Fix /api/ai/* returning 500 on every request, close credit-minting bugs, add grounding guardrail
Critical: ai_access_middleware was wired via from_fn_with_state((), ...) - passing the unit type as state - and pulled AppState from request extensions, which nothing ever populated. Every request through /api/ai/* and /api/ai/auto/* returned 500 INCOMPLETE_CONTEXT. Fixed by extracting State<AppState> properly and passing the real state at both call sites; removed the redundant, identically-broken inner middleware layer inside ai_router(). Security: ai_addon_purchase (/api/ai/addons/purchase, /api/ai/credits/buy) and ai_plan_upgrade (/api/ai/plans/upgrade) granted AI credits / plan upgrades (including enterprise) with zero payment verification - any authenticated user could mint unlimited free credits, and the frontend already called this directly. Disabled both until wired to a real payment flow. Quality: added a grounding/anti-hallucination system prompt applied to every AI feature call (orchestrator::call_feature / call_feature_with_plan, plus the handful of call sites that bypass the orchestrator). Verified against the live model that it reduces but does not eliminate fabrication on harder reasoning tasks - even the larger model invents facts not present in the input on some prompts. This is a real limitation of the two locally-hosted models, not something a system prompt alone fully solves; flagged for follow-up (e.g. a verification pass or deterministic checks for high-stakes decisions like auto-apply). Also fixed Persona/Pillar keyword detection using naive substring matching (e.g. "team" matching inside "esteemed", "lead" matching inside "leadership") - added a word-boundary-aware contains_word() helper and applied it to all keyword classifiers in this file.
This commit is contained in:
Ashwin Kumar Sivakumar
parent
64089e4da3
commit
eaae3d470f
6 changed files with 134 additions and 155 deletions
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@ -133,24 +133,13 @@ impl IntoResponse for AiAccessError {
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/// AI access. Applies to all routes under `/api/ai/*`. Non-customer roles that
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/// have no explicit subscription will automatically get a Free plan.
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pub async fn ai_access_middleware(
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State(_state): State<()>,
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State(state): State<AppState>,
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auth: AuthUser,
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request: Request,
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next: Next,
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) -> Result<Response, AiAccessError> {
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let role_code = auth.claims.active_role.clone();
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// State is not available directly in axum middleware; access the pool
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// via the request extensions where AppState was installed by `with_state`.
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let state = request
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.extensions()
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.get::<AppState>()
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.cloned()
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.ok_or_else(|| {
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tracing::error!("AppState not found in request extensions");
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AiAccessError::IncompleteContext
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})?;
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let (_sub, _plan) = plans::ensure_free_subscription(
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&state.pool,
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auth.user_id,
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@ -8,6 +8,30 @@ use crate::ai::{credits, litellm, model_router, plans, usage};
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use db::models::ai::AiFeatureCost;
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use crate::AppState;
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/// Baseline anti-hallucination / grounding instructions applied to every AI
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/// feature call, regardless of what feature-specific system prompt (if any)
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/// the caller supplies. Added after confirming the model will otherwise
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/// confidently fabricate skills/qualifications not present in the input
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/// (e.g. asserting a candidate has AWS/Kubernetes experience when only
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/// Python/SQL were listed) and use that fabrication to justify its answer.
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pub const GROUNDING_GUARDRAIL: &str = "You are an AI assistant for the Nxtgauge platform. Follow these rules strictly: \
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(1) Only use facts, skills, experience, or qualifications explicitly stated in the input below - never invent, \
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assume, or infer skills, experience, credentials, or requirements that were not explicitly mentioned. \
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(2) If information needed to fully answer is missing, say so explicitly rather than filling the gap with an assumption. \
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(3) When evaluating fit, gaps, matches, or recommendations, be honest about mismatches and missing requirements - \
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do not default to an encouraging or positive tone if the input doesn't support it. \
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(4) Do not fabricate specific facts, numbers, dates, or names not present in the input. \
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(5) Keep responses concise and directly relevant to the request.";
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/// Combine the baseline grounding guardrail with an optional feature-specific
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/// system prompt.
