chore(deps): update dependency tenacity to v9.1.4

KNOWN-ISSUES.md

@@ -60,24 +60,3 @@ hit a consent wall from EU IPs, and the inner `AU_yqL` id is only resolvable via
 Google's private batchexecute API. Do not retry. The news seed uses Bing News
 RSS instead (`ENABLE_NEWS_SEED`, real publisher URL in the `?url=` param of
 apiclick.aspx — unwrapped by `_unwrap_news_link`).
-
-## Large sources can OOM-kill the pod
-
-On 2026-07-10 the pod was OOMKilled (memory limit was 1Gi) mid-research: a
-batch of 20 concurrent sources hit a 98k-word document plus several large PDFs
-at once, and pdfplumber's parse spiked RAM past the limit. The in-memory
-research task died with the pod and its session sat in `running` forever.
-
-Mitigations now in place:
-
-- Memory limit raised to 2Gi (`k8s-manifests/researchowl/deployment.yaml`).
-- PDFs capped at 15MB (was 50MB), checked both via Content-Length and actual
-  body size; pdfplumber runs in `run_in_executor` (it is sync + CPU-heavy and
-  also froze the event loop, same class of bug as DDGS) and flushes its page
-  cache per page.
-- Extracted content is truncated to `max_content_length` (300k chars) before
-  hitting `source_contents`.
-- On startup the bot marks orphaned `running` sessions as `interrupted`.
-
-If a research still dies, the scraped sources survive in the DB: `/process`
-re-chunks and scores them without re-scraping.

requirements.txt

@@ -36,6 +36,6 @@ reportlab==4.2.5
 # Utilities
 pydantic==2.13.4
 pydantic-settings==2.14.2
-tenacity==9.0.0
+tenacity==9.1.4
 structlog==24.4.0
 python-dotenv==1.2.2

src/bot/bot.py

@@ -851,27 +851,6 @@ async def cmd_help(update: Update, ctx: ContextTypes.DEFAULT_TYPE):
 
 # ─── Bot setup ────────────────────────────────────────────────────────────────
 
-async def _mark_interrupted_on_startup(app: Application) -> None:
-    """Las tareas de research viven solo en memoria (_active_tasks): un
-    reinicio del pod las mata sin tocar la DB, y sus sesiones quedan en
-    'running' para siempre — parecen activas en /status y get_active_session.
-    """
-    db_conn = await get_db()
-    try:
-        cursor = await db_conn.execute(
-            "UPDATE research_sessions SET status = ?, updated_at = ? WHERE status = ?",
-            (ResearchStatus.INTERRUPTED, time.time(), ResearchStatus.RUNNING),
-        )
-        await db_conn.commit()
-        if cursor.rowcount:
-            logger.info("Orphaned running sessions marked interrupted",
-                        count=cursor.rowcount)
-    except Exception as e:
-        logger.warning("Interrupted-mark failed — bot continues", error=str(e))
-    finally:
-        await db_conn.close()
-
-
 async def _purge_on_startup(app: Application) -> None:
     db_conn = await get_db()
     try:
@@ -1015,7 +994,6 @@ async def _start_scheduler(app: Application) -> None:
 
 
 async def _on_startup(app: Application) -> None:
-    await _mark_interrupted_on_startup(app)
     await _purge_on_startup(app)
     await _start_scheduler(app)
 

src/config.py

@@ -47,8 +47,6 @@ class Settings(BaseSettings):
     request_timeout: int = Field(30)
     request_delay: float = Field(1.0)          # seconds between requests
     min_content_length: int = Field(200)  # chars
-    # Libros/dumps enteros (100k+ palabras) inflan RAM y DB sin aportar al RAG
-    max_content_length: int = Field(300_000)  # chars
 
     # Fuentes opcionales — desactivadas por defecto: la IP del homelab está
     # bloqueada por Reddit (403) y YouTube (transcripts vacíos), eran peso muerto.

src/db/database.py

@@ -18,7 +18,6 @@ class ResearchStatus(str, Enum):
     SATURATED = "saturated"
     FINISHED = "finished"
     ERROR = "error"
-    INTERRUPTED = "interrupted"  # el pod se reinició con el research en marcha
 
 
 class OutputType(str, Enum):

src/scraper/exhaustive.py

@@ -628,11 +628,6 @@ class ExhaustiveScraper:
                                         error="Content too short or empty")
             return
 
-        if len(content) > settings.max_content_length:
-            logger.info("Content truncated", source_id=source_id,
-                        original_length=len(content), url=url[:60])
-            content = content[:settings.max_content_length]
-
         word_count = len(content.split())
 
         await self.db.save_source_content(source_id, content)
@@ -824,49 +819,30 @@ class ExhaustiveScraper:
                     entries=len(entries), added=added)
         return added
 
-    # pdfplumber es síncrono y CPU-intensivo: parsear inline congela el event
-    # loop, y con PDFs grandes el pico de RAM puede matar el pod (OOM con
-    # límite de 1-2Gi). Ejecutar SIEMPRE vía run_in_executor.
-
-    @staticmethod
-    def _parse_pdf_sync(path: str) -> str:
-        import pdfplumber
-
-        with pdfplumber.open(path) as pdf:
-            pages = []
-            for page in pdf.pages[:50]:  # max 50 pages
-                pages.append(page.extract_text() or "")
-                page.flush_cache()  # pdfplumber cachea objetos de página: liberar
-            return "\n\n".join(pages)
-
     async def _extract_pdf(self, url: str) -> tuple[Optional[str], Optional[str]]:
         """Download and extract PDF text"""
+        import pdfplumber
         import tempfile
         import os
 
-        max_pdf_bytes = 15 * 1024 * 1024  # varios PDFs concurrentes en RAM: cap agresivo
-
         http = await self._get_http()
         try:
             async with http.get(url) as resp:
                 if resp.status != 200:
                     return None, None
                 content_length = int(resp.headers.get("content-length", 0))
-                if content_length > max_pdf_bytes:
+                if content_length > 50 * 1024 * 1024:  # skip PDFs > 50MB
                     return None, None
                 pdf_bytes = await resp.read()
-                # Sin Content-Length el check anterior no protege
-                if len(pdf_bytes) > max_pdf_bytes:
-                    return None, None
 
             with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as f:
                 f.write(pdf_bytes)
                 tmp_path = f.name
-            del pdf_bytes
 
             try:
-                loop = asyncio.get_running_loop()
-                text = await loop.run_in_executor(None, self._parse_pdf_sync, tmp_path)
+                with pdfplumber.open(tmp_path) as pdf:
+                    pages = [p.extract_text() or "" for p in pdf.pages[:50]]  # max 50 pages
+                    text = "\n\n".join(pages)
                 return text, url.split("/")[-1]
             finally:
                 os.unlink(tmp_path)