Superlinked Server

A Model Context Protocol (MCP) server that provides semantic search and RAG capabilities using Superlinked's vector search framework. This server enables AI assistants to create and query vector indexes from structured data files with support for multiple search dimensions (text similarity, recency, categories, and numbers).

Overview

This project creates an MCP server that wraps Superlinked's InMemoryExecutor, allowing any MCP-compatible client (like Claude Desktop) to perform sophisticated semantic search operations on structured data. The server provides tools to preview data files, create multi-dimensional vector indexes, and query them using natural language.

How It Works

The server uses Superlinked's vector search capabilities to create in-memory indexes that combine multiple search dimensions:

1. Text Similarity: Semantic search using sentence transformers embeddings
2. Recency: Time-based ranking for timestamp fields
3. Number: Numeric value ranking (prices, ratings, etc.)
4. Category: Categorical matching and filtering

When you create an index, the server:

• Analyzes your data file structure
• Creates appropriate vector spaces based on your column mapping
• Applies custom weights to each dimension
• Ingests the data into an in-memory index
• Makes it available for semantic queries

Features

• Multi-format support: CSV and JSON data files
• Flexible schema: Dynamic schema creation based on your data
• Multi-dimensional search: Combine text, time, numbers, and categories
• Custom weighting: Control the importance of each search dimension
• In-memory execution: Fast queries without external dependencies
• MCP compatibility: Works with any MCP client

Installation

Prerequisites

• Python 3.10+
• pip package manager

Setup

1. Clone this repository:

git clone <repository-url>
cd rag_repo

2. Install dependencies:

pip install -r requirements.txt

3. Set required environment variable:

# Required for Streamlit demo
export ANTHROPIC_API_KEY=your_api_key_here

# Optional: Change Claude model (default: claude-haiku-4-5-20251001)
export CLAUDE_MODEL=claude-haiku-4-5-20251001

MCP Server Tools

The server provides three main tools:

1. preview_file

Preview the structure and contents of a data file before indexing. Use this tool to understand your data structure and decide which spaces to create for indexing.

Parameters:

• file_path (string): Path to data file (CSV or JSON)
• rows (integer, optional): Number of sample rows to preview (default: 5)

Returns:

• Total row count
• Column names and data types
• Sample records

Example use case: "Show me what's in sample_data/business_news.json"

2. create_index

Create a searchable vector index from structured data.

Parameters:

• file_path (string): Path to data file to index
• column_mapping (dict): Maps column names to space types
  • text_similarity: For text fields (semantic search)
  • recency: For timestamp/date fields (Unix timestamps)
  • number: For numeric fields
  • category: For categorical fields
• weights (dict): Maps column names to weight values (0.0 to 1.0)
  • Higher weight = more influence on ranking
  • Example: {"description": 0.8, "timestamp": 0.3}

Returns:

• Index name (derived from filename)
• Column configuration
• Ingestion statistics
• Applied weights

Example use case: "Create an index from business_news.json with text search on the description field and recency on the date field. Put more weight on the description."

3. query_index

Search an index using natural language queries.

Parameters:

• index_name (string): Name of the index (filename without extension)
• query_text (string): Natural language search query
• limit (integer, optional): Maximum results to return (default: 5)

Returns:

• Array of results with:
  • id: Record identifier
  • score: Similarity score
  • fields: All indexed fields and their values

Example use case: "Search the business_news index for articles about strikes"

Integration with External MCP Clients

Claude Desktop

To use this server with Claude Desktop:

1. Locate your Claude Desktop configuration file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Add the server configuration:

{
  "mcpServers": {
    "superlinked-rag": {
      "command": "python",
      "args": ["/absolute/path/to/rag_repo/mcp_server.py"],
      "type": "stdio"
    }
  }
}

3. Restart Claude Desktop

4. The server tools will now be available to Claude. You can ask Claude to:

   • Preview your data files
   • Create vector indexes
   • Perform semantic searches

Streamlit Demo App

This repository includes a Streamlit chatbot demo that uses the Claude Agent SDK as an MCP client. This demonstrates how to build a conversational interface with the MCP server.

Running the Demo

1. Run the Streamlit app:

streamlit run streamlit_chatbot.py

2. Open your browser to the provided URL (typically http://localhost:8501)

3. IMPORTANT: small unfixed bug - right after fresh environment installation, the MCP server may not load on the first app start. Simply refresh the browser once after the Streamlit app loads — you only need to do this one time and it fix the problem.

Demo Features

• Real-time streaming: See AI reasoning steps as they happen
• Conversation memory: Multi-turn conversations with context
• Tool visualization: Watch the AI use MCP tools
• Interactive UI: Clean interface with execution step display

Example Conversation Flow

1. "I have this file: sample_data/business_news.json. Show me what's in it."
2. "Create an index for semantic search on the description field and time-based ranking on the date field."
3. "Who wanted to have a strike?"
4. "This news is old, put more weight on the date field."
5. "Any news related to gas? What does it say?"

Resources

• Superlinked Documentation
• Model Context Protocol
• Claude Agent SDK
• FastMCP