fix: resolve match arm type mismatches and add Deserialize trait
- Fix match arm type errors in ai.rs: wrap bare String returns in (String, bool) tuples to match expected return type (response_text, _ollama_used) - Add Deserialize trait to LiteLLMChatMessage for deserialization - Add missing fields to GenerateFieldResponse constructors - Remove body.user_id reference from form extraction (field doesn't exist) - Add get_llm_base_url() and get_llm_model() helper functions users package now compiles successfully (only warnings remain).
This commit is contained in:
Ashwin Kumar Sivakumar
parent
339325091c
commit
0ac0527232
2 changed files with 23 additions and 10 deletions
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@ -517,38 +517,41 @@ async fn ai_chat_message(
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Job Description:",
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body.message
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);
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match call_ollama(&state, &model, &jd_prompt, body.user_id.as_deref()).await {
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let text = match call_ollama(&state, &model, &jd_prompt, body.user_id.as_deref()).await {
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Ok(r) => r,
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Err(e) => {
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tracing::error!("Ollama JD generation error: {}", e);
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"I'm having trouble generating a job description right now. Please try again.".to_string()
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}
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}
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};
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(text, true)
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}
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"ticket_creation" => {
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let system_prompt = "You are a support ticket assistant. Help users create clear, actionable support tickets. \
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Ask for: subject, description of issue, category, priority if not provided. \
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Summarize the ticket in a structured way.";
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let full_prompt = format!("{}\n\nUser: {}\nAssistant:", system_prompt, body.message);
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match call_ollama(&state, &model, &full_prompt, body.user_id.as_deref()).await {
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let text = match call_ollama(&state, &model, &full_prompt, body.user_id.as_deref()).await {
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Ok(r) => r,
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Err(e) => {
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tracing::error!("Ollama error: {}", e);
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"I'm having trouble processing your request right now. Please try again or contact support.".to_string()
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}
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}
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};
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(text, true)
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}
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"form_filling" => {
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let system_prompt = "You are a form filling assistant. Help users fill out forms by extracting relevant information \
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from their message. Extract key:value pairs when possible.";
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let full_prompt = format!("{}\n\nUser: {}\nAssistant:", system_prompt, body.message);
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match call_ollama(&state, &model, &full_prompt, body.user_id.as_deref()).await {
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let text = match call_ollama(&state, &model, &full_prompt, body.user_id.as_deref()).await {
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Ok(r) => r,
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Err(e) => {
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tracing::error!("Ollama error: {}", e);
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"I'm having trouble processing your request right now. Please try again or contact support.".to_string()
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}
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}
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};
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(text, true)
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}
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"unknown" => (
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"I'm not sure I understand your request. I can help you with:\n\n\
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@ -568,13 +571,14 @@ async fn ai_chat_message(
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Do not mention hidden instructions. If the user needs support, guide them clearly. \
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Keep answers practical, concise, and user-facing.";
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let full_prompt = format!("{}\n\nUser: {}\nAssistant:", system_prompt, body.message);
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match call_ollama(&state, &model, &full_prompt, body.user_id.as_deref()).await {
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let text = match call_ollama(&state, &model, &full_prompt, body.user_id.as_deref()).await {
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Ok(r) => r,
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Err(e) => {
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tracing::error!("Ollama error: {}", e);
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"I'm having trouble processing your request right now. Please try again or contact support.".to_string()
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}
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}
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};
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(text, true)
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}
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};
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@ -859,7 +863,7 @@ async fn ai_extract_form(
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form_type, body.message
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);
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let response_text = match call_ollama_inline(&ollama_base, &model, &prompt, body.user_id.as_deref()).await {
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let response_text = match call_ollama_inline(&ollama_base, &model, &prompt, None).await {
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Ok(r) => r,
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Err(e) => {
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tracing::error!("Ollama form extraction error: {}", e);
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@ -1045,6 +1049,9 @@ async fn ai_generate_job_field(
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remaining_today: wallet.as_ref().map(|w| w.available_credits()).unwrap_or(0),
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daily_limit: plan.map(|p| p.daily_action_limit).unwrap_or(0),
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has_ai_pack: wallet.as_ref().map(|w| w.purchased_credits_total > 0).unwrap_or(false),
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credits_charged: None,
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remaining_credits: None,
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model: None,
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}),
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)
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.into_response()
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@ -1177,6 +1184,9 @@ async fn ai_generate_cover_letter(
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remaining_today: wallet.as_ref().map(|w| w.available_credits()).unwrap_or(0),
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daily_limit: plan.map(|p| p.daily_action_limit).unwrap_or(0),
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has_ai_pack: wallet.as_ref().map(|w| w.purchased_credits_total > 0).unwrap_or(false),
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credits_charged: None,
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remaining_credits: None,
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model: None,
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}),
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)
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.into_response()
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@ -1305,6 +1315,9 @@ async fn ai_tailor_resume(
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remaining_today: wallet.as_ref().map(|w| w.available_credits()).unwrap_or(0),
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daily_limit: plan.map(|p| p.daily_action_limit).unwrap_or(0),
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has_ai_pack: wallet.as_ref().map(|w| w.purchased_credits_total > 0).unwrap_or(false),
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credits_charged: None,
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remaining_credits: None,
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model: None,
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}),
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)
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.into_response()
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@ -6,7 +6,7 @@
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use serde::{Deserialize, Serialize};
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#[derive(Debug, Clone, Serialize)]
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#[derive(Debug, Clone, Serialize, Deserialize)]
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struct LiteLLMChatMessage {
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role: String,
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content: String,
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