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import logikon

your_mlops_platforms = ["wandb", "langfuse", ...]


# Log scores and artifacts to wandb and langfuse
config = logikon.DebugConfig(
    report_to=your_mlops_platforms
)

# LLM generation
...

# Debug and score reasoning
score = logikon.score(config=config, prompt=prompt, completion=completion)

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

import logikon

# Configure logikon debugger
config = logikon.DebugConfig(report_to=your_mlops_platforms)
lgk_handler = logikon.CallbackHandler(config=config)

# Set up chain and register debugger
llm = OpenAI()
prompt = PromptTemplate(
    input_variables=["question"],
    template="{question} Reason carefully before submitting your choice.",
)
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[lgk_handler])

# Run chain
print(chain.run("Vim or Emacs?", callbacks=[lgk_handler]))

import logikon
import your_mlops_platform

# Retrieve chat between human user and AI assistant
chat_history = your_mlops_platform.get_logs()

# Debug and score reasoning
score = logikon.score(messages=chat_history)

# Log scores
your_mlops_platform.log(score)