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https://github.com/explodinggradients/ragas?tab=readme-ov-file
Supercharge Your LLM Application Evaluations 🚀
https://github.com/explodinggradients/ragas?tab=readme-ov-file
evaluation llm llmops
Last synced: 7 days ago
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Supercharge Your LLM Application Evaluations 🚀
- Host: GitHub
- URL: https://github.com/explodinggradients/ragas?tab=readme-ov-file
- Owner: explodinggradients
- License: apache-2.0
- Created: 2023-05-08T17:48:04.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-29T20:23:45.000Z (14 days ago)
- Last Synced: 2024-10-29T22:47:04.674Z (14 days ago)
- Topics: evaluation, llm, llmops
- Language: Python
- Homepage: https://docs.ragas.io
- Size: 6.6 MB
- Stars: 7,050
- Watchers: 36
- Forks: 714
- Open Issues: 293
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-LLM-RAG-Application - ragas
README
Supercharge Your LLM Application Evaluations 🚀
Documentation |
Quick start |
Join Discord
Objective metrics, intelligent test generation, and data-driven insights for LLM apps
Ragas is your ultimate toolkit for evaluating and optimizing Large Language Model (LLM) applications. Say goodbye to time-consuming, subjective assessments and hello to data-driven, efficient evaluation workflows.
Don't have a test dataset ready? We also do production-aligned test set generation.
## Key Features
- 🎯 Objective Metrics: Evaluate your LLM applications with precision using both LLM-based and traditional metrics.
- 🧪 Test Data Generation: Automatically create comprehensive test datasets covering a wide range of scenarios.
- 🔗 Seamless Integrations: Works flawlessly with popular LLM frameworks like LangChain and major observability tools.
- 📊 Build feedback loops: Leverage production data to continually improve your LLM applications.
## :shield: Installation
Pypi:
```bash
pip install ragas
```
Alternatively, from source:
```bash
pip install git+https://github.com/explodinggradients/ragas
```
## :fire: Quickstart
### Evaluate your RAG with Ragas metrics
This is 4 main lines:
```python
from ragas.metrics import LLMContextRecall, Faithfulness, FactualCorrectness
from langchain_openai.chat_models import ChatOpenAI
from ragas.llms import LangchainLLMWrapper
evaluator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
metrics = [LLMContextRecall(), FactualCorrectness(), Faithfulness()]
results = evaluate(dataset=eval_dataset, metrics=metrics, llm=evaluator_llm)
```
Find the complete RAG Evaluation Quickstart here: [https://docs.ragas.io/en/latest/getstarted/rag_evaluation/](https://docs.ragas.io/en/latest/getstarted/rag_evaluation/)
🖱️Click to see preview of RESULTS
| user_input | retrieved_contexts | response | reference | context_recall | factual_correctness | faithfulness |
|------------|---------------------|----------|-----------|-----------------|---------------------|---------------|
| What are the global implications of the USA Supreme Court ruling on abortion? | "- In 2022, the USA Supreme Court ... - The ruling has created a chilling effect ..." | The global implications ... Here are some potential implications: | The global implications ... Additionally, the ruling has had an impact beyond national borders ... | 1 | 0.47 | 0.516129 |
| Which companies are the main contributors to GHG emissions ... ? | "- Fossil fuel companies ... - Between 2010 and 2020, human mortality ..." | According to the Carbon Majors database ... Here are the top contributors: | According to the Carbon Majors database ... Additionally, between 2010 and 2020, human mortality ... | 1 | 0.11 | 0.172414 |
| Which private companies in the Americas are the largest GHG emitters ... ? | "The private companies responsible ... The largest emitter amongst state-owned companies ..." | According to the Carbon Majors database, the largest private companies ... | The largest private companies in the Americas ... | 1 | 0.26 | 0 |
### Generate a test dataset for comprehensive RAG evaluation
What if you don't have the data for folks asking questions when they interact with your RAG system?
Ragas can help by generating [synthetic test set generation](https://docs.ragas.io/en/latest/getstarted/rag_testset_generation/) -- where you can seed it with your data and control the difficulty, variety, and complexity.
## 🫂 Community
If you want to get more involved with Ragas, check out our [discord server](https://discord.gg/5qGUJ6mh7C). It's a fun community where we geek out about LLM, Retrieval, Production issues, and more.
## Contributors
```yml
+----------------------------------------------------------------------------+
| +----------------------------------------------------------------+ |
| | Developers: Those who built with `ragas`. | |
| | (You have `import ragas` somewhere in your project) | |
| | +----------------------------------------------------+ | |
| | | Contributors: Those who make `ragas` better. | | |
| | | (You make PR to this repo) | | |
| | +----------------------------------------------------+ | |
| +----------------------------------------------------------------+ |
+----------------------------------------------------------------------------+
```
We welcome contributions from the community! Whether it's bug fixes, feature additions, or documentation improvements, your input is valuable.
1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request
## 🔍 Open Analytics
At Ragas, we believe in transparency. We collect minimal, anonymized usage data to improve our product and guide our development efforts.
✅ No personal or company-identifying information
✅ Open-source data collection [code](./src/ragas/_analytics.py)
✅ Publicly available aggregated [data](https://github.com/explodinggradients/ragas/issues/49)
To opt-out, set the `RAGAS_DO_NOT_TRACK` environment variable to `true`.