<h1 align="center">RagMetrics</h1>
<p align="center">
Monitor your LLM calls. Test your LLM app.
</p>
<h4 align="center">
<a href="https://pypi.org/project/ragmetrics-client/" target="_blank">
<img src="https://img.shields.io/pypi/v/ragmetrics-client.svg" alt="PyPI Version">
</a>
</h4>
[RagMetrics](https://ragmetrics.ai/) offers:
- The best LLM judge on the market (>95% human agreement)
- A/B testing for your entire LLM pipeline
- Evaluations for retrievals, not just generation
With this package, you can log your LLM calls to RagMetrics and use them as labeled data
# Quickstart
```shell
pip install ragmetrics-client
```
```python
import ragmetrics
from openai import OpenAI
## login to ragmetrics account via portal key
ragmetrics.login(key="your_ragmetrics_key")
openai_client = OpenAI()
## Monitor OpenAI, LangChain or LiteLLM clients
ragmetrics.monitor(openai_client)
## Then use as usual. All calls will be logged
resp = openai_client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": f"What is the capital of Spain?"}]
)
```
## Get your key and read docs at [RagMetrics.ai](https://ragmetrics.ai/)
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