Improve LLM

.github/workflows/docker-publish.yml

@@ -21,6 +21,15 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v4
 
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Download map assets (fonts, sprites, twemoji)
+        run: uv run python -m pipeline.download.map_assets --output frontend/public/assets
+
+      - name: Download arcgis data for finder
+        run: uv run python -m pipeline.download.arcgis --output property-data/arcgis_data.parquet
+
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
@@ -64,12 +73,6 @@ jobs:
           cache-from: type=gha,scope=screenshot
           cache-to: type=gha,mode=max,scope=screenshot
 
-      - name: Set up uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Download arcgis data for finder
-        run: uv run python -m pipeline.download.arcgis --output property-data/arcgis_data.parquet
-
       - name: Build and push finder service
         uses: docker/build-push-action@v6
         with:

docker-compose.yml

@@ -36,8 +36,8 @@ services:
       POCKETBASE_ADMIN_EMAIL: *pb-email
       POCKETBASE_ADMIN_PASSWORD: *pb-password
       SCREENSHOT_URL: http://screenshot:8002
-      OLLAMA_URL: http://host.docker.internal:11434
+      GEMINI_API_KEY: AIzaSyC2mQDcEwILHM3uOE2C-lxUQbQrKTX9Xi4
-      OLLAMA_MODEL: gpt-oss:20b
+      GEMINI_MODEL: gemini-3-flash-preview
       PUBLIC_URL: https://perfectpostcodes.schmelczer.dev
       GOOGLE_MAPS_API_KEY: "AIzaSyBgBn9LjrxHCjb9j1LZbLYpEdCJj-NkHPY"
       STRIPE_SECRET_KEY: sk_test_51SyVcePRjj2bdyn1HLkatQ5onwp8kamm41tjMcRdxXnJYWVPsVd9usMTOSNtNdGhrjbsrtNbgTdKXICg2qBiocEn00PvNDC0d3

frontend/src/components/map/AiFilterInput.tsx

@@ -42,6 +42,7 @@ interface AiFilterInputProps {
   error: string | null;
   errorType: AiFilterErrorType | null;
   notes: string | null;
+  summary: string | null;
   onSubmit: (query: string) => void;
   isLoggedIn: boolean;
   onLoginRequired: () => void;
@@ -52,6 +53,7 @@ export default memo(function AiFilterInput({
   error,
   errorType,
   notes,
+  summary,
   onSubmit,
   isLoggedIn,
   onLoginRequired,
@@ -88,7 +90,7 @@ export default memo(function AiFilterInput({
   );
 
   const hasContent = query.trim().length > 0;
-  const showExamples = expanded && !hasContent && !loading && !error && !notes;
+  const showExamples = expanded && !hasContent && !loading && !error && !notes && !summary;
 
   if (!expanded) {
     return (
@@ -173,6 +175,11 @@ export default memo(function AiFilterInput({
           {error}
         </p>
       )}
+      {summary && !error && !loading && (
+        <p className="mt-1 text-xs text-teal-600 dark:text-teal-400">
+          {summary}
+        </p>
+      )}
       {notes && !error && !loading && (
         <p className="mt-1 text-xs text-warm-500 dark:text-warm-400 italic">
           {notes}

frontend/src/components/map/Filters.tsx

@@ -92,6 +92,7 @@ interface FiltersProps {
   aiFilterError: string | null;
   aiFilterErrorType: AiFilterErrorType | null;
   aiFilterNotes: string | null;
+  aiFilterSummary: string | null;
   onAiFilterSubmit: (query: string) => void;
   isLoggedIn: boolean;
   onLoginRequired: () => void;
@@ -127,6 +128,7 @@ export default memo(function Filters({
   aiFilterError,
   aiFilterErrorType,
   aiFilterNotes,
+  aiFilterSummary,
   onAiFilterSubmit,
   isLoggedIn,
   onLoginRequired,
@@ -285,7 +287,7 @@ export default memo(function Filters({
         </div>
 
         <div ref={scrollRef} className="md:flex-1 md:overflow-y-auto">
-          <AiFilterInput loading={aiFilterLoading} error={aiFilterError} errorType={aiFilterErrorType} notes={aiFilterNotes} onSubmit={onAiFilterSubmit} isLoggedIn={isLoggedIn} onLoginRequired={onLoginRequired} />
+          <AiFilterInput loading={aiFilterLoading} error={aiFilterError} errorType={aiFilterErrorType} notes={aiFilterNotes} summary={aiFilterSummary} onSubmit={onAiFilterSubmit} isLoggedIn={isLoggedIn} onLoginRequired={onLoginRequired} />
           <div className="px-3 pb-2 space-y-2">
             <div className="flex rounded-lg bg-warm-100 dark:bg-warm-800 p-0.5">
               {(['historical', 'buy', 'rent'] as const).map((type) => {

frontend/src/components/map/MapPage.tsx

@@ -148,22 +148,33 @@ export default function MapPage({
 
   const handleAiFilterSubmit = useCallback(
     async (query: string) => {
-      const result = await aiFilters.fetchAiFilters(query);
+      // Build context from current filters for conversational refinement
+      const context = {
+        filters,
+        travelTime: travelTime.activeEntries.map((entry) => ({
+          mode: entry.mode,
+          label: entry.label,
+          min: entry.timeRange?.[0],
+          max: entry.timeRange?.[1],
+        })),
+      };
+      const hasContext =
+        Object.keys(context.filters).length > 0 || context.travelTime.length > 0;
+
+      const result = await aiFilters.fetchAiFilters(query, hasContext ? context : undefined);
       if (!result) return;
       handleSetFilters(result.filters);
-      // Apply travel time filters from AI
+      // Always sync travel time entries — clear stale ones when AI returns none
-      if (result.travelTimeFilters.length > 0) {
+      const newEntries = result.travelTimeFilters.map((tt) => ({
-        const newEntries = result.travelTimeFilters.map((tt) => ({
+        mode: tt.mode,
-          mode: tt.mode,
+        slug: tt.slug,
-          slug: tt.slug,
+        label: tt.label,
-          label: tt.label,
+        timeRange: [tt.min ?? 0, tt.max ?? 120] as [number, number],
-          timeRange: [tt.min ?? 0, tt.max ?? 120] as [number, number],
+        useBest: false,
-          useBest: false,
+      }));
-        }));
+      travelTime.handleSetEntries(newEntries);
-        travelTime.handleSetEntries(newEntries);
-      }
     },
-    [aiFilters.fetchAiFilters, handleSetFilters, travelTime.handleSetEntries]
+    [aiFilters.fetchAiFilters, handleSetFilters, travelTime.handleSetEntries, travelTime.activeEntries, filters]
   );
 
   const handleTravelTimeSetDestination = useCallback(
@@ -514,6 +525,7 @@ export default function MapPage({
       aiFilterError={aiFilters.error}
       aiFilterErrorType={aiFilters.errorType}
       aiFilterNotes={aiFilters.notes}
+      aiFilterSummary={aiFilters.summary}
       onAiFilterSubmit={handleAiFilterSubmit}
       isLoggedIn={!!user}
       onLoginRequired={onRegisterClick ?? (() => {})}

frontend/src/hooks/useAiFilters.ts

@@ -1,6 +1,6 @@
 import { useState, useCallback, useRef } from 'react';
 import type { FeatureFilters } from '../types';
-import type { TransportMode } from './useTravelTime';
+import type { TransportMode, TravelTimeEntry } from './useTravelTime';
 import { apiUrl, authHeaders, logNonAbortError } from '../lib/api';
 
 export interface AiTravelTimeFilter {
@@ -11,20 +11,54 @@ export interface AiTravelTimeFilter {
   max?: number;
 }
 
