chore: import upstream snapshot with attribution

examples/database_examples/ladybug_example.py

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+import asyncio
+import pathlib
+
+import cognee
+from cognee import SearchType
+
+
+async def main():
+    """
+    Example script demonstrating how to use Cognee with Ladybug
+
+    This example:
+    1. Configures Cognee to use Ladybug as graph database
+    2. Sets up data directories
+    3. Stores sample data with remember to Cognee
+    4. Performs different types of searches
+    """
+    # Configure Ladybug as the graph database provider
+    cognee.config.set_graph_db_config(
+        {
+            "graph_database_provider": "ladybug",  # Specify Ladybug as provider
+        }
+    )
+
+    # Set up data directories for storing documents and system files
+    # You should adjust these paths to your needs
+    current_dir = pathlib.Path(__file__).parent
+    data_directory_path = str(current_dir / "data_storage")
+    cognee.config.data_root_directory(data_directory_path)
+
+    cognee_directory_path = str(current_dir / "cognee_system")
+    cognee.config.system_root_directory(cognee_directory_path)
+
+    # Clean any existing data (optional)
+    # await cognee.forget(everything=True)
+
+    # Create a dataset
+    dataset_name = "ladybug_example"
+
+    # Add sample text to the dataset
+    sample_text = """Ladybug is a graph database system optimized for running complex graph analytics.
+    It is designed to be a high-performance graph database for data science workloads.
+    Ladybug is built with modern hardware optimizations in mind.
+    It provides support for property graphs and offers a Cypher-like query language.
+    Ladybug can handle both transactional and analytical graph workloads.
+    The database now includes vector search capabilities for AI applications and semantic search."""
+
+    # Add the sample text to the dataset
+    await cognee.remember([sample_text], dataset_name=dataset_name, self_improvement=False)
+
+    # Now let's perform some searches
+    # 1. Search for insights related to "Ladybug"
+    insights_results = await cognee.recall(
+        query_type=SearchType.GRAPH_COMPLETION, query_text="Ladybug"
+    )
+    print("\nInsights about Ladybug:")
+    for result in insights_results:
+        print(f"- {result}")
+
+    # 2. Search for text chunks related to "graph database"
+    chunks_results = await cognee.recall(
+        query_type=SearchType.CHUNKS, query_text="graph database", datasets=[dataset_name]
+    )
+    print("\nChunks about graph database:")
+    for result in chunks_results:
+        print(f"- {result}")
+
+    # 3. Get graph completion related to databases
+    graph_completion_results = await cognee.recall(
+        query_type=SearchType.GRAPH_COMPLETION, query_text="database"
+    )
+    print("\nGraph completion for databases:")
+    for result in graph_completion_results:
+        print(f"- {result}")
+
+    # Clean up (optional)
+    # await cognee.forget(everything=True)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())