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Langtrace
Overview :
Langtrace is an open-source observability tool for collecting and analyzing traces and metrics to improve the performance of large language model (LLM) applications. It supports OpenTelemetry standard tracing, can be self-hosted to avoid vendor lock-in, and offers end-to-end observability. Langtrace enables users to gain comprehensive insights into the entire machine learning process, including RAG or fine-tuned models. Additionally, Langtrace supports the establishment of feedback loops by creating gold datasets from traced LLM interactions to continuously test and enhance AI applications.
Target Users :
Langtrace's target audience includes developers and data scientists who build and maintain LLM applications. It assists them in creating and deploying more reliable AI solutions by providing advanced security, simple non-intrusive setup, support for popular LLMs and frameworks, and built-in evaluation tools.
Use Cases
Utilize Langtrace to track and optimize the performance of AI chatbots.
Create datasets with Langtrace to continuously test and improve natural language processing models.
Leverage Langtrace's feedback loop feature to enhance the accuracy of machine translation applications.
Features
Trace requests to detect bottlenecks and optimize performance.
Annotate and manually evaluate LLM requests to create a gold dataset.
Run automated evaluations based on LLMs to track performance over time.
Compare the effectiveness of prompts across different models.
Monitor costs and latency at the project, model, and user levels.
How to Use
Visit the Langtrace official website and register for an account.
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Integrate the Langtrace SDK into your project using two lines of code according to the documentation.
Utilize Langtrace to trace application requests and identify and resolve performance bottlenecks.
Annotate and manually evaluate LLM requests to create a gold dataset.
Conduct automated evaluations to monitor changes in model performance over time.
Use the Playground feature to compare the effectiveness of different model prompts.
Track costs and latency at the project, model, and user levels.
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