-interface AiFiltersResult {
+export interface AiFiltersResult {
   filters: FeatureFilters;
   travelTimeFilters: AiTravelTimeFilter[];
   notes: string;
+  /** Human-readable summary of what was set */
+  summary: string;
 }
 
 export type AiFilterErrorType = 'auth' | 'verification' | 'limit' | 'error';
 
+/** Context of currently active filters, sent for conversational refinement. */
+export interface AiFiltersContext {
+  filters: FeatureFilters;
+  travelTime: { mode: string; label: string; min?: number; max?: number }[];
+}
+
 interface UseAiFiltersResult {
-  fetchAiFilters: (query: string) => Promise<AiFiltersResult | null>;
+  fetchAiFilters: (query: string, context?: AiFiltersContext) => Promise<AiFiltersResult | null>;
   loading: boolean;
   error: string | null;
   errorType: AiFilterErrorType | null;
   notes: string | null;
+  summary: string | null;
+}
+
+/** Build a human-readable summary of the AI result. */
+function buildSummary(
+  filters: FeatureFilters,
+  travelTimeFilters: AiTravelTimeFilter[]
+): string {
+  const parts: string[] = [];
+
+  for (const [name, value] of Object.entries(filters)) {
+    if (Array.isArray(value) && value.length === 2 && typeof value[0] === 'number') {
+      parts.push(name);
+    } else if (Array.isArray(value)) {
+      parts.push(`${name}: ${(value as string[]).join(', ')}`);
+    }
+  }
+
+  for (const tt of travelTimeFilters) {
+    const bounds =
+      tt.max !== undefined ? `< ${tt.max} min` : tt.min !== undefined ? `> ${tt.min} min` : '';
+    parts.push(`${tt.mode} to ${tt.label} ${bounds}`.trim());
+  }
+
+  if (parts.length === 0) return 'No filters set';
+  return `Set ${parts.length} filter${parts.length > 1 ? 's' : ''}: ${parts.join(', ')}`;
 }
 
 export function useAiFilters(): UseAiFiltersResult {
@@ -32,77 +66,93 @@ export function useAiFilters(): UseAiFiltersResult {
   const [error, setError] = useState<string | null>(null);
   const [errorType, setErrorType] = useState<AiFilterErrorType | null>(null);
   const [notes, setNotes] = useState<string | null>(null);
+  const [summary, setSummary] = useState<string | null>(null);
   const abortRef = useRef<AbortController | null>(null);
 
-  const fetchAiFilters = useCallback(async (query: string): Promise<AiFiltersResult | null> => {
+  const fetchAiFilters = useCallback(
-    abortRef.current?.abort();
+    async (query: string, context?: AiFiltersContext): Promise<AiFiltersResult | null> => {
-    const controller = new AbortController();
+      abortRef.current?.abort();
-    abortRef.current = controller;
+      const controller = new AbortController();
+      abortRef.current = controller;
 
-    setLoading(true);
+      setLoading(true);
-    setError(null);
+      setError(null);
-    setErrorType(null);
+      setErrorType(null);
-    setNotes(null);
+      setNotes(null);
+      setSummary(null);
 
-    try {
+      try {
-      const url = apiUrl('ai-filters');
+        const url = apiUrl('ai-filters');
-      const response = await fetch(
+        const bodyObj: Record<string, unknown> = { query };
-        url,
+        if (context) {
-        authHeaders({
+          bodyObj.context = {
-          method: 'POST',
+            filters: context.filters,
-          headers: { 'Content-Type': 'application/json' },
+            travel_time: context.travelTime,
-          body: JSON.stringify({ query }),
+          };
-          signal: controller.signal,
-        })
-      );
-
-      if (!response.ok) {
-        const text = await response.text();
-        if (response.status === 401) {
-          setErrorType('auth');
-          setError(text || 'Login required');
-        } else if (response.status === 403) {
-          setErrorType('verification');
-          setError(text || 'Email verification required');
-        } else if (response.status === 429) {
-          setErrorType('limit');
-          setError(text || 'Weekly usage limit reached');
-        } else {
-          setErrorType('error');
-          setError(text || `HTTP ${response.status}`);
         }
+        const response = await fetch(
+          url,
+          authHeaders({
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify(bodyObj),
+            signal: controller.signal,
+          })
+        );
+
+        if (!response.ok) {
+          const text = await response.text();
+          if (response.status === 401) {
+            setErrorType('auth');
+            setError(text || 'Login required');
+          } else if (response.status === 403) {
+            setErrorType('verification');
+            setError(text || 'Email verification required');
+          } else if (response.status === 429) {
+            setErrorType('limit');
+            setError(text || 'Weekly usage limit reached');
+          } else {
+            setErrorType('error');
+            setError(text || `HTTP ${response.status}`);
+          }
+          setLoading(false);
+          return null;
+        }
+
+        const json = await response.json();
+        const travelTimeFilters: AiTravelTimeFilter[] = (json.travel_time_filters || []).map(
+          (tt: { mode: string; slug: string; label: string; min?: number; max?: number }) => ({
+            mode: tt.mode as TransportMode,
+            slug: tt.slug,
+            label: tt.label,
+            min: tt.min,
+            max: tt.max,
+          })
+        );
+        const filters = json.filters as FeatureFilters;
+        const summaryText = buildSummary(filters, travelTimeFilters);
+        const result: AiFiltersResult = {
+          filters,
+          travelTimeFilters,
+          notes: json.notes || '',
+          summary: summaryText,
+        };
+        setNotes(result.notes || null);
+        setSummary(summaryText);
+        setLoading(false);
+        return result;
+      } catch (err) {
+        if (controller.signal.aborted) return null;
+        logNonAbortError('ai-filters', err);
+        const message = err instanceof Error ? err.message : 'Failed to generate filters';
+        setErrorType('error');
+        setError(message);
         setLoading(false);
         return null;
       }
+    },
+    []
+  );
 
-      const json = await response.json();
+  return { fetchAiFilters, loading, error, errorType, notes, summary };
-      const travelTimeFilters: AiTravelTimeFilter[] = (json.travel_time_filters || []).map(
-        (tt: { mode: string; slug: string; label: string; min?: number; max?: number }) => ({
-          mode: tt.mode as TransportMode,
-          slug: tt.slug,
-          label: tt.label,
-          min: tt.min,
-          max: tt.max,
-        })
-      );
-      const result: AiFiltersResult = {
-        filters: json.filters as FeatureFilters,
-        travelTimeFilters,
-        notes: json.notes || '',
-      };
-      setNotes(result.notes || null);
-      setLoading(false);
-      return result;
-    } catch (err) {
-      if (controller.signal.aborted) return null;
-      logNonAbortError('ai-filters', err);
-      const message = err instanceof Error ? err.message : 'Failed to generate filters';
-      setErrorType('error');
-      setError(message);
-      setLoading(false);
-      return null;
-    }
-  }, []);
-
-  return { fetchAiFilters, loading, error, errorType, notes };
 }

pipeline/download/pois.py

@@ -7,18 +7,18 @@ import polars as pl
 from shapely.geometry import Point
 from tqdm import tqdm
 
-from pipeline.utils.england_geometry import load_england_polygon
+from pipeline.utils.england_geometry import (
+    ENGLAND_BBOX_EAST,
+    ENGLAND_BBOX_NORTH,
+    ENGLAND_BBOX_SOUTH,
+    ENGLAND_BBOX_WEST,
+    load_england_polygon,
+)
 
