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참고/neo4j-graphrag-python-main/examples/build_graph/automatic_schema_extraction/simple_kg_builder_schema_from_pdf.py

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+"""This example demonstrates how to use SimpleKGPipeline with automatic schema extraction
+from a PDF file. When no schema is provided to SimpleKGPipeline, automatic schema extraction
+is performed using the LLM.
+
+Note: This example requires an OpenAI API key to be set in the .env file.
+"""
+
+import asyncio
+import logging
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+import neo4j
+
+from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
+from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.embeddings import OpenAIEmbeddings
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure logging
+logging.basicConfig()
+logging.getLogger("neo4j_graphrag").setLevel(logging.INFO)
+
+# PDF file path
+root_dir = Path(__file__).parents[2]
+PDF_FILE = str(
+    root_dir / "data" / "Harry Potter and the Chamber of Secrets Summary.pdf"
+)
+
+
+async def run_kg_pipeline_with_auto_schema() -> None:
+    """Run the SimpleKGPipeline with automatic schema extraction from a PDF file."""
+
+    # Define Neo4j connection
+    uri = os.getenv("NEO4J_URI", "neo4j://localhost:7687")
+    user = os.getenv("NEO4J_USER", "neo4j")
+    password = os.getenv("NEO4J_PASSWORD", "password")
+
+    # Initialize the Neo4j driver
+    driver = neo4j.GraphDatabase.driver(uri, auth=(user, password))
+
+    # Create the LLM instance
+    llm = OpenAILLM(model_name="gpt-5")
+
+    # Create the embedder instance
+    embedder = OpenAIEmbeddings()
+
+    try:
+        # Create a SimpleKGPipeline instance without providing a schema
+        # This will trigger automatic schema extraction
+        kg_builder = SimpleKGPipeline(
+            llm=llm,
+            driver=driver,
+            embedder=embedder,
+            from_file=True,
+        )
+
+        print(f"Processing PDF file: {PDF_FILE}")
+        # Run the pipeline on the PDF file
+        await kg_builder.run_async(file_path=PDF_FILE)
+
+    finally:
+        # Close connections
+        await llm.aclose()
+        driver.close()
+
+
+async def main() -> None:
+    """Run the example."""
+    # Create data directory if it doesn't exist
+    data_dir = root_dir / "data"
+    data_dir.mkdir(exist_ok=True)
+
+    # Check if the PDF file exists
+    if not Path(PDF_FILE).exists():
+        print(f"Warning: PDF file not found at {PDF_FILE}")
+        print("Please replace with a valid PDF file path.")
+        return
+
+    # Run the pipeline
+    await run_kg_pipeline_with_auto_schema()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

참고/neo4j-graphrag-python-main/examples/build_graph/automatic_schema_extraction/simple_kg_builder_schema_from_text.py

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+"""This example demonstrates how to use SimpleKGPipeline with automatic schema extraction
+from a text input. When no schema is provided to SimpleKGPipeline, automatic schema extraction
+is performed using the LLM.
+
+Note: This example requires an OpenAI API key to be set in the .env file.
+"""
+
+import asyncio
+import logging
+import os
+from dotenv import load_dotenv
+import neo4j
+
+from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
+from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.embeddings import OpenAIEmbeddings
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure logging
+logging.basicConfig()
+logging.getLogger("neo4j_graphrag").setLevel(logging.DEBUG)
+
+# Sample text to build a knowledge graph from
+TEXT = """
+Acme Corporation was founded in 1985 by John Smith in New York City. 
+The company specializes in manufacturing high-quality widgets and gadgets 
+for the consumer electronics industry.
+
+Sarah Johnson joined Acme in 2010 as a Senior Engineer and was promoted to 
+Engineering Director in 2015. She oversees a team of 12 engineers working on 
+next-generation products. Sarah holds a PhD in Electrical Engineering from MIT 
+and has filed 5 patents during her time at Acme.
+
+The company expanded to international markets in 2012, opening offices in London, 
+Tokyo, and Berlin. Each office is managed by a regional director who reports 
+directly to the CEO, Michael Brown, who took over leadership in 2008.
+
+Acme's most successful product, the SuperWidget X1, was launched in 2018 and 
+has sold over 2 million units worldwide. The product was developed by a team led 
+by Robert Chen, who joined the company in 2016 after working at TechGiant for 8 years.
+
+The company currently employs 250 people across its 4 locations and had a revenue 
+of $75 million in the last fiscal year. Acme is planning to go public in 2024 
+with an estimated valuation of $500 million.
+"""
+
+
+async def run_kg_pipeline_with_auto_schema() -> None:
+    """Run the SimpleKGPipeline with automatic schema extraction from text input."""
+
+    # Define Neo4j connection
+    uri = os.getenv("NEO4J_URI", "neo4j://localhost:7687")
+    user = os.getenv("NEO4J_USER", "neo4j")
+    password = os.getenv("NEO4J_PASSWORD", "password")
+
+    # Initialize the Neo4j driver
+    driver = neo4j.GraphDatabase.driver(uri, auth=(user, password))
+
+    # Create the LLM instance
+    llm = OpenAILLM(
+        model_name="gpt-5",
+    )
+
+    # Create the embedder instance
+    embedder = OpenAIEmbeddings()
+
+    try:
+        # Create a SimpleKGPipeline instance without providing a schema
+        # This will trigger automatic schema extraction
+        kg_builder = SimpleKGPipeline(
+            llm=llm,
+            driver=driver,
+            embedder=embedder,
+            from_file=False,  # Using raw text input, not PDF
+        )
+
+        # Run the pipeline on the text
+        await kg_builder.run_async(text=TEXT)
+
+    finally:
+        # Close connections
+        await llm.aclose()
+        driver.close()
+
+
+async def main() -> None:
+    """Run the example."""
+    await run_kg_pipeline_with_auto_schema()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())