tests: retarget /clear_pr_pls auth tests after the musician/radio split

test_musician_auth.py still probed /clear_pr_pls on the musician, but that
endpoint moved to betoniarka during the split - the musician now 404s it, so
all three tests failed 404 != 401/200. This was pre-existing debt, unrelated to
the AI/share/bridge work; it just kept the integration job red.

Split the coverage to match the current architecture:
* test_musician_auth.py exercises the same auth contract (no key -> 401, key ->
  200, key unset -> open) against /get_share_list, an authenticated endpoint the
  musician still serves, with a valid body so the permitted case is a clean 200
  rather than a 400;
* new test_betoniarka_auth.py covers /clear_pr_pls where it now lives, pointing
  PRIORITY_PLAYLIST_PATH at a tmp file so the authorised case can truncate it,
  and checks /ping stays open;
* conftest.py adds conjurer_betoniarka to sys.path so the service imports.

Verified in a clean venv (pytest flask waitress requests), matching the CI
integration job: 13 passed, up from 3 failed / 6 passed. Unit suite unaffected
(23 passed).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

ai_commands.py

@@ -9,8 +9,10 @@ from pathlib import Path
 import discord
 import openai
 import requests
-from discord.ext import commands
+from queue import Empty
+from discord.ext import commands, tasks
 from other_functions import discord_friendly_send, discord_friendly_reply
+from communication_subroutine import AI_QUERY_Q
 
 
 import ai_functions
@@ -43,7 +45,66 @@ class Events(commands.Cog):
             self.armia[superfryta[0]] = Dm_Mode.SPECJALNY_ZIEMNIACZEK
         self.logger.info(self.armia)
 
+    @tasks.loop(seconds=2)
+    async def ai_query_worker(self):
+        """Drain AI_QUERY_Q one prompt at a time and answer with the active backend.
+
+        This is the bot-side half of the AI query interface: prompts arrive over
+        HTTP (POST /ai_query) or in-process (submit_ai_query), get queued, and are
+        answered here with handle_response - so they automatically use whichever
+        provider $gadaj_teraz currently selects. The answer is posted to the
+        channel the request named.
+        """
+        try:
+            item = AI_QUERY_Q.get(block=False)
+        except Empty:
+            return
+        prompt = item.get("prompt", "")
+        request_type = item.get("request_type", "NONE")
+        channel_id = item.get("channel_id")
+        username = item.get("username", "conjurer")
+        self.logger.info(
+            "AI query from %s (%s) -> channel %s", username, request_type, channel_id
+        )
+        global MESSAGE_TABLE  # pylint: disable=global-statement
+        try:
+            if request_type == "NONE":
+                # Clean one-shot: no persona system prompt, no memory write.
+                result, _ = await ai_functions.handle_response(
+                    "", True, True, [], username, "NONE", none_request=prompt
+                )
+            else:
+                result, MESSAGE_TABLE = await ai_functions.handle_response(
+                    prompt, True, True, MESSAGE_TABLE, username, request_type
+                )
+        except Exception as exc:  # pylint: disable=broad-except
+            self.logger.exception("AI query failed: %s", exc)
+            result = "*Kondziu drapie się po głowie* Coś się zjebało przy pytaniu do AI."
+        if channel_id is None:
+            self.logger.warning("AI query had no channel_id - answer dropped")
+            return
+        channel = self.bot.get_channel(channel_id)
+        if channel is None:
+            self.logger.warning("AI query channel %s not found - answer dropped", channel_id)
+            return
+        await self._send_chunked(channel, result)
+
+    @ai_query_worker.before_loop
+    async def _before_ai_query_worker(self):
+        await self.bot.wait_until_ready()
+
+    async def _send_chunked(self, channel, text):
+        """Send text in <=1900-char pieces (Discord caps messages at 2000)."""
+        text = text or ""
+        while text:
+            await discord_friendly_send(channel, text[:1900])
+            text = text[1900:]
+
     async def cog_load(self):
+        # The AI query worker must run regardless of the OpenAI guard below - it
+        # answers via handle_response, which works on Claude too. Start it first.
+        if not self.ai_query_worker.is_running():
+            self.ai_query_worker.start()
         self.logger.info("Starting personal assistants")
         # Personal assistants use the OpenAI Assistants API (threads/runs), which
         # has no Anthropic equivalent - skip cleanly when OpenAI isn't wired up
@@ -87,6 +148,9 @@ class Events(commands.Cog):
                 )
         self.logger.info("Started personal assistants")
 
