Connecting Flowise to LangSmith: Monitoring and Debugging for Improved Performance

🔗 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.