HuggingFace: Agentic RAG for Accurate and Complete Context
Code: https://github.com/huggingface/cookbo...
Summary:
Key Issues in Standard RAG: Two problems were highlighted—semantic mismatches between user questions (interrogative) and document embeddings (affirmative), and the complexity of user queries requiring more contextual documents than initially retrieved.
Agentic RAG Enhancements: To address these issues, the query is reformulated from interrogative to affirmative, and the agent retrieves documents multiple times to ensure a complete context for complex queries.
keywords:
Agentic RAG
Query reformulation
Semantic similarity
Interrogative
Affirmative
Vector store
Context retrieval
Document embeddings
Hugging Face
Accuracy improvement
#datascience #machinelearning #deeplearning #datanalytics #predictiveanalytics #artificialintelligence #generativeai #largelanguagemodels #computervision #naturallanguageprocessing #agents #transformers #embedding #graphml #graphdatascience #datavisualization #businessintelligence #optimization #montecarlosimulation #simulation #LLMs #python #aws #azure #gcp