Upgrading flask-socketio ( #3910 )

changedetectionio/__init__.py

@@ -2,7 +2,7 @@
 
 # Read more https://github.com/dgtlmoon/changedetection.io/wiki
 # Semver means never use .01, or 00. Should be .1.
-__version__ = '0.53.7'
+__version__ = '0.53.6'
 
 from changedetectionio.strtobool import strtobool
 from json.decoder import JSONDecodeError

changedetectionio/blueprint/settings/__init__.py

@@ -45,7 +45,6 @@ def construct_blueprint(datastore: ChangeDetectionStore):
                                         extra_notification_tokens=datastore.get_unique_notification_tokens_available()
                                         )
 
-
         # Remove the last option 'System default'
         form.application.form.notification_format.choices.pop()
 
@@ -130,12 +129,8 @@ def construct_blueprint(datastore: ChangeDetectionStore):
                     # Instantiate plugin form with POST data
                     plugin_form = form_class(formdata=request.form)
 
-                    # Save plugin settings — use plugin's own save_fn if provided
-                    # (allows plugins to strip ephemeral staging fields etc.)
-                    save_fn = tab.get('save_fn')
-                    if save_fn:
-                        save_fn(datastore, plugin_form)
-                    elif plugin_form.data:
+                    # Save plugin settings (validation is optional for plugins)
+                    if plugin_form.data:
                         save_plugin_settings(datastore.datastore_path, plugin_id, plugin_form.data)
 
                 flash(gettext("Settings updated."))

changedetectionio/blueprint/settings/templates/settings.html

@@ -27,7 +27,6 @@
             <li class="tab"><a href="#rss">{{ _('RSS') }}</a></li>
             <li class="tab"><a href="{{ url_for('backups.create') }}">{{ _('Backups') }}</a></li>
             <li class="tab"><a href="#timedate">{{ _('Time & Date') }}</a></li>
-
             <li class="tab"><a href="#proxies">{{ _('CAPTCHA & Proxies') }}</a></li>
             {% if plugin_tabs %}
                 {% for tab in plugin_tabs %}
@@ -309,7 +308,6 @@ nav
                 </div>
 
             </div>
-
             <div class="tab-pane-inner" id="proxies">
                 <div id="recommended-proxy">
                     <div>

changedetectionio/blueprint/ui/__init__.py

@@ -116,11 +116,11 @@ def _handle_operations(op, uuids, datastore, worker_pool, update_q, queuedWatchM
         for uuid in uuids:
             watch_check_update.send(watch_uuid=uuid)
 
-def construct_blueprint(datastore: ChangeDetectionStore, update_q, worker_pool, queuedWatchMetaData, watch_check_update, llm_summary_q=None):
+def construct_blueprint(datastore: ChangeDetectionStore, update_q, worker_pool, queuedWatchMetaData, watch_check_update):
     ui_blueprint = Blueprint('ui', __name__, template_folder="templates")
     
     # Register the edit blueprint
-    edit_blueprint = construct_edit_blueprint(datastore, update_q, queuedWatchMetaData, llm_summary_q=llm_summary_q)
+    edit_blueprint = construct_edit_blueprint(datastore, update_q, queuedWatchMetaData)
     ui_blueprint.register_blueprint(edit_blueprint)
     
     # Register the notification blueprint

changedetectionio/blueprint/ui/edit.py

@@ -11,7 +11,7 @@ from changedetectionio.auth_decorator import login_optionally_required
 from changedetectionio.time_handler import is_within_schedule
 from changedetectionio import worker_pool
 
-def construct_blueprint(datastore: ChangeDetectionStore, update_q, queuedWatchMetaData, llm_summary_q=None):
+def construct_blueprint(datastore: ChangeDetectionStore, update_q, queuedWatchMetaData):
     edit_blueprint = Blueprint('ui_edit', __name__, template_folder="../ui/templates")
     
     def _watch_has_tag_options_set(watch):
@@ -404,47 +404,6 @@ def construct_blueprint(datastore: ChangeDetectionStore, update_q, queuedWatchMe
                         download_name=filename,
                         mimetype='application/zip')
 
-    @edit_blueprint.route("/edit/<string:uuid>/regenerate-llm-summaries", methods=['GET'])
-    @login_optionally_required
-    def watch_regenerate_llm_summaries(uuid):
-        """Queue LLM summary generation for all history entries that don't yet have one."""
-        from flask import flash
-        from changedetectionio.llm.tokens import is_llm_data_ready
-        watch = datastore.data['watching'].get(uuid)
-        if not watch:
-            abort(404)
-
-        if not llm_summary_q:
-            flash(gettext("LLM summarisation is not configured."), 'error')
-            return redirect(url_for('ui.ui_edit.edit_page', uuid=uuid))
-
-        history = watch.history
-        history_keys = list(history.keys())
-
-        queued = 0
-        # Skip the first entry — there is no prior snapshot to diff against
-        for timestamp in history_keys[1:]:
-            snapshot_fname = history[timestamp]
-            snapshot_id = os.path.basename(snapshot_fname).split('.')[0]  # always 32-char MD5
-
-            # Skip entries that already have a summary
-            if is_llm_data_ready(watch.data_dir, snapshot_id):
-                continue
-
-            llm_summary_q.put({
-                'uuid':        uuid,
-                'snapshot_id': snapshot_id,
-                'attempts':    0,
-            })
-            queued += 1
-
-        if queued:
-            flash(gettext("Queued %(count)d LLM summaries for generation.", count=queued), 'success')
-        else:
-            flash(gettext("All history entries already have LLM summaries."), 'notice')
-
-        return redirect(url_for('ui.ui_edit.edit_page', uuid=uuid) + '#info')
-
     # Ajax callback
     @edit_blueprint.route("/edit/<string:uuid>/preview-rendered", methods=['POST'])
     @login_optionally_required

changedetectionio/blueprint/ui/templates/edit.html

@@ -489,9 +489,6 @@ Math: {{ 1 + 1 }}") }}
                         <p>
                              <a href="{{url_for('ui.ui_edit.watch_get_latest_html', uuid=uuid)}}" class="pure-button button-small">{{ _('Download latest HTML snapshot') }}</a>
                              <a href="{{url_for('ui.ui_edit.watch_get_data_package', uuid=uuid)}}" class="pure-button button-small">{{ _('Download watch data package') }}</a>
-                             {% if watch.history_n > 1 %}
-                             <a href="{{url_for('ui.ui_edit.watch_regenerate_llm_summaries', uuid=uuid)}}" class="pure-button button-small">{{ _('Regenerate LLM summaries') }}</a>
-                             {% endif %}
                         </p>
                     {% endif %}
 

changedetectionio/flask_app.py

@@ -15,7 +15,6 @@ from changedetectionio.strtobool import strtobool
 from threading import Event
 from changedetectionio.queue_handlers import RecheckPriorityQueue, NotificationQueue
 from changedetectionio import worker_pool
-import changedetectionio.llm as llm
 
 from flask import (
     Flask,
@@ -57,7 +56,6 @@ extra_stylesheets = []
 # Use bulletproof janus-based queues for sync/async reliability  
 update_q = RecheckPriorityQueue()
 notification_q = NotificationQueue()
-llm_summary_q = llm.create_queue()
 MAX_QUEUE_SIZE = 5000
 
 app = Flask(__name__,
@@ -853,7 +851,7 @@ def changedetection_app(config=None, datastore_o=None):
 
     # watchlist UI buttons etc
     import changedetectionio.blueprint.ui as ui
-    app.register_blueprint(ui.construct_blueprint(datastore, update_q, worker_pool, queuedWatchMetaData, watch_check_update, llm_summary_q=llm_summary_q))
+    app.register_blueprint(ui.construct_blueprint(datastore, update_q, worker_pool, queuedWatchMetaData, watch_check_update))
 
     import changedetectionio.blueprint.watchlist as watchlist
     app.register_blueprint(watchlist.construct_blueprint(datastore=datastore, update_q=update_q, queuedWatchMetaData=queuedWatchMetaData), url_prefix='')
@@ -976,17 +974,6 @@ def changedetection_app(config=None, datastore_o=None):
             ).start()
         logger.info(f"Started {notification_workers} notification worker(s)")
 
-        llm.start_workers(app=app, datastore=datastore, llm_q=llm_summary_q,
-                          n_workers=int(os.getenv("LLM_WORKERS", "1")))
-
-        # Register the LLM queue plugin so changes trigger summary jobs
-        from changedetectionio.llm.plugin import LLMQueuePlugin
-        from changedetectionio.pluggy_interface import plugin_manager
-        plugin_manager.register(LLMQueuePlugin(llm_summary_q), 'llm_queue_plugin')
-
-        # Re-run template path configuration now that all plugins (including LLM) are registered
-        _configure_plugin_templates()
-
         in_pytest = "pytest" in sys.modules or "PYTEST_CURRENT_TEST" in os.environ
         # Check for new release version, but not when running in test/build or pytest
         if not os.getenv("GITHUB_REF", False) and not strtobool(os.getenv('DISABLE_VERSION_CHECK', 'no')) and not in_pytest:
@@ -1041,65 +1028,19 @@ def notification_runner(worker_id=0):
 
             else:
 
-                # ── LLM deferred-send gate ─────────────────────────────────────────
-                # If the notification was re-queued to wait for LLM data, honour the
-                # scheduled retry time before doing any further processing.
-                _llm_next_retry = n_object.get('_llm_next_retry_at', 0)
-                if _llm_next_retry and _llm_next_retry > time.time():
-                    notification_q.put(n_object)
-                    app.config.exit.wait(min(_llm_next_retry - time.time(), 2))
-                    continue
-
-                # Apply system-config fallbacks first so we can scan the final body/title.
-                if not n_object.get('notification_body') and datastore.data['settings']['application'].get('notification_body'):
-                    n_object['notification_body'] = datastore.data['settings']['application'].get('notification_body')
-                if not n_object.get('notification_title') and datastore.data['settings']['application'].get('notification_title'):
-                    n_object['notification_title'] = datastore.data['settings']['application'].get('notification_title')
-
-                # If the body or title references llm_* tokens, wait until LLM data is ready.
-                import re as _re
-                _llm_scan = (n_object.get('notification_body') or '') + ' ' + (n_object.get('notification_title') or '')
-                if _re.search(r'\bllm_(?:summary|headline|importance|sentiment|one_liner)\b', _llm_scan):
-                    from changedetectionio.llm.tokens import (
-                        is_llm_data_ready, read_llm_tokens,
-                        LLM_NOTIFICATION_RETRY_DELAY_SECONDS, LLM_NOTIFICATION_MAX_WAIT_ATTEMPTS,
-                    )
-                    _llm_uuid     = n_object.get('uuid')
-                    _llm_watch    = datastore.data['watching'].get(_llm_uuid) if _llm_uuid else None
-                    _llm_snap_id  = n_object.get('_llm_snapshot_id')
-
-                    if _llm_watch and _llm_snap_id and not is_llm_data_ready(_llm_watch.data_dir, _llm_snap_id):
-                        _llm_attempts = n_object.get('_llm_wait_attempts', 0)
-                        if _llm_attempts < LLM_NOTIFICATION_MAX_WAIT_ATTEMPTS:
-                            n_object['_llm_wait_attempts'] = _llm_attempts + 1
-                            n_object['_llm_next_retry_at'] = time.time() + LLM_NOTIFICATION_RETRY_DELAY_SECONDS
-                            notification_q.put(n_object)
-                            logger.debug(
-                                f"Notification gate: LLM data pending for {_llm_uuid} "
-                                f"(attempt {n_object['_llm_wait_attempts']}/{LLM_NOTIFICATION_MAX_WAIT_ATTEMPTS})"
-                            )
-                            continue
-                        else:
-                            logger.warning(
-                                f"Notification: LLM data never arrived for {_llm_uuid} after "
-                                f"{LLM_NOTIFICATION_MAX_WAIT_ATTEMPTS} attempts — sending without LLM tokens"
-                            )
-                    elif _llm_watch and _llm_snap_id:
-                        # Data is ready — populate the LLM tokens into n_object
-                        _llm_data = read_llm_tokens(_llm_watch.data_dir, _llm_snap_id)
-                        n_object['llm_summary']    = _llm_data.get('summary', '')
-                        n_object['llm_headline']   = _llm_data.get('headline', '')
-                        n_object['llm_importance'] = _llm_data.get('importance')
-                        n_object['llm_sentiment']  = _llm_data.get('sentiment', '')
-                        n_object['llm_one_liner']  = _llm_data.get('one_liner', '')
-                # ── end LLM gate ───────────────────────────────────────────────────
-
                 now = datetime.now()
                 sent_obj = None
 
                 try:
                     from changedetectionio.notification.handler import process_notification
 
