Menu
Documentation for the FastAPI backend, endpoints, and dependency injection.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Overview of the project's tech stack, directory structure, and architectural patterns.
Guide to using Better Auth for client and server-side authentication.
Validates and analyzes Docusaurus MDX chapters for structure, pedagogical quality, and component usage.
Details of the RAG Chatbot, including UI and backend logic.
Interaction with NeonDB Postgres using Drizzle ORM.
Knowledge of the Vercel deployment pipeline, hybrid build scripts, and environment configuration.
| name | Backend (FastAPI) |
| description | Documentation for the FastAPI backend, endpoints, and dependency injection. |
The backend is a FastAPI application located in backend/. It powers the chatbot and RAG functionality.
backend/main.pyuvicorn backend.main:app --reload (or via npm run dev)8000.POST /api/chatChatRequest (query, history, user_context).search_qdrant).build_rag_prompt).ChatResponse (answer, contexts).POST /api/ask-selectionAskSelectionRequest (question, selected_text).backend/utils/config.py: Qdrant initialization.backend/utils/helpers.py: Embedding and Prompt building logic.backend/models.py: OpenAI/Gemini client setup.GEMINI_API_KEY: For LLM and Embeddings.QDRANT_URL, QDRANT_API_KEY: Vector DB connection.