LangSmith Setup and Token Monitoring for Cost Optimization in AI App Development

🔧 LangSmith is a unified system for debugging, testing, evaluating, and monitoring LLMs.

⚙️ By connecting your application to LangSmith, you can gather information about token usage and response time from the LLM.

💡 With this information, you can optimize cost and performance by adjusting configurations and comparing runs.

🔧 The video demonstrates the setup process for LangSmith and monitoring tokens used by LLMs to optimize cost.

💻 To install the necessary packages, a requirements.txt file is created and the packages are installed using pip install r requirements.txt.

✅ After installation, the prompt can be cleared and langsmith can be verified by using pip freeze.

🔑 Setting up LangSmith by adding project name and API keys.

📝 Creating an API key for Langsmith.

💻 Adding app.py file with imports and environment variables.

🔧 Using Chat OpenAI to assign it to LLMS and running the setup with a simple static prompt.

💻 Executing python app dot Pi in the terminal to get the answer from Chat GPT.

🌍 Introducing LangSmith as a tool to provide more insight into the application and compare different runs.

📊 We can monitor the token usage of language models in LangSmith to optimize cost.

⚙️ By adjusting the visibility of columns, we can analyze the runs and compare token usage.

🔍 We can filter and tag runs to further analyze token usage and metadata.

🔍 Assigning ChatGPT with specific settings and input variables.

💡 Using prompt templates and LLN chains to optimize cost.

📊 Adjusting temperature and associated tags for better results.

💡 LangSmith helps optimize cost by monitoring tokens used by LLMs.

📝 Work life balance is assigned to the input variable and prompt template in LangSmith.

🔍 LangSmith provides a way to easily read answers from Chat GPT without scrolling.

🔧 LangSmith helps understand how long chain components work and develop AI apps.