wware · October 26, 2024 16:13
diff --git a/0_GraphRAG.md b/0_GraphRAG.md
diff --git a/CypherLanguageBeginnerGuide.md b/CypherLanguageBeginnerGuide.md
diff --git a/GraphQueryConstructionPrompt.py b/GraphQueryConstructionPrompt.py
 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
 """
	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
	"""