 BATCH_SIZE = 50_000
 
 MIN_OCCURENCE_COUNT = 20
 
-# Bounding box for fast pre-filtering before the precise polygon check
-ENGLAND_BBOX_WEST = -6.45
-ENGLAND_BBOX_SOUTH = 49.85
-ENGLAND_BBOX_EAST = 1.77
-ENGLAND_BBOX_NORTH = 55.82
-
 POI_TAG_KEYS: list[str] = [
     "amenity",
     "building",

server-rs/src/consts.rs

@@ -15,7 +15,9 @@ pub const MAX_PRICE_HISTORY_POINTS: usize = 5000;
 pub const POSTCODE_SEARCH_OFFSET: f64 = 0.02;
 
 pub const AI_FILTERS_MAX_TOKENS: usize = 2000;
-pub const AI_FILTERS_TEMPERATURE: f32 = 0.0;
+/// Gemini 3 recommends 1.0; lower values can cause looping or degraded performance.
+pub const AI_FILTERS_TEMPERATURE: f32 = 1.0;
+pub const AI_FILTERS_WEEKLY_TOKEN_LIMIT: u64 = 10_000_000;
 
 /// Timeout for outbound HTTP service calls (seconds).
 pub const SERVICE_CALL_TIMEOUT: u64 = 120;

server-rs/src/main.rs

@@ -94,13 +94,13 @@ struct Cli {
     #[arg(long, env = "POCKETBASE_ADMIN_PASSWORD")]
     pocketbase_admin_password: String,
 
-    /// Ollama server URL (e.g. http://ollama:11434)
+    /// Gemini API key
-    #[arg(long, env = "OLLAMA_URL")]
+    #[arg(long, env = "GEMINI_API_KEY")]
-    ollama_url: String,
+    gemini_api_key: String,
 
-    /// Ollama model name
+    /// Gemini model name (e.g. gemini-2.0-flash)
-    #[arg(long, env = "OLLAMA_MODEL")]
+    #[arg(long, env = "GEMINI_MODEL")]
-    ollama_model: String,
+    gemini_model: String,
 
     /// Path to precomputed travel times directory (contains mode subdirs with parquet files)
     #[arg(long, env = "TRAVEL_TIMES")]
@@ -301,9 +301,7 @@ async fn main() -> anyhow::Result<()> {
         "Precomputed features response"
     );
 
-    let ai_filters_schema = routes::build_ollama_schema(&features_response);
+    // AI filters system prompt built after travel_time_store is loaded (needs mode counts)
-    let ai_filters_system_prompt = routes::build_system_prompt(&features_response);
-    info!("Precomputed AI filters schema and system prompt");
 
     // Record data loading metrics
     metrics::record_data_stats(
@@ -331,10 +329,7 @@ async fn main() -> anyhow::Result<()> {
         &cli.google_oauth_client_secret,
     )
     .await?;
-    info!(
+    info!("Gemini configured (model: {})", cli.gemini_model);
-        "Ollama configured: {} (model: {})",
-        cli.ollama_url, cli.ollama_model
-    );
     let tt_path = &cli.travel_times;
     if !tt_path.exists() {
         bail!(
@@ -352,6 +347,23 @@ async fn main() -> anyhow::Result<()> {
         Arc::new(store)
     };
 
+    let mode_destinations: Vec<(String, usize)> = travel_time_store
+        .available_modes
+        .iter()
+        .map(|mode| {
+            let count = travel_time_store
+                .destinations
+                .get(mode.as_str())
+                .map(|slugs| slugs.len())
+                .unwrap_or(0);
+            (mode.clone(), count)
+        })
+        .filter(|(_, count)| *count > 0)
+        .collect();
+    let ai_filters_system_prompt =
+        routes::build_system_prompt(&features_response, &mode_destinations);
+    info!("Precomputed AI filters system prompt");
+
     let token_cache = Arc::new(auth::TokenCache::new());
 
     let state = Arc::new(AppState {
@@ -370,16 +382,16 @@ async fn main() -> anyhow::Result<()> {
         features_response,
         screenshot_url: cli.screenshot_url,
         public_url: cli.public_url,
+        is_dev: index_html.is_none(),
         index_html,
         http_client,
         pocketbase_url: cli.pocketbase_url,
         pocketbase_admin_email: cli.pocketbase_admin_email,
         pocketbase_admin_password: cli.pocketbase_admin_password,
-        ollama_url: cli.ollama_url,
+        gemini_api_key: cli.gemini_api_key,
-        ollama_model: cli.ollama_model,
+        gemini_model: cli.gemini_model,
         travel_time_store,
         token_cache,
-        ai_filters_schema,
         ai_filters_system_prompt,
         google_maps_api_key: cli.google_maps_api_key,
         stripe_secret_key: cli.stripe_secret_key,
@@ -504,7 +516,7 @@ async fn main() -> anyhow::Result<()> {
         )
         .route(
             "/api/ai-filters",
-            post(move |body| routes::post_ai_filters(state_ai_filters.clone(), body))
+            post(move |ext, body| routes::post_ai_filters(state_ai_filters.clone(), ext, body))
                 .layer(ConcurrencyLimitLayer::new(5)),
         )
         .route(

server-rs/src/pocketbase.rs

@@ -240,9 +240,11 @@ async fn ensure_user_fields(
     let has_is_admin = fields.iter().any(|f| f["name"] == "is_admin");
     let has_subscription = fields.iter().any(|f| f["name"] == "subscription");
     let has_newsletter = fields.iter().any(|f| f["name"] == "newsletter");
+    let has_ai_tokens_used = fields.iter().any(|f| f["name"] == "ai_tokens_used");
+    let has_ai_tokens_week = fields.iter().any(|f| f["name"] == "ai_tokens_week");
 
-    if has_is_admin && has_subscription && has_newsletter {
+    if has_is_admin && has_subscription && has_newsletter && has_ai_tokens_used && has_ai_tokens_week {
-        info!("PocketBase users collection already has is_admin, subscription, and newsletter fields");
+        info!("PocketBase users collection already has all required fields");
         return Ok(());
     }
 
@@ -269,6 +271,20 @@ async fn ensure_user_fields(
         }));
     }
 
+    if !has_ai_tokens_used {
+        new_fields.push(serde_json::json!({
+            "name": "ai_tokens_used",
+            "type": "number",
+        }));
+    }
+
+    if !has_ai_tokens_week {
+        new_fields.push(serde_json::json!({
+            "name": "ai_tokens_week",
+            "type": "number",
+        }));
+    }
+
     let patch_resp = client
         .patch(&url)
         .header("Authorization", format!("Bearer {token}"))

server-rs/src/routes.rs

@@ -26,7 +26,7 @@ pub(crate) mod travel_time;
 mod travel_destinations;
 mod travel_modes;
 
-pub use ai_filters::{build_ollama_schema, build_system_prompt, post_ai_filters};
+pub use ai_filters::{build_system_prompt, post_ai_filters};
 pub use checkout::post_checkout;
 pub use export::get_export;
 pub use features::{build_features_response, get_features, FeatureInfo, FeaturesResponse};

server-rs/src/routes/ai_filters.rs

@@ -2,76 +2,190 @@ use std::sync::Arc;
 
 use axum::http::StatusCode;
 use axum::response::Json;
+use axum::Extension;
 use serde::{Deserialize, Serialize};
 use serde_json::{json, Value};
 use tracing::{info, warn};
 
-use crate::consts::{AI_FILTERS_MAX_TOKENS, AI_FILTERS_TEMPERATURE};
+use crate::auth::OptionalUser;
+use crate::consts::{AI_FILTERS_MAX_TOKENS, AI_FILTERS_TEMPERATURE, AI_FILTERS_WEEKLY_TOKEN_LIMIT};
+use crate::data::slugify;
+use crate::pocketbase::auth_superuser;
 use crate::routes::{FeatureInfo, FeaturesResponse};
 use crate::state::AppState;
-use crate::utils::{extract_ollama_content, ollama_chat, strip_think_blocks};
+use crate::utils::gemini_chat;
+
+#[derive(Deserialize)]
+pub struct AiFiltersContext {
+    filters: Value,
+    #[serde(default)]
+    travel_time: Vec<AiTravelTimeContext>,
+}
+
+#[derive(Deserialize)]
+pub struct AiTravelTimeContext {
+    mode: String,
+    label: String,
+    min: Option<f32>,
+    max: Option<f32>,
+}
 