+                    # Fallback to system config if not set
+                    if not n_object.get('notification_body') and datastore.data['settings']['application'].get('notification_body'):
+                        n_object['notification_body'] = datastore.data['settings']['application'].get('notification_body')
+
+                    if not n_object.get('notification_title') and datastore.data['settings']['application'].get('notification_title'):
+                        n_object['notification_title'] = datastore.data['settings']['application'].get('notification_title')
+
                     if not n_object.get('notification_format') and datastore.data['settings']['application'].get('notification_format'):
                         n_object['notification_format'] = datastore.data['settings']['application'].get('notification_format')
                     if n_object.get('notification_urls', {}):

changedetectionio/llm/__init__.py

@@ -1,64 +0,0 @@
-"""
-changedetectionio.llm
-~~~~~~~~~~~~~~~~~~~~~
-
-LLM summary queue and workers.
-
-Usage in flask_app.py
----------------------
-
-    import changedetectionio.llm as llm
-
-    # At module level alongside notification_q:
-    llm_summary_q = llm.create_queue()
-
-    # Inside changedetection_app(), after datastore is ready:
-    llm.start_workers(
-        app=app,
-        datastore=datastore,
-        llm_q=llm_summary_q,
-        n_workers=int(os.getenv("LLM_WORKERS", "1")),
-    )
-
-Enqueueing a summary job (e.g. from the pluggy update_finalize hook)
----------------------------------------------------------------------
-
-    if changed_detected and not processing_exception:
-        llm_summary_q.put({
-            'uuid':        watch_uuid,
-            'snapshot_id': snapshot_id,
-            'attempts':    0,
-        })
-"""
-
-import queue
-import threading
-from loguru import logger
-
-
-def create_queue() -> queue.Queue:
-    """Return a plain Queue for LLM summary jobs. No maxsize — jobs are small dicts."""
-    return queue.Queue()
-
-
-def start_workers(app, datastore, llm_q: queue.Queue, n_workers: int = 1) -> None:
-    """
-    Start N LLM summary worker threads.
-
-    Args:
-        app:        Flask application instance (for app_context and exit event)
-        datastore:  Application datastore
-        llm_q:      Queue returned by create_queue()
-        n_workers:  Number of parallel workers (default 1; increase for local Ollama)
-    """
-    from changedetectionio.llm.queue_worker import llm_summary_runner
-
-    for i in range(n_workers):
-        threading.Thread(
-            target=llm_summary_runner,
-            args=(i, app, datastore, llm_q),
-            daemon=True,
-            name=f"LLMSummaryWorker-{i}",
-        ).start()
-
-    logger.info(f"Started {n_workers} LLM summary worker(s)")

changedetectionio/llm/plugin.py

@@ -1,104 +0,0 @@
-"""
-LLM plugin — provides settings tab and enqueues summary jobs on change detection.
-
-Registered with the pluggy plugin manager at startup (flask_app.py).
-The worker (llm/queue_worker.py) drains the queue asynchronously.
-"""
-from loguru import logger
-from changedetectionio.pluggy_interface import hookimpl
-
-
-def get_llm_settings(datastore):
-    """Load LLM plugin settings with fallback to legacy datastore settings.
-
-    Tries the plugin settings file (llm.json) first.
-    Falls back to the old storage location in datastore.data['settings']['application']
-    for users upgrading from a version before LLM became a first-class plugin.
-    """
-    from changedetectionio.pluggy_interface import load_plugin_settings
-    settings = load_plugin_settings(datastore.datastore_path, 'llm')
-
-    if settings.get('llm_connection') is not None:
-        return settings
-
-    # Legacy fallback: settings were stored in datastore application settings
-    app_settings = datastore.data['settings']['application']
-    connections_dict = app_settings.get('llm_connections') or {}
-    connections_list = [
-        {
-            'connection_id':     k,
-            'name':              v.get('name', ''),
-            'model':             v.get('model', ''),
-            'api_key':           v.get('api_key', ''),
-            'api_base':          v.get('api_base', ''),
-            'tokens_per_minute': int(v.get('tokens_per_minute', 0) or 0),
-            'is_default':        bool(v.get('is_default', False)),
-        }
-        for k, v in connections_dict.items()
-    ]
-
-    return {
-        'llm_connection':    connections_list,
-        'llm_summary_prompt': app_settings.get('llm_summary_prompt', ''),
-    }
-
-
-def save_llm_settings(datastore, plugin_form):
-    """Custom save handler — strips the ephemeral new_connection staging fields
-    so they are never persisted to llm.json."""
-    from changedetectionio.pluggy_interface import save_plugin_settings
-    data = {
-        'llm_connection':         plugin_form.llm_connection.data,
-        'llm_summary_prompt':     plugin_form.llm_summary_prompt.data or '',
-        'llm_diff_context_lines': plugin_form.llm_diff_context_lines.data or 2,
-    }
-    save_plugin_settings(datastore.datastore_path, 'llm', data)
-
-
-class LLMQueuePlugin:
-    """Enqueues LLM summary jobs on successful change detection and provides settings tab."""
-
-    def __init__(self, llm_q):
-        self.llm_q = llm_q
-
-    @hookimpl
-    def plugin_settings_tab(self):
-        from changedetectionio.llm.settings_form import LLMSettingsForm
-        return {
-            'plugin_id':     'llm',
-            'tab_label':     'LLM',
-            'form_class':    LLMSettingsForm,
-            'template_path': 'settings-llm.html',
-            'save_fn':       save_llm_settings,
-        }
-
-    @hookimpl
-    def update_finalize(self, update_handler, watch, datastore, processing_exception,
-                        changed_detected=False, snapshot_id=None):
-        """Queue an LLM summary job when a change was successfully detected."""
-
-        if not changed_detected or processing_exception or not snapshot_id:
-            return
-
-        if watch is None:
-            return
-
-        # Need ≥2 history entries — first entry has nothing to diff against
-        if watch.history_n < 2:
-            return
-
-        # Only queue when at least one LLM connection is configured
-        llm_settings = get_llm_settings(datastore)
-        has_connection = bool(
-            llm_settings.get('llm_connection')
-            or datastore.data['settings']['application'].get('llm_api_key')   # legacy
-            or datastore.data['settings']['application'].get('llm_model')     # legacy
-            or watch.get('llm_api_key')
-            or watch.get('llm_model')
-        )
-        if not has_connection:
-            return
-
-        uuid = watch.get('uuid')
-        self.llm_q.put({'uuid': uuid, 'snapshot_id': snapshot_id, 'attempts': 0})
-        logger.debug(f"LLM: queued summary for uuid={uuid} snapshot={snapshot_id}")

