Added docs
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@ -28,22 +28,19 @@ With the integration of Keepi.ai, we encountered several challenges that made us
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Moving forward, we have adopted several new strategies, features, and design principles:
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<aside>
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💡 Propositions: Defined as atomic expressions within a text, each proposition encapsulates a unique factoid, conveyed in a succinct, standalone natural language format.
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### Propositions:
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Defined as atomic expressions within a text, each proposition encapsulates a unique factoid, conveyed in a succinct, standalone natural language format.
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We employ Large Language Models (LLMs) to break down text into propositions and link them, forming graphs with propositions as nodes and their connections as edges.
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For example, "Grass is green", and "2 + 5 = 5" are propositions. The first proposition has the truth value of "true" and the second "false".
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The inspiration was found in the following paper: https://arxiv.org/pdf/2312.06648.pdf
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<aside>
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💡 Multilayer Graph Network:
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### Multilayer Graph Network:
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A cognitive multilayer networks is both a quantitative and interpretive framework for exploring the mental lexicon, the intricate cognitive system that stores information about known words/concepts.
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@ -55,18 +52,15 @@ For example, if "John Doe" authored two New York Times cooking articles, we coul
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We used concepts from psycholinguistics described here: https://arxiv.org/abs/1507.08539
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💡 Data Loader:
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### Data Loader:
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It’s vital that we address the technical challenges associated with Retrieval-Augmented Generation (RAG), such as metadata management, context retrieval, knowledge sanitization, and data enrichment.
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The solution lies in a dependable data pipeline capable of efficiently and scalably preparing and loading data in various formats from a range of different sources. For this purpose, we can use 'dlt' as our data loader, gaining access to over 28 supported data sources.
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</aside>
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To enhance the Pythonic interface, we streamlined the use of cognee into three primary methods. Users can now execute the following steps:
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