Which integrations are available for this server?

Uses LangChain for LLM orchestration and chaining within the RAG pipeline to process queries and generate responses. Implements a state machine architecture for the RAG pipeline with multi-stage processing including guardrails, Cypher generation, retrieval, and response generation. Provides a RAG system that converts natural language queries into Cypher queries to retrieve information from a Neo4j graph database, with support for dynamic graph schema configuration and intelligent query routing. Integrates OpenAI models for dual LLM strategy: fast models for guardrails decision-making and accurate models for Cypher query generation and answering.

How do I use SOLVRO MCP - Knowledge Graph RAG System?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@SOLVRO MCP - Knowledge Graph RAG System What courses are taught by Professor Kowalski in the Computer Science department?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

SOLVRO MCP - Knowledge Graph RAG System by Solvro

┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ ToPWR API │────▶│ MCP Server │────▶│ Neo4j │ │ :8000 │ │ :8005 │ │ :7687 │ └─────────────┘ └─────────────┘ └─────────────┘ FastAPI FastMCP Knowledge Graph

Intelligent Query Routing - Guardrails system determines query relevance
Natural Language to Cypher - Converts questions to graph queries
Knowledge Graph RAG - Retrieval-Augmented Generation with Neo4j
MCP Protocol - Standard Model Context Protocol interface
Observability - Optional Langfuse tracing integration
Docker Ready - One command deployment

Quick Start

# Setup just setup cp .env.example .env # Edit with your API keys # Run with Docker just up # Start Neo4j + MCP Server + API just logs # View logs just down # Stop services

Architecture

System Overview

Service	Port	Description
`topwr-api`	8000	FastAPI backend for ToPWR app
`mcp-server`	8005	MCP server with RAG pipeline
`neo4j`	7474/7687	Knowledge graph database

RAG Pipeline

The heart of the system is a LangGraph-based RAG pipeline that intelligently processes user queries:

Pipeline Flow:

Guardrails - Fast LLM determines if query is relevant to knowledge base
Cypher Generation - Accurate LLM converts natural language to Cypher query
Retrieval - Execute query against Neo4j knowledge graph
Response - Return structured context data

Data Pipeline

Separate ETL pipeline for ingesting documents into the knowledge graph:

Pipeline Steps:

Document Loading - PDF and text document ingestion
Text Extraction - OCR and content extraction
LLM Processing - Generate Cypher queries from content
Graph Population - Execute queries to build knowledge graph

Configuration

Copy .env.example to .env and configure:

######################################## # LLM / AI Provider Keys ######################################## # OpenAI API key (optional) OPENAI_API_KEY= # DeepSeek API key (optional) DEEPSEEK_API_KEY= # Google Generative AI / PaLM API key (optional) GOOGLE_API_KEY= # CLARIN LLM API key (optional, used by API & client) CLARIN_API_KEY= ######################################## # Langfuse Observability ######################################## LANGFUSE_SECRET_KEY= LANGFUSE_PUBLIC_KEY= LANGFUSE_HOST=https://cloud.langfuse.com ######################################## # Neo4j Database ######################################## # URI used by data pipeline, MCP server and graph config NEO4J_URI=bolt://localhost:7687 NEO4J_USER=neo4j NEO4J_PASSWORD= ######################################## # MCP Server Networking ######################################## # Bind host for the MCP server process MCP_BIND_HOST=0.0.0.0 # Host/port used by API and MCP client to reach the MCP server MCP_HOST=127.0.0.1 MCP_PORT=8005

Commands

# Docker Stack just up # Start all services just down # Stop services just logs # View logs just ps # Service status just nuke # Remove everything # Local Development just mcp-server # Run MCP server just api # Run FastAPI just kg "query" # Query knowledge graph # Quality just lint # Format & lint just test # Run tests just ci # Full CI pipeline # Data Pipeline just prefect-up # Start Prefect just pipeline # Run ETL

Project Structure

src/ ├── mcp_server/ # MCP server + RAG pipeline ├── mcp_client/ # CLI client ├── topwr_api/ # FastAPI backend ├── config/ # Configuration └── data_pipeline/ # Prefect ETL flows docker/ ├── compose.stack.yml # Main stack (Neo4j + MCP + API) ├── compose.prefect.yml # Data pipeline ├── Dockerfile.mcp # MCP server image └── Dockerfile.api # FastAPI image

API Usage

Chat Endpoint

curl -X POST http://localhost:8000/api/chat \ -H "Content-Type: application/json" \ -d '{"user_id": "user1", "message": "Czym jest nagroda dziekana?"}'

Response:

{ "session_id": "abc123", "message": "Nagroda dziekana to wyróżnienie przyznawane...", "metadata": { "source": "mcp_knowledge_graph", "trace_id": "xyz789" } }

Session Management

# Get session history curl http://localhost:8000/api/sessions/{session_id}/history # List user sessions curl http://localhost:8000/api/users/{user_id}/sessions

Tech Stack

Technology	Purpose
FastMCP	Model Context Protocol server
LangGraph	RAG state machine
LangChain	LLM orchestration
Neo4j	Knowledge graph database
FastAPI	REST API backend
Langfuse	Observability (optional)
Prefect	Data pipeline orchestration
Docker	Containerization

SOLVRO MCP - Knowledge Graph RAG System