๐ง LangSmith is a unified system for debugging, testing, evaluating, and monitoring LLMs.
โ๏ธ By connecting your application to LangSmith, you can gather information about token usage and response time from the LLM.
๐ก With this information, you can optimize cost and performance by adjusting configurations and comparing runs.
๐ง The video demonstrates the setup process for LangSmith and monitoring tokens used by LLMs to optimize cost.
๐ป To install the necessary packages, a requirements.txt file is created and the packages are installed using pip install r requirements.txt.
โ After installation, the prompt can be cleared and langsmith can be verified by using pip freeze.
๐ Setting up LangSmith by adding project name and API keys.
๐ Creating an API key for Langsmith.
๐ป Adding app.py file with imports and environment variables.
๐ง Using Chat OpenAI to assign it to LLMS and running the setup with a simple static prompt.
๐ป Executing python app dot Pi in the terminal to get the answer from Chat GPT.
๐ Introducing LangSmith as a tool to provide more insight into the application and compare different runs.
๐ We can monitor the token usage of language models in LangSmith to optimize cost.
โ๏ธ By adjusting the visibility of columns, we can analyze the runs and compare token usage.
๐ We can filter and tag runs to further analyze token usage and metadata.
๐ Assigning ChatGPT with specific settings and input variables.
๐ก Using prompt templates and LLN chains to optimize cost.
๐ Adjusting temperature and associated tags for better results.
๐ก LangSmith helps optimize cost by monitoring tokens used by LLMs.
๐ Work life balance is assigned to the input variable and prompt template in LangSmith.
๐ LangSmith provides a way to easily read answers from Chat GPT without scrolling.
๐ง LangSmith helps understand how long chain components work and develop AI apps.
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