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  1. wware revised this gist Oct 26, 2024. 1 changed file with 306 additions and 0 deletions.
    306 changes: 306 additions & 0 deletions 0_GraphRAG.md
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    Original file line number Diff line number Diff line change
    @@ -1617,3 +1617,309 @@ Would you like me to:
    3. Add support for multiple external endpoints?
    4. 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:
    1. 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](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/](https://query.wikidata.org/)
    2. 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
    3. 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
    4. 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    * Historical documents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
    5. 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
    6. 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
    7. 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:
    1. Availability and Reliability:
    * Some endpoints may be occasionally down
    * Query timeouts are common
    * Rate limiting may apply
    * Consider caching frequently accessed data
    2. Data Quality:
    * Varies between sources
    * May need validation
    * Updates can be irregular
    * Inconsistencies between sources
    3. Integration Challenges:
    * Different ontologies
    * Varying data models
    * Entity resolution needed
    * Performance considerations
    4. 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:
    1. Show how to implement specific queries for any of these sources?
    2. Demonstrate integration patterns for combining multiple sources?
    3. Explain how to handle common challenges with these resources?
    4. Or explore a specific domain or use case?
  2. wware revised this gist Oct 26, 2024. 1 changed file with 691 additions and 0 deletions.
    691 changes: 691 additions & 0 deletions 0_GraphRAG.md
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    @@ -926,3 +926,694 @@ Would you like me to show you how to extend this with more advanced features lik
    * 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:
    1. 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:
    1. GraphDB (by Ontotext):
    * Excellent SPARQL support
    * Built-in reasoning
    * Vector search capabilities
    * Good documentation
    * Free tier available
    2. Stardog:
    * Enterprise-grade knowledge graph platform
    * Strong reasoning capabilities
    * Built-in vector search
    * SPARQL and GraphQL support
    * Good cloud offerings
    3. 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:
    1. Data Model:
    * Uses RDF triples instead of property graphs
    * Native support for URIs and linked data
    * Built-in support for ontologies and reasoning
    2. Query Language:
    * SPARQL instead of Cypher
    * More standardized across different RDF stores
    * Better support for federation and semantic queries
    3. Entity Linking:
    * Direct integration with DBpedia and other LOD resources
    * Easier to maintain links to external knowledge bases
    * Better support for ontology alignment
    4. 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:
    1. Federation with DBpedia's SPARQL endpoint
    2. Ontology-aware querying
    3. Reasoning-enhanced RAG
    4. 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:
    1. Federation:
    * Uses SERVICE keyword to query DBpedia
    * Combines local and DBpedia properties in single queries
    * Handles owl:sameAs relationships for entity alignment
    2. Reasoning:
    * Incorporates type hierarchies (rdfs:subClassOf)
    * Uses OWL semantics for entity equivalence
    * Can be extended with custom inference rules
    3. Knowledge Integration:
    * Merges local institutional knowledge with DBpedia facts
    * Maintains separate provenance for local vs DBpedia data
    * Handles potential conflicts between sources
    4. 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:
    1. Performance Considerations:
    * Federation can be slow for large result sets
    * Consider caching frequently accessed DBpedia data
    * Use selective SERVICE patterns
    2. Reliability:
    * DBpedia endpoint may have timeout issues
    * Add error handling for federation failures
    * Consider local mirrors for critical data
    3. Schema Alignment:
    * Use ontology mappings for property alignment
    * Consider using SKOS for concept mapping
    * Handle different naming conventions
    Would you like me to:
    1. Show how to add custom inference rules?
    2. Demonstrate more complex ontology-aware querying?
    3. Add support for multiple external endpoints?
    4. Or explore another aspect of semantic integration?
  3. wware revised this gist Oct 25, 2024. 1 changed file with 84 additions and 28 deletions.
    112 changes: 84 additions & 28 deletions 0_GraphRAG.md
    View file
    Original file line number Diff line number Diff line change
    @@ -342,36 +342,92 @@ Would you like me to:
    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:
    Neo4j is very popular for graph RAG applications, but there are several other significant players in this space. Let me break this down.

