wware/0_GraphRAG.md

Graph RAG information (Neo4J, LlamaIndex, Groq API)

What follows is a conversation I had with Anthropic's https://claude.ai/ LLM, asking it questions about how Graph RAG works in as much detail as I could think of. When I think of graph databases, I normally think RDF, but the generally preferred graph DB for LLM apps seems to be Neo4J which uses a query language called Cypher rather than SPARQL, so there are some questions below about Neo4J and Cypher syntax. The thing I really wanted to know about was the sequence of queries and responses involved, and how the queries are constructed, and how the responses are interpreted.

Prompt:

During a typical graph rag query and response, what would a langsmith trace look like?

Response:

Let me create a visualization of what a typical LangSmith trace would look like for a RAG query using a graph database.

I've created a diagram showing a typical LangSmith trace for a graph RAG query. Here's what's happening in each major section:

Query Construction Phase:
- Takes the raw user input
- Processes it into a structured format
- Generates the appropriate graph query
Graph Retrieval Phase:
- Executes the query against the graph database
- Returns relevant subgraphs
- Processes the results
Context Creation Phase:
- Formats the graph data for the LLM
- Creates an appropriate context window
LLM Generation Phase:
- Sends the combined query and context to the LLM
- Processes and generates the final response

Each phase is tracked separately in LangSmith, allowing you to:

Monitor performance metrics
Debug bottlenecks
Track token usage
Analyze query patterns

Would you like me to expand on any particular phase or add more specific metrics to the trace visualization?

Prompt:

How is the first step accomplished? I assume there is some prompt engineering magic involved?

Response:

Yes, I'll break down the query construction phase and show how prompt engineering is typically used to transform a natural language query into a graph database query.

Graph Query Construction with Prompt Engineering

Let me explain the key components of the prompt engineering approach:

System Prompt Design:
- Defines the role and expertise (graph query expert)
- Provides clear rules for query construction
- Includes the complete schema definition
- Sets expectations for output format
Schema Definition:
- Lists all node types with their properties
- Defines relationship types and their attributes
- Helps the LLM understand the graph structure
Query Construction Rules:
- Enforces consistent naming conventions
- Requires descriptive variable names
- Specifies when to use optional matches
- Includes guidance on result limiting
Implementation Features:
- Uses zero temperature for deterministic outputs
- Returns only the query without explanations
- Handles various types of questions
- Maintains consistent formatting

Some example transformations this enables:

Natural: "Who does Alice work with?"
↓
Cypher: MATCH (alice:Person {name: 'Alice'})-[:WORKS_AT]->(company:Company)
       MATCH (coworker:Person)-[:WORKS_AT]->(company)
       WHERE coworker <> alice
       RETURN DISTINCT coworker.name, company.name

Would you like me to:

Add more example transformations?
Show how to handle more complex graph patterns?
Explain how to validate the generated queries?

Prompt:

I'm unfamiliar with the Cypher syntax. Where can I learn more about it?

Response:

Let me create a beginner-friendly guide to Cypher's core syntax.

Introduction to Cypher Query Language

I've created a beginner's guide to Cypher. The key thing to remember is that Cypher queries are essentially pattern matching using ASCII art-like syntax. The queries visually represent what you're looking for in the graph.

For example, if you want to find people who work at companies:

(person:Person)-[:WORKS_AT]->(company:Company)

This pattern literally looks like what it's matching: a person node, connected by a WORKS_AT relationship, pointing to a company node.

Would you like me to:

Show more specific examples for your use case?
Explain any particular pattern in more detail?
Demonstrate how to build more complex queries step by step?

Prompt:

Is neo4j a triple store?

Response:

No, Neo4j is not a triple store, though there are some important similarities and differences worth exploring.

Key differences between Neo4j (a property graph database) and triple stores:

Data Model:
- Triple Store: Data is stored as subject-predicate-object triples
- Neo4j: Uses labeled property graphs with:
  - Nodes that can have multiple properties
  - Relationships that can have properties
  - Both nodes and relationships can have labels/types
Property Storage:
- Triple Store: Properties are stored as additional triples
- Neo4j: Properties are stored directly on nodes and relationships
Query Language:
- Triple Store: Usually SPARQL
- Neo4j: Cypher
Schema:
- Triple Store: Often uses RDF Schema or OWL ontologies
- Neo4j: Schema-optional with optional constraints and indexes
Use Cases:
- Triple Stores: Better for:
  - Semantic web applications
  - Linked data
  - Ontology-based integration
- Neo4j: Better for:
  - Complex relationship patterns
  - Path finding
  - Graph algorithms

To illustrate, here's how the same data might be represented:

# Triple Store (RDF)
:person/123 :name "John" .
:person/123 :works_at :company/456 .
:company/456 :name "Acme Corp" .

# Neo4j (Cypher)
CREATE (p:Person {id: 123, name: 'John'})
CREATE (c:Company {id: 456, name: 'Acme Corp'})
CREATE (p)-[:WORKS_AT {since: 2020}]->(c)

Would you like me to elaborate on any of these differences or explore specific use cases where one might be preferred over the other?

Prompt:

OWL can express set theoretic ideas and other useful info about your entities, so you can structure your domain knowledge nicely. Is there anything equivalent for property graphs?

Response:

Yes, property graphs can capture semantic information through Property Graph Schema (PGS) and Graph Data Science (GDS) capabilities, though it's structured differently from OWL.

Here are the key ways property graphs can express domain knowledge:

Node Label Hierarchies:

CREATE CONSTRAINT person_age IF NOT EXISTS
FOR (p:Person) REQUIRE p.age IS NOT NULL
    AND p.age >= 18;

CREATE CONSTRAINT employee_inherits IF NOT EXISTS
FOR (e:Employee) REQUIRE e:Person;

Property Constraints:

// Define required properties
CREATE CONSTRAINT person_name IF NOT EXISTS
FOR (p:Person) REQUIRE p.name IS NOT NULL;

// Define property types
CREATE CONSTRAINT person_age_type IF NOT EXISTS
FOR (p:Person) REQUIRE p.age IS INTEGER;

// Define value constraints
CREATE CONSTRAINT company_industry IF NOT EXISTS
FOR (c:Company) REQUIRE c.industry IN ['Tech', 'Finance', 'Healthcare'];

Relationship Cardinality:

// Ensure each Employee works at exactly one Company
CREATE CONSTRAINT employee_company IF NOT EXISTS
FOR ()-[r:WORKS_AT]->() 
REQUIRE r.since IS NOT NULL;

MATCH (e:Employee)
WITH e, count(()-[:WORKS_AT]->()) as company_count
WHERE company_count <> 1
RETURN e.name;

Semantic Rules via Procedures:

@procedure("domain.validateEmployeeHierarchy")
def validate_hierarchy(tx):
    # Check that no employee manages their manager
    query = """
    MATCH (e1:Employee)-[:MANAGES]->(e2:Employee)
    MATCH (e2)-[:MANAGES*]->(e1)
    RETURN e1.name, e2.name
    """
    return tx.run(query)

Graph Data Science for Inference:

// Find implicit relationships through pattern analysis
CALL gds.nodeSimilarity.stream({
    nodeProjection: ['Person'],
    relationshipProjection: {
        KNOWS: {
            type: 'KNOWS',
            orientation: 'UNDIRECTED'
        }
    }
})

Key differences from OWL:

Expressiveness:
- OWL: Formal logic, supports complex reasoning
- PGS: More practical, focused on data integrity
Reasoning:
- OWL: Built-in inference engine
- PGS: Custom procedures + Graph algorithms
Validation:
- OWL: Logical consistency checking
- PGS: Constraint checking + Custom validation
Use Cases:
- OWL: Knowledge representation, inference
- PGS: Data integrity, pattern matching

Would you like me to:

Show more examples of how to implement specific OWL-like features in property graphs?
Explain how to combine Graph Data Science with schema constraints?
Demonstrate how to build custom inference rules?

