Implementation Principles of Retrieval Enhancement Generation
Rankify's RAG module realizes the seamless integration of retrieval systems and large language models, and is the most advanced Q&A system building solution currently available.
- technology stack integrationSupport mainstream generative models such as GPT, LLaMA, T5, etc., which are automatically spliced with the search results to form enhanced hints.
- Accuracy Guarantee: Factual basis is obtained through searching, which effectively solves the LLM hallucination problem and increases the factual accuracy of the generated answers by 351 TP3T
- Application Cases: In intelligent customer service scenarios, retrieving a user question from the knowledge base before generating an answer reduces the number of incorrect responses by 60% compared to the direct generation method.
Rankify optimizes context length control and importance weighting algorithms to ensure the quality of generation.
This answer comes from the articleRankify: a Python toolkit supporting information retrieval and reorderingThe
May not be reproduced without permission:AI productivity tools " Rankify's RAG feature integrates the technical benefits of search and generation
