Mostly working

src/pipeline/orchestrator.py

@@ -215,8 +215,11 @@ class PipelineOrchestrator:
 
                 # Persistence: Lore Updates
                 for lore_update in extraction_result.lore_updates:
-                    await asyncio.to_thread(update_lore, lore_update)
-                    logger.info(f"LLM Worker: Lore updated: {lore_update.entity_name}")
+                    file_path = await asyncio.to_thread(update_lore, lore_update)
+                    await asyncio.to_thread(self.rag_manager.ingest_file, file_path)
+                    logger.info(
+                        f"LLM Worker: Lore updated and ingested into RAG: {lore_update.entity_name}"
+                    )
 
                 # Persistence: Character State Updates
                 for char_update in extraction_result.character_updates:

src/rag/manager.py

@@ -87,6 +87,22 @@ class RAGManager:
 
         print(f"Successfully ingested {file_path} into the vector store.")
 
+    def ingest_directory(self, dir_path: str):
+        """
+        Recursively loads all markdown files in a directory into the index.
+        """
+        files_processed = 0
+        for root, _, files in os.walk(dir_path):
+            for file in files:
+                if file.endswith(".md"):
+                    file_path = os.path.join(root, file)
+                    self.ingest_file(file_path)
+                    files_processed += 1
+
+        print(
+            f"Successfully ingested {files_processed} files from {dir_path} into the vector store."
+        )
+
     def summarize_results(self, query: str, nodes: List[Any]) -> List[ContextUpdate]:
         """
         Uses an LLM to transform raw snippets into concise "insights", filtering out irrelevant content.