 #[derive(Deserialize)]
 pub struct AiFiltersRequest {
     query: String,
+    /// Current filters for conversational refinement (e.g. "make it cheaper")
+    context: Option<AiFiltersContext>,
+}
+
+#[derive(Serialize)]
+pub struct TravelTimeFilter {
+    mode: String,
+    slug: String,
+    label: String,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    min: Option<f32>,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    max: Option<f32>,
 }
 
 #[derive(Serialize)]
 pub struct AiFiltersResponse {
     filters: Value,
+    #[serde(skip_serializing_if = "Vec::is_empty")]
+    travel_time_filters: Vec<TravelTimeFilter>,
     /// What the LLM couldn't map to existing filters (empty if everything matched)
     #[serde(skip_serializing_if = "String::is_empty")]
     notes: String,
 }
 
-/// Build a JSON schema for Ollama structured output.
+/// Strip markdown code fences (```json ... ``` or ``` ... ```) from LLM output.
-///
+/// Models occasionally wrap JSON in markdown fencing even when told not to.
-/// Uses two arrays (`numeric_filters` and `enum_filters`) instead of one property
+fn strip_markdown_fences(text: &str) -> &str {
-/// per feature, because Ollama converts JSON schema to GBNF grammar and a schema
+    let trimmed = text.trim();
-/// with 50+ optional keys causes a combinatorial explosion that crashes the parser.
+
-/// Array-based schema keeps the grammar small and constant-size.
+    // Try ```json\n...\n``` or ```\n...\n```
-pub fn build_ollama_schema(_features: &FeaturesResponse) -> Value {
+    if let Some(rest) = trimmed.strip_prefix("```") {
-    json!({
+        // Skip optional language tag (e.g. "json")
-        "type": "object",
+        let rest = if let Some(newline_pos) = rest.find('\n') {
-        "properties": {
+            &rest[newline_pos + 1..]
-            "numeric_filters": {
+        } else {
-                "type": "array",
+            return trimmed;
-                "items": {
+        };
-                    "type": "object",
+        if let Some(content) = rest.strip_suffix("```") {
-                    "properties": {
+            return content.trim();
-                        "name": { "type": "string" },
-                        "bound": { "type": "string", "enum": ["min", "max"] },
-                        "value": { "type": "number" }
-                    },
-                    "required": ["name", "bound", "value"]
-                }
-            },
-            "enum_filters": {
-                "type": "array",
-                "items": {
-                    "type": "object",
-                    "properties": {
-                        "name": { "type": "string" },
-                        "values": { "type": "array", "items": { "type": "string" } }
-                    },
-                    "required": ["name", "values"]
-                }
-            },
-            "notes": {
-                "type": "string"
-            }
         }
-    })
+    }
+
+    trimmed
+}
+
+/// Build the Gemini tool declaration for destination search.
+fn build_tool_declarations(state: &AppState) -> Value {
+    let modes: Vec<&str> = state
+        .travel_time_store
+        .available_modes
+        .iter()
+        .map(|mode| mode.as_str())
+        .collect();
+
+    json!([{
+        "functionDeclarations": [{
+            "name": "search_destinations",
+            "description": "Search for available travel time destinations (cities, stations, towns) that have precomputed travel time data. Call this when the user mentions wanting to be near, close to, or within a certain travel time of a specific place.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "query": {
+                        "type": "string",
+                        "description": "Place name to search for (e.g. 'Manchester', 'Kings Cross', 'Heathrow')"
+                    },
+                    "mode": {
+                        "type": "string",
+                        "enum": modes,
+                        "description": "Transport mode to search destinations for"
+                    }
+                },
+                "required": ["query", "mode"]
+            }
+        }]
+    }])
+}
+
+/// Execute a destination search against PlaceData + TravelTimeStore.
+/// Returns matching destinations as a JSON value with `results` and optional `message`.
+///
+/// Uses word-based matching: all words in the query must appear somewhere in the
+/// place name (order-independent). Also matches against slugs for short queries.
+fn execute_destination_search(state: &AppState, query: &str, mode: &str) -> Value {
+    let query_lower = query.to_lowercase();
+    let query_words: Vec<&str> = query_lower.split_whitespace().collect();
+    let query_slug = slugify(query);
+    let tt_store = &state.travel_time_store;
+    let pd = &state.place_data;
+
+    let slug_set = match tt_store.destinations.get(mode) {
+        Some(slugs) => slugs,
+        None => return json!({ "results": [], "message": format!("No travel data available for mode '{}'", mode) }),
+    };
+
+    // Find places matching the query that have travel time data.
+    // A place matches if ALL query words appear in its name, OR its slug matches the query slug.
+    let mut matches: Vec<(usize, String, u8, u32)> = pd
+        .name_lower
+        .iter()
+        .enumerate()
+        .filter_map(|(idx, name_lower)| {
+            let words_match = query_words.iter().all(|word| name_lower.contains(word));
+            let slug = slugify(&pd.name[idx]);
+            let slug_match = slug.contains(&query_slug) || query_slug.contains(&slug);
+            if !words_match && !slug_match {
+                return None;
+            }
+            if slug_set.contains(&slug) {
+                Some((idx, slug, pd.type_rank[idx], pd.population[idx]))
+            } else {
+                None
+            }
+        })
+        .collect();
+
+    // Sort: type rank asc, population desc
+    matches.sort_unstable_by(|a, b| a.2.cmp(&b.2).then(b.3.cmp(&a.3)));
+    matches.truncate(10);
+
+    if matches.is_empty() {
+        info!(query = query, mode = mode, "Destination search returned no results");
+        return json!({
+            "results": [],
+            "message": format!("No travel time data available for '{}' by {}. This destination cannot be used as a travel time filter.", query, mode)
+        });
+    }
+
+    let results: Vec<Value> = matches
+        .into_iter()
+        .map(|(idx, slug, ..)| {
+            json!({
+                "name": pd.name[idx],
+                "slug": slug,
+                "place_type": pd.place_type.get(idx).to_string(),
+            })
+        })
+        .collect();
+
+    json!({ "results": results })
 }
 
 /// Build the complete system prompt for AI filters.
 ///
-/// Contains: role instructions, feature catalogue, few-shot examples, output rules.
+/// Contains: role instructions, feature catalogue, travel time info,
+/// few-shot examples, output rules.
 /// Precomputed at startup and cached in AppState.
-pub fn build_system_prompt(features: &FeaturesResponse) -> String {
+pub fn build_system_prompt(
+    features: &FeaturesResponse,
+    mode_destinations: &[(String, usize)],
+) -> String {
     let mut parts = Vec::new();
 
-    // Role and task description
     parts.push(
         "You are a UK property search assistant. \
          The user describes their ideal property or area in natural language. \
@@ -91,10 +205,61 @@ pub fn build_system_prompt(features: &FeaturesResponse) -> String {
            (note: this counts bedrooms + living rooms combined, so 3 bed ~ min 4).\n\
          - If the user mentions something that has no matching filter, put it in \"notes\" \
            as a short phrase (e.g. \"No filter for: garden, sea view\"). \
-           If everything was matched, set \"notes\" to an empty string."
+           If everything was matched, set \"notes\" to an empty string.\n\
+         \n\
+         CONVERSATIONAL REFINEMENT:\n\
+         The user's message may include their currently active filters as context. \
+         When context is provided:\n\
+         - \"make it cheaper\" / \"lower the price\" = adjust the existing price filter down\n\
+         - \"also add ...\" / \"and good schools\" = keep existing filters and add new ones\n\
+         - \"remove the ...\" / \"drop the ...\" = return filters WITHOUT the mentioned one\n\
+         - If the request is a completely new search (not a refinement), ignore the context \
+           and build filters from scratch.\n\
+         - Always output the COMPLETE set of filters (existing + modified), not just the changes."
             .to_string(),
     );
 
