'ç”·æœ‹å�‹', 'events': [{'message_time': '2023/09/16 2:51 PM'}]}, example=False),       HumanMessage(content='å¿™ä»€ä¹ˆå‘¢', additional_kwargs={'sender': 'å¥³æœ‹å�‹', 'events': [{'message_time': '2023/09/16 3:06 PM'}]}, example=False),       AIMessage(content='ä»Šå¤©å�ªå¹²æˆ�äº†ä¸€ä»¶åƒ�æ ·çš„äº‹\né‚£å°±æ˜¯æƒ³ä½ ', additional_kwargs={'sender': 'ç”·æœ‹å�‹', 'events': [{'message_time': '2023/09/16 3:06 PM'}]}, example=False)]}]Next Stepsâ€‹You 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)PreviousTwitter (via Apify)NextWhatsApp1. Create message dump2. Define chat loader2. Create loader3. Load MessagesNext StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.
There is not yet a straightforward way to export personal WeChat messages. However if you just need no more than few hundreds 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.
There is not yet a straightforward way to export personal WeChat messages. However if you just need no more than few hundreds 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. ->: 'ç”·æœ‹å�‹', 'events': [{'message_time': '2023/09/16 2:51 PM'}]}, example=False),       HumanMessage(content='å¿™ä»€ä¹ˆå‘¢', additional_kwargs={'sender': 'å¥³æœ‹å�‹', 'events': [{'message_time': '2023/09/16 3:06 PM'}]}, example=False),       AIMessage(content='ä»Šå¤©å�ªå¹²æˆ�äº†ä¸€ä»¶åƒ�æ ·çš„äº‹\né‚£å°±æ˜¯æƒ³ä½ ', additional_kwargs={'sender': 'ç”·æœ‹å�‹', 'events': [{'message_time': '2023/09/16 3:06 PM'}]}, example=False)]}]Next Stepsâ€‹You 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)PreviousTwitter (via Apify)NextWhatsApp1. Create message dump2. Define chat loader2. Create loader3. Load MessagesNext StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.