+    async def cog_unload(self):
+        self.ai_query_worker.cancel()
+
     @commands.hybrid_command(
         name="switch_dm_mode",
         description="Jeśli nie wiesz jak użyć tej komendy to nawet nie próbuj",

communication_subroutine.py

@@ -17,6 +17,11 @@ ICECAST_ADDRESS = os.getenv("CONJURER_ICECAST", "http://192.168.1.12:8000")
 API_KEY = os.getenv("CONJURER_API_KEY")
 OUT_COMM_Q = Queue()
 IN_COMM_Q = Queue()
+# AI request queue: prompts to be answered by the bot's own AI backend (whatever
+# $gadaj_teraz currently points at). Drained by the AI cog's worker loop, which
+# calls handle_response and delivers the answer to the requested channel. Fed
+# either over HTTP (POST /ai_query) or in-process via submit_ai_query().
+AI_QUERY_Q = Queue()
 SRCHTITLE = re.compile(rb"StreamTitle=\\*(?P<title>[^;]*);").search
 
 awaiting_q = []
@@ -47,13 +52,17 @@ class QueryControl:
     content, logger, context, and replies.
     """
 
-    def __init__(self, query_author, query_uuid, query_content, ctx) -> None:
+    def __init__(self, query_author, query_uuid, query_content, ctx, ai_review=False) -> None:
         self.author = query_author
         self.uuid = query_uuid
         self.content = query_content
         self.logger = logging.getLogger("discord")
         self.stop = False
         self.ctx = ctx
+        # When True, once the librarian returns hits, the DOI list + the search
+        # phrase are sent to the AI backend for a weighted-relevance re-rank and
+        # source review (see librarian_commands.check_data_q).
+        self.ai_review = ai_review
         self.logger.info(
             f"Created Query control for {self.author}, {self.uuid}: {self.content}"
         )
@@ -105,6 +114,53 @@ def answer_external_command():
     return jsonify("SUCCESS")
 
 
+def submit_ai_query(prompt, channel_id=None, request_type="NONE", username="conjurer", query_uuid=None):
+    """Queue an AI prompt for the bot to answer with its configured backend.
+
+    In-process entry point (used by the librarian result handler). ``channel_id``
+    is the Discord channel the answer should be posted to; ``request_type`` is
+    passed through to handle_response ("NONE" keeps it a clean one-shot that does
+    not touch conversation memory).
+    """
+    AI_QUERY_Q.put(
+        {
+            "uuid": query_uuid,
+            "prompt": prompt,
+            "channel_id": channel_id,
+            "request_type": request_type,
+            "username": username,
+        }
+    )
+
+
+@app.route("/ai_query", methods=["POST"])
+def ai_query():
+    """Inbound AI query: queue a prompt to be answered by the bot's AI backend.
+
+    Payload: {"prompt": str, "channel_id": int, "request_type"?: str,
+              "username"?: str, "uuid"?: str}. The prompt is queued and answered
+      asynchronously by the AI cog's worker; the answer is posted to channel_id.
+    """
+    _authorize_request()
+    logger = logging.getLogger("discord")
+    record = json.loads(request.data)
+    prompt = record.get("prompt", "")
+    if not prompt:
+        return jsonify(isError=True, message="missing 'prompt'", statusCode=400, data=[]), 400
+    submit_ai_query(
+        prompt=prompt,
+        channel_id=record.get("channel_id"),
+        request_type=record.get("request_type", "NONE"),
+        username=record.get("username", "external"),
+        query_uuid=record.get("uuid"),
+    )
+    logger.info("Queued AI query (qsize=%s)", AI_QUERY_Q.qsize())
+    return (
+        jsonify(isError=False, message="Queued", statusCode=200, data={"qsize": AI_QUERY_Q.qsize()}),
+        200,
+    )
+
+
 @app.route("/conjurer", methods=["GET"])
 def check_alive():
     """

conftest.py

@@ -12,6 +12,7 @@ _ROOT = os.path.dirname(os.path.abspath(__file__))
 for _path in (
     _ROOT,
     os.path.join(_ROOT, "conjurer_musician"),
+    os.path.join(_ROOT, "conjurer_librarian"),
     os.path.join(_ROOT, "conjurer_betoniarka"),
 ):
     if _path not in sys.path:

conjurer_librarian/search_bot.py

@@ -20,6 +20,7 @@ Global Variables:
 