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pub fn effective_system_prompt(feature_prompt: Option<&str>) -> String {
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match feature_prompt {
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Some(p) if !p.trim().is_empty() => format!("{}\n\n{}", GROUNDING_GUARDRAIL, p),
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_ => GROUNDING_GUARDRAIL.to_string(),
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}
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}
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#[derive(Debug, thiserror::Error)]
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pub enum AiCallError {
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#[error("Plan error: {0}")]
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@ -98,7 +122,7 @@ pub async fn call_feature(
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let (text, usage, request_id) = client
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.chat_completion_text(
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&model_alias,
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system_prompt,
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Some(&effective_system_prompt(system_prompt)),
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user_message,
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max_tokens,
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)
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@ -182,7 +206,7 @@ pub async fn call_feature_with_plan(
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let (text, usage, request_id) = client
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.chat_completion_text(
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&model_alias,
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system_prompt,
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Some(&effective_system_prompt(system_prompt)),
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user_message,
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max_tokens,
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)
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@ -1,4 +1,4 @@
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use crate::ai::{credits, litellm, model_router, plans, usage};
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use crate::ai::{credits, litellm, model_router, orchestrator, plans, usage};
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use crate::AppState;
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use axum::{
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extract::{Path, Query, State},
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@ -229,7 +229,7 @@ async fn admin_ai_call(
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};
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let (text, usage, request_id) = match client
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.chat_completion_text(&model_alias, None, prompt, None)
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.chat_completion_text(&model_alias, Some(&orchestrator::effective_system_prompt(None)), prompt, None)
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.await
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{
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Ok(r) => r,
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@ -78,7 +78,7 @@ fn classify_strict_keywords(message: &str) -> Option<(&'static str, f32)> {
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"what is ", "what are ", "where do i find", "where can i find",
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"search for", "find article", "look up",
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];
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if HELP_KW.iter().any(|k| m.contains(k)) {
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if HELP_KW.iter().any(|k| contains_word(&m, k)) {
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return Some(("help_search", 0.95));
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}
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@ -90,7 +90,7 @@ fn classify_strict_keywords(message: &str) -> Option<(&'static str, f32)> {
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"having trouble with", "issue with", "problem with", "complaint",
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"refund request", "cancel my account", "billing issue", "billing problem",
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];
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if TICKET_KW.iter().any(|k| m.contains(k)) {
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if TICKET_KW.iter().any(|k| contains_word(&m, k)) {
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return Some(("ticket_creation", 0.95));
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}
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@ -100,7 +100,7 @@ fn classify_strict_keywords(message: &str) -> Option<(&'static str, f32)> {
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"extract from", "extract fields", "extract info", "extract information",
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"autofill", "auto-fill", "parse this form", "from this text",
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];
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if FORM_KW.iter().any(|k| m.contains(k)) {
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if FORM_KW.iter().any(|k| contains_word(&m, k)) {
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return Some(("form_filling", 0.95));
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}
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@ -110,7 +110,7 @@ fn classify_strict_keywords(message: &str) -> Option<(&'static str, f32)> {
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"draft a job description", "job description for", "jd for", "job posting for",
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"write job description", "generate job description",
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];
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if JD_KW.iter().any(|k| m.contains(k)) {
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if JD_KW.iter().any(|k| contains_word(&m, k)) {
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return Some(("job_description_generation", 0.95));
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}
