Added Ragas for evaluation

README.md

@@ -116,7 +116,7 @@ We also included [Perplexity and Cursor.sh prompts](#formatting-prompt-templates
 | [Braintrust Data](https://www.braintrustdata.com/) | An AI product stack featuring evaluations, prompt playgrounds, continuous integration, dataset management, and access to various AI models through a single API | <a href=https://github.com/braintrustdata/braintrust-proxy><img src="https://img.shields.io/github/stars/braintrustdata/braintrust-proxy?style=social" width=90/></a> | <a href=https://pypi.org/project/braintrust><img src="https://img.shields.io/pypi/dw/braintrust" width=150/></a> |
 | [Arize AI](https://arize.com/) | An observability platform for both LLMs and supervised ML | <a href=https://github.com/Arize-ai><img src="https://img.shields.io/github/stars/Arize-ai?style=social" width=90/></a> | <a href=https://pypi.org/project/arize><img src="https://img.shields.io/pypi/dw/arize" width=150/></a> |
 | [Weights & Biases](https://wandb.ai) | An MLOps platform for streamlining ML workflows | <a href=https://github.com/wandb/wandb><img src="https://img.shields.io/github/stars/wandb/wandb?style=social" width=90/></a> | <a href=https://pypi.org/project/wandb><img src="https://img.shields.io/pypi/dw/wandb" width=150/></a> |
-| [MLflow](https://mlflow.org/docs/latest/llms/index.html#) | A platform to streamline ML development | <a href=https://github.com/mlflow/mlflow><img src="https://img.shields.io/github/stars/mlflow/mlflow?style=social" width=90/></a> | <a href=https://pypi.org/project/mlflow><img src="https://img.shields.io/pypi/dw/mlflow" width=150/></a> |
+| [MLflow](https://mlflow.org/docs/latest/llm-tracking.html) | A platform to streamline ML development | <a href=https://github.com/mlflow/mlflow><img src="https://img.shields.io/github/stars/mlflow/mlflow?style=social" width=90/></a> | <a href=https://pypi.org/project/mlflow><img src="https://img.shields.io/pypi/dw/mlflow" width=150/></a> |
 | [PromptLayer](https://promptlayer.com/) | A platform for tracking, managing, and sharing LLM prompt engineering | <a href=https://github.com/MagnivOrg/prompt-layer-library><img src="https://img.shields.io/github/stars/MagnivOrg/prompt-layer-library?style=social" width=90/></a> | <a href=https://pypi.org/project/promptlayer><img src="https://img.shields.io/pypi/dw/promptlayer" width=150/></a> |
 | [Helicone](https://helicone.ai/) | An open-source observability platform for LLMs | <a href=https://github.com/Helicone/helicone><img src="https://img.shields.io/github/stars/Helicone/helicone?style=social" width=90/></a> | <a href=https://pypi.org/project/helicone><img src="https://img.shields.io/pypi/dw/helicone" width=150/></a> |
 | [Quotient AI](https://www.quotientai.co/) | Quotient AI is a platform for evaluating AI products on real-world use-cases, during research, development, and in production | N/A | N/A |
@@ -126,6 +126,7 @@ We also included [Perplexity and Cursor.sh prompts](#formatting-prompt-templates
 | [Patronus AI](https://www.patronus.ai/) | An automated evaluation and benchmarking platform for LLMs, providing tools for testing, scoring, and evaluating LLMs in real-world scenarios | N/A | N/A |
 | [Autoblocks AI](https://www.autoblocks.ai/) | A collaborative cloud-based workspace designed for rapid iteration on GenAI products, offering features like prompt management, observability, continuous evaluations, fine-tuning, prototyping, debugging, and scalable data ingestion & search, all in a provider-agnostic environment | N/A | <a href=https://pypi.org/project/autoblocksai><img src="https://img.shields.io/pypi/dw/autoblocksai" width=150/></a> |
 | [Context AI](https://context.ai/) | Tools for pre-launch LLM evaluations and post-launch analytics, with features such as  testing, performance monitoring, user conversation analysis, and support for various models and libraries | N/A | <a href=https://pypi.org/project/context-python><img src="https://img.shields.io/pypi/dw/context-python" width=150/></a> |
+| [Ragas](https://docs.ragas.io/en/stable/) | Framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. | <a href=https://github.com/explodinggradients/ragas><img src="https://img.shields.io/github/stars/explodinggradients/ragas?style=social" width=90/></a> | <a href=https://pypi.org/project/ragas><img src="https://img.shields.io/pypi/dw/ragas" width=90/></a> |
 | [E2b dev](https://e2b.dev/) | Services to deploy, test, and monitor AI agents, including a sandbox with a secure, long-running cloud environment for various LLMs with features like internet access | <a href=https://github.com/e2b-dev/e2b><img src="https://img.shields.io/github/stars/e2b-dev/e2b?style=social" width=90/></a> | <a href=https://pypi.org/project/e2b><img src="https://img.shields.io/pypi/dw/e2b" width=150/></a> |
 | [Agentops](https://www.agentops.ai/) | Toolkit for evaluating and developing AI agents, providing tools for agent development, monitoring capabilities, and replay analytics | <a href=https://github.com/AgentOps-AI/agentops><img src="https://img.shields.io/github/stars/AgentOps-AI/agentops?style=social" width=90/></a> | <a href=https://pypi.org/project/agentops><img src="https://img.shields.io/pypi/dw/agentops" width=150/></a> |
 | [Zenoml](https://zenoml.com/) | AI evaluation platform that enables data visualization, model performance analysis, and the creation of interactive reports for various data types | <a href=https://github.com/zeno-ml/zeno-build><img src="https://img.shields.io/github/stars/zeno-ml/zeno-build?style=social" width=90/></a> | <a href=https://pypi.org/project/zeno-client><img src="https://img.shields.io/pypi/dw/zeno-client" width=150/></a> |