Initial commit: RAG MCP Server with relationship graph

Features:
- Vector search with Pinecone + Vertex AI embeddings
- Document relationships (link, unlink, related, graph)
- Auto-link with LLM analysis
- Intelligent merge with Gemini

Modular structure:
- clients/: Pinecone, Vertex AI
- tools/: core, relations, stats
- utils/: validation, logging

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

tools/stats.py

@@ -0,0 +1,37 @@
+"""Statistics and monitoring tools."""
+from clients import get_index
+from utils.logging import get_logger
+from config import AUTO_LINK_THRESHOLD, AUTO_LINK_TOP_K
+
+logger = get_logger(__name__)
+
+def rag_stats() -> str:
+    """
+    Return RAG database statistics.
+
+    Returns:
+        Formatted statistics or error message
+    """
+    try:
+        index = get_index()
+        stats = index.describe_index_stats()
+
+        total = stats.get("total_vector_count", 0)
+
+        output = [
+            "=== RAG Statistics ===",
+            f"Total documents: {total}",
+            f"Dimension: {stats.get('dimension', 'N/A')}",
+            f"Index fullness: {stats.get('index_fullness', 'N/A')}",
+            "",
+            "=== Auto-link Settings ===",
+            f"Threshold: {AUTO_LINK_THRESHOLD}",
+            f"Top-K candidates: {AUTO_LINK_TOP_K}"
+        ]
+
+        logger.info(f"Stats retrieved: {total} documents")
+        return "\n".join(output)
+
+    except Exception as e:
+        logger.error(f"rag_stats failed: {str(e)}", exc_info=True)
+        return f"Error: {str(e)}"