プロンプトの修正

scripts/langchainapi.py

@@ -19,13 +19,14 @@ from langchain.chains import LLMChain, ConversationChain
 from langchain.output_parsers import PydanticOutputParser, OutputFixingParser
 from pydantic import BaseModel, Field, PrivateAttr
 from typing import Optional, List, Any
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-    AIMessagePromptTemplate,
-    MessagesPlaceholder,
-)
+#from langchain.prompts.chat import (
+#    ChatPromptTemplate,
+#    SystemMessagePromptTemplate,
+#    HumanMessagePromptTemplate,
+#    AIMessagePromptTemplate,
+#    MessagesPlaceholder,
+#)
+from langchain.prompts import StringPromptTemplate, PromptTemplate
 from langchain.callbacks.base import AsyncCallbackHandler
 from langchain.callbacks.manager import AsyncCallbackManager
 #from langchain_community.llms import OpenAI
@@ -40,32 +41,26 @@ class StreamingLLMCallbackHandler(AsyncCallbackHandler):
         self.recieved_message += token
 
 
-class TemplateMessagesPlaceholder(MessagesPlaceholder):
-    _human_template: str = PrivateAttr('')
-    _ai_template: str = PrivateAttr('')
+class TemplateMessagesPrompt(StringPromptTemplate):
+    template: str = ''
+    system_message: str = ''
+    history_name: str = 'history'
+    input_name: str = 'input'
 
-    def __init__(self, variable_name: str, template: str, **kwargs: Any):
-        splited = str(template).split("{prompt}")
-        self._human_template = splited[0] + "{prompt}"
-        self._ai_template = splited[1] + "{response}"
-        return super().__init__(variable_name=variable_name, **kwargs)
+    def format(self, **kwargs: Any) -> str:
+        splited = self.template.split("{prompt}")
+        human_template = splited[0] + "{prompt}"
+        ai_template = splited[1] + "{response}\n"
 
-    def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
-        input_mes_list = kwargs[self.variable_name]
-        messages = []
+        input_mes_list = kwargs[self.history_name]
+        messages = self.system_message + '\n'
         for mes in input_mes_list:
             if type(mes) is HumanMessage:
-                message_prompt = HumanMessagePromptTemplate.from_template(
-                    self._human_template,
-                )
-                message = message_prompt.format(prompt=mes.content)
-                messages.append(message)
+                messages += human_template.replace("{prompt}", mes.content)
             elif type(mes) is AIMessage:
-                message_prompt = AIMessagePromptTemplate.from_template(
-                    self._ai_template,
-                )
-                message = message_prompt.format(response=mes.content)
-                messages.append(message) 
+                messages += ai_template.replace("{response}", mes.content)
+        messages += self.template.replace("{prompt}", kwargs[self.input_name])
+        #print(messages)
         return messages
 
 
@@ -121,8 +116,7 @@ class LangChainApi:
             prompt_template_str = self.settings['llama_cpp_prompt_template']
 
         if not is_chat:
-            template = """
-You are a chatbot having a conversation with a human.
+            system_message = """You are a chatbot having a conversation with a human.
 
 You also have the function to generate image with Stable Diffusion.
 If you want to use this function, please add the following to your message.
@@ -143,21 +137,13 @@ There is no memory function, so please carry over the prompts from past conversa
 <|end_of_turn|>
 If you understand, please reply to the following:<|end_of_turn|>
 """
-            system_message_prompt = SystemMessagePromptTemplate.from_template(
-                template,
-            )
 
-            human_template = prompt_template_str.replace("{prompt}", "{input}")
-            human_message_prompt = HumanMessagePromptTemplate.from_template(
-                human_template,
+            self.prompt = TemplateMessagesPrompt(
+                system_message=system_message,
+                template=prompt_template_str,
+                input_variables=['history', 'input'],
             )
 
-            self.prompt = ChatPromptTemplate.from_messages([
-                system_message_prompt,
-                TemplateMessagesPlaceholder(variable_name="history", template=prompt_template_str),
-                human_message_prompt,
-            ])
-
             self.llm_chain = ConversationChain(prompt=self.prompt, llm=self.llm, memory=self.memory)#, verbose=True)
 
             def chat_predict(human_input):
@@ -255,6 +241,8 @@ If you understand, please reply to the following:<|end_of_turn|>
         return_message, return_prompt = self.parse_message(self.callback.recieved_message)
         if len(return_message) > 0 and return_message[-1] == '!':
             return_message = return_message[:-1]
+        if return_message.isspace():
+            return_message = None
         return return_message, return_prompt
 
     def parse_message(self, full_message):