+    // Travel time section with available modes
+    let modes_list = mode_destinations
+        .iter()
+        .map(|(mode, count)| format!("- {} ({} destinations available)", mode, count))
+        .collect::<Vec<_>>()
+        .join("\n");
+
+    parts.push(format!(
+        "\n--- TRAVEL TIME FILTERS ---\n\
+         You can add travel time filters when the user mentions commute times, \
+         proximity to places, or wanting to be near/within X minutes of somewhere.\n\
+         \n\
+         Available transport modes (only use modes that have destinations):\n\
+         {}\n\
+         - \"car\" / \"drive\" / \"driving\" = car mode\n\
+         - \"cycle\" / \"bike\" / \"cycling\" = bicycle mode\n\
+         - \"walk\" / \"walking\" / \"on foot\" = walking mode\n\
+         - \"train\" / \"tube\" / \"bus\" / \"public transport\" / \"commute\" = transit mode\n\
+         \n\
+         When the user mentions a specific place, you MUST call the search_destinations \
+         tool to find the exact slug. Use the name and slug from the search results.\n\
+         If search_destinations returns an empty array, the destination is not available — \
+         mention it in \"notes\" (e.g. \"No travel data for: Gatwick Airport\") and do NOT \
+         include a travel_time_filter for it.\n\
+         \n\
+         Travel time values are in MINUTES (0-120 range).\n\
+         - \"within 30 minutes\" = max 30\n\
+         - \"at least 10 minutes\" = min 10\n\
+         - \"30-45 minute commute\" = min 30, max 45\n\
+         - If only a max is given, omit min (and vice versa).\n\
+         \n\
+         INFERRING TRANSPORT MODE (when the user does not specify one explicitly):\n\
+         - \"commute\" to a major city centre or station = transit\n\
+         - \"near\" / \"close to\" a city centre or station = transit\n\
+         - \"near\" / \"close to\" a smaller town, village, or rural area = car\n\
+         - \"drive\" / \"driving distance\" / \"driving time\" = always car\n\
+         - If multiple modes are plausible, prefer transit for urban destinations \
+           (London, Manchester, Birmingham, Leeds, etc.) and car for everything else.",
+        modes_list,
+    ));
+
     // Feature catalogue
     parts.push("\n--- AVAILABLE FEATURES ---\n".to_string());
     for group in &features.groups {
@@ -148,6 +313,7 @@ pub fn build_system_prompt(features: &FeaturesResponse) -> String {
          Output: {\"numeric_filters\": [{\"name\": \"Last known price\", \"bound\": \"max\", \"value\": 400000}], \
          \"enum_filters\": [{\"name\": \"Leasehold/Freehold\", \"values\": [\"Freehold\"]}, \
          {\"name\": \"Property type\", \"values\": [\"Detached\", \"Semi-Detached\", \"Terraced\"]}], \
+         \"travel_time_filters\": [], \
          \"notes\": \"\"}"
             .to_string(),
     );
@@ -161,7 +327,7 @@ pub fn build_system_prompt(features: &FeaturesResponse) -> String {
          {\"name\": \"Good+ primary schools within 5km\", \"bound\": \"min\", \"value\": 5}, \
          {\"name\": \"Good+ secondary schools within 5km\", \"bound\": \"min\", \"value\": 2}, \
          {\"name\": \"Number of parks within 2km\", \"bound\": \"min\", \"value\": 3}], \
-         \"enum_filters\": [], \"notes\": \"\"}"
+         \"enum_filters\": [], \"travel_time_filters\": [], \"notes\": \"\"}"
             .to_string(),
     );
 
@@ -172,18 +338,37 @@ pub fn build_system_prompt(features: &FeaturesResponse) -> String {
          {\"name\": \"Number of bedrooms & living rooms\", \"bound\": \"min\", \"value\": 4}], \
          \"enum_filters\": [{\"name\": \"Property type\", \"values\": [\"Flats/Maisonettes\"]}, \
          {\"name\": \"Max available download speed (Mbps)\", \"values\": [\"100\", \"300\", \"1000\"]}], \
+         \"travel_time_filters\": [], \
          \"notes\": \"No filter for: beach proximity\"}"
             .to_string(),
     );
 
     parts.push(
-        "\nUser: \"large family home with a garden near restaurants\"\n\
+        "\nUser: \"within 30 minutes commute of Kings Cross, under 500k\"\n\
+         (After calling search_destinations for \"Kings Cross\" with mode \"transit\" \
+         and getting [{\"name\": \"Kings Cross\", \"slug\": \"kings-cross\", \"place_type\": \"station\"}])\n\
+         Output: {\"numeric_filters\": [\
+         {\"name\": \"Last known price\", \"bound\": \"max\", \"value\": 500000}], \
+         \"enum_filters\": [], \
+         \"travel_time_filters\": [{\"mode\": \"transit\", \"slug\": \"kings-cross\", \
+         \"label\": \"Kings Cross\", \"bound\": \"max\", \"value\": 30}], \
+         \"notes\": \"\"}"
+            .to_string(),
+    );
+
+    parts.push(
+        "\nUser: \"family home with garden, 45 min drive from Manchester, good schools\"\n\
+         (After calling search_destinations for \"Manchester\" with mode \"car\" \
+         and getting [{\"name\": \"Manchester\", \"slug\": \"manchester\", \"place_type\": \"city\"}])\n\
          Output: {\"numeric_filters\": [\
          {\"name\": \"Total floor area (sqm)\", \"bound\": \"min\", \"value\": 100}, \
          {\"name\": \"Number of bedrooms & living rooms\", \"bound\": \"min\", \"value\": 5}, \
-         {\"name\": \"Number of restaurants within 2km\", \"bound\": \"min\", \"value\": 10}], \
+         {\"name\": \"Good+ primary schools within 5km\", \"bound\": \"min\", \"value\": 5}, \
+         {\"name\": \"Good+ secondary schools within 5km\", \"bound\": \"min\", \"value\": 2}], \
          \"enum_filters\": [{\"name\": \"Property type\", \
          \"values\": [\"Detached\", \"Semi-Detached\"]}], \
+         \"travel_time_filters\": [{\"mode\": \"car\", \"slug\": \"manchester\", \
+         \"label\": \"Manchester\", \"bound\": \"max\", \"value\": 45}], \
          \"notes\": \"No filter for: garden\"}"
             .to_string(),
     );
@@ -191,7 +376,8 @@ pub fn build_system_prompt(features: &FeaturesResponse) -> String {
     // Output format reminder
     parts.push(
         "\n--- OUTPUT FORMAT ---\n\
-         {\"numeric_filters\": [{\"name\": \"...\", \"bound\": \"min\"|\"max\", \"value\": N}, ...], \"enum_filters\": [...], \"notes\": \"...\"}\n\
+         {\"numeric_filters\": [...], \"enum_filters\": [...], \"travel_time_filters\": [{\"mode\": \"...\", \"slug\": \"...\", \"label\": \"...\", \"bound\": \"min\"|\"max\", \"value\": N}, ...], \"notes\": \"...\"}\n\
+         - travel_time_filters: use ONLY slugs returned by search_destinations. If a place isn't found, mention it in notes.\n\
          Respond with ONLY the JSON object. No explanation."
             .to_string(),
     );
@@ -199,86 +385,393 @@ pub fn build_system_prompt(features: &FeaturesResponse) -> String {
     parts.join("\n")
 }
 
