refactor: decouple chatgpt session

bot/chatgpt/chat_gpt_session.py

@@ -0,0 +1,76 @@
+from bot.session_manager import Session
+from common.log import logger
+class ChatGPTSession(Session):
+    def __init__(self, session_id, system_prompt=None, model= "gpt-3.5-turbo"):
+        super().__init__(session_id, system_prompt)
+        self.messages = []
+        self.model = model
+        self.reset()
+    
+    def reset(self):
+        system_item = {'role': 'system', 'content': self.system_prompt}
+        self.messages = [system_item]
+
+    def add_query(self, query):
+        user_item = {'role': 'user', 'content': query}
+        self.messages.append(user_item)
+
+    def add_reply(self, reply):
+        assistant_item = {'role': 'assistant', 'content': reply}
+        self.messages.append(assistant_item)
+    
+    def discard_exceeding(self, max_tokens, cur_tokens= None):
+        if cur_tokens is None:
+            cur_tokens = num_tokens_from_messages(self.messages, self.model)
+        while cur_tokens > max_tokens:
+            if len(self.messages) > 2:
+                self.messages.pop(1)
+            elif len(self.messages) == 2 and self.messages[1]["role"] == "assistant":
+                self.messages.pop(1)
+                cur_tokens = num_tokens_from_messages(self.messages, self.model)
+                break
+            elif len(self.messages) == 2 and self.messages[1]["role"] == "user":
+                logger.warn("user message exceed max_tokens. total_tokens={}".format(cur_tokens))
+                break
+            else:
+                logger.debug("max_tokens={}, total_tokens={}, len(messages)={}".format(max_tokens, cur_tokens, len(self.messages)))
+                break
+            try:
+                cur_tokens = num_tokens_from_messages(self.messages, self.model)
+            except Exception as e:
+                logger.debug("Exception when counting tokens precisely for query: {}".format(e))
+                cur_tokens = cur_tokens - max_tokens
+        return cur_tokens
+    
+
+# refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
+def num_tokens_from_messages(messages, model):
+    """Returns the number of tokens used by a list of messages."""
+    import tiktoken
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        logger.debug("Warning: model not found. Using cl100k_base encoding.")
+        encoding = tiktoken.get_encoding("cl100k_base")
+    if model == "gpt-3.5-turbo":
+        return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
+    elif model == "gpt-4":
+        return num_tokens_from_messages(messages, model="gpt-4-0314")
+    elif model == "gpt-3.5-turbo-0301":
+        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
+        tokens_per_name = -1  # if there's a name, the role is omitted
+    elif model == "gpt-4-0314":
+        tokens_per_message = 3
+        tokens_per_name = 1
+    else:
+        logger.warn(f"num_tokens_from_messages() is not implemented for model {model}. Returning num tokens assuming gpt-3.5-turbo-0301.")
+        return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
+    num_tokens = 0
+    for message in messages:
+        num_tokens += tokens_per_message
+        for key, value in message.items():
+            num_tokens += len(encoding.encode(value))
+            if key == "name":
+                num_tokens += tokens_per_name
+    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
+    return num_tokens