Initial DocsMCP stack

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+# 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.