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from operator import itemgetter
from typing import Any, List, Type
from langchain.memory import ChatMessageHistory, ConversationBufferWindowMemory
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.schema import BaseMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough
from src.models.base.base_model import ContentGenerator
class QuestionGenerator(ContentGenerator):
class Question(BaseModel):
question: str = Field(alias="Question", description="A question based on the given guidelines")
def __init__(
self,
exam_prompt: str,
level: str,
description: str,
history_chat: List[BaseMessage],
openai_api_key: SecretStr,
chat_temperature: float = 0.3,
n_messages_memory: int = 20,
chat_model: str = "gpt-3.5-turbo",
) -> None:
"""
Initializes the object with the given exam prompt, level, description, history chat, and OpenAI API key.
Parameters:
exam_prompt (str): The prompt for the exam.
level (str): The level of the exam.
description (str): The description of the exam.
history_chat (List[HumanMessage | AIMessage]): List of chat messages from history.
openai_api_key (SecretStr): The API key for OpenAI.
chat_temperature (float, optional): The temperature to use for chat. Defaults to 0.3.
n_messages_memory (int, optional): The number of messages to keep in memory. Defaults to 20.
Returns:
None
"""
super().__init__(openai_api_key=openai_api_key, chat_temperature=chat_temperature, chat_model=chat_model)
self.level = level
self.exam_prompt = exam_prompt
self.description = description
self.memory = ConversationBufferWindowMemory(
chat_memory=ChatMessageHistory(messages=history_chat), return_messages=True, k=n_messages_memory
)
self.chain = self._create_chain()
def _get_system_prompt(self) -> ChatPromptTemplate:
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"{format_instructions}"
f"""
You are an excellent english teacher. You teach people to speak English by asking questions
to later evaluate its response.
You will do the following tasks for that purpose:
- Limit your questions to just once per interaction at a time.
- You will generate a question based on the following guidelines in order to
later evaluate the response given by the user.
- Don't repeat ANY previous question from the history.
- Don't ask too abstract or very specific questions.
- here below, some example structure questions are given. This is just a reference of
the kind of questions that is expected you provide. Feel free to ask another questions,
but always in the context of an {self.level} Speaking English exam. Remember to ask
just one question at a time and don't repeat ANY previous questions.
{self.description}
""",
),
MessagesPlaceholder(
variable_name="history",
),
(
"human",
"I'm ready to start, ask me a question. Do NOT repeat ANY previous AI questions from the history.",
),
]
)
return prompt
def _get_output_parser(self, pydantic_schema: Type[BaseModel]) -> PydanticOutputParser[Any]:
return PydanticOutputParser(pydantic_object=pydantic_schema)
def _create_chain(self) -> Runnable[Any, Any]:
response_parser = self._get_output_parser(self.Question)
memory_runable = RunnablePassthrough.assign(
history=RunnableLambda(self.memory.load_memory_variables) | itemgetter("history")
)
parsed_chain = (
memory_runable
| self._get_system_prompt().partial(format_instructions=response_parser.get_format_instructions())
| self.chat_llm
| response_parser
)
unparsed_chain = (
memory_runable
| self._get_system_prompt().partial(format_instructions="")
| self.chat_llm
| StrOutputParser()
)
chain = parsed_chain.with_fallbacks([unparsed_chain])
return chain
def generate(self) -> str:
"""
A function that generates a response by invoking a chain, adding the AI message to chat memory, and returning a question from the response.
"""
response = self.chain.invoke({})
if isinstance(response, BaseModel):
response = response.dict(by_alias=True)
response = response["Question"]
self.memory.chat_memory.add_ai_message(response)
return response
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