Prompt:

What does the asterisk in [:MANAGES*] mean?

Response:

The asterisk * in Cypher indicates a variable-length path - it means "follow this relationship type zero or more times" (or in other words, traverse this relationship transitively). This is incredibly useful for hierarchical or recursive relationships.

Let me show you how it works:

Here are the key variations of path matching:

// Direct management (depth = 1)
MATCH (manager)-[:MANAGES]->(employee)
RETURN manager.name, employee.name;

// Exactly two levels down (depth = 2)
MATCH (manager)-[:MANAGES*2]->(employee)
RETURN manager.name, employee.name;

// Between 1 and 3 levels (1 ≤ depth ≤ 3)
MATCH (manager)-[:MANAGES*1..3]->(employee)
RETURN manager.name, employee.name;

// All levels down (any depth)
MATCH (manager)-[:MANAGES*]->(employee)
RETURN manager.name, employee.name;

// Zero or more levels (includes self)
MATCH (person)-[:MANAGES*0..]->(employee)
RETURN person.name, employee.name;

Common use cases include:

Organizational Hierarchies:

// Find all employees under a VP
MATCH (vp:Employee {title: 'VP'})-[:MANAGES*]->(employee)
RETURN employee.name;

Cycle Detection:

// Find circular management chains
MATCH (e:Employee)-[:MANAGES*]->(e)
RETURN e.name;

Level-specific Queries:

// Find skip-level reports (reports of reports)
MATCH (manager)-[:MANAGES*2]->(skip_level_report)
RETURN manager.name, skip_level_report.name;

Path Analysis:

// Find the management chain between two people
MATCH path = (e1:Employee)-[:MANAGES*]->(e2:Employee)
WHERE e1.name = 'Alice' AND e2.name = 'Eve'
RETURN [node in nodes(path) | node.name] as chain;

Would you like me to:

Show more examples of variable-length patterns?
Explain how to optimize queries with variable-length paths?
Demonstrate how to combine this with other Cypher features?

Prompt:

It sounds like neo4j is the de facto standard graph database for graph rag applications, is that right? Are any other graph Dbs in common usage?

Response:

While Neo4j is very popular for graph RAG applications, it's not the only option. Here's a comprehensive overview:

Neo4j's dominance in graph RAG comes from several factors:

Mature vector search capabilities and GDS (Graph Data Science) library
Strong integration with major RAG frameworks (LlamaIndex, LangChain)
APOC library for extended functionality
Cypher query language which is relatively intuitive
Large community and extensive documentation
Production-proven at scale

Other significant graph databases used for RAG:

TigerGraph:

Known for excellent performance with large-scale graphs
Has machine learning workbench and built-in algorithms
GSQL query language
Native vector search capabilities
Growing integration with RAG frameworks

Amazon Neptune:

Fully managed graph service
Supports both property graphs (via openCypher) and RDF (via SPARQL)
Vector search capabilities through Neptune ML
Good integration with AWS AI/ML services
Popular for enterprises already in AWS ecosystem

ArangoDB:

Multi-model database (graph, document, key-value)
Native vector search support
AQL query language
Known for flexibility and performance
Growing adoption in RAG applications

Redis Graph (now EOL) + RediSearch:

While RedisGraph is being sunset, Redis with RediSearch/RedisVector provides graph-like capabilities
Very fast for certain use cases
Good for real-time applications
Often used in hybrid architectures

DGraph:

Built for distributed systems
GraphQL-like query language
Built-in vector search
Strong performance characteristics
Growing in popularity for RAG

Azure Cosmos DB (with Gremlin API):

Microsoft's multi-model database
Good integration with Azure AI services
Supports Apache TinkerPop/Gremlin
Popular in Microsoft-centric environments

The choice often depends on:

Scale requirements
Existing infrastructure
Query language preference
Need for managed vs self-hosted
Vector search requirements
Budget considerations
Team expertise

Neo4j is popular for RAG not necessarily because it's technically superior in all cases, but because:

It has the most mature integrations with popular RAG frameworks
Extensive documentation and examples exist
The development experience is generally good
The community is large and active
The free tier is generous for experimentation

For someone starting with graph RAG today, I would suggest:

Neo4j if you want the most straightforward path with best documentation
TigerGraph if you need enterprise-scale performance
ArangoDB if you want flexibility of multiple models
Neptune if you're already heavily invested in AWS
Cosmos DB if you're in the Azure ecosystem

Would you like me to elaborate on any particular database or provide code examples showing how the implementations might differ between them?

Prompt:

Is neo4j available as a docker image? If so, what docker-run options are typically used with it?

Response:

Yes, Neo4j is available as a Docker image. Let me show you the typical Docker setup.

Neo4j Docker Configuration

# Basic docker run command
docker run \
    --name neo4j \
    -p 7474:7474 -p 7687:7687 \
    -v $HOME/neo4j/data:/data \
    -v $HOME/neo4j/logs:/logs \
    -v $HOME/neo4j/import:/var/lib/neo4j/import \
    -v $HOME/neo4j/plugins:/plugins \
    --env NEO4J_AUTH=neo4j/password123 \
    --env NEO4J_ACCEPT_LICENSE_AGREEMENT=yes \
    neo4j:5.13.0-enterprise

# Docker Compose file for more complete setup
version: '3'
services:
  neo4j:
    image: neo4j:5.13.0-enterprise
    container_name: neo4j
    ports:
      - "7474:7474"  # HTTP
      - "7687:7687"  # Bolt
      - "7473:7473"  # HTTPS
    volumes:
      - ./data:/data
      - ./logs:/logs
      - ./import:/var/lib/neo4j/import
      - ./plugins:/plugins
      - ./conf:/conf
    environment:
      # Authentication
      - NEO4J_AUTH=neo4j/password123
      - NEO4J_ACCEPT_LICENSE_AGREEMENT=yes
      
      # Memory Configuration
      - NEO4J_server_memory_heap_initial__size=2G
      - NEO4J_server_memory_heap_max__size=2G
      - NEO4J_server_memory_pagecache_size=1G
      
      # Connection Configuration
      - NEO4J_server_default__listen__address=0.0.0.0
      - NEO4J_server_bolt_advertised__address=:7687
      - NEO4J_server_http_advertised__address=:7474
      
      # APOC Configuration
      - NEO4J_dbms_security_procedures_unrestricted=apoc.*,gds.*
      - NEO4J_dbms_security_procedures_allowlist=apoc.*,gds.*
      
      # GDS Configuration
      - NEO4J_gds_enterprise_license__file=/conf/gds.license
      
      # Logging
      - NEO4J_dbms_logs_debug_level=INFO
      
    ulimits:
      nofile:
        soft: 40000
        hard: 40000
    restart: unless-stopped
    networks:
      - neo4j_network

networks:
  neo4j_network:
    driver: bridge

Let me explain the key components:

