Add Space config and hackathon metadata

README.md

@@ -1,220 +1,37 @@
-# RAG-MCP
+---
+title: RAG-MCP Agent
+emoji: 🧠
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+app_file: app.py
+pinned: false
+---
 
-A minimal RAG (Retrieval-Augmented Generation) service with MCP tools for document ingestion and semantic search.
+# 🧠 RAG-MCP Agent
 
-Documents are chunked via LangChain's `RecursiveCharacterTextSplitter`, embedded with a HuggingFace model, and stored in Qdrant or ChromaDB.
+## Hackathon Track
 
-## Features
+Tracks:
+- "building-mcp-track-consumer"
+- "mcp-in-action-track-consumer"
 
-- **5 MCP Tools**: `ingest_documents`, `search`, `get_chunk`, `get_list`, `delete`
-- **Multiple Vector Stores**: Qdrant (default) or ChromaDB
-- **Configurable**: All settings via environment variables
-- **Docker Ready**: One-command setup with docker-compose
+## Overview
 
-## Quick Start
+RAG-MCP is a minimal MCP (Model Context Protocol) server with built-in Retrieval-Augmented Generation.
+This Space demonstrates the same RAG engine wrapped with a Gradio agent UI.
 
-### 1. Start Qdrant
+Users can ingest documents, query collections, and experiment with a working MCP-style RAG system.
 
-```bash
-docker compose up -d qdrant-service
-```
+## Project Structure
 
-### 2. Configure MCP Client
+- Core MCP server:
+  https://github.com/DataOpsFusion/Rag-MCP
 
-Add to your MCP client config (LM Studio, Claude Desktop):
+- Demo UI:
+  This Hugging Face Space (`rag-mcp-agent`) shows RAG-MCP in action.
 
-```json
-{
-  "mcpServers": {
-    "rag-mcp": {
-      "command": "uvx",
-      "args": ["--from", "/path/to/Rag-MCP", "rag-mcp-qdrant"],
-      "env": {
-        "MODEL_NAME": "jinaai/jina-embeddings-v2-base-code",
-        "MODEL_DEVICE": "cpu",
-        "QDRANT_HOST": "localhost",
-        "QDRANT_PORT": "6333"
-      }
-    }
-  }
-}
-```
+## Demo & Social
 
-Replace `/path/to/Rag-MCP` with your actual path.
-
-### 3. Use the Tools
-
-- **Ingest**: `ingest_documents(collection="docs", documents=[{"text": "..."}])`
-- **Search**: `search(collection="docs", query="find this", top_k=5)`
-- **Get Chunk**: `get_chunk(collection="docs", chunk_id="...")`
-- **List Docs**: `get_list(collection="docs")`
-- **Delete**: `delete(collection="docs", doc_id="...")`
-
-## Installation
-
-### Method 1: uvx (Recommended)
-
-```bash
-uvx --from /path/to/Rag-MCP rag-mcp-qdrant
-```
-
-First run downloads ~800MB of dependencies and may take 2-3 minutes.
-
-**Pre-install to avoid timeout:**
-```bash
-uvx --from /path/to/Rag-MCP rag-mcp-qdrant --help
-```
-
-### Method 2: pip
-
-```bash
-python3 -m venv .venv
-source .venv/bin/activate
-pip install torch --index-url https://download.pytorch.org/whl/cpu
-pip install -e .
-```
-
-Then use this MCP config:
-```json
-{
-  "mcpServers": {
-    "rag-mcp": {
-      "command": "/path/to/Rag-MCP/.venv/bin/rag-mcp-qdrant",
-      "args": [],
-      "env": {
-        "QDRANT_HOST": "localhost"
-      }
-    }
-  }
-}
-```
-
-### Method 3: Docker
-
-```bash
-docker compose up -d qdrant-service
-docker compose run --rm mcp
-```
-
-## Environment Variables
-
-| Variable | Default | Description |
-|----------|---------|-------------|
-| `MODEL_NAME` | `jinaai/jina-embeddings-v2-base-code` | HuggingFace embedding model |
-| `MODEL_DEVICE` | `cpu` | Device: `cpu`, `cuda`, `mps` |
-| `VECTOR_STORE` | `qdrant` | Backend: `qdrant` or `chroma` |
-| `QDRANT_HOST` | `localhost` | Qdrant server host |
-| `QDRANT_PORT` | `6333` | Qdrant server port |
-| `QDRANT_HTTPS` | `false` | Use HTTPS |
-| `QDRANT_API_KEY` | `None` | API key for Qdrant Cloud |
-| `CHROMA_PERSIST_DIRECTORY` | `./chroma_data` | ChromaDB storage path |
-| `COLLECTION_PREFIX` | `` | Prefix for collection names |
-| `CHUNK_SIZE` | `500` | Text chunk size |
-| `CHUNK_OVERLAP` | `50` | Overlap between chunks |
-
-## MCP Client Configuration
-
-### LM Studio
-
-File: Settings → MCP Servers
-
-```json
-{
-  "mcpServers": {
-    "rag-mcp": {
-      "command": "uv",
-      "args": ["run", "--directory", "/path/to/Rag-MCP", "rag-mcp-qdrant"],
-      "env": {
-        "MODEL_DEVICE": "cpu",
-        "QDRANT_HOST": "localhost",
-        "QDRANT_PORT": "6333"
-      }
-    }
-  }
-}
-```
-
-### Claude Desktop (macOS)
-
-File: `~/Library/Application Support/Claude/claude_desktop_config.json`
-
-```json
-{
-  "mcpServers": {
-    "rag-mcp": {
-      "command": "uvx",
-      "args": ["--from", "/path/to/Rag-MCP", "rag-mcp-qdrant"],
-      "env": {
-        "MODEL_DEVICE": "cpu",
-        "QDRANT_HOST": "localhost"
-      }
-    }
-  }
-}
-```
-
-### Claude Desktop (Windows)
-
-File: `%APPDATA%\Claude\claude_desktop_config.json`
-
-Same configuration as macOS.
-
-## Usage Examples
-
-### Python API
-
-```python
-from rag_core.config import get_config
-from rag_core.model import Model
-from rag_core.vector_store import QdrantVectorStore
-from rag_core.search import RagService
-
-cfg = get_config()
-model = Model(model_name=cfg.model_name)
-store = QdrantVectorStore.from_config(cfg)
-rag = RagService(model=model, vector_store=store, config=cfg)
-
-docs = [{"text": "Hello world", "metadata": {"source": "demo"}}]
-rag.ingest(collection="demo", documents=docs)
-
-hits = rag.search(collection="demo", query="hello", top_k=3)
-print(hits)
-```
-
-### MCP Tool Calls
-
-```python
-ingest_documents(
-    collection="knowledge",
-    documents=[{"text": "AI is transforming industries."}],
-    chunking={"chunk_size": 500, "overlap": 50}
-)
-
-search(
-    collection="knowledge",
-    query="AI transformation",
-    top_k=5,
-    score_threshold=0.5
-)
-```
-
-## Troubleshooting
-
-### Connection Refused
-```bash
-docker compose up -d qdrant-service
-curl http://localhost:6333/health
-```
-
-### Model Download Slow
-First run downloads the embedding model from HuggingFace (~400MB).
-
-### GPU Support
-```bash
-pip install torch --index-url https://download.pytorch.org/whl/cu121
-# Set MODEL_DEVICE=cuda in config
-```
-
-## License
-
-MIT
+- Demo video: (coming soon)
+- Social post: (coming soon)