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It powers the chatbot and RAG functionality.\n\n## Entry Point\n\n- **File**: `backend\u002Fmain.py`\n- **Run**: `uvicorn backend.main:app --reload` (or via `npm run dev`)\n- **Port**: Defaults to `8000`.\n\n## Endpoints\n\n### `POST \u002Fapi\u002Fchat`\n\n- **Purpose**: Main RAG chat endpoint.\n- **Input**: `ChatRequest` (query, history, user_context).\n- **Process**:\n  1. Embed query.\n  2. Search Qdrant (`search_qdrant`).\n  3. Build prompt (`build_rag_prompt`).\n  4. Generate Agent response.\n- **Output**: `ChatResponse` (answer, contexts).\n\n### `POST \u002Fapi\u002Fask-selection`\n\n- **Purpose**: Targeted Q&A on selected text.\n- **Input**: `AskSelectionRequest` (question, selected_text).\n- **Process**:\n  1. Validates selection length.\n  2. Builds selection-specific prompt.\n  3. specific Agent instructions.\n\n## Dependencies & Utils\n\n- `backend\u002Futils\u002Fconfig.py`: Qdrant initialization.\n- `backend\u002Futils\u002Fhelpers.py`: Embedding and Prompt building logic.\n- `backend\u002Fmodels.py`: OpenAI\u002FGemini client setup.\n\n## Environment Variables\n\n- `GEMINI_API_KEY`: For LLM and Embeddings.\n- `QDRANT_URL`, `QDRANT_API_KEY`: Vector DB connection.\n","# Backend Architecture (FastAPI)\n\n## Overview\n\nThe backend is a **FastAPI** application located in `backend\u002F`. It powers the chatbot and RAG functionality.\n\n## Entry Point\n\n- **File**: `backend\u002Fmain.py`\n- **Run**: `uvicorn backend.main:app --reload` (or via `npm run dev`)\n- **Port**: Defaults to `8000`.\n\n## Endpoints\n\n### `POST \u002Fapi\u002Fchat`\n\n- **Purpose**: Main RAG chat endpoint.\n- **Input**: `ChatRequest` (query, history, user_context).\n- **Process**:\n  1. Embed query.\n  2. Search Qdrant (`search_qdrant`).\n  3. 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