Essential Ports:
- 7474: HTTP for browser interface
- 7687: Bolt protocol for database connections
- 7473: HTTPS (optional)
Important Volumes:
- /data: Database files
- /logs: Log files
- /import: For importing data files
- /plugins: Custom plugins/extensions
- /conf: Configuration files
Common Environment Variables:

# Basic Setup
NEO4J_AUTH=neo4j/yourpassword  # Set initial password
NEO4J_ACCEPT_LICENSE_AGREEMENT=yes  # Required for enterprise

# Memory Configuration
NEO4J_server_memory_heap_initial__size=2G
NEO4J_server_memory_heap_max__size=2G
NEO4J_server_memory_pagecache_size=1G

# APOC and GDS (Graph Data Science)
NEO4J_dbms_security_procedures_unrestricted=apoc.*,gds.*

Quick Start Command:

docker run --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -v $HOME/neo4j/data:/data \
  -e NEO4J_AUTH=neo4j/password123 \
  neo4j:latest

Development vs Production:

# Development
docker run --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -v $HOME/neo4j/data:/data \
  -e NEO4J_AUTH=neo4j/password123 \
  -e NEO4J_ACCEPT_LICENSE_AGREEMENT=yes \
  -e NEO4J_server_memory_heap_max__size=512M \
  neo4j:latest

# Production
docker run --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -v /path/to/data:/data \
  -v /path/to/logs:/logs \
  -v /path/to/import:/var/lib/neo4j/import \
  -v /path/to/plugins:/plugins \
  -e NEO4J_AUTH=neo4j/strongpassword \
  -e NEO4J_ACCEPT_LICENSE_AGREEMENT=yes \
  -e NEO4J_server_memory_heap_max__size=16G \
  -e NEO4J_server_memory_pagecache_size=4G \
  --ulimit nofile=40000:40000 \
  --restart unless-stopped \
  neo4j:enterprise

Would you like me to:

Explain any specific configuration options in more detail?
Show how to set up clustering with Docker?
Demonstrate how to integrate with other services (like vector databases)?

Prompt:

Let's assume I want to build a Neo4J-based graph RAG system using the earlier Python code for constructing prompts and I want to do this in docker-compose using the official Neo4J docker image for one of my containers. And for the LLM, I want to use the public Groq API, accessed by either LangChain or LlamaIndex. Please show me the docker-compose.yml file I would need to do this.

Response:

I'll help you create a docker-compose file that sets up a Neo4j container and a Python service that can interact with both Neo4j and Groq's API.

Docker Compose Configuration for Neo4j Graph RAG System

version: '3.8'

services:
  neo4j:
    image: neo4j:5.13.0
    container_name: neo4j-graph-rag
    ports:
      - "7474:7474"  # HTTP
      - "7687:7687"  # Bolt
    environment:
      - NEO4J_AUTH=neo4j/your_password_here
      - NEO4J_PLUGINS=["graph-data-science", "apoc"]  # Optional but recommended plugins
      - NEO4J_dbms_memory_heap_initial__size=512M
      - NEO4J_dbms_memory_heap_max__size=2G
      - NEO4J_dbms_memory_pagecache_size=512M
    volumes:
      - neo4j_data:/data
      - neo4j_logs:/logs
      - neo4j_import:/import
      - neo4j_plugins:/plugins
    networks:
      - graph-rag-network

  rag-service:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: rag-service
    depends_on:
      - neo4j
    environment:
      - NEO4J_URI=bolt://neo4j:7687
      - NEO4J_USER=neo4j
      - NEO4J_PASSWORD=your_password_here
      - GROQ_API_KEY=your_groq_api_key_here
    volumes:
      - ./app:/app
    networks:
      - graph-rag-network
    command: python main.py  # Adjust based on your entry point

networks:
  graph-rag-network:
    driver: bridge

volumes:
  neo4j_data:
  neo4j_logs:
  neo4j_import:
  neo4j_plugins:

You'll also need a Dockerfile for the rag-service. Here it is:

Dockerfile for RAG Service

FROM python:3.11-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements file
COPY requirements.txt .

# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY ./app .

CMD ["python", "main.py"]

And here's a minimal requirements.txt file you'll need:

Python Requirements

langchain
langchain-groq
neo4j
python-dotenv
llama-index
llama-index-vector-stores-neo4j

To use this setup:

Create a project directory with the following structure:

project/
├── docker-compose.yml
├── Dockerfile
├── requirements.txt
└── app/
    └── main.py  # Your application code

Important notes:
- Replace your_password_here with a secure password for Neo4j
- Replace your_groq_api_key_here with your actual Groq API key
- The Neo4j container includes optional but recommended plugins (APOC and Graph Data Science)
- The Neo4j data, logs, and plugins are persisted using named volumes
- The services are connected via a dedicated network named graph-rag-network
- The Python service waits for Neo4j to be ready before starting
- Memory settings for Neo4j are conservative and can be adjusted based on your needs
To start the services:

docker-compose up -d

Access points:
- Neo4j Browser: http://localhost:7474
- Neo4j Bolt: bolt://localhost:7687
- Your RAG service will run inside its container and can communicate with Neo4j using the internal network

Would you like me to show you how to modify the Python code you provided to work with this Docker setup, or would you like to see how to configure environment variables for better security?

Prompt:

Please show me what a simple application would look like in Python, using LlamaIndex to access the Groq API, in a system like the one described above.

Response:

I'll create a simple application that demonstrates using LlamaIndex with Groq and Neo4j for graph-based RAG.

Graph RAG Application using LlamaIndex, Groq, and Neo4j

import os
from typing import List, Dict
from dotenv import load_dotenv
from llama_index import ServiceContext, VectorStoreIndex
from llama_index.llms import Groq
from llama_index.vector_stores.neo4j import Neo4jVectorStore
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.schema import Document
from neo4j import GraphDatabase

# Load environment variables
load_dotenv()

class GraphRAGSystem:
    def __init__(self):
        # Initialize Neo4j connection
        self.neo4j_uri = os.getenv("NEO4J_URI", "bolt://neo4j:7687")
        self.neo4j_user = os.getenv("NEO4J_USER", "neo4j")
        self.neo4j_password = os.getenv("NEO4J_PASSWORD")
        
        # Initialize Groq
        self.groq_api_key = os.getenv("GROQ_API_KEY")
        
        # Set up LlamaIndex components
        self.llm = Groq(
            api_key=self.groq_api_key,
            model_name="mixtral-8x7b-32768"  # or "llama2-70b-4096"
        )
        
        # Use HuggingFace embeddings (all-MiniLM-L6-v2 is a good default)
        self.embed_model = HuggingFaceEmbedding(
            model_name="sentence-transformers/all-MiniLM-L6-v2"
        )
        
        # Create Neo4j vector store
        self.vector_store = Neo4jVectorStore(
            url=self.neo4j_uri,
            username=self.neo4j_user,
            password=self.neo4j_password,
            embedding_dimension=384,  # matches the dimension of all-MiniLM-L6-v2
            index_name="document_index",
            node_label="Document",
            embedding_field="embedding",
            text_field="text"
        )
        
        # Create service context
        self.service_context = ServiceContext.from_defaults(
            llm=self.llm,
            embed_model=self.embed_model
        )
        
        # Initialize the index
        self.index = VectorStoreIndex.from_vector_store(
            vector_store=self.vector_store,
            service_context=self.service_context
        )

    def add_documents(self, documents: List[Dict[str, str]]) -> None:
        """
        Add documents to the vector store.
        
