Agent skill · software engineering · aiskillstore
backend-fastapi
Documentation for the FastAPI backend, endpoints, and dependency injection.
Why this skill is useful
Provides specific FastAPI endpoint documentation and dependency injection patterns that the AI wouldn't reliably generate on its own.
What it needs
Requires uvicorn installed locally. Requires openai, qdrant account access. About 1k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Backend Architecture (FastAPI) Overview The backend is a FastAPI application located in backend/. It powers the chatbot and RAG functionality. Entry Point File: backend/main.py Run: uvicorn backend.main:app --reload (or via npm run dev) Port: Defaults to 8000. Endpoints POST /api/chat Purpose: Main RAG chat endpoint. Input: ChatRequest (query, history, usercontext). Process: 1. Embed query. 2. Search Qdrant (searchqdrant). 3. Build prompt (buildragprompt). 4. Generate Agent response. Output: ChatResponse (answer, contexts). POST /api/ask-selection Purpose: Targeted Q&A on selected text. Input: AskSelectionRequest (question, selectedtext). Process: 1. Validates selection length. 2. Builds selection-specific prompt. 3. specific Agent instructions. Dependencies & Utils backend/utils/config.py: Qdrant initialization. backend/utils/helpers.py: Embedding and Prompt building logic. backend/models.py: OpenAI/Gemini client setup. Environment Variables GEMINIAPIKEY: For LLM and Embeddings. QDRANTURL, QDRANTAPIKEY: Vector DB connection.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/backend-fastapi