zenUnicorn

def movie_occurence(user_prompt, tags, metadata):
    
    comet_llm.log_prompt(
        prompt = user_prompt,
        tags = tags,
        metadata = metadata,
        output = user_prompt
    )
user_prompt = prompt_with_cost

zenUnicorn

# To get the actualy running cost
from yolopandas.utils.query_helpers import run_query_with_cost

prompt_with_cost = run_query_with_cost(movie_reviews, "Show how many times the movie tile 'The Gift' occurs in the dataset", yolo=True)

zenUnicorn

highest_reviews(user_prompt, Tags, Metadata)

zenUnicorn

def highest_reviews(user_prompt, tags, metadata):
    
    comet_llm.log_prompt(
        prompt = user_prompt,
        tags = tags,
        metadata = metadata,
        output = user_prompt
    )
    
user_prompt = highest_reviews_prompt

zenUnicorn

highest_reviews_prompt = movie_reviews.llm.query("for each movie, count the number of reviews and their average score. Show the 5 with the highest reviews,", yolo=True)

zenUnicorn

dataset = "MOVIES TITLE"

DATASET_NAME = "Movies Titles"
LLM_PROVIDER = "LangChain"

# import the dataset
movie_reviews = pd.read_csv('titles.csv')
movie_reviews

zenUnicorn

# Log a full prompt and response

comet_llm.log_prompt(
    prompt="What is this conversation about?",
    metadata= {
        "usage.prompt_tokens": 7,
        "usage.completion_tokens": 5,
        "usage.total_tokens": 12,
        "output.index": 0,
        "output.logprobs": None,

zenUnicorn

# initialize comet LLM
COMET_API_KEY = "*******"
PROJECT = "yolopandas"
PROJECT_WORKSPACE = "zenunicorn"

comet_llm.init(project= PROJECT, 
              api_key = COMET_API_KEY,
              workspace= PROJECT_WORKSPACE)

zenUnicorn

# Add your openAi Key 
import os
os.environ['OPENAI_API_KEY'] = '*******'

zenUnicorn

# import Yolopandas
from yolopandas import pd

# import CometLLM
import comet_llm