fix: bugs

api/core/agent/agent/multi_dataset_router_agent.py

-import json
 from typing import Tuple, List, Any, Union, Sequence, Optional, cast
 
 from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
-from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.prompts.chat import BaseMessagePromptTemplate
-from langchain.schema import AgentAction, AgentFinish, SystemMessage, Generation, LLMResult, AIMessage
-from langchain.schema.language_model import BaseLanguageModel
+from langchain.schema import AgentAction, AgentFinish, SystemMessage, AIMessage
 from langchain.tools import BaseTool
 from pydantic import root_validator
 
@@ -99,7 +98,7 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
         Returns:
             Action specifying what tool to use.
         """
-        agent_scratchpad = _format_intermediate_steps(intermediate_steps)
+        agent_scratchpad = format_to_openai_functions(intermediate_steps)
         selected_inputs = {
             k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
         }
@@ -119,7 +118,7 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
             }
         )
 
-        agent_decision = _parse_ai_message(ai_message)
+        agent_decision = OpenAIFunctionsAgentOutputParser._parse_ai_message(ai_message)
         return agent_decision
 
     async def aplan(

api/core/agent/agent/openai_function_call.py

 from typing import List, Tuple, Any, Union, Sequence, Optional
 
+from langchain.adapters.openai import convert_message_to_dict
 from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
 from langchain.agents.format_scratchpad import format_to_openai_functions
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
-from langchain.chat_models.openai import _convert_message_to_dict, _import_tiktoken
+from langchain.chat_models.openai import _import_tiktoken
 from langchain.memory.prompt import SUMMARY_PROMPT
 from langchain.prompts.chat import BaseMessagePromptTemplate
 from langchain.schema import AgentAction, AgentFinish, SystemMessage, AIMessage, HumanMessage, BaseMessage, \
@@ -249,15 +250,16 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
             )
         num_tokens = 0
         for m in messages:
-            message = _convert_message_to_dict(m)
+            message = convert_message_to_dict(m)
             num_tokens += tokens_per_message
             for key, value in message.items():
-                if key == "function_call":
-                    for f_key, f_value in value.items():
-                        num_tokens += len(encoding.encode(f_key))
-                        num_tokens += len(encoding.encode(f_value))
-                else:
-                    num_tokens += len(encoding.encode(value))
+                if value is not None:
+                    if key == "function_call":
+                        for f_key, f_value in value.items():
+                            num_tokens += len(encoding.encode(f_key))
+                            num_tokens += len(encoding.encode(f_value))
+                    else:
+                        num_tokens += len(encoding.encode(value))
 
                 if key == "name":
                     num_tokens += tokens_per_name

api/core/chain/llm_chain.py

 from typing import List, Dict, Any, Optional
 
-from langchain import LLMChain as LCLLMChain
+from langchain.chains.llm import LLMChain as LCLLMChain
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.schema import LLMResult, Generation
 from langchain.schema.language_model import BaseLanguageModel