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I have a rag application that now is just working with q&a csv. the app is now working with LlamaIndex and chromadb. we dont use reranking and our chunking strategy is per row in csv. we are insurance company and the rag app is responsible to answer insurance company that is very similar to each other in eahc insrance domain like car, health, fire and so on. so I emphasize very strongly on retrieval step so the app can find the most relevant and correct answer. your main task is focus on different chunking and embedding, reranking strategies. we dont need super reasoning. our most concern is about retrieval. our previous app has about 30 collections and our most problem is finding the right collection. I want to change this architecture and migrate from chromadb to qdrant and use all file types like pdf, .docs, csv, excel, .txt and so on. the research I want is: Research the key architecture decisions for our new AI chatbot (chunking, embeddings, reranking, RAG, vector DB, etc.) and prepare a concise decision report. For each topic, provide at least three options prioritized by recommendation, including a title, ranking, rationale, pros, cons, and a final recommendation based on an enterprise-scale, bilingual, on-premises deployment.