-pub async fn post_ai_filters(
+/// Monotonically increasing week number derived from Unix epoch.
-    state: Arc<AppState>,
+/// Resets every 7 days (604800 seconds). Used for weekly rate limiting.
-    Json(req): Json<AiFiltersRequest>,
+fn current_week_number() -> u64 {
-) -> Result<Json<AiFiltersResponse>, (StatusCode, String)> {
+    let secs = std::time::SystemTime::now()
-    info!(query = %req.query, "POST /api/ai-filters");
+        .duration_since(std::time::UNIX_EPOCH)
-
+        .expect("system time before epoch")
-    let url = format!("{}/api/chat", state.ollama_url);
+        .as_secs();
-    let body = json!({
+    secs / 604_800
-        "model": state.ollama_model,
-        "messages": [
-            { "role": "system", "content": state.ai_filters_system_prompt },
-            { "role": "user", "content": req.query }
-        ],
-        "stream": false,
-        "format": state.ai_filters_schema,
-        "options": {
-            "temperature": AI_FILTERS_TEMPERATURE,
-            "num_predict": AI_FILTERS_MAX_TOKENS,
-        }
-    });
-
-    // Try up to 2 attempts — LLMs occasionally return empty content (e.g. only
-    // <think> blocks with no JSON output), which is transient and usually
-    // succeeds on retry.
-    let mut last_err = None;
-    for attempt in 0..2 {
-        let raw = call_ollama_and_parse(&state.http_client, &url, &body).await;
-        match raw {
-            Ok(raw) => {
-                let filters = validate_and_convert(&raw, &state.features_response);
-                let notes = raw
-                    .get("notes")
-                    .and_then(|val| val.as_str())
-                    .unwrap_or("")
-                    .to_string();
-                return Ok(Json(AiFiltersResponse { filters, notes }));
-            }
-            Err(err) => {
-                if attempt == 0 {
-                    warn!("LLM attempt 1 failed, retrying: {}", err.1);
-                }
-                last_err = Some(err);
-            }
-        }
-    }
-
-    Err(last_err.unwrap())
 }
 
-/// Call Ollama and parse the response content as JSON.
+/// Fetch the user's current AI token usage from PocketBase.
-///
+/// Returns `(tokens_used, week_number)`.
-/// Returns an error if: the HTTP call fails, the response is malformed,
+async fn fetch_ai_usage(
-/// the content is empty after stripping think blocks, or the content is
+    state: &AppState,
-/// not valid JSON.
+    user_id: &str,
-async fn call_ollama_and_parse(
+) -> Result<(u64, u64), (StatusCode, String)> {
-    client: &reqwest::Client,
+    let pb_url = state.pocketbase_url.trim_end_matches('/');
-    url: &str,
+    let token = auth_superuser(
-    body: &Value,
+        &state.http_client,
-) -> Result<Value, (StatusCode, String)> {
+        pb_url,
-    let json_resp = ollama_chat(client, url, body).await?;
+        &state.pocketbase_admin_email,
-    let content = extract_ollama_content(&json_resp)?;
+        &state.pocketbase_admin_password,
+    )
+    .await
+    .map_err(|err| {
+        warn!("Failed to auth superuser for AI usage check: {err}");
+        (StatusCode::BAD_GATEWAY, "Internal error".into())
+    })?;
 
-    let content = strip_think_blocks(content);
+    let url = format!("{pb_url}/api/collections/users/records/{user_id}");
-    let content = content.trim();
+    let resp = state
+        .http_client
+        .get(&url)
+        .header("Authorization", format!("Bearer {token}"))
+        .send()
+        .await
+        .map_err(|err| {
+            warn!("Failed to fetch user record for AI usage: {err}");
+            (StatusCode::BAD_GATEWAY, "Internal error".into())
+        })?;
 
-    if content.is_empty() {
+    if !resp.status().is_success() {
-        warn!("LLM returned empty content after stripping think blocks");
+        let status = resp.status();
+        warn!("PocketBase user fetch failed ({status})");
+        return Err((StatusCode::BAD_GATEWAY, "Internal error".into()));
+    }
+
+    let body: Value = resp.json().await.map_err(|err| {
+        warn!("Failed to parse user record: {err}");
+        (StatusCode::BAD_GATEWAY, "Internal error".into())
+    })?;
+
+    let tokens_used = body
+        .get("ai_tokens_used")
+        .and_then(|val| val.as_u64())
+        .unwrap_or(0);
+    let week = body
+        .get("ai_tokens_week")
+        .and_then(|val| val.as_u64())
+        .unwrap_or(0);
+
+    Ok((tokens_used, week))
+}
+
+/// Update the user's AI token usage in PocketBase.
+/// Best-effort — logs warnings on failure but does not propagate errors.
+async fn update_ai_usage(state: &AppState, user_id: &str, tokens_used: u64, week: u64) {
+    let pb_url = state.pocketbase_url.trim_end_matches('/');
+    let token = match auth_superuser(
+        &state.http_client,
+        pb_url,
+        &state.pocketbase_admin_email,
+        &state.pocketbase_admin_password,
+    )
+    .await
+    {
+        Ok(tk) => tk,
+        Err(err) => {
+            warn!("Failed to auth superuser for AI usage update: {err}");
+            return;
+        }
+    };
+
+    let url = format!("{pb_url}/api/collections/users/records/{user_id}");
+    let res = state
+        .http_client
+        .patch(&url)
+        .header("Authorization", format!("Bearer {token}"))
+        .json(&json!({
+            "ai_tokens_used": tokens_used,
+            "ai_tokens_week": week,
+        }))
+        .send()
+        .await;
+
+    match res {
+        Ok(resp) if resp.status().is_success() => {}
+        Ok(resp) => {
+            let status = resp.status();
+            warn!("Failed to update AI usage ({status})");
+        }
+        Err(err) => warn!("Failed to update AI usage: {err}"),
+    }
+}
+
+/// Maximum number of round trips (function calls + retries) before giving up.
+const MAX_TOOL_ROUNDS: usize = 5;
+
+pub async fn post_ai_filters(
+    state: Arc<AppState>,
+    Extension(user): Extension<OptionalUser>,
+    Json(req): Json<AiFiltersRequest>,
+) -> Result<Json<AiFiltersResponse>, (StatusCode, String)> {
+    // Auth check
+    let user = user
+        .0
+        .ok_or((StatusCode::UNAUTHORIZED, "Login required".into()))?;
+
+    // Email verification check (skipped in dev mode)
+    if !user.verified && !state.is_dev {
         return Err((
-            StatusCode::BAD_GATEWAY,
+            StatusCode::FORBIDDEN,
-            "LLM returned empty content (no JSON output)".into(),
+            "Please verify your email to use AI filters".into(),
         ));
     }
 
