RAG Chatbot with Semantic Recommendations Platform

Scenario

A developer extracts text from unstructured enterprise documents using Bailian, ingests content into Elasticsearch, then deploys both a conversational RAG chatbot for direct Q&A and an AIRec-powered semantic recommendation engine for personalized content discovery on the same shared knowledge base.

How the products combine

1. bailian+es · document-ai-rag-pipeline-31b42e — Document AI RAG Pipeline

See _combos/document-ai-rag-pipeline-31b42e.

2. airec+opensearch+es+opensearch+oss+es+oss+opensearch+bailian+bailian+es · document-ai-rag-with-semantic-recommendations-d48dc9 — Document AI RAG with Semantic Recommendations

See _combos/document-ai-rag-with-semantic-recommendations-d48dc9.

3. bailian+es · document-extraction-to-rag-chatbot-pipeline-c495d5 — Document Extraction to RAG Chatbot Pipeline

See _combos/document-extraction-to-rag-chatbot-pipeline-c495d5.

4. airec+opensearch+es+opensearch+oss+es+oss+opensearch+airec+opensearch+es+opensearch+oss+es+oss+opensearch+bailian+bailian+es+bailian+es · enterprise-document-intelligence-and-discovery-p-662350 — Enterprise Document Intelligence and Discovery Platform

See _combos/enterprise-document-intelligence-and-discovery-p-662350.

Typical questions

• build RAG chatbot with document recommendations
• deploy Q&A bot plus personalized search from PDFs
• document extraction to chatbot and recommendation engine
• 从PDF提取构建RAG聊天机器人加智能推荐
• 文档抽取部署对话机器人和推荐系统
• RAG chatbot with AIRec recommendations
• extract index chat and recommend full pipeline
• 企业文档智能问答加推荐双通道平台

Q: How do I build and deploy a RAG chatbot with semantic document recommendations? A: You can build and deploy this platform by extracting text with Bailian, ingesting it into Elasticsearch, and running a conversational RAG chatbot alongside an AIRec-powered semantic recommendation engine on a shared knowledge base. This integrated setup enables both direct Q&A and personalized content discovery while utilizing predefined Document AI RAG pipelines.