        Args:
            documents: List of dictionaries containing 'text' and optional metadata
        """
        doc_objects = [
            Document(text=doc['text'], metadata=doc.get('metadata', {}))
            for doc in documents
        ]
        
        self.index.insert_nodes(doc_objects)
        
    def query(self, query_text: str, response_mode: str = "compact") -> str:
        """
        Query the RAG system.
        
        Args:
            query_text: The question or query text
            response_mode: One of "compact", "tree_summarize", or "accumulate"
            
        Returns:
            str: The generated response
        """
        query_engine = self.index.as_query_engine(
            response_mode=response_mode
        )
        response = query_engine.query(query_text)
        return str(response)
    
    def add_graph_relationship(self, source_id: str, target_id: str, 
                             relationship_type: str, properties: Dict = None) -> None:
        """
        Add a relationship between two nodes in the graph.
        
        Args:
            source_id: ID of the source node
            target_id: ID of the target node
            relationship_type: Type of relationship
            properties: Optional dictionary of relationship properties
        """
        with GraphDatabase.driver(self.neo4j_uri, 
                                auth=(self.neo4j_user, self.neo4j_password)) as driver:
            with driver.session() as session:
                cypher = (
                    f"MATCH (a), (b) "
                    f"WHERE id(a) = $source_id AND id(b) = $target_id "
                    f"CREATE (a)-[r:{relationship_type} $props]->(b) "
                    f"RETURN type(r)"
                )
                session.run(cypher, 
                          source_id=source_id, 
                          target_id=target_id, 
                          props=properties or {})

def main():
    # Initialize the RAG system
    rag_system = GraphRAGSystem()
    
    # Example documents
    example_docs = [
        {
            "text": "Neo4j is a graph database management system developed by Neo4j, Inc.",
            "metadata": {"type": "definition", "topic": "databases"}
        },
        {
            "text": "Graph databases store data in nodes connected by relationships.",
            "metadata": {"type": "concept", "topic": "databases"}
        }
    ]
    
    # Add documents to the system
    rag_system.add_documents(example_docs)
    
    # Example query
    query = "What is Neo4j and how does it store data?"
    response = rag_system.query(query)
    print(f"\nQuery: {query}")
    print(f"Response: {response}")

if __name__ == "__main__":
    main()

This application provides:

A complete GraphRAGSystem class that integrates:
- Neo4j for graph storage
- Groq's LLM API (using Mixtral-8x7b)
- LlamaIndex for RAG functionality
- HuggingFace embeddings for vector search
Key features:
- Document ingestion with metadata support
- Vector similarity search
- Graph relationship management
- Flexible querying with different response modes
Configuration through environment variables:

NEO4J_URI=bolt://neo4j:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_password_here
GROQ_API_KEY=your_groq_api_key_here

To use this in your Docker setup:

Save the code as app/main.py

Create a .env file in your project root:

.env Configuration

NEO4J_URI=bolt://neo4j:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_password_here
GROQ_API_KEY=your_groq_api_key_here

Update the requirements.txt to include additional dependencies:

Updated Python Requirements

langchain
langchain-groq
neo4j
python-dotenv
llama-index
llama-index-vector-stores-neo4j
llama-index-llms-groq
sentence-transformers

The system will:

Store document embeddings in Neo4j
Use Groq's API for question answering
Support graph relationships between documents
Allow for metadata-aware querying

Would you like me to show you how to extend this with more advanced features like:

Custom query templates
Relationship-aware querying
Hybrid search (combining vector and graph traversal)
Or something else specific to your use case?

Prompt:

Let's suppose I wanted to exploit Linked Data resources such as DBPedia. Would that require me to use an RDF-based database? If so, which would you recommend, and how would it change the approach you've outlined above?

Response:

This is a great question that touches on an important architectural decision in knowledge graph systems. Let me break this down:

You don't strictly need an RDF database to work with DBpedia/Linked Data, but there are significant advantages to using one:

Advantages of RDF stores:

Native SPARQL support for querying DBpedia
Direct import of RDF data without transformation
Standardized reasoning capabilities
Better alignment with Semantic Web standards
Easier federation with other SPARQL endpoints

The main RDF-native options I'd recommend considering are:

GraphDB (by Ontotext):

Excellent SPARQL support
Built-in reasoning
Vector search capabilities
Good documentation
Free tier available

Stardog:

Enterprise-grade knowledge graph platform
Strong reasoning capabilities
Built-in vector search
SPARQL and GraphQL support
Good cloud offerings

Apache Jena + Fuseki:

Open source
Good standards compliance
Lighter weight
More DIY approach

Let me show you how the implementation would change using GraphDB as an example:

RDF-based RAG Application using GraphDB

import os
from typing import List, Dict
from dotenv import load_dotenv
from llama_index import ServiceContext, VectorStoreIndex
from llama_index.llms import Groq
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.schema import Document
from rdflib import Graph, URIRef, Literal, Namespace
from rdflib.namespace import RDF, RDFS, XSD
from SPARQLWrapper import SPARQLWrapper, JSON

class RDFGraphRAGSystem:
    def __init__(self):
        # Initialize GraphDB connection
        self.graphdb_url = os.getenv("GRAPHDB_URL", "http://graphdb:7200")
        self.repository = os.getenv("GRAPHDB_REPOSITORY", "rag-knowledge-graph")
        self.sparql_endpoint = f"{self.graphdb_url}/repositories/{self.repository}"
        
        # Initialize Groq
        self.groq_api_key = os.getenv("GROQ_API_KEY")
        self.llm = Groq(
            api_key=self.groq_api_key,
            model_name="mixtral-8x7b-32768"
        )
        
        # Initialize SPARQL wrapper
        self.sparql = SPARQLWrapper(self.sparql_endpoint)
        self.sparql.setReturnFormat(JSON)
        
        # Define namespaces
        self.kg = Namespace("http://example.org/kg/")
        self.schema = Namespace("http://schema.org/")
        
        # Initialize embedding model
        self.embed_model = HuggingFaceEmbedding(
            model_name="sentence-transformers/all-MiniLM-L6-v2"
        )
        
        # Create service context
        self.service_context = ServiceContext.from_defaults(
            llm=self.llm,
            embed_model=self.embed_model
        )

    def query_dbpedia(self, entity_uri: str) -> Dict:
        """
        Query DBpedia for information about an entity
        """
        query = f"""
        CONSTRUCT {{
            <{entity_uri}> ?p ?o .
            ?o ?p2 ?o2 .
        }}
        WHERE {{
            <{entity_uri}> ?p ?o .
            OPTIONAL {{ ?o ?p2 ?o2 }}
        }}
        """
        
        self.sparql.setQuery(query)
        results = self.sparql.queryAndConvert()
        return results

    def add_document_with_entities(self, text: str, entities: List[Dict[str, str]]) -> None:
        """
        Add a document and link it to DBpedia entities
        """
        # Create embedded representation
        embedding = self.embed_model.get_text_embedding(text)
        
        # Create SPARQL INSERT
        insert_query = f"""
        PREFIX kg: <http://example.org/kg/>
        PREFIX schema: <http://schema.org/>
        PREFIX vec: <http://example.org/vec/>
        
        INSERT DATA {{
            kg:doc_{hash(text)} a schema:Article ;
                schema:text "{text}" ;
                vec:embedding "{','.join(map(str, embedding))}" .
            
