LangSmith LLM 运行
本笔记本演示了如何直接从LangSmith的LLM运行中加载数据，并在该数据上微调模型。 该过程简单，包括3个步骤。
- 选择要训练的LLM运行。
- 使用 LangSmithRunChatLoader 将运行加载为聊天会话。
- 微调你的模型。
然后你可以在你的LangChain应用中使用微调后的模型。
在深入之前，让我们先安装我们的先决条件。
先决条件
确保你已经安装了 langchain >= 0.0.311 并且已经用你的 LangSmith API 密钥配置了你的环境。
%pip install --upgrade --quiet langchain langchain-openai
import os
import uuid
uid = uuid.uuid4().hex[:6]
project_name = f"Run Fine-tuning Walkthrough {uid}"
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "YOUR API KEY"
os.environ["LANGCHAIN_PROJECT"] = project_name
1. 选择运行
第一步是选择要进行微调的运行。常见的情况是选择在获得用户积极反馈的跟踪中的LLM运行。你可以在LangSmith Cookbook和文档中找到相关示例。
为了本教程的目的，我们将生成一些运行供您在此处使用。让我们尝试微调一个简单的函数调用链。
from enum import Enum
from pydantic import BaseModel, Field
class Operation(Enum):
add = "+"
subtract = "-"
multiply = "*"
divide = "/"
class Calculator(BaseModel):
"""A calculator function"""
num1: float
num2: float
operation: Operation = Field(..., description="+,-,*,/")
def calculate(self):
if self.operation == Operation.add:
return self.num1 + self.num2
elif self.operation == Operation.subtract:
return self.num1 - self.num2
elif self.operation == Operation.multiply:
return self.num1 * self.num2
elif self.operation == Operation.divide:
if self.num2 != 0:
return self.num1 / self.num2
else:
return "Cannot divide by zero"
from pprint import pprint
from langchain_core.utils.function_calling import convert_pydantic_to_openai_function
from pydantic import BaseModel
openai_function_def = convert_pydantic_to_openai_function(Calculator)
pprint(openai_function_def)
API Reference:convert_pydantic_to_openai_function
{'description': 'A calculator function',
'name': 'Calculator',
'parameters': {'description': 'A calculator function',
'properties': {'num1': {'title': 'Num1', 'type': 'number'},
'num2': {'title': 'Num2', 'type': 'number'},
'operation': {'allOf': [{'description': 'An '
'enumeration.',
'enum': ['+',
'-',
'*',
'/'],
'title': 'Operation'}],
'description': '+,-,*,/'}},
'required': ['num1', 'num2', 'operation'],
'title': 'Calculator',
'type': 'object'}}
from langchain_core.output_parsers.openai_functions import PydanticOutputFunctionsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are an accounting assistant."),
("user", "{input}"),
]
)
chain = (
prompt
| ChatOpenAI().bind(functions=[openai_function_def])
| PydanticOutputFunctionsParser(pydantic_schema=Calculator)
| (lambda x: x.calculate())
)
math_questions = [
"What's 45/9?",
"What's 81/9?",
"What's 72/8?",
"What's 56/7?",
"What's 36/6?",
"What's 64/8?",
"What's 12*6?",
"What's 8*8?",
"What's 10*10?",
"What's 11*11?",
"What's 13*13?",
"What's 45+30?",
"What's 72+28?",
"What's 56+44?",
"What's 63+37?",
"What's 70-35?",
"What's 60-30?",
"What's 50-25?",
"What's 40-20?",
"What's 30-15?",
]
results = chain.batch([{"input": q} for q in math_questions], return_exceptions=True)
加载没有出错的运行
现在我们可以选择成功的运行来进行微调。
from langsmith.client import Client
client = Client()
successful_traces = {
run.trace_id
for run in client.list_runs(
project_name=project_name,
execution_order=1,
error=False,
)
}
llm_runs = [
run
for run in client.list_runs(
project_name=project_name,
run_type="llm",
)
if run.trace_id in successful_traces
]
2. 准备数据
现在我们可以创建LangSmithRunChatLoader的实例，并使用其lazy_load()方法加载聊天会话。
from langchain_community.chat_loaders.langsmith import LangSmithRunChatLoader
loader = LangSmithRunChatLoader(runs=llm_runs)
chat_sessions = loader.lazy_load()
API Reference:LangSmithRunChatLoader
加载聊天会话后，将其转换为适合微调的格式。
from langchain_community.adapters.openai import convert_messages_for_finetuning
training_data = convert_messages_for_finetuning(chat_sessions)
API Reference:convert_messages_for_finetuning
3. 微调模型
现在，使用OpenAI库启动微调过程。
import json
import time
from io import BytesIO
import openai
my_file = BytesIO()
for dialog in training_data:
my_file.write((json.dumps({"messages": dialog}) + "\n").encode("utf-8"))
my_file.seek(0)
training_file = openai.files.create(file=my_file, purpose="fine-tune")
job = openai.fine_tuning.jobs.create(
training_file=training_file.id,
model="gpt-3.5-turbo",
)
# Wait for the fine-tuning to complete (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)
status = openai.fine_tuning.jobs.retrieve(job.id).status
# Now your model is fine-tuned!
Status=[running]... 349.84s. 17.72s
4. 在LangChain中使用
微调后，在您的LangChain应用程序中使用生成的模型ID与ChatOpenAI模型类。
# Get the fine-tuned model ID
job = openai.fine_tuning.jobs.retrieve(job.id)
model_id = job.fine_tuned_model
# Use the fine-tuned model in LangChain
from langchain_openai import ChatOpenAI
model = ChatOpenAI(
model=model_id,
temperature=1,
)
API Reference:ChatOpenAI
(prompt | model).invoke({"input": "What's 56/7?"})
AIMessage(content='Let me calculate that for you.')
现在你已经成功地使用来自LangSmith LLM运行的数据微调了一个模型！