Add automatic context checker for RAG integration

rag_context.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+"""
+RAG Context Checker - Simple wrapper for automatic knowledge base lookup
+
+Usage from within Python/OpenClaw sessions:
+    from rag_context import check_context
+    check_context("your question here")
+
+This prints relevant context if found, otherwise silent.
+"""
+
+import sys
+sys.path.insert(0, '/home/william/.openclaw/workspace/rag')
+
+from rag_query_wrapper import search_knowledge, format_for_ai
+
+
+def check_context(query: str, max_results: int = 5, min_score: float = 0.3) -> None:
+    """
+    Check knowledge base for relevant context and print it.
+
+    This is designed as a simple call-and-forget function. If context exists,
+    it prints it. If not, it's silent.
+
+    Args:
+        query: The question or topic to search for
+        max_results: Maximum results to retrieve
+        min_score: Minimum similarity score (not used in current implementation)
+
+    Example:
+        >>> check_context("how to send SMS")
+        📚 Found 3 relevant items...
+        [prints context]
+    """
+    if not query or len(query) < 3:
+        return
+
+    try:
+        results = search_knowledge(query, n_results=max_results)
+
+        if results and results.get('count', 0) > 0:
+            print("\n" + "="*80)
+            print("📚 RELEVANT CONTEXT FROM KNOWLEDGE BASE\n")
+            formatted = format_for_ai(results)
+            print(formatted)
+            print("="*80 + "\n")
+
+    except Exception as e:
+        # Fail silently - RAG errors shouldn't break conversations
+        pass
+
+
+def get_context(query: str, max_results: int = 5) -> str:
+    """
+    Get context as a string (for automated use).
+
+    Returns formatted context string, or empty string if no results.
+
+    Args:
+        query: Search query
+        max_results: Maximum results
+
+    Returns:
+        Formatted context string, or "" if no results
+
+    Example:
+        >>> context = get_context("Reddit automation")
+        >>> if context:
+        >>>     print("Found relevant past work:")
+        >>>     print(context)
+    """
+    try:
+        results = search_knowledge(query, n_results=max_results)
+
+        if results and results.get('count', 0) > 0:
+            return format_for_ai(results)
+
+    except Exception as e:
+        pass
+
+    return ""
+
+
+# Quick test when run directly
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        query = ' '.join(sys.argv[1:])
+        check_context(query)
+    else:
+        print("Usage: python3 rag_context.py <query>")
+        print("Or import: from rag_context import check_context")