            {' '.join(f'''
            kg:doc_{hash(text)} schema:mentions <{entity['uri']}> .
            ''' for entity in entities)}
        }}
        """
        
        self.sparql.setQuery(insert_query)
        self.sparql.method = 'POST'
        self.sparql.query()

    def hybrid_query(self, question: str, max_results: int = 5) -> str:
        """
        Perform hybrid search combining vector similarity and graph patterns
        """
        # Get question embedding
        question_embedding = self.embed_model.get_text_embedding(question)
        
        # Construct hybrid SPARQL query
        query = f"""
        PREFIX vec: <http://example.org/vec/>
        PREFIX schema: <http://schema.org/>
        
        SELECT ?doc ?text ?similarity ?entity ?entityLabel
        WHERE {{
            ?doc schema:text ?text ;
                 vec:embedding ?embedding .
                 
            # Vector similarity calculation
            BIND(vec:cosine-similarity(?embedding, "{','.join(map(str, question_embedding))}") AS ?similarity)
            
            # Optional entity connections
            OPTIONAL {{
                ?doc schema:mentions ?entity .
                ?entity rdfs:label ?entityLabel .
            }}
        }}
        ORDER BY DESC(?similarity)
        LIMIT {max_results}
        """
        
        self.sparql.setQuery(query)
        results = self.sparql.queryAndConvert()
        
        # Process results and generate response using Groq
        context = self._process_sparql_results(results)
        
        response = self.llm.complete(f"""
        Context: {context}
        
        Question: {question}
        
        Answer based on the context provided above:
        """)
        
        return response.text

    def _process_sparql_results(self, results: Dict) -> str:
        """Process SPARQL results into a context string"""
        context_parts = []
        for result in results['results']['bindings']:
            text = result['text']['value']
            if 'entityLabel' in result:
                entity = result['entityLabel']['value']
                context_parts.append(f"{text} [Related to: {entity}]")
            else:
                context_parts.append(text)
        return "\n\n".join(context_parts)

def main():
    # Initialize the system
    rag_system = RDFGraphRAGSystem()
    
    # Example: Add a document about Berlin with DBpedia entities
    doc_text = "Berlin is the capital and largest city of Germany."
    entities = [
        {
            "uri": "http://dbpedia.org/resource/Berlin",
            "label": "Berlin"
        },
        {
            "uri": "http://dbpedia.org/resource/Germany",
            "label": "Germany"
        }
    ]
    
    rag_system.add_document_with_entities(doc_text, entities)
    
    # Example query
    response = rag_system.hybrid_query(
        "What is the largest city in Germany?"
    )
    print(f"Response: {response}")

if __name__ == "__main__":
    main()

And here's the corresponding Docker Compose configuration:

Docker Compose for RDF-based RAG System

version: '3.8'

services:
  graphdb:
    image: ontotext/graphdb:10.0.0
    container_name: graphdb
    ports:
      - "7200:7200"
    environment:
      - GDB_HEAP_SIZE=2g
      - GDB_MIN_MEM=1g
      - GDB_MAX_MEM=2g
    volumes:
      - graphdb_data:/opt/graphdb/home
      - graphdb_work:/opt/graphdb/work
    networks:
      - rag-network

  rag-service:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: rag-service
    depends_on:
      - graphdb
    environment:
      - GRAPHDB_URL=http://graphdb:7200
      - GRAPHDB_REPOSITORY=rag-knowledge-graph
      - GROQ_API_KEY=your_groq_api_key_here
    volumes:
      - ./app:/app
    networks:
      - rag-network

networks:
  rag-network:
    driver: bridge

volumes:
  graphdb_data:
  graphdb_work:

Key differences from the Neo4j approach:

Data Model:

Uses RDF triples instead of property graphs
Native support for URIs and linked data
Built-in support for ontologies and reasoning

Query Language:

SPARQL instead of Cypher
More standardized across different RDF stores
Better support for federation and semantic queries

Entity Linking:

Direct integration with DBpedia and other LOD resources
Easier to maintain links to external knowledge bases
Better support for ontology alignment

Vector Search:

Implementation varies by triple store
GraphDB and Stardog have built-in vector capabilities
May need additional configuration for hybrid search

The main trade-offs are:

Advantages:

Better integration with Linked Open Data
Standardized querying with SPARQL
Strong reasoning capabilities
Better semantic interoperability

Disadvantages:

Steeper learning curve
Generally more complex queries
Fewer RAG-specific integrations
May require more custom code

Would you like me to elaborate on any of these aspects or show how to implement specific features like:

Federation with DBpedia's SPARQL endpoint
Ontology-aware querying
Reasoning-enhanced RAG
Or something else specific to RDF-based systems?

Prompt:

All those topics interest me. Let's start with DBPedia federation. Suppose I have a Jena/Fuseki instance containing a graph. I've used OWL things to identify entities as same-as things in the DBPedia world, in order to ground stuff. Now I want to be able to write SPARQL queries that pull in a mix of my own graph stuff and the stuff in DBPedia. Can you show me an example of how that would be accomplished in the context of a RAG system? I think this would blend pretty naturally into both ontology-aware querying and reasoning-enhancing RAG.

Response:

I'll create an example that shows how to build a RAG system that leverages both local knowledge and DBPedia through federation, while taking advantage of OWL semantics and reasoning.

Federated RDF RAG System with Reasoning

from typing import List, Dict, Optional
import os
from dotenv import load_dotenv
from rdflib import Graph, URIRef, Literal, Namespace, OWL, RDFS, RDF
from rdflib.plugins.sparql import prepareQuery
from SPARQLWrapper import SPARQLWrapper, JSON
from llama_index import ServiceContext
from llama_index.llms import Groq
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

class FederatedRDFRagSystem:
    def __init__(self):
        # Initialize connections and namespaces
        self.fuseki_url = os.getenv("FUSEKI_URL", "http://fuseki:3030")
        self.dataset = os.getenv("FUSEKI_DATASET", "rag-kg")
        self.sparql_endpoint = f"{self.fuseki_url}/{self.dataset}/query"
        self.update_endpoint = f"{self.fuseki_url}/{self.dataset}/update"
        
        # DBPedia SPARQL endpoint
        self.dbpedia_endpoint = "http://dbpedia.org/sparql"
        
        # Initialize SPARQL wrappers
        self.local_sparql = SPARQLWrapper(self.sparql_endpoint)
        self.local_sparql.setReturnFormat(JSON)
        self.dbpedia_sparql = SPARQLWrapper(self.dbpedia_endpoint)
        self.dbpedia_sparql.setReturnFormat(JSON)
        
        # Initialize namespaces
        self.local = Namespace("http://example.org/local/")
        self.dbo = Namespace("http://dbpedia.org/ontology/")
        self.dbr = Namespace("http://dbpedia.org/resource/")
        
        # Initialize LLM and embedding model
        self.llm = Groq(
            api_key=os.getenv("GROQ_API_KEY"),
            model_name="mixtral-8x7b-32768"
        )
        self.embed_model = HuggingFaceEmbedding(
            model_name="sentence-transformers/all-MiniLM-L6-v2"
        )
        
    def add_entity_with_dbpedia_linking(
        self,
        local_uri: str,
        dbpedia_uri: str,
        properties: Dict[str, str],
        type_uri: Optional[str] = None
    ) -> None:
        """
        Add an entity to the local graph and link it to DBpedia
        """
        update_query = f"""
        PREFIX owl: <http://www.w3.org/2002/07/owl#>
        PREFIX local: <http://example.org/local/>
        PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
        
