chore: import upstream snapshot with attribution

examples/database_examples/chromadb_example.py

@@ -0,0 +1,91 @@
+import asyncio
+import os
+import pathlib
+
+# ChromaDB is available as a vector adapter, but it does not have a dataset
+# database handler for backend access control yet.
+# Set os.environ before importing Cognee: Cognee reads env-backed settings at import time, so values
+# assigned later may not override defaults or `.env`. See https://docs.cognee.ai/setup-configuration/overview#using-os-environ
+os.environ["ENABLE_BACKEND_ACCESS_CONTROL"] = "False"
+
+import cognee
+from cognee import SearchType
+
+
+async def main():
+    """
+    Example script demonstrating how to use Cognee with ChromaDB
+
+    This example:
+    1. Configures Cognee to use ChromaDB as vector database
+    2. Sets up data directories
+    3. Stores sample data with remember to Cognee
+    4. Performs different types of searches
+    """
+    # Configure ChromaDB as the vector database provider
+    cognee.config.set_vector_db_config(
+        {
+            "vector_db_url": "http://localhost:8000",  # Default ChromaDB server URL
+            "vector_db_key": "",  # ChromaDB doesn't require an API key by default
+            "vector_db_provider": "chromadb",  # Specify ChromaDB as provider
+            "vector_dataset_database_handler": "chromadb",
+        }
+    )
+
+    # 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 = "chromadb_example"
+
+    # Add sample text to the dataset
+    sample_text = """ChromaDB is an open-source embedding database.
+    It allows users to store and query embeddings and their associated metadata.
+    ChromaDB can be deployed in various ways: in-memory, on disk via sqlite, or as a persistent service.
+    It is designed to be fast, scalable, and easy to use, making it a popular choice for AI applications.
+    The database is built to handle vector search efficiently, which is essential for semantic search applications.
+    ChromaDB supports multiple distance metrics for vector similarity search and can be integrated with various ML frameworks."""
+
+    # 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 "ChromaDB"
+    insights_results = await cognee.recall(
+        query_type=SearchType.GRAPH_COMPLETION, query_text="ChromaDB"
+    )
+    print("\nInsights about ChromaDB:")
+    for result in insights_results:
+        print(f"- {result}")
+
+    # 2. Search for text chunks related to "vector search"
+    chunks_results = await cognee.recall(
+        query_type=SearchType.CHUNKS, query_text="vector search", datasets=[dataset_name]
+    )
+    print("\nChunks about vector search:")
+    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())