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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

One effective way to improve context relevance is through metadata filtering, which allows you to refine search results by pre-filtering the vector store based on custom metadata attributes. By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries.

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Dynamic metadata filtering for Amazon Bedrock Knowledge Bases with LangChain

Flipboard

Amazon Bedrock Knowledge Bases has a metadata filtering capability that allows you to refine search results based on specific attributes of the documents, improving retrieval accuracy and the relevance of responses. These metadata filters can be used in combination with the typical semantic (or hybrid) similarity search.

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AWS Glue for Handling Metadata

Analytics Vidhya

The post AWS Glue for Handling Metadata appeared first on Analytics Vidhya. The managed service offers a simple and cost-effective method of categorizing and managing big data in an enterprise. It provides organizations with […].

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Multi-tenancy in RAG applications in a single Amazon Bedrock knowledge base with metadata filtering

AWS Machine Learning Blog

One of these strategies is using Amazon Simple Storage Service (Amazon S3) folder structures and Amazon Bedrock Knowledge Bases metadata filtering to enable efficient data segmentation within a single knowledge base. The S3 bucket, containing customer data and metadata, is configured as a knowledge base data source.

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How DPG Media uses Amazon Bedrock and Amazon Transcribe to enhance video metadata with AI-powered pipelines

AWS Machine Learning Blog

With a growing library of long-form video content, DPG Media recognizes the importance of efficiently managing and enhancing video metadata such as actor information, genre, summary of episodes, the mood of the video, and more. Video data analysis with AI wasn’t required for generating detailed, accurate, and high-quality metadata.

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The Rise of Ghiblified AI Images: Privacy Concerns and Data Risks

Unite.AI

These risks go beyond data collection and include serious issues such as deepfakes , identity theft, and exposure of sensitive metadata. Metadata Exposure Digital images contain embedded metadata, such as location data, device information, and timestamps. Another critical step is metadata removal.

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LAION AI Unveils LAION-DISCO-12M: Enabling Machine Learning Research in Foundation Models with 12 Million YouTube Audio Links and Metadata

Marktechpost

Introduction to LAION-DISCO-12M To address this gap, LAION AI has released LAION-DISCO-12M—a collection of 12 million links to publicly available YouTube samples, paired with metadata designed to support foundational machine learning research in audio and music.

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