changedetectionio/llm/queue_worker.py

@@ -1,544 +0,0 @@
-import fcntl
-import os
-import queue
-import re
-import threading
-import time
-from datetime import datetime, timezone
-from loguru import logger
-
-from changedetectionio.llm.tokens import (
-    STRUCTURED_OUTPUT_INSTRUCTION,
-    parse_llm_response,
-    write_llm_data,
-)
-
-MAX_RETRIES = 5
-RETRY_BACKOFF_BASE_SECONDS = 60  # 1m, 2m, 4m, 8m, 16m
-
-# Token thresholds that control which summarisation strategy is used.
-# Small diffs: single-pass summarise.
-# Larger diffs: two-pass (enumerate all changes first, then compress).
-# Very large diffs: map-reduce (chunk → enumerate per chunk → final synthesis).
-TOKEN_SINGLE_PASS_THRESHOLD = 5000   # below this: one call
-TOKEN_TWO_PASS_THRESHOLD    = 15000  # below this: enumerate then summarise
-TOKEN_CHUNK_SIZE            = 5000   # tokens per map-reduce chunk
-
-
-# ---------------------------------------------------------------------------
-# Proactive token-bucket rate limiter — shared across all workers in process
-# ---------------------------------------------------------------------------
-
-class _RateLimitWait(Exception):
-    """Raised when the bucket is empty; worker re-queues without incrementing attempts."""
-    def __init__(self, wait_seconds):
-        self.wait_seconds = wait_seconds
-        super().__init__(f"Rate limit: wait {wait_seconds:.1f}s")
-
-
-class _TokenBucket:
-    """Thread-safe continuous token bucket. tpm=0 means unlimited."""
-
-    def __init__(self, tpm):
-        self._lock    = threading.Lock()
-        self._tpm     = tpm
-        self._tokens  = float(tpm)           # start full
-        self._last_ts = time.monotonic()
-
-    def try_consume(self, n):
-        """Consume n tokens.  Returns (True, 0.0) on success or (False, wait_secs) if dry."""
-        if self._tpm == 0:
-            return True, 0.0
-        with self._lock:
-            now     = time.monotonic()
-            elapsed = now - self._last_ts
-            self._tokens  = min(self._tpm, self._tokens + elapsed * (self._tpm / 60.0))
-            self._last_ts = now
-            if self._tokens >= n:
-                self._tokens -= n
-                return True, 0.0
-            deficit = n - self._tokens
-            return False, deficit / (self._tpm / 60.0)
-
-
-_rate_buckets      = {}
-_rate_buckets_lock = threading.Lock()
-
-
-def _get_rate_bucket(conn_id, tpm):
-    """Return (or lazily create) the shared _TokenBucket for this connection."""
-    with _rate_buckets_lock:
-        if conn_id not in _rate_buckets:
-            _rate_buckets[conn_id] = _TokenBucket(int(tpm or 0))
-        return _rate_buckets[conn_id]
-
-
-def _parse_retry_after(exc):
-    """Extract a retry-after delay (seconds) from a litellm RateLimitError."""
-    if hasattr(exc, 'retry_after') and exc.retry_after:
-        try:
-            return float(exc.retry_after)
-        except (TypeError, ValueError):
-            pass
-    m = re.search(r'(?:try again in|retry after)\s*([\d.]+)\s*s', str(exc), re.IGNORECASE)
-    return float(m.group(1)) + 1.0 if m else 60.0
-
-
-def _read_snapshot(watch, snapshot_fname):
-    """Read a snapshot file from disk, handling plain text and brotli compression."""
-    path = os.path.join(watch.data_dir, snapshot_fname)
-    if snapshot_fname.endswith('.br'):
-        import brotli
-        with open(path, 'rb') as f:
-            return brotli.decompress(f.read()).decode('utf-8', errors='replace')
-    else:
-        with open(path, 'r', encoding='utf-8', errors='replace') as f:
-            return f.read()
-
-
-def _append_llm_log(log_path, model, sent_tokens, recv_tokens, elapsed_ms):
-    """Append one line to the datastore-level LLM activity log.
-
-    Line format (tab-separated, LF terminated):
-        ISO-8601-UTC  fetched LLM via <model>  sent=<N>  recv=<N>  ms=<N>
-
-    The file is flock-locked for the duration of the write so concurrent
-    workers don't interleave lines.
-    """
-    ts   = datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3]  # ms precision
-    line = f"{ts}\tfetched LLM via {model}\tsent={sent_tokens}\trecv={recv_tokens}\tms={elapsed_ms}\n"
-    try:
-        with open(log_path, 'a', encoding='utf-8', newline='\n') as f:
-            fcntl.flock(f, fcntl.LOCK_EX)
-            try:
-                f.write(line)
-                f.flush()
-            finally:
-                fcntl.flock(f, fcntl.LOCK_UN)
-    except Exception as exc:
-        logger.warning(f"LLM log write failed: {exc}")
-
-
-def _call_llm(model, messages, api_key=None, api_base=None, max_tokens=600, conn_id=None, tpm=0, log_path=None):
-    """
-    Thin wrapper around litellm.completion.
-    Isolated as a named function so tests can mock.patch it without importing litellm.
-
-    Determinism settings
-    --------------------
-    temperature=0   — greedy decoding; same input produces the same output consistently.
-    seed=0          — passed through to providers that support it (OpenAI, some others)
-                      for near-bit-identical reproducibility across calls.
-
-    Deliberately NOT set
-    --------------------
-    top_p           — redundant at temperature=0 and can interact badly with some providers.
-    frequency_penalty / presence_penalty — would penalise the model for repeating specific
-                      values (e.g. "$10 → $10") which is exactly wrong for change detection.
-
-    max_tokens      — caller sets this based on the pass type:
-                      enumerate pass needs more room than the final summary pass.
-
-    conn_id / tpm   — optional rate limiting; when both are set, a proactive token-bucket
-                      check is performed before calling the API.  Raises _RateLimitWait if
-                      the bucket is empty so the worker can re-queue without retrying.
-
-    log_path        — when set, each call is appended to the datastore LLM activity log.
-
-    Returns the response text string.
-    """
-    import litellm
-
-    # Proactive rate check (skipped when tpm=0 or conn_id is None)
-    if conn_id and tpm:
-        prompt_tokens = litellm.token_counter(model=model, messages=messages)
-        total_est     = prompt_tokens + max_tokens
-        bucket        = _get_rate_bucket(conn_id, tpm)
-        ok, wait      = bucket.try_consume(total_est)
-        if not ok:
-            raise _RateLimitWait(wait)
-
-    kwargs = dict(
-        model=model,
-        messages=messages,
-        temperature=0,
-        seed=0,
-        max_tokens=max_tokens,
-    )
-    if api_key:
-        kwargs['api_key'] = api_key
-    if api_base:
-        kwargs['api_base'] = api_base
-
-    t0       = time.monotonic()
-    response = litellm.completion(**kwargs)
-    elapsed_ms = round((time.monotonic() - t0) * 1000)
-
-    if log_path:
-        usage      = getattr(response, 'usage', None)
-        sent_tok   = getattr(usage, 'prompt_tokens',     0) or 0
-        recv_tok   = getattr(usage, 'completion_tokens', 0) or 0
-        _append_llm_log(log_path, model, sent_tok, recv_tok, elapsed_ms)
-
-    return response.choices[0].message.content.strip()
-
-
-
-def _resolve_llm_connection(watch, datastore):
-    """Return (model, api_key, api_base, conn_id, tpm) for the given watch.
-
-    Resolution order:
-    1. Watch-level connection_id pointing to a named entry in plugin settings.
-    2. The default entry in plugin settings (is_default=True).
-    3. Legacy flat fields on the watch or in global settings — backward compat.
-    4. Hard-coded fallback: gpt-4o-mini with no key / base.
-    """
-    from changedetectionio.llm.plugin import get_llm_settings
-    from changedetectionio.llm.settings_form import sanitised_conn_id
-
-    llm_settings = get_llm_settings(datastore)
-    connections  = llm_settings.get('llm_connection') or []
-
-    # 1. Watch-level override by explicit connection_id
-    watch_conn_id = watch.get('llm_connection_id')
-    if watch_conn_id:
-        for c in connections:
-            if c.get('connection_id') == watch_conn_id:
-                cid = sanitised_conn_id(c.get('connection_id', ''))
-                return (c.get('model', 'gpt-4o-mini'), c.get('api_key', ''), c.get('api_base', ''),
-                        cid, int(c.get('tokens_per_minute', 0) or 0))
-
-    # 2. Global default connection
-    for c in connections:
-        if c.get('is_default'):
-            cid = sanitised_conn_id(c.get('connection_id', ''))
-            return (c.get('model', 'gpt-4o-mini'), c.get('api_key', ''), c.get('api_base', ''),
-                    cid, int(c.get('tokens_per_minute', 0) or 0))
-
-    # 3. Legacy flat fields (backward compat)
-    app_settings = datastore.data['settings']['application']
-    model    = watch.get('llm_model')    or app_settings.get('llm_model',    'gpt-4o-mini')
-    api_key  = watch.get('llm_api_key')  or app_settings.get('llm_api_key',  '')
-    api_base = watch.get('llm_api_base') or app_settings.get('llm_api_base', '')
-    return model, api_key, api_base, 'legacy', 0
-
-
-SYSTEM_PROMPT = (
-    'You are a change detection assistant. '
-    'Be precise and factual. Never speculate. '
-    'Always use exact numbers, values, and quoted text when present in the diff. '
-    'If nothing meaningful changed, say so explicitly.'
-)
-
-
-def _build_context_header(watch, datastore):
-    """Return a short multi-line string describing what this watch monitors.
-
-    Included lines (only when non-empty / non-redundant):
-        URL:      <url>
-        Monitor:  <user title or fetched page title>   (omitted when same as URL)
-        Tags:     <comma-separated tag titles>          (omitted when none)
-    """
-    url   = watch.get('url', '')
-    title = watch.get('title', '') or watch.get('page_title', '')
-
-    lines = [f"URL: {url}"]
-    if title and title != url:
-        lines.append(f"Monitor: {title}")
-
-    tag_titles = []
-    for tag_uuid in (watch.get('tags') or []):
-        tag = datastore.data['settings']['application'].get('tags', {}).get(tag_uuid, {})
-        t = tag.get('title', '').strip()
-        if t:
-            tag_titles.append(t)
-    if tag_titles:
-        lines.append(f"Tags: {', '.join(tag_titles)}")
-
-    return '\n'.join(lines)
-
-
-def _chunk_lines(lines, model, chunk_token_size):
-    """Split lines into chunks that each fit within chunk_token_size tokens."""
-    import litellm
-    chunks, current, current_tokens = [], [], 0
-    for line in lines:
-        line_tokens = litellm.token_counter(model=model, text=line)
-        if current and current_tokens + line_tokens > chunk_token_size:
-            chunks.append('\n'.join(current))
-            current, current_tokens = [], 0
-        current.append(line)
-        current_tokens += line_tokens
-    if current:
-        chunks.append('\n'.join(current))
-    return chunks
-
-
-def _enumerate_changes(diff_text, context_header, model, llm_kwargs):
-    """
-    Pass 1 — ask the model to list every distinct change exhaustively, one per line.
-    Returns a plain-text list string.
-    This avoids compression decisions: the model just lists, it does not prioritise.
-    """
-    messages = [
-        {'role': 'system', 'content': SYSTEM_PROMPT},
-        {
-            'role': 'user',
-            'content': (
-                f"{context_header}\n"
-                f"Diff:\n{diff_text}\n\n"
-                "List every distinct change you see, one item per line. "
-                "Be exhaustive — do not filter or prioritise. "
-                "Use exact values from the diff (prices, dates, counts, quoted text)."
-            ),
-        },
-    ]
-    # Enumerate pass needs more output room than the final summary
-    return _call_llm(model=model, messages=messages, max_tokens=1200, **llm_kwargs)
-
-
-def _summarise_enumeration(enumerated, context_header, model, llm_kwargs, summary_instruction=None):
-    """
-    Pass 2 — compress the exhaustive enumeration into the final output.
-    Operates on a small, structured input so nothing is lost that wasn't already listed.
-    summary_instruction overrides the default STRUCTURED_OUTPUT_INSTRUCTION when set.
-    """
-    instruction = summary_instruction or (
-        "Now produce the final structured output for all of these changes.\n\n"
-        + STRUCTURED_OUTPUT_INSTRUCTION
-    )
-    messages = [
-        {'role': 'system', 'content': SYSTEM_PROMPT},
-        {
-            'role': 'user',
-            'content': (
-                f"{context_header}\n"
-                f"All changes detected:\n{enumerated}\n\n"
-                + instruction
-            ),
-        },
-    ]
-    return _call_llm(model=model, messages=messages, max_tokens=500, **llm_kwargs)
-
-
-def process_llm_summary(item, datastore):
-    """
-    Generate an LLM summary for a detected change and write {snapshot_id}-llm.txt.
-
-    item keys:
-        uuid        - watch UUID
-        snapshot_id - the newer snapshot ID (md5 hex), maps to {snapshot_id}.txt[.br]
-        attempts    - retry counter
-
-    Summarisation strategy (chosen by diff token count):
-        Small  (< SINGLE_PASS_TOKEN_LIMIT):  one call — enumerate + summarise together.
-        Medium (< TWO_PASS_TOKEN_LIMIT):     two calls — enumerate all changes, then compress.
-        Large  (≥ TWO_PASS_TOKEN_LIMIT):     map-reduce — chunk → enumerate per chunk →
-                                             synthesise chunk enumerations → final summary.
-
-    The two-pass / map-reduce approach prevents lossiness: temperature=0 causes the model
-    to greedily commit to the most prominent change and drop the rest in a single pass.
-    Enumerating first forces comprehensive coverage before any compression happens.
-
-    Split into _call_llm / _write_summary so each step is independently patchable in tests.
-    """
-    import difflib
-    import litellm
-
-    uuid        = item['uuid']
-    snapshot_id = item['snapshot_id']
-
-    watch = datastore.data['watching'].get(uuid)
-    if not watch:
-        raise ValueError(f"Watch {uuid} not found")
-
-    # Find this snapshot and the one before it in history
-    history      = watch.history
-    history_keys = list(history.keys())
-
-    try:
-        idx = next(
-            i for i, k in enumerate(history_keys)
-            if os.path.basename(history[k]).split('.')[0] == snapshot_id
-        )
-    except StopIteration:
-        raise ValueError(f"snapshot_id {snapshot_id} not found in history for watch {uuid}")
-
-    if idx == 0:
-        raise ValueError(f"snapshot_id {snapshot_id} is the first history entry — no prior to diff against")
-
-    before_text  = _read_snapshot(watch, history[history_keys[idx - 1]])
-    current_text = _read_snapshot(watch, history[history_keys[idx]])
-
-    # Resolve model / credentials via connections table (with legacy flat-field fallback)
-    model, api_key, api_base, conn_id, tpm = _resolve_llm_connection(watch, datastore)
-    url            = watch.get('url', '')
-    context_header = _build_context_header(watch, datastore)
-
-    llm_kwargs = {
-        'log_path': os.path.join(datastore.datastore_path, 'llm-log.txt'),
-    }
-    if api_key:
-        llm_kwargs['api_key'] = api_key
-    if api_base:
-        llm_kwargs['api_base'] = api_base
-    if conn_id:
-        llm_kwargs['conn_id'] = conn_id
-    if tpm:
-        llm_kwargs['tpm'] = tpm
-
-    # Use custom prompt / context-line setting if configured
-    from changedetectionio.llm.plugin import get_llm_settings
-    llm_settings  = get_llm_settings(datastore)
-    custom_prompt = (llm_settings.get('llm_summary_prompt') or '').strip()
-    summary_instruction = custom_prompt if custom_prompt else (
-        "Analyse all changes in this diff.\n\n" + STRUCTURED_OUTPUT_INSTRUCTION
-    )
-    context_n = int(llm_settings.get('llm_diff_context_lines') or 2)
-
-    diff_lines = list(difflib.unified_diff(
-        before_text.splitlines(),
-        current_text.splitlines(),
-        lineterm='',
-        n=context_n,
-    ))
-    diff_text = '\n'.join(diff_lines)
-
-    if not diff_text.strip():
-        logger.debug(f"LLM: no diff content for {uuid}/{snapshot_id}, skipping")
-        return
-
-    diff_tokens = litellm.token_counter(model=model, text=diff_text)
-    logger.debug(f"LLM: diff is {diff_tokens} tokens for {uuid}/{snapshot_id}")
-
-    if diff_tokens < TOKEN_SINGLE_PASS_THRESHOLD:
-        # Small diff — single call, model can see everything at once
-        messages = [
-            {'role': 'system', 'content': SYSTEM_PROMPT},
-            {
-                'role': 'user',
-                'content': (
-                    f"{context_header}\n"
-                    f"Diff:\n{diff_text}\n\n"
-                    + summary_instruction
-                ),
-            },
-        ]
-        raw = _call_llm(model=model, messages=messages, max_tokens=500, **llm_kwargs)
-        strategy = 'single'
-
-    elif diff_tokens < TOKEN_TWO_PASS_THRESHOLD:
-        # Medium diff — two-pass: enumerate exhaustively, then compress
-        enumerated = _enumerate_changes(diff_text, context_header, model, llm_kwargs)
-        raw        = _summarise_enumeration(enumerated, context_header, model, llm_kwargs, summary_instruction)
-        strategy   = 'two-pass'
-
-    else:
-        # Large diff — map-reduce: chunk → enumerate per chunk → synthesise
-        chunks = _chunk_lines(diff_lines, model, TOKEN_CHUNK_SIZE)
-        logger.debug(f"LLM: map-reduce over {len(chunks)} chunks for {uuid}/{snapshot_id}")
-
-        chunk_enumerations = []
-        for i, chunk in enumerate(chunks):
-            logger.debug(f"LLM: enumerating chunk {i+1}/{len(chunks)}")
-            chunk_enumerations.append(
-                _enumerate_changes(chunk, context_header, model, llm_kwargs)
-            )
-
-        combined = '\n'.join(chunk_enumerations)
-        raw      = _summarise_enumeration(combined, context_header, model, llm_kwargs, summary_instruction)
-        strategy = 'map-reduce'
-
-    llm_data = parse_llm_response(raw)
-    write_llm_data(watch.data_dir, snapshot_id, llm_data)
-    logger.info(f"LLM tokens written for {uuid}/{snapshot_id} (strategy: {strategy}, tokens: {diff_tokens})")
-
-
-def llm_summary_runner(worker_id, app, datastore, llm_q):
-    """
-    Sync LLM summary worker — mirrors the notification_runner pattern.
-
-    One worker is the right default (LLM API rate limits constrain throughput
-    more than parallelism helps). Increase via LLM_WORKERS env var if using
-    a local Ollama endpoint with no rate limits.
-
-    Failed items are re-queued with exponential backoff (see MAX_RETRIES /
-    RETRY_BACKOFF_BASE_SECONDS). After MAX_RETRIES the item is dropped and
-    the failure is recorded on the watch.
-    """
-    with app.app_context():
-        while not app.config.exit.is_set():
-            try:
-                item = llm_q.get(block=False)
-            except queue.Empty:
-                app.config.exit.wait(1)
-                continue
-
-            # Honour retry delay — if the item isn't due yet, put it back
-            # and sleep briefly rather than spinning.
-            next_retry_at = item.get('next_retry_at', 0)
-            if next_retry_at > time.time():
-                llm_q.put(item)
-                app.config.exit.wait(min(next_retry_at - time.time(), 5))
-                continue
-
-            uuid        = item.get('uuid')
-            snapshot_id = item.get('snapshot_id')
-            attempts    = item.get('attempts', 0)
-
-            logger.debug(f"LLM worker {worker_id} processing uuid={uuid} snapshot={snapshot_id} attempt={attempts}")
-
-            try:
-                process_llm_summary(item, datastore)
-                logger.info(f"LLM worker {worker_id} completed summary for uuid={uuid} snapshot={snapshot_id}")
-
-            except NotImplementedError:
-                # Silently drop until the processor is implemented
-                logger.debug(f"LLM worker {worker_id} skipping — processor not yet implemented")
-
-            except _RateLimitWait as rw:
-                # Proactive bucket empty — re-queue without counting as a failure
-                item['next_retry_at'] = time.time() + rw.wait_seconds
-                llm_q.put(item)
-                logger.info(
-                    f"LLM worker {worker_id} rate-limited (proactive) for {rw.wait_seconds:.1f}s "
-                    f"uuid={uuid}"
-                )
-
-            except Exception as e:
-                # Reactive: check if the API itself returned a rate-limit error
-                try:
-                    import litellm as _litellm
-                    if isinstance(e, _litellm.RateLimitError):
-                        wait = _parse_retry_after(e)
-                        item['next_retry_at'] = time.time() + wait
-                        llm_q.put(item)
-                        logger.warning(
-                            f"LLM worker {worker_id} API rate limit for uuid={uuid}, "
-                            f"retry in {wait:.1f}s"
-                        )
-                        continue
-                except ImportError:
-                    pass
-
-                logger.error(f"LLM worker {worker_id} error for uuid={uuid} snapshot={snapshot_id}: {e}")
-
-                if attempts < MAX_RETRIES:
-                    backoff = RETRY_BACKOFF_BASE_SECONDS * (2 ** attempts)
-                    item['attempts']      = attempts + 1
-                    item['next_retry_at'] = time.time() + backoff
-                    llm_q.put(item)
-                    logger.info(
-                        f"LLM worker {worker_id} re-queued uuid={uuid} "
-                        f"attempt={item['attempts']}/{MAX_RETRIES} retry_in={backoff}s"
-                    )
-                else:
-                    logger.error(
-                        f"LLM worker {worker_id} gave up on uuid={uuid} snapshot={snapshot_id} "
-                        f"after {MAX_RETRIES} attempts"
-                    )
-                    if uuid and uuid in datastore.data['watching']:
-                        datastore.update_watch(
-                            uuid=uuid,
-                            update_obj={'last_error': f"LLM summary failed after {MAX_RETRIES} attempts: {e}"}
-                        )

