gpt model addn to conversation and seperate trimmed history + formatting

README.md

@@ -83,7 +83,7 @@ Note that `agentai` automatically parses the Python Enum type (TemperatureUnit)
 3. **Create a Conversation object and add messages**
 
 ```python
-conversation = Conversation()
+conversation = Conversation(model=GPT_MODEL)
 conversation.add_message("user", "what is the weather like today?")
 ```
 
@@ -152,7 +152,7 @@ agent_system_message = """You are ChinookGPT, a helpful assistant who gets answe
 Provide as many details as possible to your users
 Begin!"""
 
-sql_conversation = Conversation()
+sql_conversation = Conversation(model=GPT_MODEL)
 sql_conversation.add_message(role="system", content=agent_system_message)
 sql_conversation.add_message("user", "Hi, who are the top 5 artists by number of tracks")
 assistant_message = chat_complete_execute_fn(

agentai/conversation.py

@@ -13,8 +13,15 @@ class Message(BaseModel):
 
 
 class Conversation:
-    def __init__(self, history: List[Message] = [], id: Optional[str] = None, max_history_tokens: int = 200):
+    def __init__(
+        self,
+        history: List[Message] = [],
+        id: Optional[str] = None,
+        max_history_tokens: Optional[int] = None,
+        model: Optional[str] = None,
+    ) -> None:
         self.history: List[Message] = history
+        self.trimmed_history: List[Message] = []
         self.role_to_color = {
             "system": "red",
             "user": "green",
@@ -23,13 +30,16 @@ def __init__(self, history: List[Message] = [], id: Optional[str] = None, max_hi
         }
         self.id = id
         self.max_history_tokens = max_history_tokens
+        self.model = model
 
     def add_message(self, role: str, content: str, name: Optional[str] = None) -> None:
         message_dict = {"role": role, "content": content}
         if name:
             message_dict["name"] = name
         message = Message(**message_dict)
         self.history.append(message)
+        if self.max_history_tokens and self.model:
+            self.trim_history()
 
     def display_conversation(self) -> None:
         for message in self.history:
@@ -40,25 +50,42 @@ def display_conversation(self) -> None:
                 )
             )
 
-    def get_history(self) -> List[Message]:
+    def trim_history(self) -> None:
         """Function to get the conversation history based on the number of tokens"""
+
+        # raise an error if max_history_tokens or model is not set
+        if not self.max_history_tokens:
+            raise ValueError("max_history_tokens is not set in Conversation")
+        if not self.model:
+            raise ValueError("model is not set in Conversation")
+
         local = threading.local()
         try:
-            enc = local.gpt2enc
+            enc = local.encoder
         except AttributeError:
-            enc = tiktoken.get_encoding("gpt2")
-            local.gpt2enc = enc
+            enc = tiktoken.encoding_for_model(self.model)
+            local.encoder = enc
 
         total_tokens = 0
         # Iterate 2 at a time to avoid cutting in between a (prompt, response) pair
-        for i in range(len(self.history) -1, -1, -2):
+        for i in range(len(self.history) - 1, -1, -2):
             # Iterate over the messages in reverse order - from the latest to the oldest messages
             message = self.history[i]  # Message(role='User', content='I appreciate that. Take care too!', name=None)
             content = message.content
             tokens = len(enc.encode(content))
             total_tokens += tokens
             if total_tokens > self.max_history_tokens:
-                # Trim the history inplace to keep the total tokens under max_tokens
-                self.history = self.history[i + 1 :]
+                # Trim the history to keep the total tokens under max_tokens
+                # and store the trimmed history in self.trimmed_history
+                # to have a faster access to it later
+                self.trimmed_history = self.history[i + 1 :]
                 break
+
+    def get_history(self, trimmed: bool = False) -> List[Message]:
+        # Return the trimmed history if it exists and trimmed is True
+        if trimmed:
+            if not self.trimmed_history:
+                self.trim_history()
+            return self.trimmed_history
+        # Return the full history otherwise
         return self.history

docs/04_Evaluating_OpenAI_Functions.ipynb

@@ -23667,6 +23667,7 @@
    ],
    "source": [
     "import json\n",
+    "\n",
     "with open(\"evaluation_resuls.json\", \"w\") as f:\n",
     "    json.dump(evaluation_results, f, indent=4)\n",
     "\n",

docs/05_Conversation_History.ipynb

@@ -9,7 +9,10 @@
     "from agentai.conversation import Conversation\n",
     "\n",
     "# Create a conversation object with a maximum history length of 100 tokens\n",
-    "conversation = Conversation(max_history_tokens=100)"
+    "conversation = Conversation(max_history_tokens=100, model=\"gpt-3.5-turbo\")\n",
+    "# Note: max_history_tokens allows you to control the length of the conversation history in your prompt.\n",
+    "# Specify the 'model' being used if you're using the 'max_history_tokens'.\n",
+    "# 'model' here is used to pick the tokenizer for trimming the conversation history. "
    ]
   },
   {
@@ -18,7 +21,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "# Chatgpt Simulated conversations of different length\n",
     "conversation.add_message(\"User\", \"Hello!\")\n",
     "conversation.add_message(\"Assistant\", \"Hi there!\")\n",
@@ -77,7 +79,7 @@
     "    \"You're welcome! I hope these tips help you find some relief from stress. If you have any more questions or need further assistance, don't hesitate to ask. Take care!\",\n",
     ")\n",
     "conversation.add_message(\"User\", \"I appreciate that. Take care too!\")\n",
-    "conversation.add_message(\"Assistant\", \"Thank you! Have a fantastic day!\")\n"
+    "conversation.add_message(\"Assistant\", \"Thank you! Have a fantastic day!\")"
    ]
   },
   {
@@ -115,7 +117,9 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "trimmed_history = conversation.get_history(trimmed=True)"
+   ]
   }
  ],
  "metadata": {
@@ -134,7 +138,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.11.4"
   },
   "orig_nbformat": 4
  },