updated naming

lightrag/lightrag.py

@@ -396,7 +396,7 @@ class LightRAG:
             split_by_character is None, this parameter is ignored.
         """
         await self.apipeline_process_documents(string_or_strings)
-        await self.apipeline_process_chunks(split_by_character, split_by_character_only)
+        await self.apipeline_process_enqueue_documents(split_by_character, split_by_character_only)
 
     def insert_custom_chunks(self, full_text: str, text_chunks: list[str]):
         loop = always_get_an_event_loop()
@@ -465,7 +465,7 @@ class LightRAG:
             if update_storage:
                 await self._insert_done()
 
-    async def apipeline_process_documents(self, string_or_strings: str | list[str]):
+    async def apipeline_enqueue_documents(self, string_or_strings: str | list[str]):
         """Pipeline process documents
 
         1. Remove duplicate contents from the list
@@ -505,7 +505,7 @@ class LightRAG:
             logger.info("All documents have been processed or are duplicates")
             return
 
-        # 4. Store original document
+        # 4. Store status document
         await self.doc_status.upsert(new_docs)
         logger.info(f"Stored {len(new_docs)} new unique documents")
 
@@ -529,22 +529,20 @@ class LightRAG:
         
         return to_process_doc_keys
     
-    async def apipeline_process_chunks(
+    async def apipeline_process_enqueue_documents(
         self,
         split_by_character: str | None = None,
         split_by_character_only: bool = False,
     ) -> None:
-        """Pipeline process chunks
+        """
+        Process pending documents by splitting them into chunks, processing
+        each chunk for entity and relation extraction, and updating the 
+        document status.
         
-        1. Get pending documents
-        2. Split documents into chunks
-        3. Insert chunks
-
-        Args:
-            split_by_character (str | None): If not None, split the string by character, if chunk longer than
-                chunk_size, split the sub chunk by token size.
-            split_by_character_only (bool): If split_by_character_only is True, split the string by character only,
-                when split_by_character is None, this parameter is ignored.
+        1. Get all pending and failed documents
+        2. Split document content into chunks
+        3. Process each chunk for entity and relation extraction
+        4. Update the document status
         """        
         # 1. get all pending and failed documents
         pending_doc_ids = await self._get_pending_documents()
@@ -612,10 +610,11 @@ class LightRAG:
                         self.full_docs.upsert({doc_id: {"content": status_doc["content"]}})
                     )
                     
-                # check if chunks already processed  the doc
+                # Check if chunks already processed  the doc
                 if doc_id not in text_chunks_processed_doc_ids:
                     tasks[doc_id].append(self.text_chunks.upsert(chunks))
 
+                # Process document (text chunks and full docs) in parallel 
                 for doc_id, task in tasks.items():
                     try:
                         await asyncio.gather(*task)