Feedback

Harness user feedback, ai-assisted feedback, and other signals to improve, monitor, and personalize your applications:

• Streamlit Chat App: a minimal chat app that captures user feedback and shares traces of the chat application.
  • The vanilla_chain.py contains an LLMChain that powers the chat application.
  • The expression_chain.py contains an equivalent chat chain defined exclusively with LangChain expressions.
• Next.js Chat App: explore a simple TypeScript chat app demonstrating tracing and feedback capture.
  • You can check out a deployed demo version here.
• Building an Algorithmic Feedback Pipeline Automate feedback metrics for advanced monitoring and performance tuning.
• Real-time Automated Feedback: automatically generate feedback metrics for every run using an async callback. This lets you evaluate production runs in real-time.
• Real-time RAG Chat Bot Evaluation: This Streamlit walkthrough showcases an advanced application of the concepts from the Real-time Automated Feedback tutorial. It demonstrates how to automatically check for hallucinations in your RAG chat bot responses against the retrieved documents. For more information on RAG, check out the LangChain docs.
• LangChain Agents with LangSmith instrument a LangChain web-search agent with tracing and human feedback.