-    serde_json::from_str(content).map_err(|err| {
+    // Check weekly token usage
-        warn!(error = %err, content = %content, "Failed to parse LLM JSON output");
+    let current_week = current_week_number();
-        (
+    let (stored_tokens, stored_week) = fetch_ai_usage(&state, &user.id).await?;
-            StatusCode::BAD_GATEWAY,
+    let tokens_used = if stored_week == current_week {
-            format!("Failed to parse LLM output as JSON: {}", err),
+        stored_tokens
+    } else {
+        0
+    };
+
+    if tokens_used >= AI_FILTERS_WEEKLY_TOKEN_LIMIT {
+        return Err((
+            StatusCode::TOO_MANY_REQUESTS,
+            "Weekly AI usage limit reached. Resets next week.".into(),
+        ));
+    }
+
+    info!(query = %req.query, user_id = %user.id, "POST /api/ai-filters");
+
+    let tools = build_tool_declarations(&state);
+
+    // Build user message with optional context for conversational refinement
+    let user_text = if let Some(ref ctx) = req.context {
+        let mut msg = String::new();
+        msg.push_str("Currently active filters:\n");
+        msg.push_str(&serde_json::to_string(&ctx.filters).unwrap_or_default());
+        if !ctx.travel_time.is_empty() {
+            msg.push_str("\nCurrently active travel time filters:\n");
+            for tt in &ctx.travel_time {
+                let bounds = match (tt.min, tt.max) {
+                    (Some(min), Some(max)) => format!("{}-{} min", min, max),
+                    (Some(min), None) => format!("min {} min", min),
+                    (None, Some(max)) => format!("max {} min", max),
+                    (None, None) => "no range".to_string(),
+                };
+                msg.push_str(&format!("- {} to {} ({})\n", tt.mode, tt.label, bounds));
+            }
+        }
+        msg.push_str(&format!("\nUser request: {}", req.query));
+        msg
+    } else {
+        req.query.clone()
+    };
+
+    let mut contents = vec![json!({
+        "role": "user",
+        "parts": [{ "text": user_text }]
+    })];
+
+    let mut total_tokens_accumulated: u64 = 0;
+
+    // Function calling loop: model may call search_destinations, we execute and feed back
+    for round in 0..MAX_TOOL_ROUNDS {
+        let body = json!({
+            "systemInstruction": {
+                "parts": [{ "text": state.ai_filters_system_prompt }]
+            },
+            "contents": contents,
+            "tools": tools,
+            "generationConfig": {
+                "temperature": AI_FILTERS_TEMPERATURE,
+                "maxOutputTokens": AI_FILTERS_MAX_TOKENS,
+                "thinkingConfig": { "thinkingLevel": "LOW" },
+            }
+        });
+
+        let json_resp = gemini_chat(
+            &state.http_client,
+            &state.gemini_api_key,
+            &state.gemini_model,
+            &body,
         )
-    })
+        .await?;
+
+        // Accumulate token usage
+        total_tokens_accumulated += json_resp
+            .get("usageMetadata")
+            .and_then(|md| md.get("totalTokenCount"))
+            .and_then(|tc| tc.as_u64())
+            .unwrap_or(0);
+
+        let candidate = json_resp
+            .get("candidates")
+            .and_then(|cs| cs.get(0))
+            .and_then(|c| c.get("content"))
+            .ok_or_else(|| {
+                warn!("Malformed Gemini response: missing candidates[0].content");
+                (
+                    StatusCode::BAD_GATEWAY,
+                    "Malformed Gemini response".into(),
+                )
+            })?;
+
+        let parts = candidate
+            .get("parts")
+            .and_then(|p| p.as_array())
+            .ok_or_else(|| {
+                warn!("Malformed Gemini response: missing parts array");
+                (
+                    StatusCode::BAD_GATEWAY,
+                    "Malformed Gemini response".into(),
+                )
+            })?;
+
+        // Check if the model made a function call.
+        // Find the full part (includes thoughtSignature required by Gemini 3 models).
+        if let Some(fc_part) = parts.iter().find(|part| part.get("functionCall").is_some()) {
+            let fc = fc_part.get("functionCall").unwrap();
+            let fn_name = fc.get("name").and_then(|n| n.as_str()).unwrap_or("");
+            let fn_args = fc.get("args").cloned().unwrap_or(json!({}));
+
+            info!(
+                function = fn_name,
+                round = round,
+                "AI called tool"
+            );
+
+            let fn_result = if fn_name == "search_destinations" {
+                let query = fn_args
+                    .get("query")
+                    .and_then(|q| q.as_str())
+                    .unwrap_or("");
+                let mode = fn_args
+                    .get("mode")
+                    .and_then(|m| m.as_str())
+                    .unwrap_or("transit");
+                execute_destination_search(&state, query, mode)
+            } else {
+                json!({"error": "unknown function"})
+            };
+
+            // Append the model's full response (preserves thoughtSignature) + our function result
+            contents.push(candidate.clone());
+            contents.push(json!({
+                "role": "user",
+                "parts": [{
+                    "functionResponse": {
+                        "name": fn_name,
+                        "response": { "results": fn_result }
+                    }
+                }]
+            }));
+
+            // Continue the loop — model will process the results
+            continue;
+        }
+
+        // Model returned text — extract and parse as JSON
+        let text = parts
+            .iter()
+            .find_map(|part| part.get("text").and_then(|t| t.as_str()))
+            .unwrap_or("");
+        let text = strip_markdown_fences(text);
+        let text = text.trim();
+
+        if text.is_empty() {
+            warn!("Gemini returned empty text content (round {})", round);
+            // Retry by continuing the loop
+            contents.push(candidate.clone());
+            contents.push(json!({
+                "role": "user",
+                "parts": [{ "text": "Your response was empty. Please output the JSON object." }]
+            }));
+            continue;
+        }
+
+        let raw: Value = match serde_json::from_str(text) {
+            Ok(val) => val,
+            Err(err) => {
+                warn!(error = %err, round = round, "Failed to parse Gemini JSON output, retrying");
+                // Ask the model to fix its output
+                contents.push(candidate.clone());
+                contents.push(json!({
+                    "role": "user",
+                    "parts": [{ "text": "That was not valid JSON. Please output ONLY the JSON object with numeric_filters, enum_filters, travel_time_filters, and notes." }]
+                }));
+                continue;
+            }
+        };
+
+        let filters = validate_and_convert(&raw, &state.features_response);
+        let travel_time_filters = validate_travel_time_filters(&raw, &state);
+        let notes = raw
+            .get("notes")
+            .and_then(|val| val.as_str())
+            .unwrap_or("")
+            .to_string();
+
+        // Update usage with total accumulated tokens
+        let new_total = tokens_used + total_tokens_accumulated;
+        update_ai_usage(&state, &user.id, new_total, current_week).await;
+
+        return Ok(Json(AiFiltersResponse {
+            filters,
+            travel_time_filters,
+            notes,
+        }));
+    }
+
+    // Exhausted tool rounds without getting a final text response
+    warn!("AI exhausted {} tool-calling rounds without final response", MAX_TOOL_ROUNDS);
+    Err((
+        StatusCode::BAD_GATEWAY,
+        "AI could not complete the request".into(),
+    ))
+}
+
+/// Validate travel time filters from LLM output against available destinations.
+fn validate_travel_time_filters(raw: &Value, state: &AppState) -> Vec<TravelTimeFilter> {
+    let arr = match raw
+        .get("travel_time_filters")
+        .and_then(|val| val.as_array())
+    {
+        Some(arr) => arr,
+        None => return Vec::new(),
+    };
+
+    let tt_store = &state.travel_time_store;
+    let mut results = Vec::new();
+
+    for item in arr {
+        let mode = match item.get("mode").and_then(|val| val.as_str()) {
+            Some(mode) => mode,
+            None => continue,
+        };
+        let slug = match item.get("slug").and_then(|val| val.as_str()) {
+            Some(slug) => slug,
+            None => continue,
+        };
+        let label = item
+            .get("label")
+            .and_then(|val| val.as_str())
+            .unwrap_or(slug);
+
+        // Verify this destination actually exists
+        if !tt_store.has_destination(mode, slug) {
+            warn!(mode = mode, slug = slug, "AI suggested non-existent destination");
+            continue;
+        }
+
+        let bound = item.get("bound").and_then(|val| val.as_str());
+        let value = item.get("value").and_then(|val| val.as_f64());
+
+        let (min, max) = match (bound, value) {
+            (Some("min"), Some(val)) => (Some(val.max(0.0).min(120.0) as f32), None),
+            (Some("max"), Some(val)) => (None, Some(val.max(0.0).min(120.0) as f32)),
+            _ => (None, None),
+        };
+
+        // Only include if at least one bound is set
+        if min.is_some() || max.is_some() {
+            results.push(TravelTimeFilter {
+                mode: mode.to_string(),
+                slug: slug.to_string(),
+                label: label.to_string(),
+                min,
+                max,
+            });
+        }
+    }
+
+    results
 }
 
