# Getting Started

Welcome to the Context7-style MCP System documentation!

## Overview

This system provides a self-hosted, local context7-compatible MCP (Model Context Protocol) solution using Docker containers. It enables you to:

- Ingest and index your own documents
- Perform semantic search on vector embeddings
- Integrate with MCP-enabled IDEs for intelligent tool interactions

## Architecture

```
┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Client    │────▶│ docs-api    │◀────│ docs-mcp    │
│ (IDE/Tool)  │     │ (FastAPI)   │     │ (MCP Server)│
└─────────────┘     └─────────────┘     └─────────────┘
                          │
                          ▼
                    ┌─────────────┐
                    │   Qdrant    │
                    │ (Vector DB) │
                    └─────────────┘
```

## Quick Start

### 1. Start All Services

```bash
docker compose up -d --build
```

### 2. Verify Services Are Running

```bash
docker compose ps
```

You should see all three services in "Up" status:
- `qdrant` (port 6333)
- `docs-api` (port 8787)
- `docs-mcp` (port 8788)

### 3. Access the API

Open your browser and navigate to:
```
http://localhost:8787/docs
```

You should see the FastAPI documentation page.

## API Endpoints

### Health Check

```bash
curl http://localhost:8787/health
```

Expected response:
```json
{"status":"ok"}
```

### Ingest Document

Upload a text document to be processed and indexed:

```bash
curl -X POST "http://localhost:8787/api/v1/ingest" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "This is sample document content for semantic search testing.",
    "metadata": {"source": "example", "type": "text"}
  }'
```

### Search Documents

Perform a similarity search on ingested documents:

```bash
curl "http://localhost:8787/api/v1/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "semantic search",
    "top_k": 5,
    "threshold": 0.7
  }'
```

## Configuration

### Environment Variables

Copy the example environment file and customize:

```bash
cp .env.example .env
```

Key variables:
- `VECTOR_STORE_HOST`: Internal hostname of Qdrant (default: qdrant)
- `VECTOR_STORE_PORT`: Qdrant port (default: 6333)

### Docker Compose

All services are defined in `docker-compose.yml`. Key networking details:
- Services communicate internally via `context7-network`
- Qdrant uses service name `qdrant` for internal connections
- Vector store is exposed externally on port 6333 for debugging

## Next Steps

1. Review the project structure to understand component roles
2. Customize the backend API endpoints in `backend/app/main.py`
3. Implement MCP tools in `mcp-server/server.py`
4. Add more example documents in the `docs/` directory

## Troubleshooting

### Check Logs

```bash
docker compose logs -f docs-api
docker compose logs -f qdrant
docker compose logs -f docs-mcp
```

### Reset All Services

```bash
docker compose down -v
docker compose up -d --build
```

## Support

For issues, refer to the `README.md` or check the Qdrant documentation.