🔧 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.
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