refactor: decouple openai session

bot/openai/open_ai_session.py

@@ -0,0 +1,77 @@
+from bot.session_manager import Session
+from common.log import logger
+class OpenAISession(Session):
+    def __init__(self, session_id, system_prompt=None, model= "text-davinci-003"):
+        super().__init__(session_id, system_prompt)
+        self.conversation = []
+        self.model = model
+        self.reset()
+    
+    def reset(self):
+        pass
+
+    def add_query(self, query):
+        question = {'type': 'question', 'content': query}
+        self.conversation.append(question)
+
+    def add_reply(self, reply):
+        answer = {'type': 'answer', 'content': reply}
+        self.conversation.append(answer)
+    def __str__(self):
+        '''
+        e.g.  Q: xxx
+              A: xxx
+              Q: xxx
+        '''
+        prompt = self.system_prompt
+        if prompt:
+            prompt += "<|endoftext|>\n\n\n"
+        for item in self.conversation:
+            if item['type'] == 'question':
+                prompt += "Q: " + item['content'] + "\n"
+            elif item['type'] == 'answer':
+                prompt += "\n\nA: " + item['content'] + "<|endoftext|>\n"
+
+        if len(self.conversation) > 0 and self.conversation[-1]['type'] == 'question':
+            prompt += "A: "
+        return prompt
+
+    def discard_exceeding(self, max_tokens, cur_tokens= None):
+        precise = True
+        try:
+            cur_tokens = num_tokens_from_string(str(self), self.model)
+        except Exception as e:
+            precise = False
+            if cur_tokens is None:
+                raise e
+            logger.debug("Exception when counting tokens precisely for query: {}".format(e))
+        while cur_tokens > max_tokens:
+            if len(self.conversation) > 1:
+                self.conversation.pop(0)
+            elif len(self.conversation) == 1 and self.conversation[0]["type"] == "answer":
+                self.conversation.pop(0)
+                if precise:
+                    cur_tokens = num_tokens_from_string(str(self), self.model)
+                else:
+                    cur_tokens = len(str(self))
+                break
+            elif len(self.conversation) == 1 and self.conversation[0]["type"] == "question":
+                logger.warn("user question exceed max_tokens. total_tokens={}".format(cur_tokens))
+                break
+            else:
+                logger.debug("max_tokens={}, total_tokens={}, len(conversation)={}".format(max_tokens, cur_tokens, len(self.conversation)))
+                break
+            if precise:
+                cur_tokens = num_tokens_from_string(str(self), self.model)
+            else:
+                cur_tokens = len(str(self))
+        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_string(string: str, model: str) -> int:
+    """Returns the number of tokens in a text string."""
+    import tiktoken
+    encoding = tiktoken.encoding_for_model(model)
+    num_tokens = len(encoding.encode(string,disallowed_special=()))
+    return num_tokens