Uptrain

- Reduce Dependencies
- Add support for Python-3.12

We're thrilled to introduce the latest updates and enhancements in this release! 🌟

🆕 New Features and Improvements:

1. **Log_and_Evaluate**: Experience seamless logging and evaluation with our new "log_and_evaluate" feature. 📊

2. **Evaluate_Experiments**: Dive into deeper insights with "evaluate_experiments" to gain a better understanding of your model's performance. 📈

3. **Prebuilt Evaluations**: We've added prebuilt evaluations for effortless and quick assessment. 📋

4. **EvalLLM Evaluator**: Staying true to our open-source roots! 🎉 We've introduced the all-new EvalLLM evaluator for users wishing to use UpTrain for free, allowing evaluations without the need for UpTrain accounts. 💼

5. **Expanded LLM Support**: Now supporting over 100+ Language Model Models (LLMs)! 🌐

6. **Token Limiter and Fallback**: Ensure your models stay within token limits with the new token limiter, backed by a robust fallback mechanism. 🛡️

7. **New Evaluations**:
- 📜 **Guideline Adherence**: Evaluate your model's adherence to guidelines.
- 🔄 **Response Completeness wrt Context**: Assess response completeness with respect to context.

8. **New JavaScript Client**: Discover our all-new JavaScript client for a more seamless integration experience. 📦

9. **Enhanced Chart Descriptions**: Charts just got more informative with added descriptions for better data comprehension. 📊📝

10. **Download Dataset and Results**: Our client now allows you to conveniently download datasets and results with just a few clicks! 💾


We're committed to making your experience with our platform even better. Thank you for your continued support and feedback! 🙌

For more details and bug fixes, please refer to the full release documentation. Feel free to reach out to us with any questions or feedback. 📧💬

Happy experimenting! 🧪🔬🚀

Minor Fixes and Improvements
- Faster processing
- Improved API functionality
- Add Pandas fallback for Polars

We're thrilled to unveil the latest additions in UpTrain version 0.3.7, designed to elevate your evaluation and grading experience.

- Enhanced Context Analysis 🔄
- Say hello to the Context Relevance Operator, a powerful addition that allows you to assess model responses within their context, ensuring higher accuracy and coherence.
- Fact-Based Scoring 📊
- Introducing the Response Factual Score Operator, an invaluable tool for evaluating the accuracy of model-generated responses in terms of factual content.
- Language and Tone Assessment 🗣️🎵
- Dive deep into the nuances of language and communication style using the Language Critique and Tone Critique Operators. These features empower you to analyze the structure and tone of model outputs, ensuring they align perfectly with your requirements.
- Comprehensive Response Analysis ✅
- The Response Completeness Operator brings a new layer of evaluation, enabling you to gauge how thoroughly model responses address the given prompts. This ensures your outputs are not only accurate but also comprehensive.
- Improved Documentation 📚
- Updated user guides and documentation for seamless integration of new features.
- Bug Fixes and Performance 🐞
- In our commitment to providing you with a seamless experience, we've ironed out various bugs and optimized performance, making your journey with UpTrain even smoother.

- Improved documentation 📑
- Create your evaluations with ease using our new [Key Components Documentation ](https://docs.uptrain.ai/key-components)
- Better Failure Handling 🛡️
- The new failure handling significantly reduces failures due to API requests allowing you to perform robust model grading evaluations
- Custom Operators 🧪
- You can now define any custom Python functions as an Operator and use it to perform your evaluations using [CustomOperator](https://docs.uptrain.ai/tutorials/custom-operator)
- New Readers for BigQuery and DuckDB 📖
- Now, you can directly provide a query and UpTrain can read data from your BigQuery Database using [BigQueryReader](https://uptrain-ai.github.io/uptrain/operators/IO/BigQueryReader/) and DuckDB database using [DuckDBReader](https://uptrain-ai.github.io/uptrain/operators/IO/DuckDBReader/)
- New Validation Retry Logic 💡
- You can now set your own retry logic which dictates how to generate LLM responses in case of validation failures. This could be any Python function like modifying prompt, temperature, triggering a tool, returning a default response, etc.
- You can also check if the LLM response is empty or not for the given query and get back a default message instead of the LLM response.

- We added a new class called ExperimentArgs that allows you to compare the output of multiple prompts easily
- We have also revamped our plotting features:
- Directly use the plot you need by using the new LineChart, BarChart, Histogram, and ScatterPlot classes
- Create your own custom Plotly charts using the CustomPlotlyChart class
- Show multiple charts using the MultiPlot class