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