information retrieval
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Hybrid Retrieval: BM25 + FAISS on t3.medium
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A hybrid retrieval system has been developed to efficiently serve over 127,000 queries on a single AWS Lightsail instance, combining the precision of BM25 with the semantic understanding of FAISS. This system operates without a GPU for embeddings, though a GPU can be used optionally for reranking to achieve a 3x speedup. The infrastructure is cost-effective, running on a t3.medium instance for approximately $50 per month, and achieves 91% accuracy, significantly outperforming dense-only methods. The hybrid approach effectively handles complex queries by using a four-stage cascade that combines keyword precision with semantic understanding, optimizing latency and accuracy through asynchronous parallel retrieval and batch reranking. This matters because it demonstrates a cost-effective, high-performance solution for query retrieval that balances precision and semantic understanding, crucial for applications requiring accurate and efficient information retrieval.
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Multimodal vs Text Embeddings in Visual Docs
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When constructing a Retrieval-Augmented Generation (RAG) system for documents containing mixed content like text, tables, and charts, the effectiveness of multimodal embeddings was compared to text embeddings. Tests were conducted using 150 queries on datasets such as DocVQA, ChartQA, and AI2D. Results showed that multimodal embeddings significantly outperformed text embeddings for tables (88% vs. 76%) and had a slight advantage with charts (92% vs. 90%), while text embeddings excelled in pure text scenarios (96% vs. 92%). These findings suggest that multimodal embeddings are preferable for visual documents, whereas text embeddings suffice for pure text content. This matters because choosing the right embedding approach can significantly enhance the performance of systems dealing with diverse document types.
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Interact with Notion Docs Using RAG
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Retrieval-Augmented Generation (RAG) is a powerful method that allows users to interact with their Notion documents through natural language queries. By integrating RAG, users can ask questions and receive responses that are informed by the content of their documents, making information retrieval more intuitive and efficient. This approach leverages a combination of retrieval mechanisms and generative models to provide precise and contextually relevant answers, enhancing the overall user experience. Such advancements in document interaction can significantly streamline workflows and improve productivity by reducing the time spent searching for information.