Marketplace backend-fastapi

Documentation for the FastAPI backend, endpoints, and dependency injection.

install

source · Clone the upstream repo

git clone https://github.com/aiskillstore/marketplace

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/aiskillstore/marketplace "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/abdulsamad94/backend-fastapi" ~/.claude/skills/aiskillstore-marketplace-backend-fastapi && rm -rf "$T"

manifest: skills/abdulsamad94/backend-fastapi/SKILL.md

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.