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Integrate Langfuse for Enhanced Monitoring and Analytics

Open kubbot opened this issue 7 months ago • 1 comments

Feature Request: Integrate Langfuse for Enhanced Monitoring and Analytics

Why Langfuse?

Langfuse is an open-source LLM engineering platform that provides comprehensive monitoring, tracing, and analytics capabilities. It is particularly well-suited for SurfSense because:

  1. Open-Source & Self-Hostable: Aligns with SurfSense's open-source philosophy and allows for self-hosting, ensuring privacy and control.
  2. Flexible Monitoring: Supports LLM tracing, prompt management, evaluation, and manual annotation, which are critical for SurfSense's AI-driven features like RAG, podcast generation, and chat interactions.
  3. Security & Compliance: Langfuse is SOC 2 Type II and ISO 27001 certified, GDPR compliant, and HIPAA-aligned, making it ideal for handling sensitive user data.
  4. Integration Ecosystem: Offers SDKs for Python and JS/TS, and integrates seamlessly with tools like Langchain, Llama-Index, and LiteLLM, which are already part of SurfSense's tech stack.
  5. Community & Adoption: Trusted by teams at Samsara, Twilio, and Khan Academy, indicating its reliability and scalability.

Proposed Implementation

  1. Backend Integration: Use the Langfuse Python SDK to instrument FastAPI endpoints for tracing LLM calls, embeddings, and reranking operations.
  2. Frontend Integration: Leverage the JS/TS SDK to monitor user interactions, search queries, and chat sessions in the Next.js frontend.
  3. Custom Metrics: Extend Langfuse's capabilities to track SurfSense-specific metrics like podcast generation latency, RAG accuracy, and user engagement.
  4. Self-Hosting: Deploy Langfuse alongside SurfSense's existing Docker setup for a seamless experience.

Benefits

  • Debugging: Easily trace and debug LLM interactions and RAG pipelines.
  • Optimization: Identify bottlenecks in podcast generation or search performance.
  • User Insights: Gain actionable insights into how users interact with saved content and external sources.

Next Steps

  • Discuss feasibility and scope.
  • Assign priority and timeline.
  • Collaborate on implementation details.

References:

kubbot avatar May 14 '25 07:05 kubbot

This shoudn't be that difficult to implement. Looking into it 👍

MODSetter avatar May 14 '25 23:05 MODSetter