changedetectionio/llm/settings_form.py

@@ -1,139 +0,0 @@
-import re
-import uuid as _uuid
-
-from flask_babel import lazy_gettext as _l
-from wtforms import (
-    BooleanField,
-    FieldList,
-    Form,
-    FormField,
-    HiddenField,
-    IntegerField,
-    PasswordField,
-    SelectField,
-    StringField,
-    TextAreaField,
-)
-from wtforms.validators import Length, NumberRange, Optional
-
-from changedetectionio.llm.tokens import STRUCTURED_OUTPUT_INSTRUCTION
-
-# The built-in instruction appended after the diff — shown as placeholder text.
-DEFAULT_SUMMARY_PROMPT = (
-    "Analyse all changes in this diff.\n\n"
-    + STRUCTURED_OUTPUT_INSTRUCTION
-)
-
-# Allowed characters for a connection ID coming from the browser.
-_CONN_ID_RE = re.compile(r'^[a-zA-Z0-9_-]{1,64}$')
-
-
-def sanitised_conn_id(raw):
-    """Return raw if it looks like a safe identifier, otherwise a fresh UUID."""
-    s = (raw or '').strip()
-    return s if _CONN_ID_RE.match(s) else str(_uuid.uuid4())
-
-
-class LLMConnectionEntryForm(Form):
-    """Schema for a single LLM connection.
-
-    Declaring every field here is what prevents arbitrary key injection:
-    only these fields can ever reach the datastore from this form.
-    """
-    connection_id     = HiddenField()
-    name              = StringField(_l('Name'),          validators=[Optional(), Length(max=100)])
-    model             = StringField(_l('Model string'),  validators=[Optional(), Length(max=200)])
-    api_key           = StringField(_l('API Key'),       validators=[Optional(), Length(max=500)])
-    api_base          = StringField(_l('API Endpoint'),  validators=[Optional(), Length(max=500)])
-    tokens_per_minute = IntegerField(_l('Tokens/min'),   validators=[Optional(), NumberRange(min=0, max=10_000_000)], default=0)
-    is_default        = BooleanField(_l('Default'),      validators=[Optional()])
-
-
-class LLMNewConnectionForm(Form):
-    """Staging fields for the 'Add a connection' UI.
-
-    These are read client-side by llm.js to build a new FieldList entry on click.
-    They are never used server-side — render_kw sets the id attributes llm.js
-    looks up with $('#llm-add-name') etc.
-    """
-    preset = SelectField(
-        _l('Provider template'),
-        validate_choice=False,
-        # WTForms 3.x uses a dict for optgroups (has_groups() checks isinstance(choices, dict)).
-        # An empty-string key renders as <optgroup label=""> which browsers treat as ungrouped.
-        choices={
-            '': [('', '')],
-            _l('Cloud'): [
-                ('openai-mini',      'OpenAI — gpt-4o-mini'),
-                ('openai-4o',        'OpenAI — gpt-4o'),
-                ('anthropic-haiku',  'Anthropic — claude-3-haiku'),
-                ('anthropic-sonnet', 'Anthropic — claude-3-5-sonnet'),
-                ('groq-8b',          'Groq — llama-3.1-8b-instant'),
-                ('groq-70b',         'Groq — llama-3.3-70b-versatile'),
-                ('gemini-flash',     'Google — gemini-1.5-flash'),
-                ('mistral-small',    'Mistral — mistral-small'),
-                ('deepseek',         'DeepSeek — deepseek-chat'),
-                ('openrouter',       'OpenRouter (custom model)'),
-            ],
-            _l('Local'): [
-                ('ollama-llama',   'Ollama — llama3.1'),
-                ('ollama-mistral', 'Ollama — mistral'),
-                ('lmstudio',       'LM Studio'),
-            ],
-            _l('Custom'): [
-                ('custom', _l('Manual entry')),
-            ],
-        },
-        render_kw={'id': 'llm-preset'},
-    )
-    name              = StringField(_l('Name'),
-                            render_kw={'id': 'llm-add-name',  'size': 30,
-                                       'autocomplete': 'off'})
-    model             = StringField(_l('Model string'),
-                            render_kw={'id': 'llm-add-model', 'size': 40,
-                                       'placeholder': 'gpt-4o-mini', 'autocomplete': 'off'})
-    api_key           = PasswordField(_l('API Key'),
-                            render_kw={'id': 'llm-add-key',   'size': 40,
-                                       'placeholder': 'sk-…', 'autocomplete': 'off'})
-    api_base          = StringField(_l('API Endpoint'),
-                            render_kw={'id': 'llm-add-base',  'size': 40,
-                                       'placeholder': 'http://localhost:11434', 'autocomplete': 'off'})
-    tokens_per_minute = IntegerField(_l('Tokens/min'), default=0,
-                            render_kw={'id': 'llm-add-tpm',   'style': 'width: 8em;',
-                                       'min': '0', 'step': '1000'})
-
-
-class LLMSettingsForm(Form):
-    """WTForms form for the LLM settings tab.
-
-    llm_connection is a FieldList of LLMConnectionEntryForm entries.
-    llm.js emits individual hidden inputs (llm_connection-N-fieldname) on submit
-    instead of a JSON blob, so WTForms processes them through the declared schema.
-    """
-    llm_connection  = FieldList(FormField(LLMConnectionEntryForm), min_entries=0)
-    new_connection  = FormField(LLMNewConnectionForm)
-
-    llm_diff_context_lines = IntegerField(
-        _l('Diff context lines'),
-        validators=[Optional(), NumberRange(min=0, max=20)],
-        default=2,
-        description=_l(
-            'Number of unchanged lines shown around each change in the diff. '
-            'More lines give the LLM more context but increase token usage. (default: 2)'
-        ),
-        render_kw={'style': 'width: 5em;', 'min': '0', 'max': '20'},
-    )
-
-    llm_summary_prompt = TextAreaField(
-        _l('Summary prompt'),
-        validators=[Optional()],
-        description=_l(
-            'Override the instruction sent to the LLM after the diff. '
-            'Leave blank to use the built-in default (structured JSON output).'
-        ),
-        render_kw={
-            'rows': 8,
-            'placeholder': DEFAULT_SUMMARY_PROMPT,
-            'class': 'pure-input-1',
-        },
-    )

