simplified-corrective-rag and advanced-rag-router-with-amazon-bedrock
These two tools are complements, as the advanced RAG router could integrate the corrective RAG approach to enhance the routing decision-making and response generation by identifying and rectifying retrieval errors.
About simplified-corrective-rag
aws-samples/simplified-corrective-rag
How to build a simplified Corrective RAG assistant with Amazon Bedrock using LLMs, Embeddings model, Knowledge Bases for Amazon Bedrock, and Agents for Amazon Bedrock.
This project helps developers build more reliable AI assistants by addressing a common problem where large language models (LLMs) might 'hallucinate' or provide incorrect information. It takes an existing knowledge base and a user query, and if the knowledge base doesn't have the answer, it automatically performs a web search to find accurate information. This is for AI solution architects or machine learning engineers building generative AI applications who need to ensure accuracy.
About advanced-rag-router-with-amazon-bedrock
aws-samples/advanced-rag-router-with-amazon-bedrock
How to build an advanced RAG router based assistant with Amazon Bedrock using LLMs, Embeddings model, and Knowledge Bases for Amazon Bedrock.
This project helps you build an AI assistant that can answer questions using the most current and relevant information from various internal sources. You provide your business's documents or data, and the assistant can then accurately respond to user queries, reducing 'hallucinations' often seen with general AI models. It's designed for operations engineers or AI solution architects who need to deploy secure, context-aware conversational AI.
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