Facebook Messenger

This notebook shows how to load data from Facebook in a format you can fine-tune on. The overall steps are:

1. Download your messenger data to disk.
2. Create the Chat Loader and call loader.load() (or loader.lazy_load()) to perform the conversion.
3. Optionally use merge_chat_runs to combine message from the same sender in sequence, and/or map_ai_messages to convert messages from the specified sender to the “AIMessage” class. Once you’ve done this, call convert_messages_for_finetuning to prepare your data for fine-tuning.

Once this has been done, you can fine-tune your model. To do so you would complete the following steps:

1. Upload your messages to OpenAI and run a fine-tuning job.
2. Use the resulting model in your LangChain app!

Let’s begin.

1. Download Data

To download your own messenger data, following instructions here. IMPORTANT - make sure to download them in JSON format (not HTML).

We are hosting an example dump at this google drive link that we will use in this walkthrough.

# This uses some example data
import zipfile

import requests


def download_and_unzip(url: str, output_path: str = "file.zip") -> None:
    file_id = url.split("/")[-2]
    download_url = f"https://drive.google.com/uc?export=download&id={file_id}"

    response = requests.get(download_url)
    if response.status_code != 200:
        print("Failed to download the file.")
        return

    with open(output_path, "wb") as file:
        file.write(response.content)
        print(f"File {output_path} downloaded.")

    with zipfile.ZipFile(output_path, "r") as zip_ref:
        zip_ref.extractall()
        print(f"File {output_path} has been unzipped.")


# URL of the file to download
url = (
    "https://drive.google.com/file/d/1rh1s1o2i7B-Sk1v9o8KNgivLVGwJ-osV/view?usp=sharing"
)

# Download and unzip
download_and_unzip(url)

File file.zip downloaded.
File file.zip has been unzipped.

2. Create Chat Loader

We have 2 different FacebookMessengerChatLoader classes, one for an entire directory of chats, and one to load individual files. We

directory_path = "./hogwarts"

from langchain.chat_loaders.facebook_messenger import (
    FolderFacebookMessengerChatLoader,
    SingleFileFacebookMessengerChatLoader,
)

loader = SingleFileFacebookMessengerChatLoader(
    path="./hogwarts/inbox/HermioneGranger/messages_Hermione_Granger.json",
)

chat_session = loader.load()[0]
chat_session["messages"][:3]

[HumanMessage(content="Hi Hermione! How's your summer going so far?", additional_kwargs={'sender': 'Harry Potter'}),
 HumanMessage(content="Harry! Lovely to hear from you. My summer is going well, though I do miss everyone. I'm spending most of my time going through my books and researching fascinating new topics. How about you?", additional_kwargs={'sender': 'Hermione Granger'}),
 HumanMessage(content="I miss you all too. The Dursleys are being their usual unpleasant selves but I'm getting by. At least I can practice some spells in my room without them knowing. Let me know if you find anything good in your researching!", additional_kwargs={'sender': 'Harry Potter'})]

loader = FolderFacebookMessengerChatLoader(
    path="./hogwarts",
)

chat_sessions = loader.load()
len(chat_sessions)

9

3. Prepare for fine-tuning

Calling load() returns all the chat messages we could extract as human messages. When conversing with chat bots, conversations typically follow a more strict alternating dialogue pattern relative to real conversations.

You can choose to merge message “runs” (consecutive messages from the same sender) and select a sender to represent the “AI”. The fine-tuned LLM will learn to generate these AI messages.

from langchain.chat_loaders.utils import (
    map_ai_messages,
    merge_chat_runs,
)

merged_sessions = merge_chat_runs(chat_sessions)
alternating_sessions = list(map_ai_messages(merged_sessions, "Harry Potter"))

# Now all of Harry Potter's messages will take the AI message class
# which maps to the 'assistant' role in OpenAI's training format
alternating_sessions[0]["messages"][:3]

