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This notebook shows how to load chat messages from Twitter to fine-tune on. We do this by utilizing Apify. First, use Apify to export tweets. An example 
import json

from langchain_community.adapters.openai import convert_message_to_dict
from langchain.messages import AIMessage

with open("example_data/dataset_twitter-scraper_2023-08-23_22-13-19-740.json") as f:
    data = json.load(f)

# Filter out tweets that reference other tweets, because it's a bit weird
tweets = [d["full_text"] for d in data if "t.co" not in d["full_text"]]
# Create them as AI messages
messages = [AIMessage(content=t) for t in tweets]
# Add in a system message at the start
# TODO: we could try to extract the subject from the tweets, and put that in the system message.
system_message = {"role": "system", "content": "write a tweet"}
data = [[system_message, convert_message_to_dict(m)] for m in messages]
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