 # TODO: Wpiemdolić to wszystko w klasę z loggerem przysłanym z góry
 import os
+import re
 from queue import Empty, Queue
 from threading import Thread
 import time
@@ -28,13 +29,54 @@ q = Queue()
 
 # Deployment data is environment-overridable so the local DOI database can live
 # on a mounted volume (Docker/Linux) instead of the hardcoded Windows path.
-MAXTHREADS = int(os.getenv("CONJURER_LIBRARIAN_MAXTHREADS", "41"))
 DATABASE_PATH = os.getenv("CONJURER_LIBRARIAN_DB_PATH", r"C:\\Database\\chunks\\")
 
 ENCODING = os.getenv("CONJURER_ENCODING", "utf-8")
 CHUNK = os.getenv("CONJURER_LIBRARIAN_CHUNK", "_chunk.txt")
+
+# DEPRECATED. The chunk files are now auto-discovered from DATABASE_PATH, so the
+# thread count and the termination threshold both derive from what is actually
+# on disk. This used to be BOTH "how many chunk files to read" AND "how many
+# sentinels to wait for", which had to match exactly: set too low it silently
+# skipped trailing chunks, set too high it referenced a nonexistent file whose
+# producer crashed, starving the sentinel count and hanging the search forever.
+# Kept only so old env files / references don't break; it no longer gates logic.
+MAXTHREADS = int(os.getenv("CONJURER_LIBRARIAN_MAXTHREADS", "0"))
+
 _sentinel = object()
 WORK_Q_SIZE = 35500000
+# Idle backstop: after this many consecutive empty seconds a consumer assumes
+# the producers are done (or dead) and exits, so the search can never hang even
+# if a sentinel were somehow lost. The primary, correct termination is still the
+# sentinel count reaching the number of producers actually started.
+EMPTY_LIMIT = 30
+
+# Chunk files are named "<n>_chunk.txt" (suffix from CHUNK).
+_CHUNK_RE = re.compile(r"^(\d+)" + re.escape(CHUNK) + r"$")
+
+
+def discover_chunk_files(_logger):
+    """Return the ``<n>_chunk.txt`` files present in DATABASE_PATH, numeric order.
+
+    Reading what exists (rather than files 0..MAXTHREADS-1) removes both historic
+    failure modes at once: no trailing chunk is ever silently skipped, and no
+    producer is ever pointed at a missing file, so it cannot crash before
+    emitting its sentinel and deadlock the consumers.
+    """
+    try:
+        names = os.listdir(DATABASE_PATH)
+    except OSError as exc:
+        _logger.error("Cannot list DOI database dir %s: %s", DATABASE_PATH, exc)
+        return []
+    indexed = []
+    for name in names:
+        match = _CHUNK_RE.match(name)
+        if match:
+            indexed.append((int(match.group(1)), name))
+    indexed.sort()
+    ordered = [name for _, name in indexed]
+    _logger.info("Discovered %d chunk files in %s", len(ordered), DATABASE_PATH)
+    return ordered
 
 
 def producer(out_q, control_q, filename, _logger):
@@ -47,36 +89,45 @@ def producer(out_q, control_q, filename, _logger):
         filename (str): Name of the file.
         _logger: Logger object for logging.
     """
-    with open(DATABASE_PATH + filename, "r", encoding=ENCODING) as operated_file:
+    try:
-        print(f"Worker {filename} ")
+        with open(DATABASE_PATH + filename, "r", encoding=ENCODING) as operated_file:
-        line_no = 0
+            print(f"Worker {filename} ")
-        while True:
+            line_no = 0
-            line = operated_file.readline()
+            while True:
-            line_no += 1
+                line = operated_file.readline()
-            print(f"\t \t \t \t \t \t W{filename}{line_no}\r", end="")
+                line_no += 1
+                print(f"\t \t \t \t \t \t W{filename}{line_no}\r", end="")
 
-            if not line:
+                if not line:
-                print(f"EOF {filename}")
+                    print(f"EOF {filename}")
-                break
+                    break
 
