update OpenAI into ChatOpenAI

agents/code_generate_agent.py

@@ -2,6 +2,7 @@ import re
 from typing import List, Union
 from langchain.chains import LLMChain
 from langchain.chat_models import ChatOpenAI
+from langchain.llms import OpenAI
 from langchain.agents import Tool, LLMSingleActionAgent, AgentExecutor, AgentOutputParser
 from langchain.schema import AgentAction, AgentFinish
 from langchain.agents import initialize_agent

agents/tools/python_code_tool.py

@@ -1,21 +1,24 @@
 import re
-from langchain import LLMChain, OpenAI, PromptTemplate
+from langchain import LLMChain, PromptTemplate
 from langchain.chat_models import ChatOpenAI
+from langchain.llms import OpenAI
 from langchain.agents import tool
 from langchain.utilities import PythonREPL
 
 
 generate_python_code = """
-请用python写一段代码实现如下需求:
+Please write Python script to fulfill the following requirement:
 
 ---
 {input}
 ---
+
+Only output the code section without including code block symbols.
 """
 
 generate_python_code_promopt = PromptTemplate(input_variables=["input"], template=generate_python_code,)
 
-generate_code_chain = LLMChain(llm = OpenAI(temperature=0.1), prompt=generate_python_code_promopt, output_key="code")
+generate_code_chain = LLMChain(llm = ChatOpenAI(temperature=0.1), prompt=generate_python_code_promopt, output_key="code")
 
 
 @tool("Generate and Excute Python Code ", return_direct=True)

embedding.py

@@ -7,7 +7,7 @@ from langchain.chains import RetrievalQA
 from langchain.chains.question_answering import load_qa_chain
 
 from langchain.document_loaders import NotionDirectoryLoader
-from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 
@@ -32,12 +32,11 @@ class CustomEmbedding:
 
     
 
-    def getFAQChain(self, llm=OpenAI(temperature=0.7)):
+    def getFAQChain(self, llm=ChatOpenAI(temperature=0.7)):
         memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
         docsearch = FAISS.load_local(
             "./documents/business_context.faiss", self.embeddings)
         # retriever = VectorStoreRetriever(vectorstore=docsearch)
-        llm = OpenAI(temperature=0)
         _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question in chinese.
 
         Chat History: