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Scans `docs\u002F`.\n    2. Cleans MDX (removes frontmatter\u002Fimports).\n    3. Chunks text (1000 chars, 100 overlap).\n    4. Embeds using `models\u002Ftext-embedding-004`.\n    5. Upserts to Qdrant collection `physical_ai_book`.\n- **Run**: `python backend\u002Fingest.py`\n\n## Vector Search (Qdrant)\n- **Client**: `qdrant-client`\n- **Collection**: `physical_ai_book`\n- **Vector Size**: 768 (Gecko-004)\n- **Similarity**: Cosine\n\n## Prompt Engineering\n- **File**: `backend\u002Futils\u002Fhelpers.py`.\n- **RAG Prompt**: Constructs a prompt containing retrieved context chunks.\n- **Personalization**: `backend\u002Fpersonalization.py` creates system instructions based on `software_background` and `hardware_background` of the user.\n\n## Agentic Flow\nWe use a custom `Agent` class (`backend\u002Fagents.py`) that wraps the LLM calls, allowing for future expansion into multi-agent workflows.\n","# RAG Pipeline Logic\n\n## Ingestion\n- **Script**: `backend\u002Fingest.py`\n- **Process**:\n    1. Scans `docs\u002F`.\n    2. Cleans MDX (removes frontmatter\u002Fimports).\n    3. Chunks text (1000 chars, 100 overlap).\n    4. Embeds using `models\u002Ftext-embedding-004`.\n    5. 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