Appetenza
Vector databases are the search engines behind modern AI systems. In a Retrieval Augmented Gener...
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Appetenza
The error you're encountering with npm run watch suggests that there might be an issue with the...
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Appetenza
Embeddings are the mathematical backbone of modern Retrieval Augmented Generation (RAG) systems. They allow machines to understand meaning, not just keywords. Without embeddings, your chatbot would behave like a basic search engine, matching exact words instead of understanding concepts. In this...
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Appetenza
Document chunking is one of the most important — and most underestimated — parts of building a production Retrieval Augmented Generation (RAG) system. Even with the best language model and vector database, poor chunking can destroy answer quality. Chunking determines how knowledge is b...
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Ranvijay
Retrieval Augmented Generation (RAG) is one of the most important architectures powering modern AI chatbots. It combines the reasoning ability of Large Language Models (LLMs) with the accuracy of external knowledge sources. Instead of relying only on what an AI model learned during training, RAG s...
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Appetenza
Balancing Robustness, Compatibility, and Security In the early days of the internet, computers from different universities, research labs, and organizations needed to communicate across unreliable networks using software written by completely different teams. There were no mature st...
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Appetenza
Retrieval Augmented Generation (RAG) is the technique that allows an AI chatbot to answer questions using your own data, not just what a language model learned during training. Instead of relying only on an LLM’s memory, we give it a search engine + knowledge base. This is how modern AI support bo...
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