Add dataset preparation script — downloads Wikipedia/arxiv/BBC, verifies 2M+ tokens, ingests into TigerGraph

Fix #7: Update dashboard.py — 3-column layout (LLM-Only / Basic RAG / GraphRAG), fix _get_demo_passages() query matching, add LLM-Judge + BERTScore display

Fix #9: Update benchmark runner for 3-pipeline comparison + LLM-as-a-Judge + BERTScore evaluation

Update __init__.py with new exports (TGGraphRAGClient, TripleComparisonResult, LLM judge, BERTScore, etc.)

Fix #5: Install advanced GSQL queries (PPR, paths, activation) in setup_tigergraph.py

Fix #3: Add LLM-as-a-Judge (PASS/FAIL) + BERTScore evaluation — the two hackathon-required accuracy metrics

Fix #2+#4+#6: Add LLM-Only pipeline, wire NoveltyEngine, integrate TG GraphRAG client, 3-pipeline comparison

Fix #1: Add TigerGraph GraphRAG integration layer wrapping official repo REST APIs

Add advanced GSQL queries: PPR scoring, shortest paths, spreading activation, community detection"

Add GraphRAG novelties engine: PPR scoring, token budget, path pruning, spreading activation, hybrid router, incremental updates

Wire UniversalLLM into layers __init__ + add benchmark API endpoint for live runs

Add Python universal LLM layer with LiteLLM supporting 12 providers + Ollama

Add 4-tab Gradio Dashboard (Live Comparison, Batch Benchmark, Cost Analysis, Graph Explorer)

Add main entry point (CLI: dashboard, benchmark, ingest, demo)

Add TigerGraph setup script

Add ingestion pipeline

Add benchmark runner

Add Layer 2: Inference Orchestration (dual pipeline, adaptive routing, comparison)

Add Layer 4: Evaluation Layer (RAGAS + custom F1/EM metrics + benchmarking)

Add Layer 1: Graph Layer (TigerGraph schema, GSQL queries, vector search, traversal)

Add Layer 3: LLM Layer (generation, entity extraction, keyword extraction, complexity analysis)

Add configs init

Add layers init

Add settings config

Add package init