        INSERT DATA {{
            <{local_uri}> owl:sameAs <{dbpedia_uri}> .
            {f'<{local_uri}> rdf:type <{type_uri}> .' if type_uri else ''}
            
            {' '.join(f'<{local_uri}> <{pred}> "{obj}" .' 
                     for pred, obj in properties.items())}
        }}
        """
        
        self.local_sparql.setQuery(update_query)
        self.local_sparql.method = 'POST'
        self.local_sparql.query()

    def federated_entity_query(self, local_uri: str) -> Dict:
        """
        Query both local and DBpedia data for an entity
        """
        query = f"""
        PREFIX owl: <http://www.w3.org/2002/07/owl#>
        PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
        PREFIX dbo: <http://dbpedia.org/ontology/>
        
        SELECT DISTINCT ?property ?value ?dbpedia_property ?dbpedia_value
        WHERE {{
            # Local graph pattern
            <{local_uri}> ?property ?value .
            
            # DBpedia federation
            SERVICE <{self.dbpedia_endpoint}> {{
                ?dbpedia_uri owl:sameAs <{local_uri}> .
                ?dbpedia_uri ?dbpedia_property ?dbpedia_value .
                
                # Filter relevant DBpedia properties
                FILTER(STRSTARTS(STR(?dbpedia_property), STR(dbo:)))
            }}
        }}
        """
        
        self.local_sparql.setQuery(query)
        results = self.local_sparql.queryAndConvert()
        return results

    def reasoning_enhanced_query(self, question: str) -> str:
        """
        Perform a query that leverages OWL reasoning and federation
        """
        # First, get question embedding
        question_embedding = self.embed_model.get_text_embedding(question)
        
        # Complex federated query with reasoning
        query = f"""
        PREFIX vec: <http://example.org/vec/>
        PREFIX owl: <http://www.w3.org/2002/07/owl#>
        PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
        PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
        PREFIX dbo: <http://dbpedia.org/ontology/>
        
        SELECT DISTINCT ?entity ?localProp ?localVal ?dbProp ?dbVal ?type ?superType
        WHERE {{
            # Local graph patterns with vector similarity
            ?doc vec:embedding ?embedding ;
                 rdfs:seeAlso ?entity .
                 
            # Vector similarity calculation
            BIND(vec:cosine-similarity(?embedding, "{','.join(map(str, question_embedding))}") AS ?similarity)
            
            # Get local properties
            ?entity ?localProp ?localVal .
            
            # Get entity type and supertype through reasoning
            ?entity rdf:type ?type .
            OPTIONAL {{ ?type rdfs:subClassOf ?superType }}
            
            # Federation with DBpedia
            SERVICE <{self.dbpedia_endpoint}> {{
                ?dbpedia_entity owl:sameAs ?entity ;
                               ?dbProp ?dbVal .
                               
                # Filter for relevant DBpedia properties
                FILTER(STRSTARTS(STR(?dbProp), STR(dbo:)))
            }}
        }}
        ORDER BY DESC(?similarity)
        LIMIT 5
        """
        
        self.local_sparql.setQuery(query)
        results = self.local_sparql.queryAndConvert()
        
        # Process results into context
        context = self._process_reasoning_results(results)
        
        # Generate response using Groq
        prompt = f"""
        Given the following context that combines local knowledge and DBpedia information:
        
        {context}
        
        Question: {question}
        
        Please provide a comprehensive answer that integrates both local and DBpedia knowledge.
        Consider the hierarchical relationships (subtypes/supertypes) when relevant.
        
        Answer:
        """
        
        response = self.llm.complete(prompt)
        return response.text

    def _process_reasoning_results(self, results: Dict) -> str:
        """
        Process SPARQL results into a structured context
        """
        context_parts = []
        current_entity = None
        entity_info = {}
        
        for binding in results['results']['bindings']:
            entity = binding['entity']['value']
            
            if entity != current_entity:
                if current_entity and entity_info:
                    context_parts.append(self._format_entity_info(entity_info))
                current_entity = entity
                entity_info = {
                    'uri': entity,
                    'local_props': {},
                    'dbpedia_props': {},
                    'types': set(),
                    'supertypes': set()
                }
            
            if 'localProp' in binding:
                prop = binding['localProp']['value']
                val = binding['localVal']['value']
                entity_info['local_props'][prop] = val
                
            if 'dbProp' in binding:
                prop = binding['dbProp']['value']
                val = binding['dbVal']['value']
                entity_info['dbpedia_props'][prop] = val
                
            if 'type' in binding:
                entity_info['types'].add(binding['type']['value'])
                
            if 'superType' in binding and binding['superType']['value']:
                entity_info['supertypes'].add(binding['superType']['value'])
        
        if current_entity and entity_info:
            context_parts.append(self._format_entity_info(entity_info))
            
        return "\n\n".join(context_parts)

    def _format_entity_info(self, info: Dict) -> str:
        """Format entity information into readable text"""
        parts = [f"Entity: {info['uri'].split('/')[-1]}"]
        
        if info['types']:
            types_str = ', '.join(t.split('/')[-1] for t in info['types'])
            parts.append(f"Types: {types_str}")
            
        if info['supertypes']:
            supertypes_str = ', '.join(t.split('/')[-1] for t in info['supertypes'])
            parts.append(f"Supertypes: {supertypes_str}")
            
        if info['local_props']:
            parts.append("Local Properties:")
            for prop, val in info['local_props'].items():
                prop_name = prop.split('/')[-1]
                parts.append(f"  - {prop_name}: {val}")
                
        if info['dbpedia_props']:
            parts.append("DBpedia Properties:")
            for prop, val in info['dbpedia_props'].items():
                prop_name = prop.split('/')[-1]
                parts.append(f"  - {prop_name}: {val}")
                
        return "\n".join(parts)

def main():
    # Initialize system
    rag_system = FederatedRDFRagSystem()
    
    # Example: Add a local researcher entity linked to DBpedia
    rag_system.add_entity_with_dbpedia_linking(
        local_uri="http://example.org/local/researcher/ada_lovelace",
        dbpedia_uri="http://dbpedia.org/resource/Ada_Lovelace",
        properties={
            "http://example.org/local/department": "Computer Science",
            "http://example.org/local/research_area": "Mathematical Logic"
        },
        type_uri="http://example.org/local/Researcher"
    )
    
    # Example query
    response = rag_system.reasoning_enhanced_query(
        "What were Ada Lovelace's contributions to computer science and what department does she work in?"
    )
    print(f"Response: {response}")

if __name__ == "__main__":
    main()

This example demonstrates several key concepts:

Federation:

Uses SERVICE keyword to query DBpedia
Combines local and DBpedia properties in single queries
Handles owl:sameAs relationships for entity alignment

Reasoning:

Incorporates type hierarchies (rdfs:subClassOf)
Uses OWL semantics for entity equivalence
Can be extended with custom inference rules

Knowledge Integration:

Merges local institutional knowledge with DBpedia facts
Maintains separate provenance for local vs DBpedia data
Handles potential conflicts between sources

RAG Enhancement:

Uses semantic types to improve context selection
Leverages hierarchical relationships for better understanding
Combines vector similarity with semantic relations

