SKU/Artículo: AMZ-B0G4H3VZGM
GRAPH-RAG Foundations: From Beginners to Expert: Master Knowledge Graphs, Retrieval-Augmented Generation, Vector Search, and Next-Gen AI Reasoning Systems for Real-World Applications
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Paperback
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0.33 kg
0.33 kg
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Amazon
Amazon
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USA
USA
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- The year 2023 marked the explosive arrival of Retrieval-Augmented Generation (RAG), a simple yet transformative idea: instead of forcing a large language model (LLM) to memorize the entire world inside its parameters, let it retrieve relevant documents at inference time and condition its answer on fresh, verifiable evidence. Within months, RAG became the default paradigm for building reliable, knowledge-intensive applications—chatbots that quote company policy, medical assistants that cite the latest studies, legal co-pilots that reference case law. The hallucination problem, long considered an inevitable side-effect of scaling, suddenly appeared solvable. Yet by early 2025, the limitations of classical RAG have become equally obvious. Vector similarity search over flat lists of text chunks excels at locating topically relevant passages, but it struggles with multi-hop questions, complex relationships, temporal reasoning, hierarchical knowledge, and any scenario that demands true understanding rather than sophisticated pattern matching. When an analyst asks “Which pharmaceutical companies that partnered with BioNTech before 2020 later received FDA emergency authorization for a non-COVID therapeutic in 2024?”, traditional RAG typically fails—not because the information is absent, but because the connections are scattered across dozens of documents and no single chunk contains the full reasoning chain. This is precisely where Graph-RAG enters the picture. Graph-RAG is not merely an incremental improvement; it represents a paradigm shift in how we organize, retrieve, and reason over knowledge. By combining the semantic density of vector embeddings with the explicit relational structure of knowledge graphs, Graph-RAG enables language models to perform genuine reasoning rather than statistical autocomplete over retrieved snippets. Relationships become first-class citizens. Entities are disambiguated. Facts are linked through typed, traversable edges. The result is a new generation of AI systems that can answer questions traditional RAG cannot, trace their reasoning back to source documents, and maintain consistency across thousands of interactions.
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AR$47.335
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