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@ -119,7 +119,7 @@ fn classify_strict_keywords(message: &str) -> Option<(&'static str, f32)> {
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"cover letter", "coverletter", "write a letter", "application letter",
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"letter of interest", "motivation letter",
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];
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if CL_KW.iter().any(|k| m.contains(k)) {
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if CL_KW.iter().any(|k| contains_word(&m, k)) {
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return Some(("generate_cover_letter", 0.95));
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}
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@ -130,7 +130,7 @@ fn classify_strict_keywords(message: &str) -> Option<(&'static str, f32)> {
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"update my resume", "update resume", "fix my resume", "optimize my resume",
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"customize my resume", "adjust my resume", "polish my resume",
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];
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if RESUME_KW.iter().any(|k| m.contains(k)) {
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if RESUME_KW.iter().any(|k| contains_word(&m, k)) {
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return Some(("improve_resume", 0.95));
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}
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@ -141,7 +141,7 @@ fn classify_strict_keywords(message: &str) -> Option<(&'static str, f32)> {
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"unlock lead", "unlock contact", "lead contact", "view lead",
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"request to view",
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];
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if CONTACT_KW.iter().any(|k| m.contains(k)) {
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if CONTACT_KW.iter().any(|k| contains_word(&m, k)) {
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return Some(("request_view_contact", 0.95));
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}
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@ -150,7 +150,7 @@ fn classify_strict_keywords(message: &str) -> Option<(&'static str, f32)> {
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"auto apply", "auto-apply", "apply to all", "apply for me",
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"apply on my behalf", "apply automatically", "bulk apply", "mass apply",
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];
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if APPLY_KW.iter().any(|k| m.contains(k)) {
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if APPLY_KW.iter().any(|k| contains_word(&m, k)) {
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return Some(("auto_apply_job", 0.95));
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}
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@ -614,7 +614,7 @@ async fn ai_chat_generate(
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}
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};
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return match client
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.chat_completion_text("askash-fast", None, prompt, None)
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.chat_completion_text("askash-fast", Some(&orchestrator::effective_system_prompt(None)), prompt, None)
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.await
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{
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Ok((text, _, _)) => (text, true),
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@ -689,7 +689,7 @@ async fn ai_chat_generate(
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let (text, usage, request_id) = match client
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.chat_completion_text(&model_alias,
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None,
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Some(&orchestrator::effective_system_prompt(None)),
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prompt,
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None,
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)
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@ -1871,6 +1871,57 @@ async fn ai_usage_status(
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// - DISCOVER : help find things (search, recommendations)
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// - IMPROVE : optimize existing (analytics, suggestions)
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/// Word-boundary aware substring check, so a keyword like "team" doesn't
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/// match inside unrelated words like "steam" or "esteemed", and "lead"
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/// doesn't match inside "leadership". Keywords with a trailing/leading
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/// space (e.g. "make a") are treated literally; the check still applies
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/// boundary rules to the overall match span.
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fn contains_word(haystack: &str, needle: &str) -> bool {
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let bytes = haystack.as_bytes();
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let needle_bytes = needle.as_bytes();
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if needle_bytes.is_empty() {
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return false;
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}
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let is_boundary = |c: Option<u8>| match c {
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None => true,
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Some(b) => !(b as char).is_alphanumeric(),
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};
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// Only enforce a boundary on an edge if the needle itself is alphanumeric
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// there — a needle like "how do i " already carries its own boundary via
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// the trailing space, so requiring another non-alphanumeric char after
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// that would over-constrain it.
