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WhatsApp

This notebook shows how to use the WhatsApp chat loader. This class helps map exported WhatsApp conversations to LangChain chat messages.

The process has three steps: 1. Export the chat conversations to computer 2. Create the WhatsAppChatLoader with the file path pointed to the json file or directory of JSON files 3. Call loader.load() (or loader.lazy_load()) to perform the conversion.

1. Create message dump​

To make the export of your WhatsApp conversation(s), complete the following steps:

  1. Open the target conversation
  2. Click the three dots in the top right corner and select “More”.
  3. Then select “Export chat” and choose “Without media”.

An example of the data format for each conversation is below:

%%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. Create the Chat Loader​

The WhatsAppChatLoader accepts the resulting zip file, unzipped directory, or the path to any of the chat .txt files therein.

Provide that as well as the user name you want to take on the role of “AI” when fine-tuning.

from langchain_community.chat_loaders.whatsapp import WhatsAppChatLoader

API Reference:

loader = WhatsAppChatLoader(
    path="./whatsapp_chat.txt",
)

3. Load messages​

The load() (or lazy_load) methods return a list of “ChatSessions” that currently store the list of messages per loaded conversation.

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)]}]

Next Steps​

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_openai import ChatOpenAI

llm = ChatOpenAI()

for chunk in llm.stream(messages[0]["messages"]):
    print(chunk.content, end="", flush=True)

API Reference:

Thank you for the encouragement! I'll do my best to continue studying and sharing fascinating insights about parrot communication.

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