RAGAs- A Framework for Evaluating RAG Applications

#langchain #rag #llm #chatbot #chatgpt #ai #machinelearning
Enter RAG pipelines combine retrieval and language generation modules to enhance natural language processing tasks. With RAGAS, you can assess the performance of RAG systems without relying on human annotations, making evaluation cycles faster and more efficient.

Timestamps:
0:00 - Intro
01:11 - What are RAGAs
01:43 - Evaluate the RAG pipeline
02:24 - Evaluation Metrics
02:37 - FAITHFULNESS
02:53 - ANSWER RELEVANCE
02:59 - CONTEXT RELEVANCE
03:13 - CONTEXT RECALL
03:23 - CONTEXT PRECISION
03:42 - Install
04:14 - OpenAI
04:45 - PDF
05:27 - Index, split, embeddings
06:41 -Create List ground_truths
07:32 - Convert dict to dataset
07:53 - Conclusion

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[Link Used]

Blog: https://docs.ragas.io/
Github: https://github.com/explodinggradients/ragas
Code : https://github.com/weaviate/recipes/blob/main/evaluation/Ragas/RAGAs-RAG-langchain.ipynb

Additional Tags and Keywords:
#AI #Chatbot #langchain #RAG #RAGAs #SuperAI #python #Technology #Innovation #ArtificialIntelligence #MachineLearning #NaturalLanguageProcessing #VirtualAssistant #Automation #HumanComputerInteraction #ConversationalAI #DigitalTransformation #Tech #video #llmlingua #microsoftresearch

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