changedetectionio/llm/templates/settings-llm.html

@@ -1,103 +0,0 @@
-<script src="{{url_for('static_content', group='js', filename='llm.js')}}" defer></script>
-<script>
-var LLM_CONNECTIONS = (function () {
-    var list = {{ plugin_form.llm_connection.data|tojson }};
-    var out  = {};
-    (list || []).forEach(function (c) { if (c && c.connection_id) out[c.connection_id] = c; });
-    return out;
-}());
-var LLM_I18N = {
-    noConnections:     '{{ _("No connections configured yet.") }}',
-    setDefault:        '{{ _("Set as default") }}',
-    remove:            '{{ _("Remove") }}',
-    show:              '{{ _("show") }}',
-    hide:              '{{ _("hide") }}',
-    nameModelRequired: '{{ _("Name and Model string are required.") }}'
-};
-</script>
-
-{# ── Configured connections table ──────────────────── #}
-<fieldset>
-    <legend>{{ _('LLM Connections') }}</legend>
-
-    <table class="pure-table pure-table-horizontal llm-connections">
-        <thead>
-            <tr>
-                <th class="llm-col-def" title="{{ _('Default') }}">{{ _('Default') }}</th>
-                <th class="llm-col-name">{{ _('Name') }}</th>
-                <th class="llm-col-model">{{ _('Model') }}</th>
-                <th class="llm-col-key">{{ _('API Key') }}</th>
-                <th class="llm-col-tpm" title="{{ _('Tokens per minute limit (0 = unlimited)') }}">{{ _('TPM') }}</th>
-                <th class="llm-col-del"></th>
-            </tr>
-        </thead>
-        <tbody id="llm-connections-tbody">
-        </tbody>
-    </table>
-</fieldset>
-
-{# ── Add connection ─────────────────────────────────── #}
-{% set nf = plugin_form.new_connection.form %}
-<fieldset>
-    <legend>{{ _('Add a connection') }}</legend>
-
-    <div class="pure-control-group">
-        {{ nf.preset.label }}
-        {{ nf.preset() }}
-    </div>
-
-    <div class="pure-control-group">
-        {{ nf.name.label }}
-        {{ nf.name(placeholder=_('e.g. My OpenAI')) }}
-    </div>
-    <div class="pure-control-group">
-        {{ nf.model.label }}
-        {{ nf.model() }}
-    </div>
-    <div class="pure-control-group">
-        <label for="llm-add-key">
-            {{ _('API Key') }}
-            <span class="pure-form-message-inline">({{ _('leave blank for local') }})</span>
-        </label>
-        <div class="llm-key-wrap">
-            {{ nf.api_key() }}
-            <button type="button" id="llm-key-toggle" class="pure-button">{{ _('show') }}</button>
-        </div>
-    </div>
-    <div class="pure-control-group" id="llm-base-group" style="display:none">
-        <label for="llm-add-base">
-            {{ _('API Endpoint') }}
-            <span class="pure-form-message-inline">({{ _('optional') }})</span>
-        </label>
-        {{ nf.api_base() }}
-    </div>
-    <div class="pure-control-group">
-        <label for="llm-add-tpm">
-            {{ _('Tokens/min limit') }}
-            <span class="pure-form-message-inline">({{ _('0 = unlimited') }})</span>
-        </label>
-        {{ nf.tokens_per_minute() }}
-    </div>
-
-    <div class="pure-controls">
-        <button type="button" id="llm-btn-add" class="pure-button pure-button-primary">{{ _('+ Add connection') }}</button>
-    </div>
-</fieldset>
-
-{# ── Prompt configuration ────────────────────────────────── #}
-<fieldset>
-    <legend>{{ _('Summary Prompt') }}</legend>
-    <div class="pure-control-group">
-        {{ plugin_form.llm_diff_context_lines.label }}
-        {{ plugin_form.llm_diff_context_lines() }}
-        <span class="pure-form-message-inline">
-            {{ _('Unchanged lines shown around each change in the diff sent to the LLM. More lines = more context but higher token cost. (default: 2)') }}
-        </span>
-    </div>
-    <div class="pure-control-group">
-        {{ render_field(plugin_form.llm_summary_prompt) }}
-        <span class="pure-form-message-inline">
-            {{ _('Instruction appended after the diff in every LLM call. Leave blank to use the built-in default (structured JSON output).') }}
-        </span>
-    </div>
-</fieldset>

changedetectionio/llm/tokens.py

@@ -1,197 +0,0 @@
-"""
-LLM notification token definitions and file I/O helpers.
-
-All LLM data for a snapshot is stored under a dedicated subdirectory:
-    {data_dir}/llm/{snapshot_id}-llm.json
-
-A plain-text {snapshot_id}-llm.txt is also written containing just the
-summary field, for backward compatibility with any code that already reads it.
-
-Token catalogue
----------------
-llm_summary     1-3 sentence description of all changes, exact values.
-llm_headline    5-8 word punchy title — ideal for the notification subject line.
-llm_importance  Numeric 1-10 significance score; enables routing rules like
-                "only escalate if llm_importance >= 8".
-llm_sentiment   Machine-readable: "positive", "negative", or "neutral".
-                Useful for trend tracking and coloured alert styling.
-llm_one_liner   Shortest useful summary — one sentence for SMS, Pushover,
-                and other character-limited channels.
-"""
-
-import os
-import json
-from loguru import logger
-
-# ── Constants ──────────────────────────────────────────────────────────────
-
-LLM_TOKEN_NAMES = (
-    'llm_summary',
-    'llm_headline',
-    'llm_importance',
-    'llm_sentiment',
-    'llm_one_liner',
-)
-
-# How long the notification runner waits for LLM data before giving up.
-LLM_NOTIFICATION_RETRY_DELAY_SECONDS = int(os.getenv('LLM_NOTIFICATION_RETRY_DELAY', '10'))
-LLM_NOTIFICATION_MAX_WAIT_ATTEMPTS   = int(os.getenv('LLM_NOTIFICATION_MAX_WAIT',    '18'))  # 18 × 10s = 3 min
-
-# JSON prompt fragment — embedded in the final summarisation call.
-STRUCTURED_OUTPUT_INSTRUCTION = (
-    'Return ONLY a valid JSON object — no markdown fences, no extra text — using exactly these keys:\n'
-    '{"summary":"1-3 sentences covering ALL changes; use exact values from the diff.","headline":"5-8 word punchy title for this specific change","importance":7,"sentiment":"positive","one_liner":"One sentence for SMS/push character limits."}\n'
-    'importance: 1=trivial whitespace, 5=moderate content change, 10=critical price/availability change.\n'
-    'sentiment: "positive" (desirable for the user), "negative" (undesirable), or "neutral" (informational only).'
-)
-
-
-# ── File I/O ───────────────────────────────────────────────────────────────
-
-def llm_subdir(data_dir: str) -> str:
-    """Return the llm/ subdirectory path (does not create it)."""
-    return os.path.join(data_dir, 'llm')
-
-
-def llm_json_path(data_dir: str, snapshot_id: str) -> str:
-    return os.path.join(llm_subdir(data_dir), f"{snapshot_id}-llm.json")
-
-
-def llm_txt_path(data_dir: str, snapshot_id: str) -> str:
-    return os.path.join(llm_subdir(data_dir), f"{snapshot_id}-llm.txt")
-
-
-def is_llm_data_ready(data_dir: str, snapshot_id: str) -> bool:
-    """Return True if LLM data has been written for this snapshot."""
-    return os.path.exists(llm_json_path(data_dir, snapshot_id)) or \
-           os.path.exists(llm_txt_path(data_dir, snapshot_id))
-
-
-def read_llm_tokens(data_dir: str, snapshot_id: str) -> dict:
-    """
-    Read LLM token data for a snapshot.
-
-    Tries JSON first (new format), falls back to plain .txt (old format).
-    Returns an empty dict if no data is available yet.
-    """
-    json_file = llm_json_path(data_dir, snapshot_id)
-    if os.path.exists(json_file):
-        try:
-            with open(json_file, 'r', encoding='utf-8') as f:
-                data = json.load(f)
-            if isinstance(data, dict):
-                return _normalise(data)
-        except Exception as exc:
-            logger.warning(f"LLM tokens: failed to read {json_file}: {exc}")
-
-    txt_file = llm_txt_path(data_dir, snapshot_id)
-    if os.path.exists(txt_file):
-        try:
-            with open(txt_file, 'r', encoding='utf-8') as f:
-                summary = f.read().strip()
-            return _normalise({'summary': summary, 'one_liner': summary[:200]})
-        except Exception as exc:
-            logger.warning(f"LLM tokens: failed to read {txt_file}: {exc}")
-
-    return {}
-
-
-def write_llm_data(data_dir: str, snapshot_id: str, data: dict) -> str:
-    """
-    Atomically write LLM data to the llm/ subdirectory.
-
-    Writes:
-      llm/{snapshot_id}-llm.json  — full structured data (all tokens)
-      llm/{snapshot_id}-llm.txt   — plain summary text (backward compat)
-
-    Returns the path of the JSON file.
-    """
-    normalised = _normalise(data)
-
-    subdir = llm_subdir(data_dir)
-    os.makedirs(subdir, exist_ok=True)
-
-    json_file = llm_json_path(data_dir, snapshot_id)
-    _atomic_write_text(json_file, json.dumps(normalised, ensure_ascii=False))
-
-    txt_file = llm_txt_path(data_dir, snapshot_id)
-    _atomic_write_text(txt_file, normalised.get('summary', ''))
-
-    return json_file
-
-
-def parse_llm_response(response: str) -> dict:
-    """
-    Parse a structured JSON response from the LLM.
-
-    Tries strict JSON parse, then extracts from markdown code fences,
-    then a bare object search.  Falls back to treating the whole response
-    as the 'summary' field if nothing parses.
-    """
-    import re
-    text = response.strip()
-
-    # 1. Direct JSON parse
-    try:
-        obj = json.loads(text)
-        if isinstance(obj, dict):
-            return _normalise(obj)
-    except (json.JSONDecodeError, ValueError):
-        pass
-
-    # 2. Markdown code fence: ```json { ... } ```
-    m = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL)
-    if m:
-        try:
-            obj = json.loads(m.group(1))
-            if isinstance(obj, dict):
-                return _normalise(obj)
-        except (json.JSONDecodeError, ValueError):
-            pass
-
-    # 3. Bare JSON object anywhere in the response
-    m = re.search(r'\{[^{}]*\}', text, re.DOTALL)
-    if m:
-        try:
-            obj = json.loads(m.group(0))
-            if isinstance(obj, dict):
-                return _normalise(obj)
-        except (json.JSONDecodeError, ValueError):
-            pass
-
-    # 4. Fallback — treat entire response as summary
-    logger.debug("LLM response was not valid JSON — using raw text as summary")
-    return _normalise({'summary': text, 'one_liner': text[:200] if len(text) > 200 else text})
-
-
-# ── Internal helpers ───────────────────────────────────────────────────────
-
-def _normalise(data: dict) -> dict:
-    """Return a clean token dict with all expected keys present."""
-    importance = data.get('importance')
-    if importance is not None:
-        try:
-            importance = max(1, min(10, int(float(importance))))
-        except (TypeError, ValueError):
-            importance = None
-
-    sentiment = str(data.get('sentiment', '')).lower().strip()
-    if sentiment not in ('positive', 'negative', 'neutral'):
-        sentiment = ''
-
-    return {
-        'summary':    str(data.get('summary',   '') or '').strip(),
-        'headline':   str(data.get('headline',  '') or '').strip(),
-        'importance': importance,
-        'sentiment':  sentiment,
-        'one_liner':  str(data.get('one_liner', '') or '').strip(),
-    }
-
-
-def _atomic_write_text(path: str, text: str) -> None:
-    tmp = path + '.tmp'
-    with open(tmp, 'w', encoding='utf-8') as f:
-        f.write(text)
-        f.flush()
-        os.fsync(f.fileno())
-    os.replace(tmp, path)

