Address import LlamaIndexRagWorker problem

crazy_functions/Rag_Interface.py

@@ -1,9 +1,12 @@
-import os, glob
-
+import os,glob
 from typing import List
-from toolbox import report_exception
-from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
+
+from llama_index.core import Document
 from shared_utils.fastapi_server import validate_path_safety
+
+from toolbox import report_exception
+from crazy_functions.rag_fns.rag_file_support import extract_text, supports_format
+from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
 from crazy_functions.crazy_utils import input_clipping
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 
@@ -12,44 +15,14 @@ MAX_HISTORY_ROUND = 5
 MAX_CONTEXT_TOKEN_LIMIT = 4096
 REMEMBER_PREVIEW = 1000
 
-# import vector store lib
-VECTOR_STORE_TYPE = "Milvus"
-if VECTOR_STORE_TYPE == "Milvus":
-    try:
-        from crazy_functions.rag_fns.milvus_worker import MilvusRagWorker as LlamaIndexRagWorker
-    except:
-        VECTOR_STORE_TYPE = "Simple"
-if VECTOR_STORE_TYPE == "Simple":
-    from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
-
 @CatchException
-def handle_document_upload(files: List[str], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+def handle_document_upload(files: List[str], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, rag_worker):
     """
     Handles document uploads by extracting text and adding it to the vector store.
-
-    Args:
-        files (List[str]): List of file paths to process.
-        llm_kwargs: Language model keyword arguments.
-        plugin_kwargs: Plugin keyword arguments.
-        chatbot: Chatbot instance.
-        history: Chat history.
-        system_prompt: System prompt.
-        user_request: User request.
     """
-    from llama_index.core import Document
-    from crazy_functions.rag_fns.rag_file_support import extract_text, supports_format
     user_name = chatbot.get_user()
     checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
 
-    if user_name in RAG_WORKER_REGISTER:
-        rag_worker = RAG_WORKER_REGISTER[user_name]
-    else:
-        rag_worker = RAG_WORKER_REGISTER[user_name] = LlamaIndexRagWorker(
-            user_name,
-            llm_kwargs,
-            checkpoint_dir=checkpoint_dir,
-            auto_load_checkpoint=True
-        )
 
     for file_path in files:
         try:
@@ -73,10 +46,19 @@ def handle_document_upload(files: List[str], llm_kwargs, plugin_kwargs, chatbot,
 @CatchException
 def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
 
+    # import vector store lib
+    VECTOR_STORE_TYPE = "Milvus"
+    if VECTOR_STORE_TYPE == "Milvus":
+        try:
+            from crazy_functions.rag_fns.milvus_worker import MilvusRagWorker as LlamaIndexRagWorker
+        except:
+            VECTOR_STORE_TYPE = "Simple"
+    if VECTOR_STORE_TYPE == "Simple":
+        from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
+
     # 1. we retrieve rag worker from global context
     user_name = chatbot.get_user()
     checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
-
     if user_name in RAG_WORKER_REGISTER:
         rag_worker = RAG_WORKER_REGISTER[user_name]
     else:
@@ -100,7 +82,7 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
         chatbot.append([txt, f'正在处理上传的文档 ({current_context}) ...'])
         yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
 
-        yield from handle_document_upload(file_paths, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
+        yield from handle_document_upload(file_paths, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, rag_worker)
         return
 
     elif txt == "清空向量数据库":
@@ -145,7 +127,6 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
     # 6. Search vector store and build prompts
     nodes = rag_worker.retrieve_from_store_with_query(i_say)
     prompt = rag_worker.build_prompt(query=i_say, nodes=nodes)
-
     # 7. Query language model
     if len(chatbot) != 0:
         chatbot.pop(-1)  # Pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`