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let needle_starts_alnum = (needle_bytes[0] as char).is_alphanumeric();
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let needle_ends_alnum = (needle_bytes[needle_bytes.len() - 1] as char).is_alphanumeric();
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let mut start = 0;
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while let Some(pos) = haystack[start..].find(needle) {
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let match_start = start + pos;
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let match_end = match_start + needle_bytes.len();
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let before_ok = !needle_starts_alnum || {
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let before = if match_start == 0 { None } else { Some(bytes[match_start - 1]) };
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is_boundary(before)
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};
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let after_ok = !needle_ends_alnum || {
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let after = bytes.get(match_end).copied();
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is_boundary(after)
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};
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if before_ok && after_ok {
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return true;
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}
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start = match_start + 1;
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if start >= haystack.len() {
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break;
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}
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}
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false
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}
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
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#[serde(rename_all = "snake_case")]
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pub enum Persona {
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@ -1901,7 +1952,7 @@ impl Persona {
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"recruitment", "team", "employer", "organization", "org ", "staff",
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"headcount", "workforce", "b2b", "enterprise",
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];
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if COMPANIES.iter().any(|k| m.contains(k)) {
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if COMPANIES.iter().any(|k| contains_word(&m, k)) {
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return Some(Persona::Companies);
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}
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@ -1911,7 +1962,7 @@ impl Persona {
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"resume", "cv ", "interview", "hiring me", "salary", "offer letter",
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"job board", "job listing", "vacancy", "position", "candidate",
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];
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if JOB_SEEKERS.iter().any(|k| m.contains(k)) {
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if JOB_SEEKERS.iter().any(|k| contains_word(&m, k)) {
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return Some(Persona::JobSeekers);
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}
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@ -1921,7 +1972,7 @@ impl Persona {
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"quote", "quotation", "order", "checkout", "payment", "invoice me",
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"subscription", "plan", "package",
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];
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if CUSTOMERS.iter().any(|k| m.contains(k)) {
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if CUSTOMERS.iter().any(|k| contains_word(&m, k)) {
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return Some(Persona::Customers);
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}
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@ -1931,7 +1982,7 @@ impl Persona {
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"freelancer", "consultant", "contractor", "side hustle", "service provider",
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"lead", "leads", "client", "project", "deliverable",
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];
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if PROFESSIONALS.iter().any(|k| m.contains(k)) {
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if PROFESSIONALS.iter().any(|k| contains_word(&m, k)) {
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return Some(Persona::Professionals);
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}
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@ -1968,7 +2019,7 @@ impl Pillar {
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"generate", "new ", "add a", "set up", "setup ", "post a",
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"publish", "start a", "begin a", "launch",
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];
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if CREATE.iter().any(|k| m.contains(k)) {
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if CREATE.iter().any(|k| contains_word(&m, k)) {
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return Some(Pillar::Create);
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}
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@ -1978,7 +2029,7 @@ impl Pillar {
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"verification", "onboard", "onboarding", "fill in", "fill out",
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"resume setup", "complete my", "finish my", "pick up where",
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];
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if COMPLETE.iter().any(|k| m.contains(k)) {
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if COMPLETE.iter().any(|k| contains_word(&m, k)) {
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return Some(Pillar::Complete);
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}
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@ -1988,7 +2039,7 @@ impl Pillar {
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"suggest", "show me", "browse", "discover", "explore", "best",
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"top ", "near me", "nearby", "available",
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];
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if DISCOVER.iter().any(|k| m.contains(k)) {
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if DISCOVER.iter().any(|k| contains_word(&m, k)) {
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return Some(Pillar::Discover);
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}
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@ -1998,7 +2049,7 @@ impl Pillar {
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"performance", "metrics", "stats", "statistics", "better", "enhance",
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"upgrade", "polish", "refine", "tweak", "fix my",
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];
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if IMPROVE.iter().any(|k| m.contains(k)) {
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if IMPROVE.iter().any(|k| contains_word(&m, k)) {
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return Some(Pillar::Improve);
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}
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@ -2122,7 +2173,7 @@ fn is_support_intent(message: &str) -> bool {
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"can't", "cant ", "cannot", "unable to", "issue", "problem",
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"help me fix", "stuck", "blocked",
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];
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SUPPORT_KW.iter().any(|k| m.contains(k))
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SUPPORT_KW.iter().any(|k| contains_word(&m, k))
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}
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// ── Phase 2: auto-create a support ticket for support-intent queries ──────────
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@ -3873,121 +3924,23 @@ pub mod phase3 {
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}
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/// POST /api/ai/addons/purchase — purchase a credit pack.
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///
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/// This previously granted credits with no payment verification at all -
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/// any authenticated user could mint unlimited free AI credits. Disabled
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/// until it's wired to a real payment flow (see the PayU-verified
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/// nxtgauge-rust-payments ai_credits flow for the pattern to follow).
