c756a5766b
- Add rag_indexer.py: build LightRAG index from OCR with OpenCode API - Add rag_query.py: query the knowledge graph - Add vlm_describer.py: generate VLM descriptions via LM Studio - Add test_model.py: quick check for LightRAG-compatible models - Add run_pipeline.sh and run_pipeline.bat: full OCR → VLM → RAG pipeline - Fix rapidocr import (rapidocr_onnxruntime) - Fix process_any_pdf.py paths for cross-platform use - Add .env.example, README_RAG.md, AGENTS.md - Update .gitignore for outputs and secrets
39 lines
1.1 KiB
Bash
Executable File
39 lines
1.1 KiB
Bash
Executable File
#!/bin/bash
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# run_pipeline.sh — полный pipeline: OCR → VLM → RAG индекс
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set -e
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PDF="${1:-123.pdf}"
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OUT="${2:-output_123}"
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PYTHON="/opt/homebrew/bin/python3.11"
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echo "=============================================="
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echo " Pipeline: $PDF → $OUT"
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echo "=============================================="
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# 1. OCR
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echo ""
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echo "[1/4] OCR: PDF → PNG + JSON"
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$PYTHON process_any_pdf.py "$PDF" "$OUT"
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# 2. VLM descriptions
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echo ""
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echo "[2/4] VLM: PNG → описания (LM Studio)"
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$PYTHON vlm_describer.py "$OUT" --model qwen/qwen3-vl-4b
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# 3. RAG Index
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echo ""
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echo "[3/4] RAG индекс: JSON + VLM → граф знаний"
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rm -rf "$OUT/lightrag_cache"
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$PYTHON rag_indexer.py "$OUT" --backend opencode --model nemotron-3-super-free --vlm-desc
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# 4. Test query
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echo ""
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echo "[4/4] Тестовый запрос"
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$PYTHON rag_query.py "$OUT" "Какие оси есть в чертеже?" --backend opencode --mode hybrid
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echo ""
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echo "=============================================="
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echo " Готово! Индекс в $OUT/lightrag_cache"
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echo "=============================================="
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