What is Langfuse
Langfuse: Observability and Analytics for LLM-Powered Applications
Langfuse is an open-source platform designed to provide comprehensive observability and product analytics for applications built on Large Language Models (LLMs). As organizations increasingly harness the power of Generative AI, Langfuse offers a suite of developer tools focused on visibility and insights, enabling product and engineering teams to optimize their LLM-based applications.
Key Features and Benefits:
Comprehensive Tracing and Control Flow Visibility:
- Capture the full context of complex LLM applications, including chained or agentic calls to foundation models.
- Model and framework-agnostic client SDKs and integrations.
- Track LLM inference, embedding retrieval, API usage, and interactions with internal systems.
- Automated instrumentation for popular frameworks like Langchain.
Quality Monitoring and Evaluation:
- Attach scores to production traces for measuring output quality.
- Support for model-based evaluations, user feedback, manual labeling, and implicit data signals.
- Monitor quality trends over time, by user segments, and across application versions.
User Intent Analysis:
- Classify and analyze varying user inputs and intents.
- Gain insights into real-world usage patterns and unexpected user behaviors.
Langfuse in action
Why Use Langfuse with Zilliz Cloud?
This Integration seamlessly combines Langfuse's observability capabilities with Zilliz Cloud's vector database solutions to help enhance retrieval augmented generation (RAG) workflows by monitoring embedding quality and relevance. You can also use this to optimize vector search performance and accuracy through detailed analytics.
By integrating Langfuse with Zilliz Cloud, developers can gain deep insights into their LLM applications' performance, quality, and user interactions. This powerful combination allows for continuous improvement of AI-driven features, ensuring that vector search and retrieval processes are finely tuned and aligned with user needs.
Leverage the synergy between Langfuse's observability tools and Zilliz Cloud's vector capabilities to build more robust, efficient, and user-centric LLM applications.
Learn
The best way to start is with a hands-on tutorial. This tutorial will walk you through how to enhance your Retrieval Augmented Generation solutions with Langfuse and Zilliz Cloud.