Phoenix
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Example Notebooks

Explore the capabilities of Phoenix with notebook tutorials for concrete use-cases

LLM Traces

Trace through the execution of your LLM application to understand its internal structure and to troubleshoot issues with retrieval, tool execution, LLM calls, and more.
Title
Topics
Links
Tracing and Evaluating a LlamaIndex + OpenAI RAG Application
  • LlamaIndex
  • OpenAI
  • retrieval-augmented generation
Tracing and Evaluating a LlamaIndex OpenAI Agent
  • LlamaIndex
  • OpenAI
  • agents
  • function calling
Tracing and Evaluating a Structured Data Extraction Application with OpenAI Function Calling
  • OpenAI
  • structured data extraction
  • function calling
Tracing and Evaluating a LangChain + OpenAI RAG Application
  • LangChain
  • OpenAI
  • retrieval-augmented generation
Tracing and Evaluating a LangChain Agent
  • LangChain
  • OpenAI
  • agents
  • function calling
Tracing and Evaluating a LangChain + Vertex AI RAG Application
  • LangChain
  • Vertex AI
  • retrieval-augmented generation
Tracing and Evaluating a LangChain + Google PaLM RAG Application
  • LangChain
  • Google PaLM
  • retrieval-augmented generation

LLM Evals

Leverage the power of large language models to evaluate your generative model or application for hallucinations, toxicity, relevance of retrieved documents, and more.
Title
Topics
Links
Evaluating Hallucinations
  • hallucinations
Evaluating Toxicity
  • toxicity
Evaluating Relevance of Retrieved Documents
  • document relevance
Evaluating Question-Answering
  • question-answering
Evaluating Summarization
  • summarization
Evaluating Code Readability
  • code readability

Retrieval-Augmented Generation Analysis

Visualize your generative application's retrieval process to surface failed retrievals and to find topics not addressed by your knowledge base.
Title
Topics
Links
Evaluating and Improving Search and Retrieval Applications
  • LlamaIndex
  • retrieval-augmented generation
Evaluating and Improving Search and Retrieval Applications
  • LlamaIndex
  • Milvus
  • retrieval-augmented generation
Evaluating and Improving Search and Retrieval Applications
  • LangChain
  • Pinecone
  • retrieval-augmented generation

Embedding Analysis

Explore lower-dimensional representations of your embedding data to identify clusters of high-drift and performance degradation.
Title
Topics
Links
Active Learning for a Drifting Image Classification Model
  • image classification
  • fine-tuning
Root-Cause Analysis for a Drifting Sentiment Classification Model
  • NLP
  • sentiment classification
Troubleshooting an LLM Summarization Task
  • summarization
Collect Chats with GPT
  • LLMs
Find Clusters, Export, and Explore with GPT
  • LLMs
  • exploratory data analysis

Structured Data Analysis

Statistically analyze your structured data to perform A/B analysis, temporal drift analysis, and more.
Title
Topics
Links
Detecting Fraud with Tabular Embeddings
  • tabular data
  • anomaly detection