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from langchain_core.pydantic_v1 import BaseModel, Field
from typing import List
from typing import Literal
from langchain.prompts import ChatPromptTemplate
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
class IntentCategorizer(BaseModel):
"""Analyzing the user message input"""
environment: bool = Field(
description="Return 'True' if the question relates to climate change, the environment, nature, etc. (Example: should I eat fish?). Return 'False' if the question is just chit chat or not related to the environment or climate change.",
)
def make_chitchat_intent_categorization_chain(llm):
openai_functions = [convert_to_openai_function(IntentCategorizer)]
llm_with_functions = llm.bind(functions = openai_functions,function_call={"name":"IntentCategorizer"})
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant, you will analyze, translate and reformulate the user input message using the function provided"),
("user", "input: {input}")
])
chain = prompt | llm_with_functions | JsonOutputFunctionsParser()
return chain
def make_chitchat_intent_categorization_node(llm):
categorization_chain = make_chitchat_intent_categorization_chain(llm)
def categorize_message(state):
output = categorization_chain.invoke({"input": state["user_input"]})
print(f"\n\nChit chat output intent categorization: {output}\n")
state["search_graphs_chitchat"] = output["environment"]
print(f"\n\nChit chat output intent categorization: {state}\n")
return state
return categorize_message
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