[AIMessage(content="Professor Snape, I was hoping I could speak with you for a moment about something that's been concerning me lately.", additional_kwargs={'sender': 'Harry Potter'}),
 HumanMessage(content="What is it, Potter? I'm quite busy at the moment.", additional_kwargs={'sender': 'Severus Snape'}),
 AIMessage(content="I apologize for the interruption, sir. I'll be brief. I've noticed some strange activity around the school grounds at night. I saw a cloaked figure lurking near the Forbidden Forest last night. I'm worried someone may be plotting something sinister.", additional_kwargs={'sender': 'Harry Potter'})]

Now we can convert to OpenAI format dictionaries

from langchain.adapters.openai import convert_messages_for_finetuning

training_data = convert_messages_for_finetuning(alternating_sessions)
print(f"Prepared {len(training_data)} dialogues for training")

Prepared 9 dialogues for training

training_data[0][:3]

[{'role': 'assistant',
  'content': "Professor Snape, I was hoping I could speak with you for a moment about something that's been concerning me lately."},
 {'role': 'user',
  'content': "What is it, Potter? I'm quite busy at the moment."},
 {'role': 'assistant',
  'content': "I apologize for the interruption, sir. I'll be brief. I've noticed some strange activity around the school grounds at night. I saw a cloaked figure lurking near the Forbidden Forest last night. I'm worried someone may be plotting something sinister."}]

OpenAI currently requires at least 10 training examples for a fine-tuning job, though they recommend between 50-100 for most tasks. Since we only have 9 chat sessions, we can subdivide them (optionally with some overlap) so that each training example is comprised of a portion of a whole conversation.

Facebook chat sessions (1 per person) often span multiple days and conversations, so the long-range dependencies may not be that important to model anyhow.

# Our chat is alternating, we will make each datapoint a group of 8 messages,
# with 2 messages overlapping
chunk_size = 8
overlap = 2

training_examples = [
    conversation_messages[i : i + chunk_size]
    for conversation_messages in training_data
    for i in range(0, len(conversation_messages) - chunk_size + 1, chunk_size - overlap)
]

len(training_examples)

100

4. Fine-tune the model

It’s time to fine-tune the model. Make sure you have openai installed and have set your OPENAI_API_KEY appropriately

# %pip install -U openai --quiet

import json
import time
from io import BytesIO

import openai

# We will write the jsonl file in memory
my_file = BytesIO()
for m in training_examples:
    my_file.write((json.dumps({"messages": m}) + "\n").encode("utf-8"))

my_file.seek(0)
training_file = openai.files.create(file=my_file, purpose="fine-tune")

# OpenAI audits each training file for compliance reasons.
# This make take a few minutes
status = openai.files.retrieve(training_file.id).status
start_time = time.time()
while status != "processed":
    print(f"Status=[{status}]... {time.time() - start_time:.2f}s", end="\r", flush=True)
    time.sleep(5)
    status = openai.files.retrieve(training_file.id).status
print(f"File {training_file.id} ready after {time.time() - start_time:.2f} seconds.")

File file-ULumAXLEFw3vB6bb9uy6DNVC ready after 0.00 seconds.

With the file ready, it’s time to kick off a training job.

job = openai.fine_tuning.jobs.create(
    training_file=training_file.id,
    model="gpt-3.5-turbo",
)

Grab a cup of tea while your model is being prepared. This may take some time!

status = openai.fine_tuning.jobs.retrieve(job.id).status
start_time = time.time()
while status != "succeeded":
    print(f"Status=[{status}]... {time.time() - start_time:.2f}s", end="\r", flush=True)
    time.sleep(5)
    job = openai.fine_tuning.jobs.retrieve(job.id)
    status = job.status

Status=[running]... 874.29s. 56.93s

print(job.fine_tuned_model)

ft:gpt-3.5-turbo-0613:personal::8QnAzWMr

5. Use in LangChain

You can use the resulting model ID directly the ChatOpenAI model class.

from langchain.chat_models import ChatOpenAI

model = ChatOpenAI(
    model=job.fine_tuned_model,
    temperature=1,
)

from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_messages(
    [
        ("human", "{input}"),
    ]
)

chain = prompt | model | StrOutputParser()

for tok in chain.stream({"input": "What classes are you taking?"}):
    print(tok, end="", flush=True)

I'm taking Charms, Defense Against the Dark Arts, Herbology, Potions, Transfiguration, and Ancient Runes. How about you?