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pub async fn ai_addon_purchase(
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State(state): State<AppState>,
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auth: contracts::auth_middleware::AuthUser,
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Json(body): Json<AddonPurchaseBody>,
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State(_state): State<AppState>,
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_auth: contracts::auth_middleware::AuthUser,
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Json(_body): Json<AddonPurchaseBody>,
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) -> impl axum::response::IntoResponse {
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let code = body.addon_code.trim().to_uppercase();
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let addon_amount = match code.as_str() {
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"STARTER" => 50,
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"GROWTH" => 150,
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"POWER" => 500,
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"ENTERPRISE" => 1000,
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_ => {
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match AiCreditPackageRepository::list_active(&state.pool).await {
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Ok(packages) => packages
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.into_iter()
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.find(|pkg| {
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pkg.name
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.to_uppercase()
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.replace([' ', '-'], "_")
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.contains(&code)
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})
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.map(|pkg| pkg.credits),
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Err(e) => {
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tracing::error!("Failed to list AI credit packages: {}", e);
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None
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}
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}
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.unwrap_or(0)
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}
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};
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if addon_amount <= 0 {
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return (
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axum::http::StatusCode::BAD_REQUEST,
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axum::Json(serde_json::json!({
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"success": false,
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"message": format!("Unknown addon code: {}", body.addon_code),
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})),
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)
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.into_response();
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}
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let _ = match crate::ai::plans::ensure_free_subscription(
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&state.pool,
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auth.user_id,
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Some(&auth.claims.active_role),
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)
|
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.await
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{
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Ok(data) => data,
|
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Err(e) => {
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return (e.status_code(), axum::Json(e.error_body())).into_response();
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}
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};
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||||
|
||||
if let Err(e) = UserAiSubscriptionRepository::add_purchased_credits(
|
||||
&state.pool,
|
||||
auth.user_id,
|
||||
addon_amount,
|
||||
)
|
||||
.await
|
||||
{
|
||||
tracing::error!("ai_addon_purchase update failed: {}", e);
|
||||
return (
|
||||
axum::http::StatusCode::INTERNAL_SERVER_ERROR,
|
||||
axum::Json(AddonPurchaseResponse {
|
||||
success: false,
|
||||
addon_balance: 0,
|
||||
message: "Failed to process purchase".to_string(),
|
||||
}),
|
||||
)
|
||||
.into_response();
|
||||
}
|
||||
|
||||
let Some(updated_sub) = UserAiSubscriptionRepository::get_by_user_id(&state.pool, auth.user_id)
|
||||
.await
|
||||
.ok()
|
||||
.flatten() else {
|
||||
return (
|
||||
axum::http::StatusCode::INTERNAL_SERVER_ERROR,
|
||||
axum::Json(AddonPurchaseResponse {
|
||||
success: false,
|
||||
addon_balance: 0,
|
||||
message: "Subscription not found after purchase".to_string(),
|
||||
}),
|
||||
)
|
||||
.into_response();
|
||||
};
|
||||
|
||||
let purchased_remaining =
|
||||
(updated_sub.purchased_credits_total - updated_sub.purchased_credits_used).max(0);
|
||||
let remaining_credits = crate::ai::credits::remaining_credits(&updated_sub);
|
||||
|
||||
if let Err(e) = AiCreditTransactionRepository::create(
|
||||
&state.pool,
|
||||
auth.user_id,
|
||||
"credit",
|
||||
"purchase",
|
||||
addon_amount,
|
||||
remaining_credits,
|
||||
None,
|
||||
Some(&format!("addon_code={}", body.addon_code)),
|
||||
)
|
||||
.await
|
||||
{
|
||||
tracing::error!("Failed to record AI credit purchase transaction: {}", e);
|
||||
}
|
||||
|
||||
(
|
||||
axum::http::StatusCode::OK,
|
||||
axum::Json(AddonPurchaseResponse {
|
||||
success: true,
|
||||
addon_balance: purchased_remaining,
|
||||
message: format!("Successfully added {} AI credits", addon_amount),
|
||||
}),
|
||||
axum::http::StatusCode::PAYMENT_REQUIRED,
|
||||
axum::Json(serde_json::json!({
|
||||
"success": false,
|
||||
"message": "Direct credit purchase is temporarily unavailable. Please use the AI credits checkout flow.",
|
||||
"code": "PAYMENT_VERIFICATION_REQUIRED",
|
||||
})),
|
||||
)
|
||||
.into_response()
|
||||
}
|
||||
|
|
@ -4006,11 +3959,28 @@ pub mod phase3 {
|
|||
}
|
||||
|
||||
/// POST /api/ai/plans/upgrade — upgrade the user's AI plan.
