3b07f3c08d
<!-- .github/pull_request_template.md --> ## Description <!-- Provide a clear description of the changes in this PR --> ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --------- Co-authored-by: Boris <boris@topoteretes.com> Co-authored-by: Vasilije <8619304+Vasilije1990@users.noreply.github.com>
94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
import os
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import pathlib
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import asyncio
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import cognee
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from cognee.modules.search.types import SearchType
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async def main():
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"""
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Example script demonstrating how to use Cognee with Neo4j
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This example:
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1. Configures Cognee to use Neo4j as graph database
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2. Sets up data directories
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3. Adds sample data to Cognee
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4. Processes (cognifies) the data
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5. Performs different types of searches
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"""
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# Set up Neo4j credentials in .env file and get the values from environment variables
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neo4j_url = os.getenv("GRAPH_DATABASE_URL")
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neo4j_user = os.getenv("GRAPH_DATABASE_USERNAME")
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neo4j_pass = os.getenv("GRAPH_DATABASE_PASSWORD")
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# Configure Neo4j as the graph database provider
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cognee.config.set_graph_db_config(
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{
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"graph_database_url": neo4j_url, # Neo4j Bolt URL
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"graph_database_provider": "neo4j", # Specify Neo4j as provider
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"graph_database_username": neo4j_user, # Neo4j username
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"graph_database_password": neo4j_pass, # Neo4j password
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}
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)
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# Set up data directories for storing documents and system files
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# You should adjust these paths to your needs
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current_dir = pathlib.Path(__file__).parent
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data_directory_path = str(current_dir / "data_storage")
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cognee.config.data_root_directory(data_directory_path)
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cognee_directory_path = str(current_dir / "cognee_system")
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cognee.config.system_root_directory(cognee_directory_path)
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# Clean any existing data (optional)
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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# Create a dataset
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dataset_name = "neo4j_example"
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# Add sample text to the dataset
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sample_text = """Neo4j is a graph database management system.
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It stores data in nodes and relationships rather than tables as in traditional relational databases.
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Neo4j provides a powerful query language called Cypher for graph traversal and analysis.
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It now supports vector indexing for similarity search with the vector index plugin.
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Neo4j allows embedding generation and vector search to be combined with graph operations.
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Applications can use Neo4j to connect vector search with graph context for more meaningful results."""
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# Add the sample text to the dataset
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await cognee.add([sample_text], dataset_name)
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# Process the added document to extract knowledge
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await cognee.cognify([dataset_name])
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# Now let's perform some searches
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# 1. Search for insights related to "Neo4j"
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insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Neo4j")
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print("\nInsights about Neo4j:")
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for result in insights_results:
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print(f"- {result}")
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# 2. Search for text chunks related to "graph database"
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chunks_results = await cognee.search(
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query_type=SearchType.CHUNKS, query_text="graph database", datasets=[dataset_name]
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)
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print("\nChunks about graph database:")
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for result in chunks_results:
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print(f"- {result}")
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# 3. Get graph completion related to databases
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graph_completion_results = await cognee.search(
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query_type=SearchType.GRAPH_COMPLETION, query_text="database"
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)
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print("\nGraph completion for databases:")
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for result in graph_completion_results:
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print(f"- {result}")
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# Clean up (optional)
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# await cognee.prune.prune_data()
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# await cognee.prune.prune_system(metadata=True)
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if __name__ == "__main__":
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asyncio.run(main())
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