    #### Graph Databases in RAG Applications

    *There was a document here showing some different databases but somehow the document
    got corrupted so this information was lost.*

    Some key observations about the current landscape:

    1. **Neo4j's Popularity** comes from:
    * Large existing userbase
    * Strong integration with common RAG frameworks
    * Good documentation for AI/ML use cases
    * Vector search capabilities
    2. **Emerging Trends**:
    * Many databases are adding native vector support
    * Hybrid architectures becoming more common
    * Focus on scalability and real-time updates
    3. **Selection Factors**:
    * Scale of data
    * Query patterns
    * Infrastructure requirements
    * Team expertise
    * Cost considerations
    While Neo4j is very popular for graph RAG applications, it's not the only option. Here's a comprehensive overview:

    Would you like me to:
    1. 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

    2. 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:

    1. Scale requirements
    2. Existing infrastructure
    3. Query language preference
    4. Need for managed vs self-hosted
    5. Vector search requirements
    6. Budget considerations
    7. Team expertise

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

    1. It has the most mature integrations with popular RAG frameworks
    2. Extensive documentation and examples exist
    3. The development experience is generally good
    4. The community is large and active
    5. 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

    1. Dive deeper into any particular database?
    2. Explain specific RAG integration patterns?
    3. Compare query languages across these databases?
    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?
  4. wware revised this gist Oct 25, 2024. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion 0_GraphRAG.md
    View file
    Original file line number Diff line number Diff line change
    @@ -1,4 +1,4 @@
    # What is this document?
    # 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
  5. wware revised this gist Oct 25, 2024. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion 0_GraphRAG.md
    View file
    Original file line number Diff line number Diff line change
    @@ -117,7 +117,7 @@ Is neo4j a triple store?
    ## Response:
    No, Neo4j is not a triple store, though there are some important similarities and differences worth exploring.

    ![Triple Store vs Property Graph Model](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/98621df0d8c47565d1e4b67fa5cf05e84fcb0f80/Property-Graph-vs-Triple-Store.png)
    ![Triple Store vs Property Graph Model](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/5a06c0ed12cc8bc5ad8fbce340edc66944a494f0/Property-Graph-vs-Triple-Store.png)

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

  6. wware revised this gist Oct 25, 2024. 1 changed file with 71 additions and 7 deletions.
    78 changes: 71 additions & 7 deletions 0_GraphRAG.md
    View file
    Original file line number Diff line number Diff line change
    @@ -117,9 +117,7 @@ Is neo4j a triple store?
    ## Response:
    No, Neo4j is not a triple store, though there are some important similarities and differences worth exploring.

    Triple Store vs Property Graph Model

    Click to open diagram
    ![Triple Store vs Property Graph Model](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/98621df0d8c47565d1e4b67fa5cf05e84fcb0f80/Property-Graph-vs-Triple-Store.png)

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

    @@ -346,9 +344,10 @@ It sounds like neo4j is the de facto standard graph database for graph rag appli
    ## Response:
    Neo4j is very popular for graph RAG applications, but there are several other significant players in this space. Let me break this down.

    Graph Databases in RAG Applications
    #### Graph Databases in RAG Applications

    Click to open document
    *There was a document here showing some different databases but somehow the document
    got corrupted so this information was lost.*

    Some key observations about the current landscape:

    @@ -380,9 +379,74 @@ Is neo4j available as a docker image? If so, what docker-run options are typical
    ## Response:
    Yes, Neo4j is available as a Docker image. Let me show you the typical Docker setup.

    Neo4j Docker Configuration
    #### 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
    Click to open code
    networks:
    neo4j_network:
    driver: bridge
    ```

    Let me explain the key components:

  7. wware revised this gist Oct 25, 2024. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion 0_GraphRAG.md
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    Original file line number Diff line number Diff line change
    @@ -170,7 +170,7 @@ OWL can express set theoretic ideas and other useful info about your entities, s
    ## 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.

    ![Property Graph Schema and Constraints](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/XXX/Property-Graph-Schema-and-Constraints.png)
    ![Property Graph Schema and Constraints](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/894ac1c2cab4c623c36095c40bacfcd6d0da9a27/Property-Graph-Schema-and-Constraints.png)

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

  8. wware revised this gist Oct 25, 2024. 2 changed files with 2 additions and 4 deletions.
    6 changes: 2 additions & 4 deletions 0_GraphRAG.md
    View file
    Original file line number Diff line number Diff line change
    @@ -170,9 +170,7 @@ OWL can express set theoretic ideas and other useful info about your entities, s
    ## 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.