changedetectionio/model/Watch.py

@@ -1009,31 +1009,14 @@ class model(EntityPersistenceMixin, watch_base):
 
 
     def extra_notification_token_values(self):
-        from changedetectionio.llm.tokens import read_llm_tokens
-        history = self.history
-        if not history:
-            return {}
-        latest_fname = history[list(history.keys())[-1]]
-        snapshot_id = os.path.basename(latest_fname).split('.')[0]  # always 32-char MD5
-        data = read_llm_tokens(self.data_dir, snapshot_id)
-        if not data:
-            return {}
-        return {
-            'llm_summary':    data.get('summary', ''),
-            'llm_headline':   data.get('headline', ''),
-            'llm_importance': data.get('importance'),
-            'llm_sentiment':  data.get('sentiment', ''),
-            'llm_one_liner':  data.get('one_liner', ''),
-        }
+        # Used for providing extra tokens
+        # return {'widget': 555}
+        return {}
 
     def extra_notification_token_placeholder_info(self):
-        return [
-            ('llm_summary',    "LLM: 1-3 sentence summary of all changes with exact values"),
-            ('llm_headline',   "LLM: 5-8 word punchy title for this specific change"),
-            ('llm_importance', "LLM: Significance score 1-10 (1=trivial, 10=critical)"),
-            ('llm_sentiment',  "LLM: Change sentiment — positive, negative, or neutral"),
-            ('llm_one_liner',  "LLM: One sentence for SMS/push character limits"),
-        ]
+        # Used for providing extra tokens
+        # return [('widget', "Get widget amounts")]
+        return []
 
 
     def extract_regex_from_all_history(self, regex):

changedetectionio/notification_service.py

@@ -6,7 +6,6 @@ Extracted from update_worker.py to provide standalone notification functionality
 for both sync and async workers
 """
 import datetime
-import os
 
 import pytz
 from loguru import logger
@@ -83,12 +82,6 @@ class NotificationContextData(dict):
             'watch_tag': None,
             'watch_title': None,
             'watch_url': 'https://WATCH-PLACE-HOLDER/',
-            # LLM-generated tokens (populated by notification_runner once LLM data is ready)
-            'llm_headline':   None,
-            'llm_importance': None,
-            'llm_one_liner':  None,
-            'llm_sentiment':  None,
-            'llm_summary':    None,
         })
 
         # Apply any initial data passed in
@@ -281,11 +274,6 @@ class NotificationService:
                                                     timestamp_changed=dates[date_index_to]))
 
         if self.notification_q:
-            # Store snapshot_id hint so notification_runner can gate on LLM data readiness
-            if watch and len(dates) > 0:
-                latest_fname = watch.history.get(dates[date_index_to], '')
-                if latest_fname:
-                    n_object['_llm_snapshot_id'] = os.path.basename(latest_fname).split('.')[0]
             logger.debug("Queued notification for sending")
             self.notification_q.put(n_object)
         else:

changedetectionio/pluggy_interface.py

@@ -151,8 +151,7 @@ class ChangeDetectionSpec:
         pass
 
     @hookspec
-    def update_finalize(update_handler, watch, datastore, processing_exception,
-                        changed_detected=False, snapshot_id=None):
+    def update_finalize(update_handler, watch, datastore, processing_exception):
         """Called after watch processing completes (success or failure).
 
         This hook is called in the finally block after all processing is complete,
@@ -169,10 +168,6 @@ class ChangeDetectionSpec:
             processing_exception: The exception from the main processing block, or None if successful.
                                  This does NOT include cleanup exceptions - only exceptions from
                                  the actual watch processing (fetch, diff, etc).
-            changed_detected: True when the processor detected a content change (default False).
-            snapshot_id: MD5 hex string of the new snapshot, matches the prefix of the history
-                         filename (e.g. 'abc123…' → 'abc123….txt[.br]').  None when no snapshot
-                         was saved (first run, error, same content).
 
         Returns:
             None: This hook doesn't return a value
@@ -585,8 +580,7 @@ def apply_update_handler_alter(update_handler, watch, datastore):
     return current_handler
 
 
-def apply_update_finalize(update_handler, watch, datastore, processing_exception,
-                          changed_detected=False, snapshot_id=None):
+def apply_update_finalize(update_handler, watch, datastore, processing_exception):
     """Apply update_finalize hooks from all plugins.
 
     Called in the finally block after watch processing completes, allowing plugins
@@ -597,8 +591,6 @@ def apply_update_finalize(update_handler, watch, datastore, processing_exception
         watch: The watch dict that was processed (may be None)
         datastore: The application datastore
         processing_exception: The exception from processing, or None if successful
-        changed_detected: True when the processor detected a content change.
-        snapshot_id: MD5 hex string of the new snapshot, or None.
 
     Returns:
         None
@@ -609,9 +601,7 @@ def apply_update_finalize(update_handler, watch, datastore, processing_exception
             update_handler=update_handler,
             watch=watch,
             datastore=datastore,
-            processing_exception=processing_exception,
-            changed_detected=changed_detected,
-            snapshot_id=snapshot_id,
+            processing_exception=processing_exception
         )
     except Exception as e:
         # Don't let plugin errors crash the worker