|
||||
///
|
||||
/// This previously upgraded to ANY plan (including enterprise) with no
|
||||
/// payment verification at all - only "free" is allowed here now, since
|
||||
/// there is no pricing/payment linkage for paid plans in this table yet.
|
||||
pub async fn ai_plan_upgrade(
|
||||
State(state): State<AppState>,
|
||||
auth: contracts::auth_middleware::AuthUser,
|
||||
Json(body): Json<PlanUpgradeBody>,
|
||||
) -> impl axum::response::IntoResponse {
|
||||
if !body.plan_code.trim().eq_ignore_ascii_case("free") {
|
||||
return (
|
||||
axum::http::StatusCode::PAYMENT_REQUIRED,
|
||||
axum::Json(PlanUpgradeResponse {
|
||||
success: false,
|
||||
plan: "Free".to_string(),
|
||||
monthly_limit: 10,
|
||||
message: "Paid plan upgrades require a verified payment and are not yet available through this endpoint.".to_string(),
|
||||
}),
|
||||
)
|
||||
.into_response();
|
||||
}
|
||||
|
||||
let plan = match AiPlanRepository::get_by_code(&state.pool, &body.plan_code).await {
|
||||
Ok(Some(plan)) => plan,
|
||||
Ok(None) => {
|
||||
|
|
@ -4443,10 +4413,6 @@ pub fn ai_router() -> Router<AppState> {
|
|||
.route("/plans/upgrade", post(phase3::ai_plan_upgrade))
|
||||
.merge(crate::handlers::ai_auto::ai_auto_router())
|
||||
.merge(crate::handlers::ai_phase4::phase4_router())
|
||||
.layer(axum::middleware::from_fn_with_state(
|
||||
(),
|
||||
crate::ai::middleware::ai_access_middleware,
|
||||
))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
use crate::ai::{credits, litellm, model_router, plans, usage};
|
||||
use crate::ai::{credits, litellm, model_router, orchestrator, plans, usage};
|
||||
use crate::AppState;
|
||||
use axum::{
|
||||
extract::{Query, State},
|
||||
|
|
@ -306,7 +306,7 @@ async fn ai_suggest(
|
|||
|
||||
let full_prompt = format!("{}\n\nPreferences/Context: {}\n\nSuggestions:", system_prompt, text);
|
||||
let (response_text, usage, request_id) = match client
|
||||
.chat_completion_text(&model_alias, None, &full_prompt, None)
|
||||
.chat_completion_text(&model_alias, Some(&orchestrator::effective_system_prompt(None)), &full_prompt, None)
|
||||
.await
|
||||
{
|
||||
Ok(r) => r,
|
||||
|
|
|
|||
|
|
@ -119,11 +119,11 @@ async fn main() {
|
|||
.nest("/api/admin/ai", handlers::admin_ai::admin_ai_router())
|
||||
// ── AI Assistant ──────────────────────────────────────────────────
|
||||
.nest("/api/ai", handlers::ai::ai_router().layer(axum::middleware::from_fn_with_state(
|
||||
(),
|
||||
state.clone(),
|
||||
crate::ai::middleware::ai_access_middleware,
|
||||
)))
|
||||
.nest("/api/ai/auto", handlers::ai_auto::ai_auto_router().layer(axum::middleware::from_fn_with_state(
|
||||
(),
|
||||
state.clone(),
|
||||
crate::ai::middleware::ai_access_middleware,
|
||||
)))
|
||||
.route("/health", get(|| async { "Users OK" }))
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue