Phoenix - Freely Monitor your AI Application Locally
AI Summary
Summary: Arise Phoenix - AI Tracing and Evaluation Tool
- Introduction to Arise Phoenix:
- Open-source tool for tracing and evaluating generative AI applications.
- Features easy setup, prompt iteration, and data clustering.
- Runs locally, tracing calls from OpenAI, Lang chain, and Llama index.
- Capabilities:
- Allows tracing of AI application components: LLM chain tool, agent embedding, Retriever, and Ranker.
- Monitors backend processes: converting user queries to embeddings, data filtering, prompt optimization, and response generation.
- Helps identify and fix points of failure in AI applications.
- Installation and Usage:
- Install with
pip install arise_phoenix.- Integrate with OpenAI, Lang chain, and Llama index applications for tracing.
- Start a local Phoenix server using a simple Python script.
- OpenAI Integration:
- Export OpenAI API key and integrate GPT-4 models into Python applications.
- Modify OpenAI SDK with
instrumentfunction for tracing.- Example provided for creating a conversation and receiving a meal plan response.
- Lang Chain Integration:
- Similar to OpenAI, uses
instrumentfunction for tracing.- Example application retrieves data, splits text, embeds, and saves to a database.
- Processes user questions and provides answers with full traceability.
- Llama Index Integration:
- Uses
instrumentfunction for tracing, similar to previous integrations.- Indexes data and answers questions with traceable steps.
- Additional Features:
- Evaluation for hallucinations and metric plotting.
- Analysis of retrieved data, relevance, summarization, code generation, and toxicity.
- Monitoring of Retrieve and Generate (RAG) applications.
- Conclusion:
- The tool offers comprehensive tracing and evaluation for AI applications.
- Future videos will explore more features.
- Encourages viewers to like, share, subscribe for more AI-related content.
For more detailed instructions and examples, refer to the video tutorial.