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import asyncio
import dotenv
import os
import pandas as pd
import sys
import typing as t
from dataclasses import dataclass, field
from datetime import datetime
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field
from ragas import SingleTurnSample
from ragas.llms.base import LangchainLLMWrapper
from ragas.metrics.base import MetricType
from ragas.metrics.base import MetricWithLLM, SingleTurnMetric
from ragas.prompt.pydantic_prompt import PydanticPrompt
from typing import List, Tuple
# Load environment variables from .env file
dotenv.load_dotenv()
# Access the OpenAI API key
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
class ObjectionInput(BaseModel):
user_input: str = Field(description="The objection text")
response: str = Field(default="", description="The response to the objection")
reference: str = Field(default="", description="Any reference related to the objection")
class ObjectionOutput(BaseModel):
satisfy: bool = Field(description="Boolean indicating if the objection was satisfied")
def process_salesbud_file(file_path: str) -> List[Tuple[ObjectionInput, ObjectionOutput]]:
"""
Process the salesbud CSV file and return a list of examples for ObjectionlPrompt.
Args:
file_path (str): The path to the salesbud CSV file.
Returns:
List[Tuple[ObjectionInput, ObjectionOutput]]: A list of tuples containing ObjectionInput and ObjectionOutput.
"""
# Print the timestamp and the file being processed
print(f"{datetime.now()}: Processing file: salesbud_examples.csv")
# Read the CSV file into a DataFrame
df = pd.read_csv('data/salesbud_examples.csv')
# List to hold the processed objections
examples = [] # List to hold examples
# Process each row in the DataFrame
for index, row in df.iterrows():
# Create an ObjectionInput instance for each row
objection_input = ObjectionInput(
user_input=row['objection'], # Assuming your CSV has a column named 'objection'
response=row.get('response', ""), # Use .get() to avoid KeyError if the column doesn't exist
reference=row.get('reference', "") # Use .get() to avoid KeyError if the column doesn't exist
)
# Create an ObjectionOutput instance (you can modify the logic for 'satisfy' as needed)
objection_output = ObjectionOutput(
satisfy= row['satisfy']
)
# Append the example tuple to the examples list
examples.append((objection_input, objection_output))
#print (examples[0])
return examples
class ObjectionlPrompt(PydanticPrompt[ObjectionInput, ObjectionOutput]):
instruction = "You are an expert technology sales rep that is tasked with judging if response satisfies potential customer's objection (user input). \
Given an user input and sales rep response, output True if the response satisfies the objection by the potential customer"
input_model = ObjectionInput
output_model = ObjectionOutput
examples = process_salesbud_file('salesbud_examples.csv')
@dataclass
class SatisfyRate(MetricWithLLM, SingleTurnMetric):
name: str = "satisfy_rate"
_required_columns: t.Dict[MetricType, t.Set[str]] = field(
default_factory=lambda: {MetricType.SINGLE_TURN: {"response", "reference"}}
)
objection_prompt: PydanticPrompt = ObjectionlPrompt()
async def _ascore(self, row):
pass
async def _single_turn_ascore(self, sample, callbacks):
prompt_input = ObjectionInput(
user_input=sample.user_input, response=sample.response
)
prompt_response = await self.objection_prompt.generate(
data=prompt_input, llm=self.llm
)
return int(prompt_response.satisfy)
async def generate_objection_score(question_answer):
print("generate_objection_scores()")
# user_response= pd.read_csv(file_path)
openai_model = LangchainLLMWrapper(ChatOpenAI(model_name="gpt-4o", api_key=OPENAI_API_KEY))
scorer = SatisfyRate(llm=openai_model)
sample = SingleTurnSample(user_input=question_answer['objection'], response=question_answer['answer'])
#(user_response['objection'][num], user_response['response'][num])
satisfy_0_1 = await scorer.single_turn_ascore(sample)
print(satisfy_0_1)
print (question_answer['objection'], question_answer['answer'], satisfy_0_1)
# Implement your logic to generate a response based on the user's input
return satisfy_0_1 #f"Response to your objection: {user_response['objection'][num]}, {user_response['response'][num]}, {satisfy_0_1}"
async def generate_response_to_objection(file_path, num):
from langchain_openai import ChatOpenAI
from ragas.llms.base import LangchainLLMWrapper
import pandas as pd
user_response= pd.read_csv(file_path)
openai_model = LangchainLLMWrapper(ChatOpenAI(model_name="gpt-4o", api_key=OPENAI_API_KEY))
scorer = SatisfyRate(llm=openai_model)
sample = SingleTurnSample(user_input=user_response['objection'][num], response=user_response['response'][num])
#(user_response['objection'][num], user_response['response'][num])
satisfy_0_1 = await scorer.single_turn_ascore(sample)
print (user_response['objection'][num], user_response['response'][num], satisfy_0_1)
# Implement your logic to generate a response based on the user's input
return satisfy_0_1 #f"Response to your objection: {user_response['objection'][num]}, {user_response['response'][num]}, {satisfy_0_1}"
async def main(file_path):
# Call the async function
#examples_file = process_salesbud_file()
response = await generate_response_to_objection(file_path, 0)
if __name__ == "__main__":
# Check if the file path is provided as a command-line argument
if len(sys.argv) != 2:
print("Usage: python objection_eval.py <path_to_salesbud.csv>")
sys.exit(1)
# Get the file path from the command-line argument
file_path = sys.argv[1]
# Run the main async function
asyncio.run(main(file_path)) |