feat: upgrade langchain (#430)

Co-authored-by: jyong <718720800@qq.com>

api/core/agent/agent_builder.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from langchain import LLMChain
 from langchain.agents import ZeroShotAgent, AgentExecutor, ConversationalAgent
-from langchain.callbacks import CallbackManager
+from langchain.callbacks.manager import CallbackManager
 from langchain.memory.chat_memory import BaseChatMemory
 
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
@@ -16,23 +16,20 @@ class AgentBuilder:
     def to_agent_chain(cls, tenant_id: str, tools, memory: Optional[BaseChatMemory],
                        dataset_tool_callback_handler: DatasetToolCallbackHandler,
                        agent_loop_gather_callback_handler: AgentLoopGatherCallbackHandler):
-        llm_callback_manager = CallbackManager([agent_loop_gather_callback_handler, DifyStdOutCallbackHandler()])
         llm = LLMBuilder.to_llm(
             tenant_id=tenant_id,
             model_name=agent_loop_gather_callback_handler.model_name,
             temperature=0,
             max_tokens=1024,
-            callback_manager=llm_callback_manager
+            callbacks=[agent_loop_gather_callback_handler, DifyStdOutCallbackHandler()]
         )
 
-        tool_callback_manager = CallbackManager([
-            agent_loop_gather_callback_handler,
-            dataset_tool_callback_handler,
-            DifyStdOutCallbackHandler()
-        ])
-
         for tool in tools:
-            tool.callback_manager = tool_callback_manager
+            tool.callbacks = [
+                agent_loop_gather_callback_handler,
+                dataset_tool_callback_handler,
+                DifyStdOutCallbackHandler()
+            ]
 
         prompt = cls.build_agent_prompt_template(
             tools=tools,
@@ -54,7 +51,7 @@ class AgentBuilder:
             tools=tools,
             agent=agent,
             memory=memory,
-            callback_manager=agent_callback_manager,
+            callbacks=agent_callback_manager,
             max_iterations=6,
             early_stopping_method="generate",
             # `generate` will continue to complete the last inference after reaching the iteration limit or request time limit