tools.py

from langchain_astradb import AstraDBVectorStore
from langchain_huggingface import HuggingFaceEndpointEmbeddings
from langchain.tools.retriever import create_retriever_tool
from langchain_huggingface import HuggingFaceEmbeddings
import os
import pandas as pd
import requests
import yaml

HOLIDAY_KEYWORDS ={
    "christmas": ["christmas", "santa", "carol", "holiday"]}



class RetrievalTool:
    def __init__(self):
        # self.embeddings = HuggingFaceEndpointEmbeddings(
        # model= "sentence-transformers/all-MiniLM-L6-v2",
        # task="feature-extraction",
        # huggingfacehub_api_token= os.environ["HF_TOKEN"])
        
        self.embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    
        self.vector_store = AstraDBVectorStore(collection_name="program_astra",
                                           embedding=self.embeddings,
                                           api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
                                           token= os.environ["ASTRA_DB_APPLICATION_TOKEN"],
                                           namespace= os.environ["ASTRA_DB_NAMESPACE"])  
        
        self.calendarKey = os.environ["CALENDARIFIC_API_KEY"]
        self.config = yaml.safe_load(open("config.yaml")) 
    
    def get_HolidayInCalendar(self,scheduleDate:str):        
        schedule_date = pd.to_datetime(scheduleDate, errors='coerce')
        if schedule_date is pd.NaT:
            raise ValueError("Invalid date format. Please use YYYY-MM-DD.")
        schedule_date = schedule_date.date()
        
        year = schedule_date.year
        month = schedule_date.month
        country = "US"
        holidaylist = []

        endpoint_url = f"https://calendarific.com/api/v2/holidays?api_key={self.calendarKey}&country={country}&year={year}&month={month}"
        response = requests.get(endpoint_url)
        eventName = ""
        if response.status_code == 200:
            holidays = response.json()['response']['holidays']
            holidaylist = list(map(lambda x: x['description'],holidays))
            
            if any("Christmas" in sentence for sentence in holidaylist):
                eventName = "christmas"
            elif any("New year" in sentence for sentence in holidaylist):
                eventName = "new year"  
            return eventName  
        else:        
            return ""   
    
    #This is what will be coming from the front end to populate genre and schedule date for astra filtering    
    def buildDataToIncludeHolidayEvents(self, genre:str, scheduleDate:str):        
        if not genre or not scheduleDate:
            raise ValueError("Genre and schedule date are required.")
        genre_data = genre.strip().lower()  
        
        holidayEvent = self.get_HolidayInCalendar(scheduleDate)
        
        return genre_data, holidayEvent   
    
    
    def get_retrievers(self, user_genres: list, holiday_event: str = None):
        astraConfig = self.config["astra_db"]
        astra_filter_genre = {"genre": {"$in": user_genres}}

        if holiday_event:
            keywords = HOLIDAY_KEYWORDS.get(holiday_event.lower(), [])
        
            astra_filter_holiday = {
            "$or": [
                {"synopsis": {"$in": keywords}}
            ]}

            retriever_holiday = self.vector_store.as_retriever(search_kwargs={"filter": astra_filter_holiday, "k": astraConfig["holidaySearch"]["k"]})
            retriever_genre = self.vector_store.as_retriever(search_kwargs={"filter": astra_filter_genre, "k": astraConfig["genreSearchWithEvent"]["k"]})

            return retriever_genre,retriever_holiday

        else:        
            retriever = self.vector_store.as_retriever(search_kwargs={"filter": astra_filter_genre, "k": astraConfig["genreSearchWithoutEvent"]["k"]})
            return retriever, None  
    
    #This is the function to pull the relevant docs based on the genre and date
    def get_relevant_programmes(self, genre: str, scheduleDate: str)-> pd.DataFrame:
        """Retrieves relevant documents from the vector store based on the genre and date."""
        if not self.vector_store or not self.embeddings:
            raise ValueError("Vector store or embeddings not initialized.")  
        
        genre, holidayEvents = self.buildDataToIncludeHolidayEvents(genre, scheduleDate)
        
        retriever_genre, retriever_holiday = self.get_retrievers([genre], holidayEvents) 
        documents= []

        if retriever_holiday:
            documents.extend(retriever_holiday.invoke(holidayEvents))

        if retriever_genre:
            documents.extend(retriever_genre.invoke(f"{genre} genre based programs"))  
            
        if not documents:
            raise ValueError("No relevant documents found.")
        program_df = pd.DataFrame([doc.metadata for doc in documents])
        
        formatted_entries = []
        for _, row in program_df.iterrows():
            title = row['programme_title']
            duration = row['duration']
            rating = row['ratings']
            synopsis = " ".join(row['synopsis']) if isinstance(row['synopsis'], list) else str(row['synopsis'])
            genre = " ".join(row['genre']) if isinstance(row['genre'], list) else str(row['genre'])

            formatted_entries.append(
            f"programme_title: {title}, duration: {duration}, ratings: {rating}, synopsis: {synopsis}, genre: {genre}"
            )

        # Join all formatted strings with newline
        docs = "\n".join(formatted_entries)
        return docs, holidayEvents  

# if __name__ == "__main__":    
#     tool = RetrievalTool()  
#     tool2 = tool.get_relevant_documents("comedy", "2023-10-25")  
#     print(tool2)