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WhatsApp

本笔记本展示了如何使用WhatsApp聊天加载器。该类帮助将导出的WhatsApp对话映射到LangChain聊天消息。

该过程分为三个步骤：

  1. 将聊天对话导出到电脑
  2. 使用指向 JSON 文件或 JSON 文件目录的文件路径创建 WhatsAppChatLoader
  3. 调用 loader.load()（或 loader.lazy_load()）来执行转换。

1. 创建消息转储​

要导出您的WhatsApp对话，请完成以下步骤：

  1. 打开目标对话
  2. 点击右上角的三个点并选择“更多”。
  3. 然后选择“导出聊天记录”并选择“不包含媒体文件”。

每个对话的数据格式示例如下：

%%writefile whatsapp_chat.txt
[8/15/23, 9:12:33 AM] Dr. Feather: ‎Messages and calls are end-to-end encrypted. No one outside of this chat, not even WhatsApp, can read or listen to them.
[8/15/23, 9:12:43 AM] Dr. Feather: I spotted a rare Hyacinth Macaw yesterday in the Amazon Rainforest. Such a magnificent creature!
‎[8/15/23, 9:12:48 AM] Dr. Feather: ‎image omitted
[8/15/23, 9:13:15 AM] Jungle Jane: That's stunning! Were you able to observe its behavior?
‎[8/15/23, 9:13:23 AM] Dr. Feather: ‎image omitted
[8/15/23, 9:14:02 AM] Dr. Feather: Yes, it seemed quite social with other macaws. They're known for their playful nature.
[8/15/23, 9:14:15 AM] Jungle Jane: How's the research going on parrot communication?
‎[8/15/23, 9:14:30 AM] Dr. Feather: ‎image omitted
[8/15/23, 9:14:50 AM] Dr. Feather: It's progressing well. We're learning so much about how they use sound and color to communicate.
[8/15/23, 9:15:10 AM] Jungle Jane: That's fascinating! Can't wait to read your paper on it.
[8/15/23, 9:15:20 AM] Dr. Feather: Thank you! I'll send you a draft soon.
[8/15/23, 9:25:16 PM] Jungle Jane: Looking forward to it! Keep up the great work.
Writing whatsapp_chat.txt

2. 创建聊天加载器​

WhatsAppChatLoader 接受生成的 zip 文件、解压后的目录或其中任何聊天 .txt 文件的路径。

提供您想要在微调时承担“AI”角色的用户名。

from langchain_community.chat_loaders.whatsapp import WhatsAppChatLoader
API Reference:WhatsAppChatLoader
loader = WhatsAppChatLoader(
    path="./whatsapp_chat.txt",
)

3. 加载消息​

load()（或lazy_load）方法返回一个“ChatSessions”列表，该列表当前存储每个加载对话的消息列表。

from typing import List

from langchain_community.chat_loaders.utils import (
    map_ai_messages,
    merge_chat_runs,
)
from langchain_core.chat_sessions import ChatSession

raw_messages = loader.lazy_load()
# Merge consecutive messages from the same sender into a single message
merged_messages = merge_chat_runs(raw_messages)
# Convert messages from "Dr. Feather" to AI messages
messages: List[ChatSession] = list(
    map_ai_messages(merged_messages, sender="Dr. Feather")
)
[{'messages': [AIMessage(content='I spotted a rare Hyacinth Macaw yesterday in the Amazon Rainforest. Such a magnificent creature!', additional_kwargs={'sender': 'Dr. Feather', 'events': [{'message_time': '8/15/23, 9:12:43 AM'}]}, example=False),
   HumanMessage(content="That's stunning! Were you able to observe its behavior?", additional_kwargs={'sender': 'Jungle Jane', 'events': [{'message_time': '8/15/23, 9:13:15 AM'}]}, example=False),
   AIMessage(content="Yes, it seemed quite social with other macaws. They're known for their playful nature.", additional_kwargs={'sender': '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)]}]

下一步​

然后，您可以根据需要利用这些消息，例如微调模型、选择少量示例，或直接预测下一条消息。

from langchain_openai import ChatOpenAI

llm = ChatOpenAI()

for chunk in llm.stream(messages[0]["messages"]):
    print(chunk.content, end="", flush=True)
API Reference:ChatOpenAI
Thank you for the encouragement! I'll do my best to continue studying and sharing fascinating insights about parrot communication.

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