75 @@ impl ChatOrchestrator {
 
 fn classify_intent(message: &str) -> String {
     let text = message.to_lowercase();
-    if text.contains("job description") || text.contains("jd") || text.contains("role") {
+
+    if text.contains("job description")
+        || text.contains("generate job")
+        || text.contains("create job")
+        || text.contains("write job")
+        || (text.contains("jd") && text.len() < 10)
+    {
         return "job_description_generation".to_string();
     }
+
+    if text.contains("cover letter")
+        || text.contains("write a letter")
+        || text.contains("generate letter")
+    {
+        return "generate_cover_letter".to_string();
+    }
+
+    if text.contains("resume")
+        && (text.contains("improve")
+            || text.contains("rewrite")
+            || text.contains("summary")
+            || text.contains("tailor"))
+    {
+        return "improve_resume_summary".to_string();
+    }
+
     if text.contains("form") || text.contains("field") || text.contains("fill") {
         return "form_filling_assistance".to_string();
     }
-    if text.contains("help")
+
+    if text.contains("search kb")
+        || text.contains("find article")
+        || text.contains("how do i")
+        || text.contains("how to")
+        || text.contains("where do i")
+        || (text.contains("help") && !text.contains("help me"))
         || text.contains("article")
         || text.contains("kb")
         || text.contains("docs")
+        || text.contains("documentation")
     {
-        return "help_article_retrieval".to_string();
+        return "search_kb".to_string();
     }
+
     if text.contains("ticket")
         || text.contains("support")
         || text.contains("issue")
         || text.contains("bug")
+        || text.contains("problem")
+        || text.contains("not working")
+        || text.contains("error")
     {
         return "support_ticket_creation".to_string();
     }
+
+    if text.contains("ai plan")
+        || text.contains("ai limit")
+        || text.contains("ai package")
+        || text.contains("ai credit")
+        || text.contains("upgrade ai")
+    {
+        return "explain_plan_limits".to_string();
+    }
+
+    if text.contains("balance")
+        && (text.contains("ai") || text.contains("credit") || text.contains("action"))
+    {
+        return "check_ai_pack_balance".to_string();
+    }
+
     "general".to_string()
 }

src/config/mod.rs

@@ -5,9 +5,13 @@ pub struct AppConfig {
     pub app_host: String,
     pub app_port: u16,
     pub database_url: Option<String>,
+    pub llm_provider: String,
     pub ollama_base_url: String,
     pub ollama_chat_model: String,
     pub ollama_embed_model: String,
+    pub litellm_base_url: String,
+    pub litellm_api_key: String,
+    pub litellm_model: String,
     pub help_center_seed_path: String,
     pub tickets_source: String,
     pub nxtgauge_users_url: String,
@@ -21,12 +25,19 @@ impl AppConfig {
             database_url: std::env::var("DATABASE_URL")
                 .ok()
                 .filter(|v| !v.trim().is_empty()),
+            llm_provider: env_or_default("LLM_PROVIDER", "ollama"),
             ollama_base_url: env_or_default(
                 "OLLAMA_BASE_URL",
                 "http://ollama.nxtgauge-ai.svc.cluster.local:11434",
             ),
             ollama_chat_model: env_or_default("OLLAMA_CHAT_MODEL", "gemma3:270m"),
             ollama_embed_model: env_or_default("OLLAMA_EMBED_MODEL", "nomic-embed-text"),
+            litellm_base_url: env_or_default(
+                "LITELLM_BASE_URL",
+                "https://llm.nxtgauge.com/v1",
+            ),
+            litellm_api_key: std::env::var("LITELLM_API_KEY").unwrap_or_default(),
+            litellm_model: env_or_default("LITELLM_MODEL", "askash-main"),
             help_center_seed_path: env_or_default(
                 "HELP_CENTER_SEED_PATH",
                 "./seeds/help_articles.json",

src/cover_letter/mod.rs

@@ -0,0 +1,2 @@
+pub mod models;
+pub mod service;

src/cover_letter/models.rs

@@ -0,0 +1,18 @@
+use serde::{Deserialize, Serialize};
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct GenerateCoverLetterRequest {
+    pub job_title: String,
+    pub company_name: Option<String>,
+    pub applicant_name: Option<String>,
+    pub applicant_skills: Option<Vec<String>>,
+    pub applicant_experience: Option<String>,
+    pub tone: Option<String>,
+    pub additional_notes: Option<String>,
+}
+
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct GenerateCoverLetterResponse {
+    pub cover_letter: String,
+    pub raw_markdown: String,
+}