To use this with Apache Jena/Fuseki, you'd need this Docker configuration:

Docker Compose for Jena/Fuseki RAG System

version: '3.8'

services:
  fuseki:
    image: stain/jena-fuseki
    container_name: fuseki
    environment:
      - ADMIN_PASSWORD=admin123
      - JVM_ARGS=-Xmx4g
    ports:
      - "3030:3030"
    volumes:
      - fuseki_data:/fuseki
      - ./config:/fuseki/configuration
      - ./ontologies:/fuseki/ontologies
    networks:
      - rag-network

  rag-service:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: rag-service
    depends_on:
      - fuseki
    environment:
      - FUSEKI_URL=http://fuseki:3030
      - FUSEKI_DATASET=rag-kg
      - GROQ_API_KEY=your_groq_api_key_here
    volumes:
      - ./app:/app
    networks:
      - rag-network

networks:
  rag-network:
    driver: bridge

volumes:
  fuseki_data:

Some key points about this approach:

Performance Considerations:

Federation can be slow for large result sets
Consider caching frequently accessed DBpedia data
Use selective SERVICE patterns

Reliability:

DBpedia endpoint may have timeout issues
Add error handling for federation failures
Consider local mirrors for critical data

Schema Alignment:

Use ontology mappings for property alignment
Consider using SKOS for concept mapping
Handle different naming conventions

Would you like me to:

Show how to add custom inference rules?
Demonstrate more complex ontology-aware querying?
Add support for multiple external endpoints?
Or explore another aspect of semantic integration?

Prompt:

What are some of the currently available Linked Data resources one can find online? What kind of information do they provide?

Response:

Let me break down the major Linked Data resources available online and their key offerings:

General Knowledge Bases:

DBpedia:

Structured data extracted from Wikipedia
Covers virtually all Wikipedia topics
Multiple language editions
Regular updates from Wikipedia
Excellent for general knowledge queries
SPARQL endpoint: https://dbpedia.org/sparql

Wikidata:

Collaborative knowledge base by Wikimedia
More structured than DBpedia
Contains uniquely identified entities
Strong cross-language support
Highly active community maintenance
SPARQL endpoint: https://query.wikidata.org/

Scientific and Academic:

Bio2RDF:

Large collection of biological/biomedical datasets
Includes GenBank, KEGG, DrugBank
Valuable for life sciences research
Links between different biological databases

LinkedCT:

Clinical trials data
Detailed medical study information
Connected to other healthcare resources

DBLP (as Linked Data):

Computer science bibliography
Academic publication metadata
Author networks and collaborations

Government and Geographic:

GeoNames:

Worldwide geographical database
Place names in multiple languages
Hierarchical relationships
Population data
Geographical coordinates

LinkedGeoData:

OpenStreetMap as linked data
Detailed geographic features
Points of interest
Infrastructure data

UK Government Linked Data:

Official UK government statistics
Public sector information
Legislative data
Companies House data

Cultural and Media:

Europeana:

European cultural heritage
Digital artifacts from museums
Historical documents
Artwork metadata
Cultural institution information

BBC Things:

Media-related entities
Program information
Music and artist data
News topic categorization

Scientific Disciplines:

ChEMBL RDF:

Bioactive molecules
Drug-like compounds
Binding and activity data
Pharmaceutical research data

UniProt:

Protein sequence and annotation data
Functional information
Scientific literature references
Cross-references to other databases

Linguistic Resources:

BabelNet:

Multilingual lexical database
Word senses and meanings
Translations and definitions
Semantic relationships

Lexvo:

Language-related data
Information about languages
Writing systems
Geographical language usage

Libraries and Publications:

WorldCat (as Linked Data):

Library catalog information
Book metadata
Author information
Publication details

CrossRef:

Academic citation metadata
DOI resolution
Publication relationships
Author identifiers

Practical example of combining multiple sources:

Multi-Source Linked Data Query Example

from SPARQLWrapper import SPARQLWrapper, JSON
from typing import Dict, List
import json

class LinkedDataExplorer:
    def __init__(self):
        self.endpoints = {
            'dbpedia': SPARQLWrapper('https://dbpedia.org/sparql'),
            'wikidata': SPARQLWrapper('https://query.wikidata.org/sparql'),
            'geonames': SPARQLWrapper('http://factforge.net/repositories/geonames')
        }
        
        for endpoint in self.endpoints.values():
            endpoint.setReturnFormat(JSON)

    def explore_scientist(self, scientist_name: str) -> Dict:
        """
        Gather information about a scientist from multiple sources
        """
        results = {}
        
        # DBpedia query for basic info and publications
        dbpedia_query = f"""
        SELECT DISTINCT ?abstract ?field ?publication WHERE {{
            ?person rdfs:label "{scientist_name}"@en ;
                   dbo:abstract ?abstract ;
                   dbo:field ?field .
            OPTIONAL {{ ?person dbo:notableWork ?publication }}
            FILTER(LANG(?abstract) = 'en')
        }}
        """
        self.endpoints['dbpedia'].setQuery(dbpedia_query)
        results['dbpedia'] = self.endpoints['dbpedia'].query().convert()

        # Wikidata query for awards and institutions
        wikidata_query = f"""
        SELECT ?award ?institution WHERE {{
            ?person wdt:P31 wd:Q5 ;  # is a human
                   rdfs:label "{scientist_name}"@en .
            OPTIONAL {{ ?person wdt:P166 ?award }}  # awards received
            OPTIONAL {{ ?person wdt:P108 ?institution }}  # employer/institution
        }}
        """
        self.endpoints['wikidata'].setQuery(wikidata_query)
        results['wikidata'] = self.endpoints['wikidata'].query().convert()

        return self._process_results(results)

    def explore_location(self, place_name: str) -> Dict:
        """
        Gather geographic and cultural information about a location
        """
        results = {}
        
        # GeoNames query for geographic data
        geonames_query = f"""
        SELECT ?lat ?long ?population ?country WHERE {{
            ?place gn:name "{place_name}" ;
                   wgs84_pos:lat ?lat ;
                   wgs84_pos:long ?long .
            OPTIONAL {{ ?place gn:population ?population }}
            OPTIONAL {{ ?place gn:parentCountry ?country }}
        }}
        """
        self.endpoints['geonames'].setQuery(geonames_query)
        results['geonames'] = self.endpoints['geonames'].query().convert()

        # DBpedia query for cultural and historical information
        dbpedia_query = f"""
        SELECT ?abstract ?landmark ?event WHERE {{
            ?place rdfs:label "{place_name}"@en ;
                   dbo:abstract ?abstract .
            OPTIONAL {{ ?landmark dbo:location ?place }}
            OPTIONAL {{ 
                ?event dbo:place ?place ;
                       a dbo:HistoricalEvent
            }}
            FILTER(LANG(?abstract) = 'en')
        }}
        """
        self.endpoints['dbpedia'].setQuery(dbpedia_query)
        results['dbpedia'] = self.endpoints['dbpedia'].query().convert()

        return self._process_results(results)

    def explore_drug(self, drug_name: str) -> Dict:
        """
        Gather pharmaceutical and chemical information about a drug
        """
        # Example combining DrugBank and ChEMBL data
        chembl_query = f"""
        SELECT ?molecule ?mechanism ?target WHERE {{
            ?drug rdfs:label "{drug_name}"@en ;
                  chembl:mechanismOfAction ?mechanism ;
                  chembl:hasTarget ?target ;
                  chembl:molecularFormula ?molecule .
        }}
        """
        # Note: This is a simplified example. Real implementation would need
        # appropriate endpoint URLs and authentication

    def _process_results(self, results: Dict) -> Dict:
        """Process and clean up SPARQL results"""
        processed = {}
        for source, data in results.items():
            if 'results' in data and 'bindings' in data['results']:
                processed[source] = [
                    {k: v['value'] for k, v in binding.items()}
                    for binding in data['results']['bindings']
                ]
        return processed

def main():
    explorer = LinkedDataExplorer()
    