changedetectionio/static/js/llm.js

@@ -1,187 +0,0 @@
-/* llm.js — LLM Connections management (settings page)
- * Depends on: jQuery (global), LLM_CONNECTIONS + LLM_I18N injected by Jinja2 template.
- */
-(function ($) {
-    'use strict';
-
-    // Provider presets: [value, label, model, api_base, tpm]
-    // tpm = tokens-per-minute limit (0 = unlimited / local).
-    // Defaults reflect free-tier or conservative tier-1 limits.
-    var LLM_PRESETS = [
-        ['openai-mini',      'OpenAI — gpt-4o-mini',             'gpt-4o-mini',                              '',                          200000],
-        ['openai-4o',        'OpenAI — gpt-4o',                  'gpt-4o',                                   '',                           30000],
-        ['anthropic-haiku',  'Anthropic — claude-3-haiku',       'anthropic/claude-3-haiku-20240307',        '',                          100000],
-        ['anthropic-sonnet', 'Anthropic — claude-3-5-sonnet',    'anthropic/claude-3-5-sonnet-20241022',     '',                           40000],
-        ['groq-8b',          'Groq — llama-3.1-8b-instant',      'groq/llama-3.1-8b-instant',               '',                            6000],
-        ['groq-70b',         'Groq — llama-3.3-70b-versatile',   'groq/llama-3.3-70b-versatile',            '',                            6000],
-        ['gemini-flash',     'Google — gemini-1.5-flash',        'gemini/gemini-1.5-flash',                 '',                         1000000],
-        ['mistral-small',    'Mistral — mistral-small',          'mistral/mistral-small-latest',            '',                          500000],
-        ['deepseek',         'DeepSeek — deepseek-chat',         'deepseek/deepseek-chat',                  '',                           50000],
-        ['openrouter',       'OpenRouter (custom model)',         'openrouter/',                             '',                           20000],
-        ['ollama-llama',     'Ollama — llama3.1 (local)',        'ollama/llama3.1',    'http://localhost:11434',     0],
-        ['ollama-mistral',   'Ollama — mistral (local)',         'ollama/mistral',     'http://localhost:11434',     0],
-        ['lmstudio',         'LM Studio (local)',                'openai/local',       'http://localhost:1234/v1',   0],
-    ];
-
-    var presetMap = {};
-    $.each(LLM_PRESETS, function (_, p) { presetMap[p[0]] = p; });
-
-    function escHtml(s) {
-        return $('<div>').text(String(s)).html();
-    }
-
-    function maskKey(k) {
-        if (!k) return '<span style="color:var(--color-grey-700)">—</span>';
-        return escHtml(k.substring(0, 4)) + '••••';
-    }
-
-    // Emit WTForms FieldList hidden inputs (llm_connection-N-fieldname) so the
-    // server processes connections through the declared schema — no arbitrary keys.
-    function serialise() {
-        var $form = $('form.settings');
-        $form.find('input[data-llm-gen]').remove();
-
-        var ids = Object.keys(LLM_CONNECTIONS);
-        $.each(ids, function (i, id) {
-            var c      = LLM_CONNECTIONS[id];
-            var prefix = 'llm_connection-' + i + '-';
-            var fields = {
-                connection_id:     id,
-                name:              c.name              || '',
-                model:             c.model             || '',
-                api_key:           c.api_key           || '',
-                api_base:          c.api_base          || '',
-                tokens_per_minute: parseInt(c.tokens_per_minute || 0, 10)
-            };
-            $.each(fields, function (field, value) {
-                $('<input>').attr({ type: 'hidden', name: prefix + field, value: value, 'data-llm-gen': '1' }).appendTo($form);
-            });
-            // BooleanField: only emit when true (absence == false in WTForms)
-            if (c.is_default) {
-                $('<input>').attr({ type: 'hidden', name: prefix + 'is_default', value: 'y', 'data-llm-gen': '1' }).appendTo($form);
-            }
-        });
-    }
-
-    function renderTable() {
-        var $tbody = $('#llm-connections-tbody');
-        $tbody.empty();
-        var ids = Object.keys(LLM_CONNECTIONS);
-        if (!ids.length) {
-            $tbody.html('<tr class="llm-empty"><td colspan="6">' + escHtml(LLM_I18N.noConnections) + '</td></tr>');
-            return;
-        }
-        $.each(ids, function (_, id) {
-            var c   = LLM_CONNECTIONS[id];
-            var tpm = parseInt(c.tokens_per_minute || 0, 10);
-            var tpmLabel = tpm ? tpm.toLocaleString() : '<span style="color:var(--color-grey-700)">∞</span>';
-            $tbody.append(
-                '<tr>' +
-                '<td class="llm-col-def">' +
-                    '<input type="radio" class="llm-default-radio" name="llm_default_radio"' +
-                    ' title="' + escHtml(LLM_I18N.setDefault) + '"' +
-                    (c.is_default ? ' checked' : '') +
-                    ' data-id="' + escHtml(id) + '">' +
-                '</td>' +
-                '<td class="llm-col-name">'  + escHtml(c.name)  + '</td>' +
-                '<td class="llm-col-model">' + escHtml(c.model) + '</td>' +
-                '<td class="llm-col-key">'   + maskKey(c.api_key) + '</td>' +
-                '<td class="llm-col-tpm">'   + tpmLabel + '</td>' +
-                '<td class="llm-col-del">' +
-                    '<button type="button" class="llm-del"' +
-                    ' title="' + escHtml(LLM_I18N.remove) + '"' +
-                    ' data-id="' + escHtml(id) + '">×</button>' +
-                '</td>' +
-                '</tr>'
-            );
-        });
-    }
-
-    $(function () {
-        // Event delegation on tbody — survives re-renders
-        $('#llm-connections-tbody')
-            .on('change', '.llm-default-radio', function () {
-                var chosen = String($(this).data('id'));
-                $.each(LLM_CONNECTIONS, function (k) {
-                    LLM_CONNECTIONS[k].is_default = (k === chosen);
-                });
-                serialise();
-            })
-            .on('click', '.llm-del', function () {
-                var id = String($(this).data('id'));
-                delete LLM_CONNECTIONS[id];
-                var remaining = Object.keys(LLM_CONNECTIONS);
-                if (remaining.length && !remaining.some(function (k) { return LLM_CONNECTIONS[k].is_default; })) {
-                    LLM_CONNECTIONS[remaining[0]].is_default = true;
-                }
-                renderTable();
-                serialise();
-            });
-
-        function updateBaseVisibility() {
-            var val     = $('#llm-preset').val();
-            var preset  = presetMap[val];
-            var hasBase = preset ? !!preset[3] : (val === 'custom');
-            var show    = (val === 'custom') || hasBase;
-            $('#llm-base-group').toggle(show);
-        }
-
-        // Preset dropdown pre-fills add form
-        $('#llm-preset').on('change', function () {
-            var val = $(this).val();
-            var p   = presetMap[val];
-            if (p) {
-                $('#llm-add-name').val(p[1].replace(/\s*—.*/, '').trim());
-                $('#llm-add-model').val(p[2]);
-                $('#llm-add-base').val(p[3]);
-                $('#llm-add-tpm').val(p[4] !== undefined ? p[4] : 0);
-                $('#llm-add-key').val('');
-            }
-            updateBaseVisibility();
-        });
-
-        // Add connection
-        $('#llm-btn-add').on('click', function () {
-            var name  = $.trim($('#llm-add-name').val());
-            var model = $.trim($('#llm-add-model').val());
-            var key   = $.trim($('#llm-add-key').val());
-            var base  = $.trim($('#llm-add-base').val());
-            var tpm   = parseInt($('#llm-add-tpm').val(), 10) || 0;
-            if (!name || !model) {
-                alert(LLM_I18N.nameModelRequired);
-                return;
-            }
-            var id = 'llm-' + Date.now();
-            var isFirst = !Object.keys(LLM_CONNECTIONS).length;
-            LLM_CONNECTIONS[id] = {
-                name: name, model: model, api_key: key, api_base: base,
-                tokens_per_minute: tpm, is_default: isFirst
-            };
-            $('#llm-preset, #llm-add-name, #llm-add-model, #llm-add-key, #llm-add-base').val('');
-            $('#llm-add-tpm').val('0');
-            $('#llm-base-group').hide();
-            renderTable();
-            serialise();
-        });
-
-        // Show/hide API key visibility
-        $('#llm-key-toggle').on('click', function () {
-            var $inp = $('#llm-add-key');
-            if ($inp.attr('type') === 'password') {
-                $inp.attr('type', 'text');
-                $(this).text(LLM_I18N.hide);
-            } else {
-                $inp.attr('type', 'password');
-                $(this).text(LLM_I18N.show);
-            }
-        });
-
-        // Serialise connections to hidden field before form submit
-        $('form.settings').on('submit', serialise);
-
-        // Init
-        renderTable();
-        serialise();
-    });
-
-}(jQuery));

changedetectionio/static/styles/scss/parts/_llm.scss

@@ -1,57 +0,0 @@
-#llm {
-  // ── Key field wrapper — input + show/hide toggle inline ───────────────
-  .llm-key-wrap {
-    display: flex;
-    gap: 0.3em;
-    align-items: center;
-
-    input { flex: 1; min-width: 0; }
-
-    button {
-      flex: 0 0 auto;
-    }
-  }
-
-  // ── Pure-grid column padding consistency ──────────────────────────────
-  .pure-u-md-1-2 {
-    .pure-control-group {
-      padding-right: 1em;
-    }
-  }
-
-  // ── Connections table ─────────────────────────────────────────────────
-  table.llm-connections {
-    width: 100%;
-
-    .llm-col-def   { width: 3em;   text-align: center; }
-    .llm-col-name  { font-weight: 500; }
-    .llm-col-model { font-family: monospace; font-size: 0.85em; color: var(--color-grey-400); }
-    .llm-col-key   {
-      font-family: monospace; font-size: 0.82em; color: var(--color-grey-600);
-      max-width: 140px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap;
-    }
-    .llm-col-del   { width: 2.5em; text-align: center; }
-
-    .llm-del {
-      background: none;
-      border: none;
-      cursor: pointer;
-      color: var(--color-grey-600);
-      padding: 0.15em 0.4em;
-      border-radius: 3px;
-      font-size: 1.1em;
-      line-height: 1;
-      &:hover { color: var(--color-dark-red); background: #ffeaea; }
-    }
-
-    .llm-empty td {
-      text-align: center;
-      color: var(--color-grey-600);
-      padding: 1.8em;
-      font-style: italic;
-      font-size: 0.92em;
-    }
-  }
-
-  .llm-default-radio { cursor: pointer; }
-}

changedetectionio/static/styles/scss/styles.scss

@@ -32,7 +32,6 @@
 @use "parts/toast";
 @use "parts/login_form";
 @use "parts/tabs";
-@use "parts/llm";
 
 // Smooth transitions for theme switching
 body,

changedetectionio/tests/conftest.py

@@ -249,14 +249,13 @@ def prepare_test_function(live_server, datastore_path):
     # CRITICAL: Get datastore and stop it from writing stale data
     datastore = live_server.app.config.get('DATASTORE')
 
-    # Clear the queues before starting the test to prevent state leakage
-    from changedetectionio.flask_app import update_q, llm_summary_q
-    for q in (update_q, llm_summary_q):
-        while not q.empty():
-            try:
-                q.get_nowait()
-            except:
-                break
+    # Clear the queue before starting the test to prevent state leakage
+    from changedetectionio.flask_app import update_q
+    while not update_q.empty():
+        try:
+            update_q.get_nowait()
+        except:
+            break
 
     # Add test helper methods to the app for worker management
     def set_workers(count):

changedetectionio/tests/test_llm.py

@@ -1,260 +0,0 @@
-"""
-Tests for LLM summary queue, worker, and regenerate route.
-
-Mocking strategy
-----------------
-- `_call_llm` is patched at the module level so no real LiteLLM/API calls are made.
-- `_write_summary` is left un-patched so we can assert the file was actually written.
-- `process_llm_summary` is called directly in unit tests (no worker thread needed).
-"""
-
-import os
-import queue
-import time
-from unittest.mock import patch, MagicMock
-
-import pytest
-from flask import url_for
-
-from changedetectionio.tests.util import set_original_response, set_modified_response, wait_for_all_checks
-
-
-# ---------------------------------------------------------------------------
-# Unit tests — process_llm_summary directly, no HTTP, no worker thread
-# ---------------------------------------------------------------------------
-
-class TestProcessLlmSummary:
-
-    def _make_watch_with_two_snapshots(self, client, datastore_path):
-        """Helper: returns (datastore, uuid, snapshot_id) with 2 history entries."""
-        set_original_response(datastore_path=datastore_path)
-        datastore = client.application.config['DATASTORE']
-        test_url  = url_for('test_endpoint', _external=True)
-
-        uuid = datastore.add_watch(url=test_url)
-        client.get(url_for("ui.form_watch_checknow"), follow_redirects=True)
-        wait_for_all_checks(client)
-
-        set_modified_response(datastore_path=datastore_path)
-        client.get(url_for("ui.form_watch_checknow"), follow_redirects=True)
-        wait_for_all_checks(client)
-
-        watch        = datastore.data['watching'][uuid]
-        history_keys = list(watch.history.keys())
-        snapshot_id  = os.path.basename(watch.history[history_keys[1]]).split('.')[0]
-        return datastore, uuid, snapshot_id
-
-    def test_writes_summary_file(self, client, live_server, datastore_path):
-        """process_llm_summary writes {snapshot_id}-llm.txt when _call_llm succeeds."""
-        datastore, uuid, snapshot_id = self._make_watch_with_two_snapshots(client, datastore_path)
-        watch = datastore.data['watching'][uuid]
-        item  = {'uuid': uuid, 'snapshot_id': snapshot_id, 'attempts': 0}
-
-        from changedetectionio.llm.queue_worker import process_llm_summary
-        with patch('changedetectionio.llm.queue_worker._call_llm', return_value='Price dropped from $10 to $8.') as mock_llm:
-            process_llm_summary(item, datastore)
-
-        assert mock_llm.called
-        summary_path = os.path.join(watch.data_dir, f"{snapshot_id}-llm.txt")
-        assert os.path.exists(summary_path), "Summary file was not written"
-        assert open(summary_path).read() == 'Price dropped from $10 to $8.'
-
-    def test_call_llm_uses_temperature_zero_and_seed(self, client, live_server, datastore_path):
-        """_call_llm always passes temperature=0 and seed=0 to litellm for determinism."""
-        import litellm
-        from changedetectionio.llm.queue_worker import _call_llm
-
-        messages = [{'role': 'user', 'content': 'hello'}]
-        mock_response = MagicMock()
-        mock_response.choices[0].message.content = 'ok'
-
-        with patch('litellm.completion', return_value=mock_response) as mock_completion:
-            _call_llm(model='gpt-4o-mini', messages=messages)
-
-        call_kwargs = mock_completion.call_args.kwargs
-        assert call_kwargs['temperature'] == 0,   "temperature must be 0"
-        assert call_kwargs['seed']        == 0,   "seed must be 0 for reproducibility"
-        assert 'top_p'              not in call_kwargs, "top_p must not be set (redundant at temp=0)"
-        assert 'frequency_penalty'  not in call_kwargs, "frequency_penalty must not be set"
-        assert 'presence_penalty'   not in call_kwargs, "presence_penalty must not be set"
-
-    def test_skips_first_history_entry(self, client, live_server, datastore_path):
-        """process_llm_summary raises ValueError for the first history entry (no prior to diff)."""
-        set_original_response(datastore_path=datastore_path)
-        datastore = client.application.config['DATASTORE']
-        test_url = url_for('test_endpoint', _external=True)
-
-        uuid = datastore.add_watch(url=test_url)
-        client.get(url_for("ui.form_watch_checknow"), follow_redirects=True)
-        wait_for_all_checks(client)
-
-        watch = datastore.data['watching'][uuid]
-        history_keys = list(watch.history.keys())
-        first_fname = watch.history[history_keys[0]]
-        snapshot_id = os.path.basename(first_fname).split('.')[0]
-
-        item = {'uuid': uuid, 'snapshot_id': snapshot_id, 'attempts': 0}
-
-        from changedetectionio.llm.queue_worker import process_llm_summary
-        with pytest.raises(ValueError, match="first history entry"):
-            process_llm_summary(item, datastore)
-
-    def test_raises_for_unknown_watch(self, client, live_server, datastore_path):
-        """process_llm_summary raises ValueError if the watch UUID doesn't exist."""
-        datastore = client.application.config['DATASTORE']
-        item = {'uuid': 'does-not-exist', 'snapshot_id': 'abc123', 'attempts': 0}
-
-        from changedetectionio.llm.queue_worker import process_llm_summary
-        with pytest.raises(ValueError, match="not found"):
-            process_llm_summary(item, datastore)
-
-
-# ---------------------------------------------------------------------------
-# Unit tests — worker retry logic, no HTTP
-# ---------------------------------------------------------------------------
-
-class TestWorkerRetry:
-
-    def test_requeues_on_failure_with_backoff(self, client, live_server, datastore_path):
-        """Worker re-queues a failed item with incremented attempts and future next_retry_at."""
-        from changedetectionio.llm.queue_worker import MAX_RETRIES, RETRY_BACKOFF_BASE_SECONDS
-
-        llm_q   = queue.Queue()
-        app     = client.application
-        datastore = client.application.config['DATASTORE']
-
-        item = {'uuid': 'fake-uuid', 'snapshot_id': 'abc123', 'attempts': 0}
-        llm_q.put(item)
-
-        from changedetectionio.llm.queue_worker import process_llm_summary
-        with patch('changedetectionio.llm.queue_worker.process_llm_summary', side_effect=RuntimeError("API down")):
-            # Run one iteration manually (don't start the full runner thread)
-            from changedetectionio.llm import queue_worker
-            got = llm_q.get(block=False)
-            try:
-                queue_worker.process_llm_summary(got, datastore)
-            except Exception as e:
-                got['attempts'] += 1
-                got['next_retry_at'] = time.time() + RETRY_BACKOFF_BASE_SECONDS * (2 ** (got['attempts'] - 1))
-                llm_q.put(got)
-
-        assert llm_q.qsize() == 1
-        requeued = llm_q.get_nowait()
-        assert requeued['attempts'] == 1
-        assert requeued['next_retry_at'] > time.time()
-
-    def test_drops_after_max_retries(self, client, live_server, datastore_path):
-        """Worker drops item and records last_error after MAX_RETRIES exhausted."""
-        set_original_response(datastore_path=datastore_path)
-        datastore = client.application.config['DATASTORE']
-        test_url = url_for('test_endpoint', _external=True)
-        uuid = datastore.add_watch(url=test_url)
-
-        from changedetectionio.llm.queue_worker import MAX_RETRIES
-        item = {'uuid': uuid, 'snapshot_id': 'abc123', 'attempts': MAX_RETRIES}
-
-        llm_q = queue.Queue()
-        llm_q.put(item)
-
-        with patch('changedetectionio.llm.queue_worker.process_llm_summary', side_effect=RuntimeError("still down")):
-            from changedetectionio.llm import queue_worker
-            got = llm_q.get(block=False)
-            try:
-                queue_worker.process_llm_summary(got, datastore)
-            except Exception as e:
-                if got['attempts'] < MAX_RETRIES:
-                    llm_q.put(got)
-                else:
-                    datastore.update_watch(uuid=uuid, update_obj={'last_error': str(e)})
-
-        # Queue should be empty — item was dropped
-        assert llm_q.empty()
-        watch = datastore.data['watching'][uuid]
-        assert 'still down' in (watch.get('last_error') or '')
-
-
-# ---------------------------------------------------------------------------
-# Route tests — GET /edit/<uuid>/regenerate-llm-summaries
-# ---------------------------------------------------------------------------
-
-class TestRegenerateLlmSummariesRoute:
-
-    def test_queues_missing_summaries(self, client, live_server, datastore_path):
-        """Route queues one item per history entry that lacks a -llm.txt file."""
-        set_original_response(datastore_path=datastore_path)
-        datastore = client.application.config['DATASTORE']
-        test_url  = url_for('test_endpoint', _external=True)
-
-        uuid = datastore.add_watch(url=test_url)
-        client.get(url_for("ui.form_watch_checknow"), follow_redirects=True)
-        wait_for_all_checks(client)
-
-        set_modified_response(datastore_path=datastore_path)
-        client.get(url_for("ui.form_watch_checknow"), follow_redirects=True)
-        wait_for_all_checks(client)
-
-        watch = datastore.data['watching'][uuid]
-        assert watch.history_n >= 2
-
-        from changedetectionio.flask_app import llm_summary_q
-
-        res = client.get(
-            url_for('ui.ui_edit.watch_regenerate_llm_summaries', uuid=uuid),
-            follow_redirects=True,
-        )
-        assert res.status_code == 200
-
-        # history_n - 1 items queued (first entry skipped, no prior to diff)
-        expected = watch.history_n - 1
-        assert llm_summary_q.qsize() == expected
-
-        # Each item has the right shape
-        items = []
-        while not llm_summary_q.empty():
-            items.append(llm_summary_q.get_nowait())
-
-        for item in items:
-            assert item['uuid'] == uuid
-            assert item['attempts'] == 0
-            assert len(item['snapshot_id']) == 32  # MD5 hex
-
-    def test_skips_already_summarised_entries(self, client, live_server, datastore_path):
-        """Route skips entries where {snapshot_id}-llm.txt already exists."""
-        set_original_response(datastore_path=datastore_path)
-        datastore = client.application.config['DATASTORE']
-        test_url  = url_for('test_endpoint', _external=True)
-
-        uuid = datastore.add_watch(url=test_url)
-        client.get(url_for("ui.form_watch_checknow"), follow_redirects=True)
-        wait_for_all_checks(client)
-
-        set_modified_response(datastore_path=datastore_path)
-        client.get(url_for("ui.form_watch_checknow"), follow_redirects=True)
-        wait_for_all_checks(client)
-
-        watch = datastore.data['watching'][uuid]
-        history_keys  = list(watch.history.keys())
-        second_fname  = watch.history[history_keys[1]]
-        snapshot_id   = os.path.basename(second_fname).split('.')[0]
-
-        # Pre-write a summary file
-        summary_path = os.path.join(watch.data_dir, f"{snapshot_id}-llm.txt")
-        with open(summary_path, 'w') as f:
-            f.write('already done')
-
-        from changedetectionio.flask_app import llm_summary_q
-
-        client.get(
-            url_for('ui.ui_edit.watch_regenerate_llm_summaries', uuid=uuid),
-            follow_redirects=True,
-        )
-
-        # That entry should have been skipped — queue should be empty
-        assert llm_summary_q.empty()
-
-    def test_404_for_unknown_watch(self, client, live_server, datastore_path):
-        res = client.get(
-            url_for('ui.ui_edit.watch_regenerate_llm_summaries', uuid='does-not-exist'),
-            follow_redirects=False,
-        )
-        assert res.status_code == 404

changedetectionio/worker.py

@@ -518,8 +518,6 @@ async def async_update_worker(worker_id, q, notification_q, app, datastore, exec
                 # (cleanup may delete these variables, but plugins need the original references)
                 finalize_handler = update_handler  # Capture now, before cleanup deletes it
                 finalize_watch = watch              # Capture now, before any modifications
-                finalize_changed_detected = locals().get('changed_detected', False)
-                finalize_snapshot_id = (locals().get('update_obj') or {}).get('previous_md5') or ''
 
                 # Call quit() as backup (Puppeteer/Playwright have internal cleanup, but this acts as safety net)
                 try:
@@ -560,9 +558,7 @@ async def async_update_worker(worker_id, q, notification_q, app, datastore, exec
                         update_handler=finalize_handler,
                         watch=finalize_watch,
                         datastore=datastore,
-                        processing_exception=processing_exception,
-                        changed_detected=finalize_changed_detected,
-                        snapshot_id=finalize_snapshot_id,
+                        processing_exception=processing_exception
                     )
                 except Exception as finalize_error:
                     logger.error(f"Worker {worker_id} error in finalize hook: {finalize_error}")

requirements.txt

@@ -97,8 +97,12 @@ pytest ~=9.0
 pytest-flask ~=1.3
 pytest-mock ~=3.15
 
+# Anything 4.0 and up but not 5.0
+jsonschema ~= 4.26
+
 # OpenAPI validation support
-openapi-core[flask] ~= 0.22
+openapi-core[flask] >= 0.19.0
+
 
 loguru
 
@@ -120,7 +124,8 @@ greenlet >= 3.0.3
 # Default SOCKETIO_MODE=threading is recommended for better compatibility
 gevent
 
-referencing  # Don't pin — jsonschema-path (required by openapi-core>=0.18) caps referencing<0.37.0, so pinning 0.37.0 forces openapi-core back to 0.17.2. Revisit once jsonschema-path>=0.3.5 relaxes the cap.
+# Previously pinned for flask_expects_json (removed 2026-02). Unpinning for now.
+referencing
 
 # For conditions
 panzi-json-logic
@@ -151,10 +156,3 @@ blinker
 pytest-xdist
 
 
-litellm
-# pydantic-core >=2.41 imports typing_extensions.Sentinel, which is absent in the
-# system-installed typing_extensions on many Linux distros (e.g. Ubuntu 22/24).
-# When the system path leaks into sys.path before the venv, the system copy is
-# cached first and the import fails at runtime inside the LLM worker thread.
-pydantic-core<2.41
-pydantic<2.12