    Property Graph Schema and Constraints

    Click to open diagram
    ![Property Graph Schema and Constraints](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/XXX/Property-Graph-Schema-and-Constraints.png)

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

    @@ -807,4 +805,4 @@ Would you like me to show you how to extend this with more advanced features lik
    * Custom query templates
    * Relationship-aware querying
    * Hybrid search (combining vector and graph traversal)
    * Or something else specific to your use case?
    * Or something else specific to your use case?
    Binary file added Property-Graph-Schema-and-Constraints.png
    View file
    Loading
    Sorry, something went wrong. Reload?
    Sorry, we cannot display this file.
    Sorry, this file is invalid so it cannot be displayed.
  9. wware revised this gist Oct 25, 2024. 1 changed file with 217 additions and 1 deletion.
    218 changes: 217 additions & 1 deletion 0_GraphRAG.md
    View file
    Original file line number Diff line number Diff line change
    @@ -591,4 +591,220 @@ docker-compose up -d
    * 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?
    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:

    1. 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
    2. Key features:
    * Document ingestion with metadata support
    * Vector similarity search
    * Graph relationship management
    * Flexible querying with different response modes
    3. Configuration through environment variables:

    ```bash
    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:

    1. Save the code as `app/main.py`
    2. 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
    ```
    3. 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?
  10. wware revised this gist Oct 25, 2024. 1 changed file with 136 additions and 1 deletion.
    137 changes: 136 additions & 1 deletion 0_GraphRAG.md
    View file
    Original file line number Diff line number Diff line change
    @@ -456,4 +456,139 @@ Would you like me to:

    1. Explain any specific configuration options in more detail?
    2. Show how to set up clustering with Docker?
    3. Demonstrate how to integrate with other services (like vector databases)?
    3. 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:

    1. Create a project directory with the following structure:

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

    2. 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
    3. To start the services:

    ```bash
    docker-compose up -d
    ```

    4. Access points:
    * Neo4j Browser: [http://localhost:7474](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?
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    @@ -46,9 +46,7 @@ How is the first step accomplished? I assume there is some prompt engineering ma
    ## 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

    Click to open code
    [Graph Query Construction with Prompt Engineering](#file-graphqueryconstructionprompt-py)

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

    @@ -95,7 +93,7 @@ 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](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a#file-cypherlanguagebeginnerguide-md)
    [Introduction to Cypher Query Language](#file-cypherlanguagebeginnerguide-md)

    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.

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    # What is this document?

    *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?

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    @@ -269,7 +269,7 @@ The asterisk `*` in Cypher indicates a variable-length path - it means "follow t

    Let me show you how it works:

    ![Variable-Length Path Matching in Cypher](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/98621df0d8c47565d1e4b67fa5cf05e84fcb0f80/Variable-Length-Path-Matching-in-Cypher.png)
    ![Variable-Length Path Matching in Cypher](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/a51d03f27536749413d1eefe910817e85e10eb29/Variable-Length-Path-Matching-in-Cypher.png)

    Here are the key variations of path matching:

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    Let me show you how it works:

    Variable-Length Path Matching in Cypher

    Click to open diagram
    ![Variable-Length Path Matching in Cypher](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/98621df0d8c47565d1e4b67fa5cf05e84fcb0f80/Variable-Length-Path-Matching-in-Cypher.png)

    Here are the key variations of path matching:

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    @@ -87,9 +87,7 @@ 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

    Click to open document
    [Introduction to Cypher Query Language](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a#file-cypherlanguagebeginnerguide-md)

    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.

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

    LangSmith Trace for Graph RAG Query

    Click to open diagram
    ![LangSmith Trace for Graph RAG Query](https://gist.github.com/wware/4c9b2ea74f02b68c1ffa00b3a2d49b9a/raw/98621df0d8c47565d1e4b67fa5cf05e84fcb0f80/LangSmithTrace.png)

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

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  21. wware created this gist Oct 24, 2024.
    102 changes: 102 additions & 0 deletions CypherLanguageBeginnerGuide.md
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    # 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
    1. Nodes

    * Represented with parentheses: `()`
    * Labels use a colon: `(:Person)`
    * Properties use curly braces: `(:Person {name: "John"})`

    ```MATCH (p:Person {name: "John"}) RETURN p```
    2. 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```
    3. 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
    ```cypher
    // Find John's friends
    MATCH (john:Person {name: "John"})-[:FRIENDS_WITH]->(friend:Person)
    RETURN friend.name
    ```
    ### Creating Relationships
    ```cypher
    // 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
    ```cypher
    // 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
    ```cypher
    // 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
    96 changes: 96 additions & 0 deletions GraphQueryConstructionPrompt.py
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    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
    """
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    ## 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.

