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aws-samples / fine-tune-embedding-models-on-sagemaker

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This repository contains samples for fine-tuning embedding models using Amazon SageMaker. Embedding models are useful for tasks such as semantic similarity, text clustering, and information retrieval. Fine-tuning these models on your specific domain data can greatly improve their performance.

aws embeddings embedding-models sagemaker aws-blog sagemaker-endpoint sagemaker-studio generative-ai
  • Updated Feb 25, 2025
  • Jupyter Notebook

aws-samples / Reducing-Hallucinations-in-LLM-Agents-with-a-Verified-Semantic-Cache

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This repository contains sample code demonstrating how to implement a verified semantic cache using Amazon Bedrock Knowledge Bases to prevent hallucinations in Large Language Model (LLM) responses while improving latency and reducing costs.

agent aws demo bedrock rag aws-blog llm semantic-cache llm-agent amazon-bedrock amazon-bedrock-agents amazon-bedrock-knowledge-bases
  • Updated Feb 3, 2025
  • Jupyter Notebook

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