    # Example: Explore information about Marie Curie
    scientist_info = explorer.explore_scientist("Marie Curie")
    print(json.dumps(scientist_info, indent=2))
    
    # Example: Explore information about Paris
    location_info = explorer.explore_location("Paris")
    print(json.dumps(location_info, indent=2))

if __name__ == "__main__":
    main()

Some key considerations when working with these resources:

Availability and Reliability:

Some endpoints may be occasionally down
Query timeouts are common
Rate limiting may apply
Consider caching frequently accessed data

Data Quality:

Varies between sources
May need validation
Updates can be irregular
Inconsistencies between sources

Integration Challenges:

Different ontologies
Varying data models
Entity resolution needed
Performance considerations

Best Practices:

Use federation when possible
Implement proper error handling
Cache frequently accessed data
Consider local copies of essential datasets

Would you like me to:

Show how to implement specific queries for any of these sources?
Demonstrate integration patterns for combining multiple sources?
Explain how to handle common challenges with these resources?
Or explore a specific domain or use case?

Cypher Query Language - A Beginner's Guide

Core Concept: ASCII Art Pattern Matching

Cypher queries look like ASCII art! The syntax visually represents the patterns you're trying to match in your graph. (node1)-[relationship]->(node2)

Basic Building Blocks

Nodes
- Represented with parentheses: ()
- Labels use a colon: (:Person)
- Properties use curly braces: (:Person {name: "John"})
MATCH (p:Person {name: "John"}) RETURN p
Relationships
- Represented with square brackets: []
- Arrows show direction: -> or <-
- Types use colon: [:WORKS_AT]
- Properties in curly braces: [:WORKS_AT {since: 2020}]
MATCH (:Person)-[r:WORKS_AT]->(c:Company) RETURN r, c
Common Commands
- MATCH: Find patterns in your graph
- WHERE: Filter results
- RETURN: Specify what to output
- CREATE: Make new nodes/relationships
- MERGE: Create if doesn't exist
- SET: Update properties
- DELETE: Remove nodes/relationships

Common Patterns

Finding Connected Nodes

// Find John's friends
MATCH (john:Person {name: "John"})-[:FRIENDS_WITH]->(friend:Person)
RETURN friend.name

Creating Relationships

// Make John work at Acme Corp
MATCH (p:Person {name: "John"})
MATCH (c:Company {name: "Acme Corp"})
CREATE (p)-[:WORKS_AT {since: 2024}]->(c)

Optional Matching

// Find all people and their managers (if they have one)
MATCH (p:Person)
OPTIONAL MATCH (p)-[:MANAGED_BY]->(manager:Person)
RETURN p.name, manager.name

Aggregation

// Count employees per company
MATCH (c:Company)<-[:WORKS_AT]-(p:Person)
RETURN c.name, COUNT(p) as employee_count

Best Practices

Start Small

Begin with simple patterns
Add complexity gradually
Test each part separately

Use Parameters

Instead of: {name: "John"}
Use: {name: $personName}
Prevents injection, improves caching

Name Your Patterns

Use meaningful variable names
Makes queries more readable
Helps with maintenance

Limit Results

Use LIMIT for large datasets
Add ORDER BY for consistency
Consider pagination

Interactive Learning Resources

Neo4j's Official Sandbox: https://neo4j.com/sandbox/
- Free interactive environment
- Comes with sample datasets
- Guided tutorials
Neo4j Browser Tutorial:
- Type :play cypher in Neo4j Browser
- Interactive, step-by-step lessons
- Real-time feedback
Cypher Manual: https://neo4j.com/docs/cypher-manual/
- Comprehensive reference
- Detailed examples
- Best practices

	from langchain.prompts import ChatPromptTemplate
	from langchain.chat_models import ChatOpenAI
	from langchain.chains import LLMChain

	# Define the system prompt that teaches the LLM how to construct graph queries
	SYSTEM_TEMPLATE = """You are a graph query expert that converts natural language questions into Cypher queries.
	Follow these rules when constructing queries:
	1. Always use lowercase for relationship types and property names
	2. Use descriptive variable names (e.g., 'person' instead of 'p')
	3. Include relevant properties in the RETURN clause
	4. Add appropriate WHERE clauses for filtering
	5. Use OPTIONAL MATCH when the relationship might not exist
	6. Limit results when appropriate

	The graph schema is:
	- Nodes:
	* Person(id, name, age, title)
	* Company(id, name, industry)
	* Project(id, name, status)
	- Relationships:
	* WORKS_AT(since)
	* MANAGES(role)
	* WORKS_ON(role, hours)

	Convert the user's question into a Cypher query."""

	# Define the human prompt template
	HUMAN_TEMPLATE = """Question: {question}

	Create a Cypher query that answers this question.
	Return only the Cypher query with no explanations."""

	# Create the full prompt template
	prompt = ChatPromptTemplate.from_messages([
	("system", SYSTEM_TEMPLATE),
	("human", HUMAN_TEMPLATE)
	])

	def construct_graph_query(question: str, llm: ChatOpenAI) -> str:
	"""
	Constructs a Cypher query from a natural language question.

	Args:
	question: Natural language question
	llm: Language model instance

	Returns:
	str: Generated Cypher query
	"""
	# Create the chain
	chain = LLMChain(llm=llm, prompt=prompt)

	# Generate the query
	result = chain.invoke({"question": question})

	return result["text"].strip()

	# Example usage
	if __name__ == "__main__":
	example_questions = [
	"Who are all the senior managers at tech companies?",
	"Which projects have no one working on them?",
	"Find people who work at multiple companies"
	]

	llm = ChatOpenAI(temperature=0, model="gpt-4")

	for question in example_questions:
	query = construct_graph_query(question, llm)
	print(f"\nQuestion: {question}")
	print(f"Generated Query: {query}")

	# Example outputs:
	"""
	Question: Who are all the senior managers at tech companies?
	Generated Query:
	MATCH (person:Person)-[m:MANAGES]->(company:Company)
	WHERE company.industry = 'tech' AND m.role CONTAINS 'senior'
	RETURN person.name, company.name, m.role
	ORDER BY company.name

	Question: Which projects have no one working on them?
	Generated Query:
	MATCH (project:Project)
	WHERE NOT EXISTS ((person:Person)-[:WORKS_ON]->(project))
	RETURN project.name, project.status

	Question: Find people who work at multiple companies
	Generated Query:
	MATCH (person:Person)-[w:WORKS_AT]->(company:Company)
	WITH person, COUNT(company) as company_count
	WHERE company_count > 1
	MATCH (person)-[w:WORKS_AT]->(company:Company)
	RETURN person.name, COLLECT(company.name) as companies, COUNT(company) as num_companies
	ORDER BY num_companies DESC
	"""