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Commit ·
0db65ad
1
Parent(s): f7874af
fix: align all UI model documentation strings with Tencent Hy3 + Gemini 3.1 Flash backend
Browse files
RAG_FULL_APPLICATION_FRONTEND/src/pages/AdvancedRagPage.jsx
CHANGED
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@@ -12,7 +12,7 @@ export default function AdvancedRagPage() {
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const [graphData, setGraphData] = useState(null);
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const [graphLoading, setGraphLoading] = useState(false);
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const [inputText, setInputText] = useState(
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"Artificial Intelligence and Machine Learning models rely on Vector Embeddings and Supabase pgvector storage. HyDE query expansion uses Large Language Models like
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);
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// Guardrails State
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const [graphData, setGraphData] = useState(null);
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const [graphLoading, setGraphLoading] = useState(false);
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const [inputText, setInputText] = useState(
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+
"Artificial Intelligence and Machine Learning models rely on Vector Embeddings and Supabase pgvector storage. HyDE query expansion uses Large Language Models like Tencent Hy3 and Gemini 3.1 Flash to generate synthetic document responses for reciprocal rank fusion retrieval."
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);
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// Guardrails State
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RAG_FULL_APPLICATION_FRONTEND/src/pages/FundamentalsPage.jsx
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@@ -7,8 +7,8 @@ export default function FundamentalsPage() {
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{ title: '1. Knowledge Ingestion', desc: 'Parsing PDF, TXT, DOCX, CSV, PNG documents and tokenizing text into discrete chunks.' },
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{ title: '2. Vector Indexing', desc: 'Generating 1024-dim dense embeddings using bge-m3 and indexing in Supabase pgvector & local BM25.' },
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{ title: '3. Strategic Retrieval', desc: 'Executing Hybrid Search (BM25 + Vector RRF), HyDE, Re-ranking, or Agentic RAG to fetch context chunks.' },
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{ title: '4. Augmented Generation', desc: 'Dispatching formatted context prompts to
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{ title: '5. Quality Evaluation (RAGAS)', desc: 'Evaluating Faithfulness, Relevancy, Precision, and Recall using
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];
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return (
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{ title: '1. Knowledge Ingestion', desc: 'Parsing PDF, TXT, DOCX, CSV, PNG documents and tokenizing text into discrete chunks.' },
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{ title: '2. Vector Indexing', desc: 'Generating 1024-dim dense embeddings using bge-m3 and indexing in Supabase pgvector & local BM25.' },
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{ title: '3. Strategic Retrieval', desc: 'Executing Hybrid Search (BM25 + Vector RRF), HyDE, Re-ranking, or Agentic RAG to fetch context chunks.' },
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{ title: '4. Augmented Generation', desc: 'Dispatching formatted context prompts to Tencent Hy3 (Primary) with Gemini 3.1 Flash backup.' },
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{ title: '5. Quality Evaluation (RAGAS)', desc: 'Evaluating Faithfulness, Relevancy, Precision, and Recall using Gemini 3.1 Flash as LLM Judge.' }
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];
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return (
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