backend-fastapi
Documentation for the FastAPI backend, endpoints, and dependency injection.
Best use case
backend-fastapi is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Documentation for the FastAPI backend, endpoints, and dependency injection.
Teams using backend-fastapi should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/backend-fastapi/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How backend-fastapi Compares
| Feature / Agent | backend-fastapi | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Documentation for the FastAPI backend, endpoints, and dependency injection.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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, user_context). - **Process**: 1. Embed query. 2. Search Qdrant (`search_qdrant`). 3. Build prompt (`build_rag_prompt`). 4. Generate Agent response. - **Output**: `ChatResponse` (answer, contexts). ### `POST /api/ask-selection` - **Purpose**: Targeted Q&A on selected text. - **Input**: `AskSelectionRequest` (question, selected_text). - **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 - `GEMINI_API_KEY`: For LLM and Embeddings. - `QDRANT_URL`, `QDRANT_API_KEY`: Vector DB connection.
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