feat: initial knowledge-inbox pipeline implementation

- Oracle ADB queue table (sql/schema.sql)
- Queue CRUD: core/queue_db.py
- YouTube transcript: core/youtube.py
- Web page fetch: core/web.py
- LLM enrichment via OCI GenAI Gemini Flash: core/enricher.py
- Text chunker: core/chunker.py
- Obsidian note writer: core/obsidian.py
- Oracle vector store insertion: core/vector.py
- Polling daemon: daemon/worker.py
- Telegram bot: bot/telegram_bot.py
- Main runner: main.py

core/chunker.py

@@ -0,0 +1,28 @@
+"""Simple sliding-window text chunking."""
+
+
+def chunk_text(text: str, size: int = 2000, overlap: int = 200) -> list[str]:
+    """Split text into overlapping chunks.
+
+    Args:
+        text: The full text to split.
+        size: Maximum characters per chunk.
+        overlap: Characters of overlap between consecutive chunks.
+
+    Returns:
+        List of text chunks. Returns single-item list for short text.
+    """
+    if len(text) <= size:
+        return [text]
+
+    chunks: list[str] = []
+    step = size - overlap
+    start = 0
+    while start < len(text):
+        end = start + size
+        chunks.append(text[start:end])
+        if end >= len(text):
+            break
+        start += step
+
+    return chunks