    LangSmith Trace for Graph RAG Query

    Click to open diagram

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

    1. Query Construction Phase:
    * Takes the raw user input
    * Processes it into a structured format
    * Generates the appropriate graph query
    2. Graph Retrieval Phase:
    * Executes the query against the graph database
    * Returns relevant subgraphs
    * Processes the results
    3. Context Creation Phase:
    * Formats the graph data for the LLM
    * Creates an appropriate context window
    4. 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

    Click to open code

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

    1. **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
    2. **Schema Definition**:
    * Lists all node types with their properties
    * Defines relationship types and their attributes
    * Helps the LLM understand the graph structure
    3. **Query Construction Rules**:
    * Enforces consistent naming conventions
    * Requires descriptive variable names
    * Specifies when to use optional matches
    * Includes guidance on result limiting
    4. **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:

    1. Add more example transformations?
    2. Show how to handle more complex graph patterns?
    3. 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

    Click to open document

    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:

    ```cypher
    (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:

    1. Show more specific examples for your use case?
    2. Explain any particular pattern in more detail?
    3. 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.

    Triple Store vs Property Graph Model

    Click to open diagram

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

    1. **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
    2. **Property Storage**:
    * Triple Store: Properties are stored as additional triples
    * Neo4j: Properties are stored directly on nodes and relationships
    3. **Query Language**:
    * Triple Store: Usually SPARQL
    * Neo4j: Cypher
    4. **Schema**:
    * Triple Store: Often uses RDF Schema or OWL ontologies
    * Neo4j: Schema-optional with optional constraints and indexes
    5. **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.

    Property Graph Schema and Constraints

    Click to open diagram

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

    1. **Node Label Hierarchies**:

    ```cypher
    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;
    ```

    2. **Property Constraints**:

    ```cypher
    // 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'];
    ```

    3. **Relationship Cardinality**:

    ```cypher
    // 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;
    ```

    4. **Semantic Rules via Procedures**:

    ```python
    @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)
    ```

    5. **Graph Data Science for Inference**:

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

    Key differences from OWL:

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

    Would you like me to:

    1. Show more examples of how to implement specific OWL-like features in property graphs?
    2. Explain how to combine Graph Data Science with schema constraints?
    3. 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:

    Variable-Length Path Matching in Cypher

    Click to open diagram

    Here are the key variations of path matching:

    ```cypher
    // 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:

    1. **Organizational Hierarchies**:

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

    2. **Cycle Detection**:

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

    3. **Level-specific Queries**:

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

    4. **Path Analysis**:

    ```cypher
    // 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:

    1. Show more examples of variable-length patterns?
    2. Explain how to optimize queries with variable-length paths?
    3. 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:
    Neo4j is very popular for graph RAG applications, but there are several other significant players in this space. Let me break this down.

    Graph Databases in RAG Applications

    Click to open document

    Some key observations about the current landscape:

    1. **Neo4j's Popularity** comes from:
    * Large existing userbase
    * Strong integration with common RAG frameworks
    * Good documentation for AI/ML use cases
    * Vector search capabilities
    2. **Emerging Trends**:
    * Many databases are adding native vector support
    * Hybrid architectures becoming more common
    * Focus on scalability and real-time updates
    3. **Selection Factors**:
    * Scale of data
    * Query patterns
    * Infrastructure requirements
    * Team expertise
    * Cost considerations

    Would you like me to:

    1. Dive deeper into any particular database?
    2. Explain specific RAG integration patterns?
    3. Compare query languages across these databases?

    ## 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

    Click to open code

    Let me explain the key components:

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

    ```bash
    # 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.*
    ```

    4. **Quick Start Command**:

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

    5. **Development vs Production**:

    ```bash
    # 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:

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