'Dr. Feather', 'events': [{'message_time': '8/15/23, 9:14:02 AM'}]}, example=False),       HumanMessage(content="How's the research going on parrot communication?", additional_kwargs={'sender': 'Jungle Jane', 'events': [{'message_time': '8/15/23, 9:14:15 AM'}]}, example=False),       AIMessage(content="It's progressing well. We're learning so much about how they use sound and color to communicate.", additional_kwargs={'sender': 'Dr. Feather', 'events': [{'message_time': '8/15/23, 9:14:50 AM'}]}, example=False),       HumanMessage(content="That's fascinating! Can't wait to read your paper on it.", additional_kwargs={'sender': 'Jungle Jane', 'events': [{'message_time': '8/15/23, 9:15:10 AM'}]}, example=False),       AIMessage(content="Thank you! I'll send you a draft soon.", additional_kwargs={'sender': 'Dr. Feather', 'events': [{'message_time': '8/15/23, 9:15:20 AM'}]}, example=False),       HumanMessage(content='Looking forward to it! Keep up the great work.', additional_kwargs={'sender': 'Jungle Jane', 'events': [{'message_time': '8/15/23, 9:25:16 PM'}]}, example=False)]}]Next Steps‚ÄãYou can then use these messages how you see fit, such as fine-tuning a model, few-shot example selection, or directly make predictions for the next message.from langchain.chat_models import ChatOpenAIllm = ChatOpenAI()for chunk in llm.stream(messages[0]['messages']):    print(chunk.content, end="", flush=True)    Thank you for the encouragement! I'll do my best to continue studying and sharing fascinating insights about parrot communication.PreviousWeChat1. Create message dump2. Create the Chat Loader3. Load messagesNext StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
This notebook shows how to use the WhatsApp chat loader. This class helps map exported Telegram conversations to LangChain chat messages.
This notebook shows how to use the WhatsApp chat loader. This class helps map exported Telegram conversations to LangChain chat messages. ->: 'Dr. Feather', 'events': [{'message_time': '8/15/23, 9:14:02 AM'}]}, example=False),       HumanMessage(content="How's the research going on parrot communication?", additional_kwargs={'sender': 'Jungle Jane', 'events': [{'message_time': '8/15/23, 9:14:15 AM'}]}, example=False),       AIMessage(content="It's progressing well. We're learning so much about how they use sound and color to communicate.", additional_kwargs={'sender': 'Dr. Feather', 'events': [{'message_time': '8/15/23, 9:14:50 AM'}]}, example=False),       HumanMessage(content="That's fascinating! Can't wait to read your paper on it.", additional_kwargs={'sender': 'Jungle Jane', 'events': [{'message_time': '8/15/23, 9:15:10 AM'}]}, example=False),       AIMessage(content="Thank you! I'll send you a draft soon.", additional_kwargs={'sender': 'Dr. Feather', 'events': [{'message_time': '8/15/23, 9:15:20 AM'}]}, example=False),       HumanMessage(content='Looking forward to it! Keep up the great work.', additional_kwargs={'sender': 'Jungle Jane', 'events': [{'message_time': '8/15/23, 9:25:16 PM'}]}, example=False)]}]Next Steps‚ÄãYou can then use these messages how you see fit, such as fine-tuning a model, few-shot example selection, or directly make predictions for the next message.from langchain.chat_models import ChatOpenAIllm = ChatOpenAI()for chunk in llm.stream(messages[0]['messages']):    print(chunk.content, end="", flush=True)    Thank you for the encouragement! I'll do my best to continue studying and sharing fascinating insights about parrot communication.PreviousWeChat1. Create message dump2. Create the Chat Loader3. Load messagesNext StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.