src/cover_letter/service.rs

@@ -0,0 +1,46 @@
+use std::sync::Arc;
+
+use crate::{cover_letter::models::*, error::AppError, providers::llm::ai_provider::AiProvider};
+
+#[derive(Clone)]
+pub struct CoverLetterService {
+    ai_provider: Arc<dyn AiProvider>,
+}
+
+impl CoverLetterService {
+    pub fn new(ai_provider: Arc<dyn AiProvider>) -> Self {
+        Self { ai_provider }
+    }
+
+    pub async fn generate(
+        &self,
+        request: GenerateCoverLetterRequest,
+    ) -> Result<GenerateCoverLetterResponse, AppError> {
+        let system = "You are a professional cover letter writer. Write compelling, concise cover letters that highlight relevant skills and experience. Keep the tone professional but engaging.";
+        let user_prompt = format!(
+            "Write a cover letter for:\n\
+             Job Title: {}\n\
+             Company Name: {}\n\
+             Applicant Name: {}\n\
+             Key Skills: {:?}\n\
+             Experience: {}\n\
+             Tone: {}\n\
+             Additional Notes: {:?}\n\n\
+             Write a professional cover letter with 3-4 short paragraphs. Do not invent details not provided.",
+            request.job_title,
+            request.company_name.unwrap_or_else(|| "the company".to_string()),
+            request.applicant_name.unwrap_or_else(|| "the applicant".to_string()),
+            request.applicant_skills.clone().unwrap_or_default(),
+            request.applicant_experience.unwrap_or_else(|| "relevant experience".to_string()),
+            request.tone.unwrap_or_else(|| "professional".to_string()),
+            request.additional_notes
+        );
+
+        let generated = self.ai_provider.complete(system, &user_prompt).await?;
+
+        Ok(GenerateCoverLetterResponse {
+            cover_letter: generated.lines().take(10).collect::<Vec<_>>().join("\n"),
+            raw_markdown: generated,
+        })
+    }
+}

src/handlers/cover_letter.rs

@@ -0,0 +1,19 @@
+use axum::{extract::State, Json};
+
+use crate::{
+    cover_letter::models::{GenerateCoverLetterRequest, GenerateCoverLetterResponse},
+    state::AppState,
+    error::AppError,
+};
+
+pub async fn generate_cover_letter(
+    State(state): State<AppState>,
+    Json(request): Json<GenerateCoverLetterRequest>,
+) -> Result<Json<GenerateCoverLetterResponse>, AppError> {
+    if request.job_title.trim().is_empty() {
+        return Err(AppError::BadRequest("job_title is required".to_string()));
+    }
+
+    let generated = state.cover_letter_service.generate(request).await?;
+    Ok(Json(generated))
+}

src/handlers/mod.rs

@@ -1,4 +1,5 @@
 pub mod chat;
+pub mod cover_letter;
 pub mod forms;
 pub mod health;
 pub mod help;

src/main.rs

@@ -1,5 +1,6 @@
 mod chat;
 mod config;
+mod cover_letter;
 mod db;
 mod error;
 mod forms;
@@ -16,7 +17,9 @@ use std::sync::Arc;
 use config::AppConfig;
 use providers::help_center::nxtgauge_help_center_provider::NxtgaugeHelpCenterProvider;
 use providers::llm::ollama_provider::OllamaAiProvider;
+use providers::llm::litellm_provider::LiteLLMProvider;
 use providers::tickets::nxtgauge_ticket_provider::NxtgaugeTicketProvider;
+use providers::llm::ai_provider::AiProvider;
 use retrieval::embeddings::ollama_embedding_provider::OllamaEmbeddingProvider;
 use state::AppState;
 use tracing::{info, warn};
@@ -35,12 +38,22 @@ async fn main() {
         }
     };
 
-    let ai_provider = Arc::new(OllamaAiProvider::new(
+    let ai_provider: Arc<dyn AiProvider> = if cfg.llm_provider == "litellm" {
+        info!("Using LiteLLM provider with model {}", cfg.litellm_model);
+        Arc::new(LiteLLMProvider::new(
+            cfg.litellm_base_url.clone(),
+            cfg.litellm_api_key.clone(),
+            cfg.litellm_model.clone(),
+        )) as Arc<dyn AiProvider>
+    } else {
+        info!("Using Ollama provider with model {}", cfg.ollama_chat_model);
+        Arc::new(OllamaAiProvider::new(
             cfg.ollama_base_url.clone(),
             cfg.ollama_chat_model.clone(),
-    ));
+        )) as Arc<dyn AiProvider>
+    };
 
-    let embedding_provider = Arc::new(OllamaEmbeddingProvider::new(
+    let _embedding_provider = Arc::new(OllamaEmbeddingProvider::new(
         cfg.ollama_base_url.clone(),
         cfg.ollama_embed_model.clone(),
     ));

