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July was fire??! We're thrilled to announce that we crossed a significant milestone by hitting 100K+ followers on LinkedIn! This incredible community support fuels our passion for innovation. From client visits to expanding our footprint with a new office in Chennai, it's been a whirlwind of growth. Our Hyderabad HQ is buzzing with energy and enthusiasm as we welcome fresh talent & internship programs. We're also on the cusp of achieving core cloud competencies, a testament to our team's dedication. And to top it off, our #techtalk launch was a huge success! We're incredibly grateful for this fantastic momentum and can't wait to see what Q3 brings. Let's keep the energy high! #grateful #excited #growingstronger #cloud #newbeginnings #teamworkmakesdreamwork #digitaltransformation

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We're #hiring a new Salesforce QA in Hyderabad, Telangana. Apply today or share this post with your network.

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Is Chunking Holding Back RAG? How Landmark Embedding Solves the Puzzle As LLMs evolve, RAG’s ability to draw from external knowledge is essential for applications requiring extensive data. However, the existing retrieval methods usually work with the chunked context, which is prone to inferior quality of semantic representation and incomplete retrieval of useful information A research paper introduces BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models https://lnkd.in/d4X6KJMX Abstract: The paper introduces "Landmark Embedding," an approach for improving retrieval-augmented long-context language models. Unlike existing methods that rely on chunking, which can disrupt context and lose information, Landmark Embedding retains context coherence, improving the quality of information retrieval. It leverages a chunking-free architecture, a position-aware objective, and a multi-stage learning algorithm. Landmark Embedding significantly enhances the performance of LLMs for long-context tasks. Landmark Embedding solves this by eliminating chunking. It uses special tokens (landmarks) to maintain coherent context and applies a position-aware objective that ensures comprehensive retrieval. A multi-stage learning process is used to train the model efficiently with both real and synthetic data. 10 Key Takeaways: - Chunking-Free Architecture: Landmark Embedding removes the need for chunking, preserving context integrity for long-context language models. - Landmark Tokens: Special tokens (landmarks) are inserted at the end of each sentence, helping to retain the context for effective retrieval. - Position-Aware Objective: It emphasizes the retrieval of the complete set of relevant sentences, focusing on the ultimate boundary of useful information. - Multi-Stage Learning: The model is trained using a combination of distant supervision, weak supervision, and fine-tuning, maximizing cost-effectiveness and performance. - Improved Performance: In experiments, Landmark Embedding outperformed baseline methods on LLaMA-2 and ChatGPT-3.5 across multiple long-context datasets. - Efficient Retrieval: Despite using fewer tokens for input, Landmark Embedding achieved better retrieval results compared to models like ChatGPT with longer input windows. - Fine-Tuning: Even moderate fine-tuning with synthetic data significantly enhanced the model's retrieval capability. - Broader Applications: The method can be applied to any task requiring retrieval from long-context inputs, such as question answering and reading comprehension. All credits go to the authors of the paper #RAG #Chunking #VectorDB #Embedding