LLM Evaluations
Project description
arize-phoenix-evals
Phoenix provides tooling to evaluate LLM applications, including tools to determine the relevance or irrelevance of documents retrieved by retrieval-augmented generation (RAG) application, whether or not the response is toxic, and much more.
Phoenix's approach to LLM evals is notable for the following reasons:
- Includes pre-tested templates and convenience functions for a set of common Eval “tasks”
- Data science rigor applied to the testing of model and template combinations
- Designed to run as fast as possible on batches of data
- Includes benchmark datasets and tests for each eval function
Installation
Install the arize-phoenix sub-package via pip
pip install arize-phoenix-evals
Note you will also have to install the LLM vendor SDK you would like to use with LLM Evals. For example, to use OpenAI's GPT-4, you will need to install the OpenAI Python SDK:
pip install 'openai>=1.0.0'
Usage
Here is an example of running the RAG relevance eval on a dataset of Wikipedia questions and answers:
import os
from phoenix.evals import (
RAG_RELEVANCY_PROMPT_TEMPLATE,
RAG_RELEVANCY_PROMPT_RAILS_MAP,
OpenAIModel,
download_benchmark_dataset,
llm_classify,
)
from sklearn.metrics import precision_recall_fscore_support, confusion_matrix, ConfusionMatrixDisplay
os.environ["OPENAI_API_KEY"] = "<your-openai-key>"
# Download the benchmark golden dataset
df = download_benchmark_dataset(
task="binary-relevance-classification", dataset_name="wiki_qa-train"
)
# Sample and re-name the columns to match the template
df = df.sample(100)
df = df.rename(
columns={
"query_text": "input",
"document_text": "reference",
},
)
model = OpenAIModel(
model="gpt-4",
temperature=0.0,
)
rails =list(RAG_RELEVANCY_PROMPT_RAILS_MAP.values())
df[["eval_relevance"]] = llm_classify(df, model, RAG_RELEVANCY_PROMPT_TEMPLATE, rails)
#Golden dataset has True/False map to -> "irrelevant" / "relevant"
#we can then scikit compare to output of template - same format
y_true = df["relevant"].map({True: "relevant", False: "irrelevant"})
y_pred = df["eval_relevance"]
# Compute Per-Class Precision, Recall, F1 Score, Support
precision, recall, f1, support = precision_recall_fscore_support(y_true, y_pred)
To learn more about LLM Evals, see the LLM Evals documentation.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for arize_phoenix_evals-0.9.0.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 52b389584011fcc169382dbba1963cf405409934450fa5fae9d97ffb2715f6f6 |
|
MD5 | b61862ea7c870f5816da672e5f9d1f1a |
|
BLAKE2b-256 | f7eeb8c3b585359823f2288dcdd2f9a87069eb5e4adb1b18030a8307539dce99 |
Hashes for arize_phoenix_evals-0.9.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | de8a5dd34bf81299006dc2e73052a19556d4bb03d168cb7c5578aeceb684cd35 |
|
MD5 | 1255b5392562f8cf6d3ae39ed96c1456 |
|
BLAKE2b-256 | 782c9234db58906e90cc58dc2aa09532efef5c6a5db7205c859bae1750f25431 |