Update src/rag_engine.py

src/rag_engine.py

@@ -150,6 +150,35 @@ def save_uploaded_file(uploaded_file, username: str = "default") -> str:
         logger.error(f"Error saving file: {e}")
         return None
 
+def process_and_add_text(text: str, source_name: str, username: str) -> Tuple[bool, str]:
+    """
+    Ingests raw text string (e.g., from the Flattener tool) directly into Chroma.
+    """
+    try:
+        user_db_path = os.path.join(CHROMA_PATH, username)
+        emb_fn = get_embedding_func()
+        
+        # Initialize DB
+        db = Chroma(persist_directory=user_db_path, embedding_function=emb_fn)
+
+        # Create a Document object directly
+        doc = Document(
+            page_content=text,
+            metadata={
+                "source": source_name, 
+                "strategy": "flattened_text",
+                "file_type": "generated"
+            }
+        )
+
+        # Add single document
+        db.add_documents([doc])
+        return True, f"Successfully indexed flattened text: {source_name}"
+        
+    except Exception as e:
+        logger.error(f"Error indexing raw text: {e}")
+        return False, f"Error: {str(e)}"
+
 def ingest_file(file_path: str, username: str, strategy: str = "paragraph") -> Tuple[bool, str]:
     """
     The High-Level Bridge: Takes a file path, chunks it, and saves to Vector DB.