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iMessage

本笔记本展示了如何使用 iMessage 聊天加载器。该类帮助将 iMessage 对话转换为 LangChain 聊天消息。

在 MacOS 上,iMessage 将对话存储在 ~/Library/Messages/chat.db 的 sqlite 数据库中(至少适用于 macOS Ventura 13.4)。 IMessageChatLoader 从此数据库文件中加载数据。

  1. 创建 IMessageChatLoader,并将文件路径指向您想要处理的 chat.db 数据库。
  2. 调用 loader.load()(或 loader.lazy_load())以执行转换。可选地使用 merge_chat_runs 来按顺序合并来自同一发送方的消息,和/或使用 map_ai_messages 将指定发送方的消息转换为 "AIMessage" 类。

1. 访问聊天数据库

您的终端可能无法访问 ~/Library/Messages。要使用此类,您可以将数据库复制到可访问的目录(例如,文档)并从那里加载。或者(不推荐),您可以在系统设置 > 安全性与隐私 > 完整磁盘访问中为您的终端模拟器授予完全磁盘访问权限。

我们创建了一个示例数据库,您可以在 此链接的驱动器文件 中使用。

# This uses some example data
import requests


def download_drive_file(url: str, output_path: str = "chat.db") -> 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.")


url = (
"https://drive.google.com/file/d/1NebNKqTA2NXApCmeH6mu0unJD2tANZzo/view?usp=sharing"
)

# Download file to chat.db
download_drive_file(url)
File chat.db downloaded.

2. 创建聊天加载器

提供加载器与 zip 目录的文件路径。您可以选择性地指定映射到 AI 消息的用户 ID,以及配置是否合并消息运行。

from langchain_community.chat_loaders.imessage import IMessageChatLoader
loader = IMessageChatLoader(
path="./chat.db",
)

3. 加载消息

load()(或 lazy_load)方法返回一个“ChatSessions”列表,该列表当前仅包含每个加载的对话的消息列表。所有消息最初都映射到“HumanMessage”对象。

您可以选择合并消息“运行”(来自同一发送者的连续消息),并选择一个发送者来代表“AI”。经过微调的 LLM 将学习生成这些 AI 消息。

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 "Tortoise" to AI messages. Do you have a guess who these conversations are between?
chat_sessions: List[ChatSession] = list(
map_ai_messages(merged_messages, sender="Tortoise")
)
# Now all of the Tortoise's messages will take the AI message class
# which maps to the 'assistant' role in OpenAI's training format
chat_sessions[0]["messages"][:3]
[AIMessage(content="Slow and steady, that's my motto.", additional_kwargs={'message_time': 1693182723, 'sender': 'Tortoise'}, example=False),
HumanMessage(content='Speed is key!', additional_kwargs={'message_time': 1693182753, 'sender': 'Hare'}, example=False),
AIMessage(content='A balanced approach is more reliable.', additional_kwargs={'message_time': 1693182783, 'sender': 'Tortoise'}, example=False)]

3. 准备微调

现在是将我们的聊天消息转换为 OpenAI 字典的时刻。我们可以使用 convert_messages_for_finetuning 工具来完成此操作。

from langchain_community.adapters.openai import convert_messages_for_finetuning
training_data = convert_messages_for_finetuning(chat_sessions)
print(f"Prepared {len(training_data)} dialogues for training")
Prepared 10 dialogues for training

4. 微调模型

现在是微调模型的时候了。确保您已安装 openai 并已正确设置 OPENAI_API_KEY

%pip install --upgrade --quiet  langchain-openai
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_data:
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-zHIgf4r8LltZG3RFpkGd4Sjf ready after 10.19 seconds.

文件准备好后,接下来是启动训练任务。

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

在您的模型准备期间,喝杯茶吧。这可能需要一些时间!

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]... 524.95s
print(job.fine_tuned_model)
ft:gpt-3.5-turbo-0613:personal::7sKoRdlz

5. 在 LangChain 中使用

您可以直接在 ChatOpenAI 模型类中使用生成的模型 ID。

from langchain_openai import ChatOpenAI

model = ChatOpenAI(
model=job.fine_tuned_model,
temperature=1,
)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
[
("system", "You are speaking to hare."),
("human", "{input}"),
]
)

chain = prompt | model | StrOutputParser()
for tok in chain.stream({"input": "What's the golden thread?"}):
print(tok, end="", flush=True)
A symbol of interconnectedness.

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