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. It is especially useful for teams working in multi. Documentation for the FastAPI backend, endpoints, and dependency injection.
Documentation for the FastAPI backend, endpoints, and dependency injection.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "backend-fastapi" skill to help with this workflow task. Context: Documentation for the FastAPI backend, endpoints, and dependency injection.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
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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