Setup LangSmith
When developing state-of-the-art AI applications, it’s essential to fully understand and utilize every tool at your disposal. Among these, LangSmith stands out as a go-to platform for debugging, testing, evaluating, and monitoring your LLMs.
By offering deep insights into token usage, execution times, and other pivotal metrics, LangSmith plays a critical role in enhancing both performance and cost-efficiency.
For those familiar with our video tutorial on the business24_ai YouTube channel titled “Setup LangSmith and Monitor Tokens used by LLMs to Optimize Cost”, this article serves as a textual companion. It provides a summary to the video content.
Summary of the Transcript:
1. Introduction to LangSmith: A unified system that aids in debugging, testing, evaluating, and monitoring LLMs, providing detailed insights about code execution, such as token usage and response times.
2. Setting Up the Environment: Step-by-step instructions on creating a dedicated project directory, launching Visual Studio Code, and establishing a virtual environment.
3. Package Management: A walkthrough of creating a ‘requirements.TXT’ file, installing necessary packages, and ensuring LangSmith’s successful installation.
4. Environment Variables & API Keys: Guidance on how to set up `.env` files for storing project-specific information and obtaining API keys from OpenAI and LangSmith.
5. Creating the App: Building a basic `app.py` file to interact with ChatGPT, adjust configurations like temperature, and monitor token usage through LangSmith’s dashboard.
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
# Load env variables
load_dotenv()
llm = ChatOpenAI(temperature=0, tags=["tag-source-app-py"])
print(llm.predict("Why do we finish a setup with hello, world!"))6. Interactive Development with Jupyter Notebook: Introduction to creating and managing Jupyter notebooks, adjusting LLM configurations, and running multiple test scenarios.
7. Wrapping Up: A conclusive note on the advantages of using LangSmith, especially when understanding the workings of LangChain components during AI app development.