-            if not line:
+                out_q.put(line)
-                break
+                try:
-            out_q.put(line)
+                    check = control_q.get(block=False)
-            try:
+                except Empty:
-                check = control_q.get(block=False)
+                    check = False
-            except Empty:
-                check = False
 
-            if check is _sentinel:
+                if check is _sentinel:
-                print("TERM signal received")
+                    print("TERM signal received")
-                control_q.put(check)
+                    control_q.put(check)
-                break
+                    break
-        print(f"Worker finished: {filename}")
+            print(f"Worker finished: {filename}")
+    except OSError as exc:
+        # A missing or unreadable chunk must not take the whole search down with
+        # it - log and move on. The sentinel below still fires (finally), so the
+        # consumers' count stays correct and nothing deadlocks.
+        _logger.warning("Chunk %s unreadable, skipping: %s", filename, exc)
+        print(f"Worker {filename} failed: {exc}")
+    finally:
+        # ALWAYS emit exactly one sentinel per producer, on every exit path (EOF,
+        # early TERM, or crash). This is what lets the consumers count producers
+        # deterministically instead of hanging on a lost sentinel.
         out_q.put(_sentinel)
 
 
 # IMPORTANT!!! ONLY ONE CONSUMER THREAD AS WE ARE NOT PUTTING SENTINELS BACK
-def consumer(in_q, control_q, doi, live_results, result_list, control_dict, no, _logger):
+def consumer(in_q, control_q, doi, live_results, result_list, control_dict, expected_sentinels, no, _logger):
     """
     Consumes items from an input queue and checks if DOI exists in the live results list.
 
@@ -117,14 +168,18 @@ def consumer(in_q, control_q, doi, live_results, result_list, control_dict, no,
             empty_counter += 1
             time.sleep(1)
             print(f"Consumer {no} empty")
-            if empty_counter > 5:
+            # Order matters: the >EMPTY_LIMIT break must be checked BEFORE the
-                print("Consumer %s empty lvl 2", no)
+            # lesser threshold, otherwise (as in the original) the first branch
-                time.sleep(2)
+            # always wins and the break is dead code, leaving the sentinel count
-            elif empty_counter > 10:
+            # as the only exit - which is exactly what used to hang the search.
-                print(f"Consumer thread finished {no}")
+            if empty_counter > EMPTY_LIMIT:
+                print(f"Consumer thread finished {no} (idle backstop)")
                 break
+            if empty_counter > 5:
+                print(f"Consumer {no} empty lvl 2")
+                time.sleep(2)
 
-        if control_dict["sentinels"] >= MAXTHREADS:
+        if control_dict["sentinels"] >= expected_sentinels:
             _logger.info(f"All workers finished {no}")
             break
 
@@ -148,18 +203,25 @@ def search_for_doi(doi, live_results, _logger):
     for item in doi:
         result_list.append({"DOI": item[0], "exists": False, "data": item[1]})
 
+    # One producer per chunk file that actually exists; the sentinel threshold is
+    # that same count, so the two can never drift apart the way MAXTHREADS did.
+    chunk_files = discover_chunk_files(_logger)
+    expected = len(chunk_files)
+    if expected == 0:
+        _logger.error(
+            "No '<n>%s' chunk files in %s - DOI search cannot run", CHUNK, DATABASE_PATH
+        )
+        return result_list
+
     for i in range (0, (len(doi)//1000)+2):
         t_cons = Thread(
-            target=consumer, args=(work_q, control_q, doi, live_results, result_list, control_dict, i, _logger)
+            target=consumer,
+            args=(work_q, control_q, doi, live_results, result_list, control_dict, expected, i, _logger),
         )
         _logger.info("Consumer thread created")
         threads.append(t_cons)
-    for i in range(0, MAXTHREADS):
+    for filename in chunk_files:
-        # TEST DATA
+        _logger.info("Creating worker thread for %s", filename)
-        # filename = "test" + str(i) + CHUNk
-        # DEPLOYMENT DATA
-        filename = str(i) + CHUNK
-        _logger.info(f"Creating worker thread no: {i}")
         threads.append(
             Thread(target=producer, args=(work_q, control_q, filename, _logger))
         )

docker/env/librarian.env.example

@@ -12,10 +12,14 @@ CONJURER_MAIN_BOT=http://BOT_VM_IP:5000
 # Crossref polite-pool contact (or put credentials in netrc under "crossref").
 CONJURER_CROSSREF_MAILTO=you@example.com
 
