Update file parsing progress info (infiniflow#3780)

### What problem does this PR solve?

Refine the file parsing progress info

### Type of change

- [x] Refactoring

Signed-off-by: jinhai <haijin.chn@gmail.com>

rag/svr/task_executor.py

@@ -370,72 +370,86 @@ def run_raptor(row, chat_mdl, embd_mdl, callback=None):
     return res, tk_count, vector_size
 
 
-def do_handle_task(r):
-    callback = partial(set_progress, r["id"], r["from_page"], r["to_page"])
+def do_handle_task(task):
+    task_id = task["id"]
+    task_from_page = task["from_page"]
+    task_to_page = task["to_page"]
+    task_tenant_id = task["tenant_id"]
+    task_embedding_id = task["embd_id"]
+    task_language = task["language"]
+    task_llm_id = task["llm_id"]
+    task_dataset_id = task["kb_id"]
+    task_doc_id = task["doc_id"]
+    task_document_name = task["name"]
+    task_parser_config = task["parser_config"]
+
+    # prepare the progress callback function
+    progress_callback = partial(set_progress, task_id, task_from_page, task_to_page)
     try:
-        embd_mdl = LLMBundle(r["tenant_id"], LLMType.EMBEDDING, llm_name=r["embd_id"], lang=r["language"])
+        # bind embedding model
+        embedding_model = LLMBundle(task_tenant_id, LLMType.EMBEDDING, llm_name=task_embedding_id, lang=task_language)
     except Exception as e:
-        callback(-1, msg=str(e))
+        progress_callback(-1, msg=f'Fail to bind embedding model: {str(e)}')
         raise
-    if r.get("task_type", "") == "raptor":
+
+    # Either using RAPTOR or Standard chunking methods
+    if task.get("task_type", "") == "raptor":
         try:
-            chat_mdl = LLMBundle(r["tenant_id"], LLMType.CHAT, llm_name=r["llm_id"], lang=r["language"])
-            cks, tk_count, vector_size = run_raptor(r, chat_mdl, embd_mdl, callback)
+            # bind LLM for raptor
+            chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
+
+            # run RAPTOR
+            chunks, tk_count, vector_size = run_raptor(task, chat_model, embedding_model, progress_callback)
         except Exception as e:
-            callback(-1, msg=str(e))
+            progress_callback(-1, msg=f'Fail to bind LLM used by RAPTOR: {str(e)}')
             raise
     else:
-        st = timer()
-        cks = build(r)
-        logging.info("Build chunks({}): {}".format(r["name"], timer() - st))
-        if cks is None:
+        # Standard chunking methods
+        start_ts = timer()
+        chunks = build(task)
+        logging.info("Build document {}: {:.2f}s".format(task_document_name, timer() - start_ts))
+        if chunks is None:
             return
-        if not cks:
-            callback(1., "No chunk! Done!")
+        if not chunks:
+            progress_callback(1., msg=f"No chunk built from {task_document_name}")
             return
         # TODO: exception handler
-        ## set_progress(r["did"], -1, "ERROR: ")
-        callback(
-            msg="Generate {} chunks ({:.2f}s). Embedding chunks.".format(len(cks), timer() - st)
-        )
-        st = timer()
+        ## set_progress(task["did"], -1, "ERROR: ")
+        progress_callback(msg="Generate {} chunks".format(len(chunks)))
+        start_ts = timer()
         try:
-            tk_count, vector_size = embedding(cks, embd_mdl, r["parser_config"], callback)
+            tk_count, vector_size = embedding(chunks, embedding_model, task_parser_config, progress_callback)
         except Exception as e:
-            callback(-1, "Embedding error:{}".format(str(e)))
-            logging.exception("run_rembedding got exception")
+            progress_callback(-1, "Generate embedding error:{}".format(str(e)))
+            logging.exception("run_embedding got exception")
             tk_count = 0
             raise
-        logging.info("Embedding elapsed({}): {:.2f}".format(r["name"], timer() - st))
-        callback(msg="Finished embedding ({:.2f}s)!".format(timer() - st))
-    # logging.info(f"task_executor init_kb index {search.index_name(r["tenant_id"])} embd_mdl {embd_mdl.llm_name} vector length {vector_size}")
-    init_kb(r, vector_size)
-    chunk_count = len(set([c["id"] for c in cks]))
-    st = timer()
+        logging.info("Embedding {} elapsed: {:.2f}".format(task_document_name, timer() - start_ts))
+        progress_callback(msg="Embedding chunks ({:.2f}s)".format(timer() - start_ts))
+    # logging.info(f"task_executor init_kb index {search.index_name(task_tenant_id)} embedding_model {embedding_model.llm_name} vector length {vector_size}")
+    init_kb(task, vector_size)
+    chunk_count = len(set([chunk["id"] for chunk in chunks]))
+    start_ts = timer()
     es_r = ""
     es_bulk_size = 4
-    for b in range(0, len(cks), es_bulk_size):
-        es_r = settings.docStoreConn.insert(cks[b:b + es_bulk_size], search.index_name(r["tenant_id"]), r["kb_id"])
+    for b in range(0, len(chunks), es_bulk_size):
+        es_r = settings.docStoreConn.insert(chunks[b:b + es_bulk_size], search.index_name(task_tenant_id), task_dataset_id)
         if b % 128 == 0:
-            callback(prog=0.8 + 0.1 * (b + 1) / len(cks), msg="")
-    logging.info("Indexing elapsed({}): {:.2f}".format(r["name"], timer() - st))
+            progress_callback(prog=0.8 + 0.1 * (b + 1) / len(chunks), msg="")
+    logging.info("Indexing {} elapsed: {:.2f}".format(task_document_name, timer() - start_ts))
     if es_r:
-        callback(-1,
-                 "Insert chunk error, detail info please check log file. Please also check Elasticsearch/Infinity status!")
-        settings.docStoreConn.delete({"doc_id": r["doc_id"]}, search.index_name(r["tenant_id"]), r["kb_id"])
+        progress_callback(-1, "Insert chunk error, detail info please check log file. Please also check Elasticsearch/Infinity status!")
+        settings.docStoreConn.delete({"doc_id": task_doc_id}, search.index_name(task_tenant_id), task_dataset_id)
         logging.error('Insert chunk error: ' + str(es_r))
         raise Exception('Insert chunk error: ' + str(es_r))
 
-    if TaskService.do_cancel(r["id"]):
-        settings.docStoreConn.delete({"doc_id": r["doc_id"]}, search.index_name(r["tenant_id"]), r["kb_id"])
+    if TaskService.do_cancel(task_id):
+        settings.docStoreConn.delete({"doc_id": task_doc_id}, search.index_name(task_tenant_id), task_dataset_id)
         return
 
-    callback(1., msg="Index cost {:.2f}s.".format(timer() - st))
-    DocumentService.increment_chunk_num(
-        r["doc_id"], r["kb_id"], tk_count, chunk_count, 0)
-    logging.info(
-        "Chunk doc({}), token({}), chunks({}), elapsed:{:.2f}".format(
-            r["id"], tk_count, len(cks), timer() - st))
+    progress_callback(1., msg="Finish Index ({:.2f}s)".format(timer() - start_ts))
+    DocumentService.increment_chunk_num(task_doc_id, task_dataset_id, tk_count, chunk_count, 0)
+    logging.info("Chunk doc({}), token({}), chunks({}), elapsed:{:.2f}".format(task_id, tk_count, len(chunks), timer() - start_ts))
 
 
 def handle_task():