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/api/server.py

@@ -19,8 +19,7 @@ from aiohttp import web
 from loguru import logger
 
 from nanobot.config.paths import get_media_dir
-from nanobot.utils.document import extract_text
-from nanobot.utils.helpers import detect_image_mime, safe_filename
+from nanobot.utils.helpers import safe_filename
 from nanobot.utils.runtime import EMPTY_FINAL_RESPONSE_MESSAGE
 
 MAX_FILE_SIZE = 10 * 1024 * 1024  # 10 MB
@@ -162,40 +161,6 @@ async def _parse_multipart(request: web.Request) -> tuple[str, list[str], str |
     return text, media_paths, session_id
 
 
-# ---------------------------------------------------------------------------
-# Pre-processing: extract document text at the API boundary
-# ---------------------------------------------------------------------------
-
-def _extract_documents(text: str, media_paths: list[str]) -> tuple[str, list[str]]:
-    """Separate images from documents in *media_paths*.
-
-    Documents (PDF, DOCX, XLSX, PPTX, …) have their text extracted and
-    appended to *text*.  Only image paths are kept in the returned list so
-    that downstream layers (ContextBuilder) only need to handle vision
-    blocks.
-    """
-    image_paths: list[str] = []
-    doc_texts: list[str] = []
-
-    for path_str in media_paths:
-        p = Path(path_str)
-        if not p.is_file():
-            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
-
-
 # ---------------------------------------------------------------------------
 # Route handlers
 # ---------------------------------------------------------------------------
@@ -232,10 +197,6 @@ async def handle_chat_completions(request: web.Request) -> web.Response:
         logger.exception("Error parsing upload")
         return _error_json(413, "File too large or invalid upload")
 
-    # Extract document text at the API boundary; only images stay in media.
-    if media_paths:
-        text, media_paths = _extract_documents(text, media_paths)
-
     session_key = f"api:{session_id}" if session_id else API_SESSION_KEY
     session_locks: dict[str, asyncio.Lock] = request.app["session_locks"]
     session_lock = session_locks.setdefault(session_key, asyncio.Lock())