Why Use This
This skill provides specialized capabilities for aiskillstore's codebase.
Use Cases
- Developing new features in the aiskillstore repository
- Refactoring existing code to follow aiskillstore standards
- Understanding and working with aiskillstore's codebase structure
Install Guide
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https://github.com/aiskillstore/marketplace/tree/main/skills/abdulsamad94/backend-fastapi
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Updated At Jan 19, 2026, 04:39 AM
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License NOASSERTION
---
name: backend-fastapi
description: Documentation for the FastAPI backend, endpoints, and dependency injection.
---
# 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.