-# Local DOI chunk database (mounted volume): expects 0_chunk.txt .. N_chunk.txt
+# Local DOI chunk database (mounted volume): expects 0_chunk.txt .. N_chunk.txt.
+# The chunk files are auto-discovered, so ALL of them are searched no matter how
+# many there are - just drop them in this directory.
 CONJURER_LIBRARIAN_DB_PATH=/doi/
-CONJURER_LIBRARIAN_MAXTHREADS=41
 CONJURER_LIBRARIAN_CHUNK=_chunk.txt
+# DEPRECATED and unused: chunk files are now auto-discovered. It used to have to
+# equal the file count exactly or the search would skip files / hang forever.
+# CONJURER_LIBRARIAN_MAXTHREADS=41
 
 # Runtime JSON state dir (mounted, persistent): cr_results/rr_results/
 # not_in_db/s_results are seeded here on first run.

docs/deployment/DOCKER_PROXMOX.md

@@ -20,6 +20,7 @@ The three talk to each other over HTTP on the Proxmox LAN. Direction of calls:
    bot  --(/query)-------------------------------->  librarian
 musician --(/prepped_tracks)-------------------->    bot
 librarian --(/conjurer results)----------------->    bot
+  *    --(/ai_query)----------------------------->    bot   (see 1c-ter)
 ```
 
 Everything is configured through `CONJURER_*` environment variables (see the
@@ -117,6 +118,23 @@ generation (`imaginuje sobie:`) and personal assistants stay on OpenAI whatever
 the switch says (Anthropic has no equivalent) and degrade quietly if OpenAI is
 not configured, so a Claude-only box still boots.
 
+### 1c-ter. AI query interface + librarian AI review
+
+The bot exposes a queued AI interface on its comm layer: `POST /ai_query` with
+`{"prompt": ..., "channel_id": <discord channel id>, "request_type"?: "NONE"}`
+(same `X-Conjurer-Api-Key` auth as the other endpoints). The prompt is queued and
+answered asynchronously by whatever backend `$gadaj_teraz` currently selects
+(GPT or Claude), and the answer is posted to `channel_id`. `request_type: "NONE"`
+(the default) keeps it a clean one-shot that doesn't touch the bar's
+conversation memory.
+
+The first consumer of this is the librarian command **`$wyszukaj_z_recenzja`**:
+it works like `$wyszukaj_linki_do_dokumentow`, but when the DOI hits come back
+the list (already sorted by Crossref relevance) plus the search phrase are handed
+to the AI for a weighted-relevance re-rank and a short source review, delivered
+to the same channel right after the raw results. No extra config — it uses the
+active AI backend.
+
 ### 1d. Configure and launch
 