 /// Validate LLM output against feature metadata and convert to FeatureFilters format.
@@ -374,3 +867,32 @@ fn validate_and_convert(raw: &Value, features: &FeaturesResponse) -> Value {
 
     Value::Object(result)
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn strip_fences_json_tag() {
+        let input = "```json\n{\"a\": 1}\n```";
+        assert_eq!(strip_markdown_fences(input), "{\"a\": 1}");
+    }
+
+    #[test]
+    fn strip_fences_no_tag() {
+        let input = "```\n{\"a\": 1}\n```";
+        assert_eq!(strip_markdown_fences(input), "{\"a\": 1}");
+    }
+
+    #[test]
+    fn strip_fences_passthrough() {
+        let input = "{\"a\": 1}";
+        assert_eq!(strip_markdown_fences(input), "{\"a\": 1}");
+    }
+
+    #[test]
+    fn strip_fences_whitespace() {
+        let input = "  ```json\n  {\"a\": 1}  \n```  ";
+        assert_eq!(strip_markdown_fences(input), "{\"a\": 1}");
+    }
+}

server-rs/src/routes/invites.rs

@@ -178,7 +178,7 @@ pub async fn get_invite(
     }
 
     // Dev-only: return a fake valid admin invite without hitting PocketBase
-    if state.index_html.is_none() && code == DEV_INVITE_CODE {
+    if state.is_dev && code == DEV_INVITE_CODE {
         return Json(InviteValidation {
             valid: true,
             invite_type: "admin".to_string(),
@@ -294,7 +294,7 @@ pub async fn post_redeem_invite(
     }
 
     // Dev-only: fake redeem — just return "licensed" without touching PocketBase
-    if state.index_html.is_none() && req.code == DEV_INVITE_CODE {
+    if state.is_dev && req.code == DEV_INVITE_CODE {
         info!(user_id = %user.id, "Dev invite redeemed (no-op)");
         return Json(RedeemResponse {
             result: "licensed".to_string(),

server-rs/src/state.rs

@@ -34,6 +34,8 @@ pub struct AppState {
     pub screenshot_url: String,
     /// Public-facing URL for absolute og:image URLs (e.g. https://perfectpostcodes.schmelczer.dev)
     pub public_url: String,
+    /// True when --dist is not provided (no static serving, relaxed auth checks)
+    pub is_dev: bool,
     /// Contents of index.html read at startup, used for crawler OG injection (None when --dist omitted)
     pub index_html: Option<String>,
     /// Shared HTTP client for proxying to the screenshot service and PocketBase
@@ -44,16 +46,14 @@ pub struct AppState {
     pub pocketbase_admin_email: String,
     /// PocketBase superuser password
     pub pocketbase_admin_password: String,
-    /// Ollama server URL for AI area summaries (e.g. http://ollama:11434)
+    /// Gemini API key for AI filters
-    pub ollama_url: String,
+    pub gemini_api_key: String,
-    /// Ollama model name for area summaries (e.g. gemma3:12b)
+    /// Gemini model name (e.g. gemini-2.0-flash)
-    pub ollama_model: String,
+    pub gemini_model: String,
     /// Precomputed travel time data store
     pub travel_time_store: Arc<TravelTimeStore>,
     /// Token validation cache (60s TTL)
     pub token_cache: Arc<TokenCache>,
-    /// JSON schema for Ollama structured output in AI filters
-    pub ai_filters_schema: serde_json::Value,
     /// Complete system prompt for AI filters (features + examples + instructions)
     pub ai_filters_system_prompt: String,
     /// Google Maps API key for Street View metadata lookups

server-rs/src/utils.rs

@@ -6,7 +6,7 @@ mod llm;
 pub use grid_index::GridIndex;
 pub use hash::{generate_priorities, splitmix64_hash};
 pub use interned_column::InternedColumn;
-pub use llm::{extract_ollama_content, ollama_chat, strip_think_blocks};
+pub use llm::{extract_gemini_content, gemini_chat};
 
 /// Normalize a UK postcode: uppercase, strip spaces, insert canonical space before inward code.
 /// e.g. "e142dg" → "E14 2DG", "E14  2DG" → "E14 2DG", "EC1A1BB" → "EC1A 1BB"

server-rs/src/utils/llm.rs

@@ -4,68 +4,62 @@ use tracing::warn;
 
 pub type LlmError = (StatusCode, String);
 
-/// Send a chat request to Ollama and return the parsed JSON response.
+/// Send a generateContent request to the Gemini API and return the parsed JSON response.
 ///
 /// Handles connection errors, non-success status codes, and JSON parse failures
 /// uniformly as `BAD_GATEWAY` errors.
-pub async fn ollama_chat(
+pub async fn gemini_chat(
     client: &reqwest::Client,
-    url: &str,
+    api_key: &str,
+    model: &str,
     body: &Value,
 ) -> Result<Value, LlmError> {
-    let response = client.post(url).json(body).send().await.map_err(|err| {
+    let url = format!(
-        warn!(error = %err, "Failed to connect to Ollama");
+        "https://generativelanguage.googleapis.com/v1beta/models/{}:generateContent?key={}",
+        model, api_key
+    );
+
+    let response = client.post(&url).json(body).send().await.map_err(|err| {
+        warn!(error = %err, "Failed to connect to Gemini API");
         (
             StatusCode::BAD_GATEWAY,
-            format!("Failed to connect to Ollama: {}", err),
+            format!("Failed to connect to Gemini API: {}", err),
         )
     })?;
 
     if !response.status().is_success() {
         let status = response.status();
         let body_text = response.text().await.unwrap_or_default();
-        warn!(status = %status, body = %body_text, "Ollama returned error");
+        warn!(status = %status, body = %body_text, "Gemini API returned error");
         return Err((
             StatusCode::BAD_GATEWAY,
-            format!("Ollama error {}: {}", status, body_text),
+            format!("Gemini API error {}: {}", status, body_text),
         ));
     }
 
     response.json().await.map_err(|err| {
-        warn!(error = %err, "Failed to parse Ollama response");
+        warn!(error = %err, "Failed to parse Gemini API response");
         (
             StatusCode::BAD_GATEWAY,
-            format!("Failed to parse Ollama response: {}", err),
+            format!("Failed to parse Gemini API response: {}", err),
         )
     })
 }
 
-/// Extract content from Ollama native response (`message.content`)
+/// Extract text content from Gemini response (`candidates[0].content.parts[0].text`)
-pub fn extract_ollama_content(json: &Value) -> Result<&str, LlmError> {
+pub fn extract_gemini_content(json: &Value) -> Result<&str, LlmError> {
-    json.get("message")
+    json.get("candidates")
-        .and_then(|msg| msg.get("content"))
+        .and_then(|c| c.get(0))
-        .and_then(|ct| ct.as_str())
+        .and_then(|c| c.get("content"))
+        .and_then(|c| c.get("parts"))
+        .and_then(|p| p.get(0))
+        .and_then(|p| p.get("text"))
+        .and_then(|t| t.as_str())
         .ok_or_else(|| {
-            warn!("Malformed Ollama response: missing message.content");
+            warn!("Malformed Gemini response: missing candidates[0].content.parts[0].text");
             (
                 StatusCode::BAD_GATEWAY,
-                "Malformed LLM response: missing message.content".into(),
+                "Malformed Gemini response: missing content".into(),
             )
         })
 }
-
-/// Strip `<think>...</think>` blocks from model output
-pub fn strip_think_blocks(text: &str) -> String {
-    let mut result = String::new();
-    let mut remaining = text;
-    while let Some(start) = remaining.find("<think>") {
-        result.push_str(&remaining[..start]);
-        if let Some(end) = remaining[start..].find("</think>") {
-            remaining = &remaining[start + end + 8..];
-        } else {
-            return result;
-        }
-    }
-    result.push_str(remaining);
-    result
-}