feat: use gpt 4o

backend/api/main.py

@@ -29,7 +29,7 @@ def start():
         temperature=0.3,
         max_tokens=512,
         client=None,
-        model="gpt-3.5-turbo",
+        model="gpt-4o",
         verbose=True,
     )
     ai = AI(

backend/data/food_to_cook/food_i_eat.txt

@@ -22,3 +22,4 @@ Indomie noodles
 Yam porridge
 Fried plantain and egg sauce
 Asun (spicy goat meat)
+Afang

backend/plugins/a1_german_teacher.py

@@ -5,12 +5,16 @@
 from langchain.memory import ConversationBufferMemory
 
 class GermanTeacherPlugin():
-  def __init__(self, model):
-      self.model = ChatOpenAI(temperature=0.5, max_tokens=512, client=None)
-  def get_lang_chain_tool(self):
-    # get the file use_words.txt
-    german_teacher_prompt = PromptTemplate.from_template(
-    """You are a german friend that speaks Deutsch.
+
+    def __init__(self, model):
+        self.model = ChatOpenAI(
+            temperature=0.5, max_tokens=512, client=None, model="gpt-4o"
+        )
+
+    def get_lang_chain_tool(self):
+        # get the file use_words.txt
+        german_teacher_prompt = PromptTemplate.from_template(
+            """You are a german friend that speaks Deutsch.
     Make sure your responses are not too long so that the user can understand you.
     Feel free to talk about any topic of your choice.
     Your goal is to teach the grammar and vocabulary of the german language through conversation.
@@ -47,9 +51,9 @@ def get_lang_chain_tool(self):
     human: {prompt}
     response:
     """
-    )
-    todo_chain = LLMChain(llm=self.model, prompt=german_teacher_prompt, memory=ConversationBufferMemory())
-    return [Tool(
+        )
+        todo_chain = LLMChain(llm=self.model, prompt=german_teacher_prompt, memory=ConversationBufferMemory())
+        return [Tool(
           name="Specialized German Model",
           description="This tool is a German language model designed for engaging in German conversations. It excels at understanding and generating responses in German. you MUST use this tool when the query contains /a1teacher. also all deutsch conversations MUST use this tool. It takes the user's query as input and provides the best German response.",
           func=todo_chain.run,

backend/plugins/chat_gpt.py

@@ -5,7 +5,7 @@
 
 class ChatGptPlugin():
   def __init__(self, model):
-      self.model = ChatOpenAI(temperature=0.7, client=None)
+      self.model = ChatOpenAI(temperature=0.7, client=None, model="gpt-4o")
   def get_lang_chain_tool(self):
 
     template = """

backend/plugins/convo.py

@@ -6,7 +6,7 @@
 
 class ConvoPlugin:
     def __init__(self, model):
-        self.model = ChatOpenAI(temperature=0.7, client=None)
+        self.model = ChatOpenAI(temperature=0.7, client=None, model="gpt-4o")
 
     def get_lang_chain_tool(self):
         template = """

backend/plugins/devotionals.py

@@ -7,10 +7,13 @@
 from langchain import LLMChain, PromptTemplate
 
 class DevotionalsPlugin():
-  def __init__(self, model):
-      self.model = ChatOpenAI(temperature=1.0, max_tokens=1024, client=None)
-      self.theologian_prompt = PromptTemplate.from_template(
-          """You are a Christian and theologian
+
+    def __init__(self, model):
+        self.model = ChatOpenAI(
+            temperature=1.0, max_tokens=1024, client=None, model="gpt-4o"
+        )
+        self.theologian_prompt = PromptTemplate.from_template(
+            """You are a Christian and theologian
           Create a devotional message for the chapter after {previous_chapter}, it should be in an interesting story telling fashion but must try to be as factual as possible.
 
           In a another section, add a Reflection and Application section, where you reflect on the chapter and apply it practically to today's world.
@@ -22,11 +25,11 @@ def __init__(self, model):
 
           Token limit: 1024
           """
-      )
-      self.chain = LLMChain(llm=self.model, prompt=self.theologian_prompt, verbose=True)
-      
-  def get_lang_chain_tool(self):
-    return [DevotionalsPluginTool(chain=self.chain)]
+        )
+        self.chain = LLMChain(llm=self.model, prompt=self.theologian_prompt, verbose=True)
+
+    def get_lang_chain_tool(self):
+        return [DevotionalsPluginTool(chain=self.chain)]
 
 class DevotionalsPluginTool(BaseTool):
     name = "Daily devotionals generator"

backend/plugins/food_to_cook.py

@@ -9,7 +9,7 @@
 
 class FoodToCookPlugin():
     def __init__(self, model):
-        self.model = ChatOpenAI(temperature=1.0, max_tokens=1024, client=None)
+        self.model = ChatOpenAI(temperature=1.0, max_tokens=1024, client=None, model="gpt-4o")
         self.theologian_prompt = PromptTemplate.from_template(
             """You are my personal chef, here is a list of food i eat. and the last series of food i ate.
             Suggest the next food i should cook for a meal.

backend/plugins/talker.py

@@ -5,7 +5,7 @@
 
 class TalkerPlugin():
   def __init__(self, model):
-      self.model = ChatOpenAI(temperature=0.7, client=None)
+      self.model = ChatOpenAI(temperature=0.7, client=None, model="gpt-4o")
   def get_lang_chain_tool(self):
 
     template = """

backend/plugins/used_words_practice.py

@@ -7,7 +7,7 @@
 
 class UsedWordsPracticePlugin:
     def __init__(self, model):
-        self.model = ChatOpenAI(temperature=0.5, max_tokens=512, client=None)
+        self.model = ChatOpenAI(temperature=0.5, max_tokens=512, client=None, model="gpt-4o")
 
     def get_lang_chain_tool(self):
         # get the file use_words.txt