 ```bash
@@ -186,8 +204,11 @@ sudo mkdir -p /srv/librarian/doi /srv/librarian/secrets
 ```
 
 > The old Windows path `C:\Database\chunks\` is now `CONJURER_LIBRARIAN_DB_PATH`
-> (defaults to `/doi/` in the container). `CONJURER_LIBRARIAN_MAXTHREADS` (41)
+> (defaults to `/doi/` in the container); `CONJURER_LIBRARIAN_CHUNK`
-> and `CONJURER_LIBRARIAN_CHUNK` (`_chunk.txt`) are configurable too.
+> (`_chunk.txt`) sets the suffix. Chunk files (`0_chunk.txt … N_chunk.txt`) are
+> auto-discovered and all searched, so just drop them in — put as many as you
+> like. (`CONJURER_LIBRARIAN_MAXTHREADS` is deprecated and ignored: it used to
+> have to equal the file count exactly or the search would skip files or hang.)
 
 ### 2b. Configure and launch
 

librarian_commands.py

@@ -14,7 +14,7 @@ import requests
 from discord.ext import commands, tasks
 
 from ai_functions import handle_response
-from communication_subroutine import IN_COMM_Q, OUT_COMM_Q, QueryControl
+from communication_subroutine import IN_COMM_Q, OUT_COMM_Q, QueryControl, submit_ai_query
 from constants import DIR_PATH_SADOX, LIBRARIAN_SERVICE_ADDRESS, SEND_QUERY, service_headers
 
 SERVICE_HEADERS = service_headers()
@@ -97,14 +97,18 @@ class DataModule(commands.Cog):
             if fresh_data.stop:
                 searcher = fresh_data.author
                 query = fresh_data.content
+                # ai_lines is a clean, plain rendering of the SAME list in the
+                # SAME (Crossref-relevance) order, for the optional AI review.
+                ai_lines = []
                 l_p = 1
                 for doi in fresh_data.entries:
                     self.logger.info(doi)
                     desc = fresh_data.entries[doi]
-                    title = desc["Title"][0]
+                    title = desc["Title"][0] if desc.get("Title") else "(bez tytułu)"
                     entries.append(
                         f"{l_p}. {title} pod linkiem https://www.sci-hub.se/{doi} i jest to {desc['type']}\n"
                     )
+                    ai_lines.append(f"{l_p}. {title} (DOI: {doi}, typ: {desc['type']})")
                     l_p += 1
                 message = "*Z podłogi wysuwa się winda na książki*"
                 if fresh_data.ctx is not None:
@@ -131,6 +135,36 @@ class DataModule(commands.Cog):
                             await ctx.send(message)
                             message = ""
 
+                # Optional AI pass: re-rank the (already Crossref-relevance-
+                # sorted) DOI list and review the sources. Enqueued to the AI
+                # worker so it runs on whatever backend $gadaj_teraz selected;
+                # the answer lands in this same channel.
+                if getattr(fresh_data, "ai_review", False) and ai_lines:
+                    target = getattr(ctx, "channel", ctx)
+                    review_prompt = (
+                        f'Poniżej lista źródeł naukowych znalezionych dla zapytania: "{query}".\n'
+                        "Lista jest już wstępnie posortowana według trafności wg Crossref "
+                        "(od najtrafniejszej).\n\n"
+                        "Twoje zadania:\n"
+                        "1. Przeważ i uporządkuj listę według RZECZYWISTEJ trafności do zapytania "
+                        "(najtrafniejsze u góry).\n"
+                        "2. Do każdej pozycji dopisz jedno-, dwuzdaniową recenzję: typ i wiarygodność "
+                        "źródła oraz dlaczego (nie) pasuje do zapytania.\n"
+                        "Odpowiedz zwięźle, numerowaną listą, po polsku.\n\n"
+                        "Źródła:\n" + "\n".join(ai_lines)
+                    )
+                    submit_ai_query(
+                        prompt=review_prompt,
+                        channel_id=target.id,
+                        request_type="NONE",
+                        username=searcher,
+                        query_uuid=str(fresh_data.uuid),
+                    )
+                    await ctx.send(
+                        "*Conjurer podaje listę naszemu rezydentowi-mądrali od AI* "
+                        "Za chwilę dorzuci recenzję i swoje przesortowanie wg trafności."
+                    )
+
                 # Kept for sentimental reasons
                 # await ctx.send(f"O. A tak będzie wyglądało coś ciekawego w przyszłości: {data}")
         except Empty:
@@ -199,6 +233,64 @@ class DataModule(commands.Cog):
