Deepeval

In DeepEval v0.21.74, we have:
- Agnetic evaluation metric to evaluate tool calling correctness for LLM agents: https://docs.confident-ai.com/docs/metrics-tool-correctness
- Pydantic Schemas to enforce JSON outputs for custom, smaller LLMs: https://docs.confident-ai.com/docs/guides-using-custom-llms
- Asynchronous support for synthetic data generation: https://docs.confident-ai.com/docs/evaluation-datasets-synthetic-data
- Tracing integration for LLamaIndex and LangChain: https://docs.confident-ai.com/docs/confident-ai-tracing

In DeepEval v0.21.62, we:
- added an option to print out intermediate steps during metric execution, which can be configured via the `verbose_mode` parameter: https://docs.confident-ai.com/docs/metrics-answer-relevancy#example
- hyperparameters can be logged to Confident AI via the evaluate() function: https://docs.confident-ai.com/docs/getting-started#optimizing-hyperparameters
- Synthetic data generation now gives more realistic results and is more customizable: https://docs.confident-ai.com/docs/evaluation-datasets-synthetic-data

For deepeval's latest release v0.21.15, we release:
- Synthetic Data generation. Generate synthetic data from documents easily: https://docs.confident-ai.com/docs/evaluation-datasets-synthetic-data
- caching. If you're running 10k test cases and it fails at the 9999th test case, you no longer have to rerun the first 9999 test case as you can just read from cache using the `-c` flag: https://docs.confident-ai.com/docs/evaluation-introduction#cache
- repeats. If you want to repeat each test case for statistical significant, use the `-r` flag: https://docs.confident-ai.com/docs/evaluation-introduction#repeats
- LLM Benchmarks. Supporting popular benchmarks such as MMLU, HellaSwag, and BIG-BH so anyone can evaluate ANY model on research backed benchmarks in a few lines of code.
- G-Eval improvements. The G-Eval metric now supports using logprobs of tokens to find the weighted summed score.

- asynchronous support throughout deepeval, and no longer using threads. Users can also call individual metrics asynchronously: https://docs.confident-ai.com/docs/metrics-introduction#measuring-metrics-in-async
- improved the way in which you create a custom LLM for evaluation. You'll now have to implement an asynchronous generate() method to use deepeval's async features: https://docs.confident-ai.com/docs/metrics-introduction#using-a-custom-llm
- strict mode for all metrics!
- improve `evaluate()` function for more customizability: https://docs.confident-ai.com/docs/evaluation-introduction#evaluating-without-pytest

In DeepEval's latest release, there is now:
- conversational metrics: https://docs.confident-ai.com/docs/metrics-knowledge-retention. This metric evaluates whether your LLM is able to retain factual information presented to it throughout a conversation
- synthetic data generation. Generate evaluation datasets from scratch: https://docs.confident-ai.com/docs/evaluation-datasets#generate-an-evaluation-dataset

For the newest release, deepeval now is now stable for production use:
- reduced package size
- separated functionality of pytest vs deepeval test run command
- included coverage score for summarization
- fix contextual precision node error
- released docs for better transparency into metrics calculation
- allows users to configure RAGAS metrics for custom embedding models: https://docs.confident-ai.com/docs/metrics-ragas#example
- fixed bugs with checking for package updates