将检索和生成结合起来,构建一个简单的内容推荐系统: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 defrecommend_content(user_history):all_recommendations=[]forhistoryinuser_history:retrieved_docs=retrieve(history)prompt="Based on your interest, I recommend: "+" ".join(retrieved_docs)recommendation=...
Introducing RAG for Retail The RAG-based recommendation system redefines personalization by dynamically integrating vast amounts of data, from market trends to individual customer interactions, into the recommendation process. This ensures that product suggestions are relevant and timely, enhancing the s...
For example,healthcare applicationsleverage RAG to retrieve up-to-date medical research, enabling accurate diagnoses. Similarly,legal systemsuse RAG to synthesize case law, ensuring precise, context-aware recommendations. A common misconception is that RAG merely aggregates data. Instead, itsynthesizes ...
Business intelligence.Companies can use RAG to help GenAI models pull relevant market data for automated production of insights and reports. This data can include market research, competitor analysis, sales volume, and customer feedback. Content recommendations.RAG can improve content recommendation ...
a document hierarchy, a RAG system can more reliably answer a question about regulations for a ...
Recommendation:For customer support automation ahybrid approachmight be optimal. Finetuning can ensure that the chatbot aligns with the company’s branding, tone, and general knowledge, handling the majority of typical customer queries. RAG can then serve as a complementary system, stepping in for ...
The system responds: Recommendation: Switzerland offers scenic hiking trails, especially in the Alps. Consider Zermatt for breathtaking views. Below is a diagram showing a simplified travel recommendation knowledge graph: **Nodes:**Switzerland, Hiking, Scenic Landscapes.Edges: Connect Switzerland to hikin...
Recommendation:For this use casea RAG systemseems to be the more fitting choice. Given the need for dynamic access to the organisation’s evolving internal databases and the potential requirement for transparency in the answering process, RAG offers capabilities that align well with these needs...
: 0, 'max_retries': 10, 'max_retry_wait': 10.0, 'sleep_on_rate_limit_recommendation': ...
RAG can streamline your recommendation system and take it to the next level with the power of human-like responses and semantic similarity, understanding what your user actually needs. You can start by adding a large language model layer between your database ...