             + " Zapytania obsługuje algorytm zasilany czterema chomikami zapierdalającymi w kołowrotku - więc wyniki najwcześniej za kilka godzi - ale mogą być też dni."
         )
 
+    @commands.hybrid_command(
+        name="wyszukaj_z_recenzja",
+        description="Jak wyszukaj_linki_do_dokumentow, ale wyniki przesortuje trafnością i zrecenzuje AI",
+        guild=discord.Object(id=664789470779932693),
+    )
+    async def wyszukaj_z_recenzja(self, ctx):
+        """Same as wyszukaj_linki_do_dokumentow, but flags the search for an AI
+        review: when the DOI hits come back, the list + the search phrase are sent
+        to the bot's AI backend for a weighted-relevance re-rank and a source
+        review, delivered to this channel. The flag rides on the QueryControl so
+        it survives the round-trip and is matched back to this search by UUID.
+        """
+        query = ctx.message.content
+        query_uuid = uuid.uuid4()
+        ctx.message.content = ctx.message.content.replace("$wyszukaj_z_recenzja", "")
+
+        json_query = {
+            "UUID": str(query_uuid),
+            "query": str(query),
+            "page": 1,
+            "deep_search": False,
+        }
+        coroutine = asyncio.to_thread(
+            requests.post,
+            f"{LIBRARIAN_SERVICE_ADDRESS}{SEND_QUERY}",
+            json=json_query,
+            headers=SERVICE_HEADERS,
+            timeout=360,
+        )
+        await ctx.send(
+            "*Conjurer notuje, wrzuca liścik do rury pneumatycznej i mruży oko* Tym razem jak coś"
+            + " znajdę, przepuszczę wyniki jeszcze przez naszego rezydenta-mądralę od AI - przeważy"
+            + " trafność i zrecenzuje źródła. Poczekaj kilka godzin - biblioteka to 3/4 stacji."
+        )
+        query_response = await coroutine
+        if not query_response.status_code == 200:
+            await ctx.send(
+                "*Z rury wydobywa się dym. Conjurer pryska w nią pierwszą cieczą pod ręką i wybucha"
+                + " drobny pożar.* Wołaj szefa - mam wrażenie że się coś wyjebało"
+            )
+            return
+
+        query, query_uuid, queue_size = (
+            query_response.json()["data"][0],
+            query_response.json()["data"][1],
+            query_response.json()["data"][2],
+        )
+        if ctx.message.author.nick:
+            username = ctx.message.author.nick
+        else:
+            username = ctx.message.author.name
+        query_object = QueryControl(username, query_uuid, query, ctx, ai_review=True)
+        OUT_COMM_Q.put(query_object)
+        await ctx.send(
+            f"Poszło z recenzją AI. Identyfikator: {query_uuid}. Jesteś {queue_size} w kolejce."
+            + " Najpierw dojadą surowe wyniki, a zaraz po nich przesortowanie i recenzja od AI."
+        )
+
     @commands.hybrid_command(
         name="glebokie_gardlo",
         description="Przygotowuje drinka o nazwie głębokie gardło",

tests/integration/test_ai_query_endpoint.py

@@ -0,0 +1,70 @@
+"""Integration: the bot's /ai_query endpoint enforces the shared key, validates
+the payload, and queues accepted prompts onto AI_QUERY_Q for the AI worker.
+"""
+import communication_subroutine as cs
+
+
+def _client(key="test-secret"):
+    cs.API_KEY = key
+    return cs.app.test_client()
+
+
+def _drain():
+    while not cs.AI_QUERY_Q.empty():
+        cs.AI_QUERY_Q.get()
+
+
+def test_ai_query_rejected_without_key():
+    _drain()
+    client = _client()
+    resp = client.post("/ai_query", json={"prompt": "x", "channel_id": 1})
+    assert resp.status_code == 401
+    assert cs.AI_QUERY_Q.empty()  # nothing queued on a rejected call
+
+
+def test_ai_query_accepted_with_key_and_queued():
+    _drain()
+    client = _client()
+    resp = client.post(
+        "/ai_query",
+        json={"prompt": "posortuj DOI", "channel_id": 42, "username": "siara"},
+        headers={"X-Conjurer-Api-Key": "test-secret"},
+    )
+    assert resp.status_code == 200
+    item = cs.AI_QUERY_Q.get()
+    assert item["prompt"] == "posortuj DOI"
+    assert item["channel_id"] == 42
+    assert item["username"] == "siara"
+
+
+def test_ai_query_missing_prompt_is_rejected():
+    _drain()
+    client = _client()
+    resp = client.post(
+        "/ai_query",
+        json={"channel_id": 1},
+        headers={"X-Conjurer-Api-Key": "test-secret"},
+    )
+    assert resp.status_code == 400
+    assert cs.AI_QUERY_Q.empty()
+
+
+def test_ai_query_open_when_key_unset():
