From d97fc3c047f060bb99f4762ecf92dd67a239a1d9 Mon Sep 17 00:00:00 2001 From: Yongtae Date: Sat, 30 Sep 2023 21:20:20 +0900 Subject: [PATCH] Add technologies for question answering and scoring --- README.md | 16 ++++++---------- documents/image/concept.png | Bin 2219736 -> 882243 bytes 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 4a4ad2e..c75e6b3 100644 --- a/README.md +++ b/README.md @@ -21,23 +21,19 @@ Any feedback, bug reports, and suggestions are appreciated! ## 🔧 Main Technologies -- [LangChain](https://github.com/langchain-ai/langchain) : LangChain is a framework for developing applications powered by large language models. It can be divided conceptually into components (like llm component, embedding component), which are well-abstracted and easy to switch. This is the reason we can treat each component as hyperparameter. - -- [LangSmith](https://docs.smith.langchain.com/) : LangSmith is a web-based GUI tool that helps developers debug, test, evaluate, and monitor their LLM (Language Learning Model) applications. we can use LangSmith for evaluation and monitoring. - - [Hydra](https://github.com/facebookresearch/hydra) : Hydra is an open-source Python framework that simplifies the development of research and other complex applications. It has the ability to dynamically create a hierarchical configuration system, which is its key feature - [Optuna](https://github.com/optuna/optuna) : Optuna is an open-source Python library for hyperparameter optimization. It offers a define-by-run API that allows users to construct search spaces, a mixture of efficient searching, and pruning algorithm to enhance the cost-effectiveness of optimization. Optuna also provides a web dashboard for visualization and evaluation of studies in real-time. -- [ragas](https://github.com/explodinggradients/ragas) : Ragas is an evaluation framework for Retrieval Augmented Generation (RAG) pipelines that provides tools based on the latest research for evaluating LLM-generated text to give insights about the RAG pipeline. - # 🚀 How to use -I will explain how to use this repository by using question answering as an example. In example, we use +I will explain how to use this repository by using question answering as an example. In this example, we will use following technologies. + +- [LangChain for model](https://github.com/langchain-ai/langchain) : LangChain is a framework for developing applications powered by large language models. It can be divided conceptually into components (like llm component, embedding component), which are well-abstracted and easy to switch. This is the reason we can treat each component as hyperparameter. + +- [ragas for scoring](https://github.com/explodinggradients/ragas) : Ragas is an evaluation framework for Retrieval Augmented Generation (RAG) pipelines that provides tools based on the latest research for evaluating LLM-generated text to give insights about the RAG pipeline. -- **LangChain** : for model -- **Ragas** : for evaluation -- **LangSmith** : for evaluation and monitoring +- [LangSmith for scoring and monitoring](https://docs.smith.langchain.com/) : LangSmith is a web-based GUI tool that helps developers debug, test, evaluate, and monitor their LLM (Language Learning Model) applications. we can use LangSmith for evaluation and monitoring. ## Step 0 : Environment Setup diff --git a/documents/image/concept.png b/documents/image/concept.png index efe63a45d48ca425ced86d65c7baae54e7e804f3..dfb49502db45ef095237aac950dfd5d0abe89aea 100644 GIT binary patch literal 882243 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