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LangSmith Chat Datasets

This notebook demonstrates an easy way to load a LangSmith chat dataset fine-tune a model on that data. The process is simple and comprises 3 steps.

  1. Create the chat dataset.
  2. Use the LangSmithDatasetChatLoader to load examples.
  3. Fine-tune your model.

Then you can use the fine-tuned model in your LangChain app.

Before diving in, let’s install our prerequisites.

Prerequisites​

Ensure you’ve installed langchain >= 0.0.311 and have configured your environment with your LangSmith API key.

%pip install --upgrade --quiet  langchain langchain-openai
import os
import uuid

uid = uuid.uuid4().hex[:6]
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "YOUR API KEY"

1. Select a dataset​

This notebook fine-tunes a model directly on selecting which runs to fine-tune on. You will often curate these from traced runs. You can learn more about LangSmith datasets in the docs docs.

For the sake of this tutorial, we will upload an existing dataset here that you can use.

from langsmith.client import Client

client = Client()
import requests

url = "https://raw.githubusercontent.com/langchain-ai/langchain/master/docs/docs/integrations/chat_loaders/example_data/langsmith_chat_dataset.json"
response = requests.get(url)
response.raise_for_status()
data = response.json()
dataset_name = f"Extraction Fine-tuning Dataset {uid}"
ds = client.create_dataset(dataset_name=dataset_name, data_type="chat")
_ = client.create_examples(
    inputs=[e["inputs"] for e in data],
    outputs=[e["outputs"] for e in data],
    dataset_id=ds.id,
)

2. Prepare Data​

Now we can create an instance of LangSmithRunChatLoader and load the chat sessions using its lazy_load() method.

from langchain_community.chat_loaders.langsmith import LangSmithDatasetChatLoader

loader = LangSmithDatasetChatLoader(dataset_name=dataset_name)

chat_sessions = loader.lazy_load()

With the chat sessions loaded, convert them into a format suitable for fine-tuning.​

from langchain_community.adapters.openai import convert_messages_for_finetuning

training_data = convert_messages_for_finetuning(chat_sessions)

3. Fine-tune the Model​

Now, initiate the fine-tuning process using the OpenAI library.

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]... 429.55s. 46.34s

4. Use in LangChain​

After fine-tuning, use the resulting model ID with the ChatOpenAI model class in your LangChain app.

# 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:

model.invoke("There were three ravens sat on a tree.")
AIMessage(content='[{"s": "There were three ravens", "object": "tree", "relation": "sat on"}, {"s": "three ravens", "object": "a tree", "relation": "sat on"}]')

Now you have successfully fine-tuned a model using data from LangSmith LLM runs!

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