refactor: centralize document extraction in AgentLoop._process_message

Move extract_documents() to nanobot.utils.document as a reusable helper
and call it once in AgentLoop._process_message, the single entry point
for all message processing (API + all channels).

This replaces the previous API-only _extract_documents() in server.py,
ensuring Telegram, Feishu, Slack, WeChat, and all other channels also
benefit from automatic document text extraction.

Adds a configurable max_file_size guard (default 50 MB) to skip
oversized files gracefully, preventing unbounded memory/CPU usage
from channel-downloaded attachments.

- server.py: removed _extract_documents and related imports
- document.py: added extract_documents() with size limit
- loop.py: calls extract_documents() at the top of _process_message
- Tests updated: 70 related tests pass

Made-with: Cursor

nanobot/utils/document.py

@@ -1,9 +1,12 @@
 """Document text extraction utilities for nanobot."""
 
+import mimetypes
 from pathlib import Path
 
 from loguru import logger
 
+from nanobot.utils.helpers import detect_image_mime
+
 try:
     from pypdf import PdfReader
 except ImportError:
@@ -204,3 +207,60 @@ def _is_text_extension(ext: str) -> bool:
         ".ini",
         ".cfg",
     }
+
+
+# ---------------------------------------------------------------------------
+# High-level helper: split media into images + extracted document text
+# ---------------------------------------------------------------------------
+
+_MAX_EXTRACT_FILE_SIZE = 50 * 1024 * 1024  # 50 MB
+
+
+def extract_documents(
+    text: str,
+    media_paths: list[str],
+    *,
+    max_file_size: int = _MAX_EXTRACT_FILE_SIZE,
+) -> tuple[str, list[str]]:
+    """Separate images from documents in *media_paths*.
+
+    Documents (PDF, DOCX, XLSX, PPTX, plain-text, …) have their text
+    extracted and appended to *text*.  Only image paths are kept in the
+    returned list so that downstream layers only need to handle vision
+    blocks.
+
+    Files larger than *max_file_size* bytes are skipped with a warning
+    to avoid unbounded memory / CPU usage.
+    """
+    image_paths: list[str] = []
+    doc_texts: list[str] = []
+
+    for path_str in media_paths:
+        p = Path(path_str)
+        if not p.is_file():
+            continue
+
+        try:
+            size = p.stat().st_size
+        except OSError:
+            continue
+        if size > max_file_size:
+            logger.warning(
+                "Skipping oversized file for extraction: {} ({:.1f} MB > {} MB limit)",
+                p.name, size / (1024 * 1024), max_file_size // (1024 * 1024),
+            )
+            continue
+
+        raw = p.read_bytes()
+        mime = detect_image_mime(raw) or mimetypes.guess_type(path_str)[0]
+        if mime and mime.startswith("image/"):
+            image_paths.append(path_str)
+        else:
+            extracted = extract_text(p)
+            if extracted and not extracted.startswith("[error:"):
+                doc_texts.append(f"[File: {p.name}]\n{extracted}")
+
+    if doc_texts:
+        text = text + "\n\n" + "\n\n".join(doc_texts)
+
+    return text, image_paths