Basta Spring 2025 Turbo RAG: AI-basierte Retriever-Auswahl mit Semantic Router Marco Frodl [email protected] Principal Consultant for Generative AI @marcofrodl

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router About Me Marco Frodl Principal Consultant for Generative AI Thinktecture AG X: @marcofrodl E-Mail: [email protected] LinkedIn: https://www.linkedin.com/in/marcofrodl/ https://www.thinktecture.com/thinktects/marco-frodl/

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Turbo 🚀 https://www.aurelio.ai/semantic-router Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow, unreliable LLM generations to make tool-use or safety decisions, we use the magic of semantic vector space — routing our requests using semantic meaning.

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Turbo 🚀 https://www.aurelio.ai/semantic-router Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow, unreliable LLM generations to make tool-use or safety decisions, we use the magic of semantic vector space — routing our requests using semantic meaning. It’s perfect for: input guarding, topic routing, tool-use decisions.

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Turbo 🚀 in Numbers In my RAG example, a Semantic Router using remote services is 3.4 times faster than an LLM and it is 30 times less expensive. A local Semantic Router is 7.7 times faster than an LLM and it is 60 times less expensive.

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Really? Safety Speed Budget

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Refresher: What is RAG? “Retrieval-Augmented Generation (RAG) extends the capabilities of LLMs to an organization's internal knowledge, all without the need to retrain the model.

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Refresher: What is RAG? https://aws.amazon.com/what-is/retrieval-augmented-generation/ “Retrieval-Augmented Generation (RAG) extends the capabilities of LLMs to an organization's internal knowledge, all without the need to retrain the model. It references an authoritative knowledge base outside of its training data sources before generating a response”

Ask me anything Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Simple RAG Question Prepare Search Search Results Question Answer LLM Vector DB Embedding Model Question as Vector Workflow Terms - Retriever - Chain Elements Embedding- Model Vector- DB Python LLM LangChain

Our sample content Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Simple RAG in a nutshell

Which retriever do you want? Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Multiple Retriever

Best source determination before the search Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Advanced RAG Question Retriever Selection 0-N Search Results Question Answer LLM Embedding Model Vector DB A Question as Vector Vector DB B LLM Prepare Search or

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Demo: Dynamic Retriever Selection with LLM

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Embedding Model

Best source determination before the search Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Advanced RAG Question Retriever Selection 0-N Search Results Question Answer LLM Embedding Model Vector DB A Question as Vector Vector DB B LLM Prepare Search or

Best source determination before the search Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Advanced RAG w/ Semantic Router Question Retriever Selection 0-N Search Results Question Answer Embedding Model Vector DB A Question as Vector Vector DB B LLM Prepare Search or Embedding Model

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Demo: Semantic Router with RAG

LLM as Router Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Turbo 🐌

Semantic Router with remote embedding model Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Turbo 🚀

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Demo: Semantic Router running locally

Semantic Router with local embedding model Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Turbo 🚀

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Speed & Budget in Numbers SR Remote is 3.4 times faster than LLM (0,62s vs 0,18s) SR Local is 7.75 times faster than LLM (0,62s vs 0,08s) SR Remote is 30 times cheaper than LLM ($0,60 vs $0,02) SR Local is 60 times cheaper than LLM ($0,60 vs $0,01)

Turbo RAG AI-basierte Retriever-Auswahl mit Semantic Router Yes, please! Safety Speed Budget

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Thank you! Any questions? Marco Frodl @marcofrodl Principal Consultant for Generative AI