π Flowise and LangSmith can be easily connected to monitor and debug flowwise chart flows.
π¬ LangSmith allows tracing conversations and provides useful information about the runs, such as the number of tokens used and latency.
π οΈ Connecting Flowise to LangSmith is crucial for testing professional AI apps before deployment.
π Clone the repo to the folder 'LangSmith/Flowise' in the project folder
π₯οΈ Start Visual Studio Code from inside the directory and navigate to the 'packages/server' folder
π Connect Flowise to LangSmith by uncommenting four lines and providing a project name and API
β¨ Navigate to smith.langchin.com and create an API key
βοΈ Paste the API key into the code in Visual Studio code
π¦ Install the latest packages and build Flowise using yarn
π Using Flowise to create chat flows
π Setting up an open AI key in Flowise
π§ͺ Testing the created chat flow
π€ The video discusses the concept of AGI (Artificial General Intelligence).
π‘ Before using LangSmith, the debug information had to be obtained from the terminal, but now it can be accessed easily on the LangSmith dashboard.
π The video demonstrates the ease of integrating LangSmith into a Python project.
π The Flowise project can be configured in the dot end file to access information about runs, components, messages, prompts, and contents.
π LangSmith provides detailed information about flows, including meta information like the library used and other useful configurations.
π‘ Flowise has many features that were only briefly discussed in the video.
π‘ Flowise is used in conjunction with LangSmith for tracing.
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