Phoenix
Open-source LLM tracing and evaluation
Description
Phoenix is an open-source tool developed by the Arize AI team, designed for comprehensive LLM (Large Language Model) tracing and evaluation. It empowers developers to effectively evaluate, experiment with, and optimize their AI applications in real time. The platform is built upon OpenTelemetry (OTEL), ensuring a seamless setup process, complete transparency in operations, and freedom from vendor lock-in, which allows users to start, scale, or transition their projects without restrictions.
The tool provides robust capabilities for enhancing AI application development and maintenance. Users can leverage application tracing for total visibility into LLM app data, utilizing automatic instrumentation or opting for manual control. Phoenix features an interactive prompt playground equipped with an efficient evaluation library, pre-built templates customizable for any task, and the ability to incorporate human feedback. It also offers streamlined evaluations and annotations, alongside dataset clustering and visualization tools that use embeddings to identify semantically similar data points and isolate areas of poor performance, thus aiding in debugging and improving LLM workflows.
Key Features
- Open-source LLM Tracing and Evaluation: Enables comprehensive tracking and assessment of Large Language Model applications.
- OpenTelemetry (OTEL) Integration: Ensures seamless setup, full transparency, and no vendor lock-in for flexible scaling.
- Application Tracing for Total Visibility: Collects LLM app data with automatic or manual instrumentation for complete oversight.
- Interactive Prompt Playground: Provides an agile evaluation library with pre-built, customizable templates and human feedback options.
- Streamlined Evaluations and Annotations: Offers an ergonomic eval library for quick, customizable task evaluations, including human input.
- Dataset Clustering & Visualization: Uses embeddings to uncover semantic similarities and isolate performance issues in data.
- Flexible and Agnostic Architecture: Built on OpenTelemetry, supporting various vendors, frameworks, and languages.
- Extensive Integrations: Compatible with popular LLM tools like LlamaIndex, Langchain, OpenAI, Mistral, and more.
Use Cases
- Debugging and troubleshooting LLM-powered applications.
- Identifying and mitigating LLM hallucinations.
- Optimizing prompt performance and fine-tuning models.
- Understanding LLM decision-making processes.
- Isolating poor performance in RAG (Retrieval Augmented Generation) systems.
- Analyzing unstructured text data for insights on user inputs and LLM responses.
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