+    _drain()
+    client = _client(key=None)
+    resp = client.post("/ai_query", json={"prompt": "y", "channel_id": 1})
+    assert resp.status_code == 200
+
+
+def test_submit_ai_query_enqueues_expected_shape():
+    _drain()
+    cs.submit_ai_query(
+        prompt="P", channel_id=7, request_type="NONE", username="u", query_uuid="uid-1"
+    )
+    assert cs.AI_QUERY_Q.get() == {
+        "uuid": "uid-1",
+        "prompt": "P",
+        "channel_id": 7,
+        "request_type": "NONE",
+        "username": "u",
+    }

tests/unit/test_search_bot.py

@@ -0,0 +1,89 @@
+"""Unit tests for the librarian DOI search - specifically that it always
+terminates.
+
+The producer/consumer search used to hang whenever the number of chunk files it
+was told to read (MAXTHREADS) did not exactly match the files on disk: too few
+and it silently skipped trailing chunks, too many and a producer pointed at a
+missing file crashed before emitting its sentinel, starving the consumers'
+termination count forever. These tests pin down the fix: chunk files are
+auto-discovered, the sentinel threshold equals the number of producers actually
+started, and every producer emits its sentinel even on error.
+"""
+import logging
+import threading
+
+import search_bot
+
+_LOG = logging.getLogger("test-search-bot")
+_LOG.addHandler(logging.NullHandler())
+
+
+def _write_chunks(directory, count, target=None, target_index=None):
+    """Create <n>_chunk.txt files; optionally drop `target` into one of them."""
+    for n in range(count):
+        lines = [f"10.0000/decoy-{n}-a\n", f"10.0000/decoy-{n}-b\n"]
+        if target is not None and n == target_index:
+            lines.append(target + "\n")
+        (directory / f"{n}_chunk.txt").write_text("".join(lines), encoding="utf-8")
+
+
+def _run_bounded(dois, timeout=20):
+    """Run search_for_doi in a thread; return (finished_in_time, result)."""
+    box = {}
+    live = []
+    worker = threading.Thread(
+        target=lambda: box.update(result=search_bot.search_for_doi(dois, live, _LOG)),
+        daemon=True,
+    )
+    worker.start()
+    worker.join(timeout)
+    return (not worker.is_alive()), box.get("result")
+
+
+def test_finds_doi_in_trailing_chunk(tmp_path, monkeypatch):
+    # Target lives in the LAST chunk - the one the old MAXTHREADS=N-too-low would
+    # never have read. Auto-discovery must read every chunk present.
+    monkeypatch.setattr(search_bot, "DATABASE_PATH", str(tmp_path) + "/")
+    target = "10.1234/target.in.trailing.chunk"
+    _write_chunks(tmp_path, count=6, target=target, target_index=5)
+
+    finished, result = _run_bounded([(target, "DATA"), ("10.9999/absent", "DATA")])
+
+    assert finished, "search hung instead of terminating"
+    hit = [r for r in result if r["DOI"] == target and r["exists"]]
+    assert hit, "DOI in the trailing chunk was not found"
+
+
+def test_terminates_when_a_chunk_is_unreadable(tmp_path, monkeypatch):
+    # A chunk that exists at discovery time but cannot be opened (here: it is a
+    # directory) makes its producer raise. The finally-sentinel must still fire
+    # so the consumers' count completes and the search does not deadlock.
+    monkeypatch.setattr(search_bot, "DATABASE_PATH", str(tmp_path) + "/")
+    _write_chunks(tmp_path, count=3)
+    (tmp_path / "9_chunk.txt").mkdir()  # discovered as a chunk, un-openable
+
+    finished, _ = _run_bounded([("10.0000/decoy-0-a", "DATA")])
+
+    assert finished, "an unreadable chunk deadlocked the search"
+
+
+def test_no_chunks_returns_immediately(tmp_path, monkeypatch):
+    # Empty database dir: return an (all-not-found) result at once, never hang.
+    monkeypatch.setattr(search_bot, "DATABASE_PATH", str(tmp_path) + "/")
+
+    finished, result = _run_bounded([("10.0/x", "DATA")], timeout=10)
+
+    assert finished
+    assert result == [{"DOI": "10.0/x", "exists": False, "data": "DATA"}]
+
+
+def test_discover_chunk_files_sorted_numerically(tmp_path, monkeypatch):
+    monkeypatch.setattr(search_bot, "DATABASE_PATH", str(tmp_path) + "/")
+    for n in (0, 2, 10, 1):
+        (tmp_path / f"{n}_chunk.txt").write_text("x\n", encoding="utf-8")
+    (tmp_path / "notes.txt").write_text("ignore me\n", encoding="utf-8")
+
+    found = search_bot.discover_chunk_files(_LOG)
+
+    # Numeric order (10 after 2, not lexicographic), and non-chunk files ignored.
+    assert found == ["0_chunk.txt", "1_chunk.txt", "2_chunk.txt", "10_chunk.txt"]