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#4162 Tools for KG: Constitution & Bill of Rights

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#4162 Tools for KG: Constitution & Bill of Rights #4162  Given Google AI Studio, Google Jules, Google AntiGravity and Neo4j AuraDB Professional, what is the best tools to implement a web knowledge graph where common users can ask questions about the U.S. Constitution and Bill of Rights? You don’t actually have to choose just one; these four tools represent distinct, highly complementary layers of a modern, agent-driven GraphRAG (Retrieval-Augmented Generation) stack. To build a web application where users can query the U.S. Constitution and Bill of Rights, here is the best way to leverage each tool for the implementation: 1. Neo4j AuraDB Professional: The Knowledge Engine This is your database layer. The Constitution is a highly structured, interconnected document, making it perfect for a graph database. The Role: You will use AuraDB to store the ontological structure of the documents (e.g., (Article)-[:HAS_SECTION]->(Section) or (Amendment)-[:GUARANTEES]->(Right) ) . W...
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#4161 Knowledge Graph of U.S. Constitution and Bill of Rights; +Website

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#4161 Knowledge Graph of U.S. Constitution and Bill of Rights; +Website #4161  Explain step-by-step how to implement GraphRAG in Python. Include sequence of queries, with both pre-processing and post-processing. Implementing GraphRAG (Graph Retrieval-Augmented Generation) is a powerful way to enhance standard RAG systems. While standard RAG retrieves isolated text chunks using vector similarity, GraphRAG builds a Knowledge Graph (KG) from your documents, allowing the LLM to understand complex, multi-hop relationships and answer broad, thematic questions ("Global Search") as well as specific entity questions ("Local Search"). Here is a step-by-step breakdown of how to implement the GraphRAG pipeline in Python, from pre-processing (indexing) to querying and post-processing. Phase 1: Pre-Processing (The Indexing Pipeline) This phase transforms your raw text documents into a structured Knowledge Graph with summarized communities. 1. Document Ingestion & Chunking Fir...
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#4160 Upload File Types for LLMs: PDF or HTML; +PDF-to-XYZ Tools

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#4160 Upload File Types for LLMs: PDF or HTML; +PDF-to-XYZ Tools #4160  When accepting files as input, do LLMs work better with PDF or HTML files? In 2026, the consensus among developers and AI researchers is that HTML (and its distilled cousin, Markdown) is significantly more effective for LLM input than PDF. While modern multimodal models (like Gemini 2.0 Flash or Claude 3.7) are remarkably good at "seeing" PDFs, HTML provides a native semantic structure that LLMs interpret with much higher reliability and lower token overhead. Why HTML Wins Semantic Hierarchy: HTML tags like <h1> , <table> , and <ul> provide explicit signals about data relationships. In contrast, a PDF is a "display format" where a header is just text with a larger font size. Token Efficiency: "Raw" PDFs often contain redundant layout data. Converting them to "clean" HTML or Markdown can reduce token counts by up to 90% without losing information, allow...
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