Evaluate RAG - LLM's using RAGAS Python Pytest Framework
Data will be fed to embedded model which stores the data into vector database based on semantic understanding. LLM(like chatgpt, gemini, anthropic) interacts with this vector database and replies based on the user prompts RAG (Retrieval Augmented Generation) data retrieved from vector database articulation of whole question with necessary context is called augmentation Generation is about giving the answer In traditional software testing, we assert on the system responses, but in LLM's we evaluate quality of the response using benchmark metrics. Here we make use of RAGAS framework Benchmark metrics for document retrieval system context precision --> no of relavant docs retrieved / total number of docs retrieved => 3/6 50% context recall => no of relavent docs retrieved / total number of relavent docs retrieved Install Python Install Pycharm IDE make sure to configure path of python while importing project (use which python3 to know the path of installation Let...