src/providers/llm/litellm_provider.rs

@@ -0,0 +1,118 @@
+use async_trait::async_trait;
+use reqwest::Client;
+use serde::{Deserialize, Serialize};
+
+use crate::{error::AppError, providers::llm::ai_provider::AiProvider};
+
+#[derive(Clone)]
+pub struct LiteLLMProvider {
+    client: Client,
+    base_url: String,
+    api_key: String,
+    model: String,
+}
+
+impl LiteLLMProvider {
+    pub fn new(base_url: String, api_key: String, model: String) -> Self {
+        Self {
+            client: Client::new(),
+            base_url,
+            api_key,
+            model,
+        }
+    }
+
+    fn fallback_response(user_prompt: &str) -> String {
+        format!(
+            "LiteLLM model is unavailable right now. I captured your request so workflow can continue: {}",
+            user_prompt
+        )
+    }
+}
+
+#[derive(Debug, Serialize)]
+struct ChatMessage {
+    role: String,
+    content: String,
+}
+
+#[derive(Debug, Serialize)]
+struct ChatCompletionRequest {
+    model: String,
+    messages: Vec<ChatMessage>,
+    temperature: f32,
+    max_tokens: i32,
+}
+
+#[derive(Debug, Deserialize)]
+struct ChatCompletionResponse {
+    choices: Vec<Choice>,
+}
+
+#[derive(Debug, Deserialize)]
+struct Choice {
+    message: Message,
+}
+
+#[derive(Debug, Deserialize)]
+struct Message {
+    content: String,
+}
+
+#[async_trait]
+impl AiProvider for LiteLLMProvider {
+    async fn complete(&self, system_prompt: &str, user_prompt: &str) -> Result<String, AppError> {
+        let url = format!("{}/chat/completions", self.base_url.trim_end_matches('/'));
+
+        let payload = ChatCompletionRequest {
+            model: self.model.clone(),
+            messages: vec![
+                ChatMessage {
+                    role: "system".to_string(),
+                    content: system_prompt.to_string(),
+                },
+                ChatMessage {
+                    role: "user".to_string(),
+                    content: user_prompt.to_string(),
+                },
+            ],
+            temperature: 0.2,
+            max_tokens: 2048,
+        };
+
+        let res = self
+            .client
+            .post(url)
+            .header("Authorization", format!("Bearer {}", self.api_key))
+            .header("Content-Type", "application/json")
+            .json(&payload)
+            .send()
+            .await;
+
+        let Ok(res) = res else {
+            return Ok(Self::fallback_response(user_prompt));
+        };
+
+        if !res.status().is_success() {
+            let status = res.status();
+            let body = res.text().await.unwrap_or_default();
+            tracing::warn!("LiteLLM request failed: {} - {}", status, body);
+            return Ok(Self::fallback_response(user_prompt));
+        }
+
+        let body: Result<ChatCompletionResponse, _> = res.json().await;
+        match body {
+            Ok(parsed) => {
+                if let Some(choice) = parsed.choices.first() {
+                    Ok(choice.message.content.trim().to_string())
+                } else {
+                    Ok(Self::fallback_response(user_prompt))
+                }
+            }
+            Err(e) => {
+                tracing::warn!("Failed to parse LiteLLM response: {}", e);
+                Ok(Self::fallback_response(user_prompt))
+            }
+        }
+    }
+}

src/providers/llm/mod.rs

@@ -1,2 +1,3 @@
 pub mod ai_provider;
 pub mod ollama_provider;
+pub mod litellm_provider;

src/routes/mod.rs

@@ -17,6 +17,10 @@ pub fn build_router(state: AppState) -> Router {
                     "/jobs/generate-description",
                     post(handlers::jobs::generate_description),
                 )
+                .route(
+                    "/cover-letter/generate",
+                    post(handlers::cover_letter::generate_cover_letter),
+                )
                 .route("/forms/extract", post(handlers::forms::extract))
                 .route("/tickets/create", post(handlers::tickets::create))
                 .route("/help/search", post(handlers::help::search)),

src/state.rs

@@ -3,6 +3,7 @@ use std::sync::Arc;
 use crate::{
     chat::orchestrator::ChatOrchestrator,
     config::AppConfig,
+    cover_letter::service::CoverLetterService,
     db::Database,
     forms::service::FormService,
     jobs::service::JobsService,
@@ -20,6 +21,7 @@ pub struct AppState {
     pub chat_orchestrator: ChatOrchestrator,
     pub jobs_service: JobsService,
     pub form_service: FormService,
+    pub cover_letter_service: CoverLetterService,
     pub ticket_service: TicketService,
     pub help_center: Arc<dyn HelpCenterProvider>,
 }
@@ -34,13 +36,14 @@ impl AppState {
     ) -> Self {
         let jobs_service = JobsService::new(ai_provider.clone());
         let form_service = FormService::new(ai_provider.clone());
+        let cover_letter_service = CoverLetterService::new(ai_provider.clone());
         let ticket_service = TicketService::new(ticket_provider, db.clone());
         let chat_orchestrator = ChatOrchestrator::new(
             jobs_service.clone(),
             form_service.clone(),
             help_center.clone(),
             ticket_service.clone(),
-            ai_provider,
+            ai_provider.clone(),
         );
 
         Self {
@@ -49,6 +52,7 @@ impl AppState {
             chat_orchestrator,
             jobs_service,
             form_service,
+            cover_letter_service,
             ticket_service,
             help_center,
         }