Examples

Explore the capabilities of Phoenix with notebooks

Tutorials

Application Examples

Example full-stack applications instrumented using OpenInference and observed via phoenix server instances.

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.

TitleTopicsLinks

Retrieval Example with Evaluations: Fast UI Viz

  • Evaluations

  • Retrieval

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

Tracing and Evaluation a DSPy 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.

TitleTopicsLinks

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.

TitleTopicsLinks

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.

TitleTopicsLinks

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.

TitleTopicsLinks

Detecting Fraud with Tabular Embeddings

  • tabular data

  • anomaly detection

Last updated