ComponentsChat loadersChat loaders📄️ DiscordThis notebook shows how to create your own chat loader that works on copy-pasted messages (from dms) to a list of LangChain messages.📄️ Facebook MessengerThis notebook shows how to load data from Facebook in a format you can finetune on. The overall steps are:📄️ GMailThis loader goes over how to load data from GMail. There are many ways you could want to load data from GMail. This loader is currently fairly opionated in how to do so. The way it does it is it first looks for all messages that you have sent. It then looks for messages where you are responding to a previous email. It then fetches that previous email, and creates a training example of that email, followed by your email.📄️ iMessageThis notebook shows how to use the iMessage chat loader. This class helps convert iMessage conversations to LangChain chat messages.📄️ Fine-Tuning on LangSmith Chat DatasetsThis notebook demonstrates an easy way to load a LangSmith chat dataset fine-tune a model on that data.📄️ Fine-Tuning on LangSmith LLM RunsThis notebook demonstrates how to directly load data from LangSmith's LLM runs and fine-tune a model on that data.📄️ SlackThis notebook shows how to use the Slack chat loader. This class helps map exported slack conversations to LangChain chat messages.📄️ TelegramThis notebook shows how to use the Telegram chat loader. This class helps map exported Telegram conversations to LangChain chat messages.📄️ Twitter (via Apify)This notebook shows how to load chat messages from Twitter to finetune on. We do this by utilizing Apify.📄️ WeChatThere is not yet a straightforward way to export personal WeChat messages. However if you just need no more than few hundrudes of messages for model fine-tuning or few-shot examples, this notebook shows how to create your own chat loader that works on copy-pasted WeChat messages to a list of LangChain messages.📄️ WhatsAppThis notebook shows how to use the WhatsApp chat loader. This class helps map exported Telegram conversations to LangChain chat messages.PreviousTrubricsNextDiscord