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>
89 lines
3.2 KiB
Python
89 lines
3.2 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 Milvus
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This example:
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1. Configures Cognee to use Milvus as vector 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 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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local_milvus_db_path = os.path.join(cognee_directory_path, "databases", "milvus.db")
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# Configure Milvus as the vector database provider
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cognee.config.set_vector_db_config(
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{
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"vector_db_url": local_milvus_db_path, # Enter Milvus Endpoint if exist
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"vector_db_key": "", # Enter Token
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"vector_db_provider": "milvus", # Specify Milvus as provider
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}
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)
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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 = "milvus_example"
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# Add sample text to the dataset
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sample_text = """Milvus is an open-source vector database built to power AI applications.
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It is designed for storing, indexing, and querying large-scale vector datasets.
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Milvus implements efficient approximate nearest neighbor search algorithms.
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It features advanced indexing techniques like HNSW, IVF, PQ, and more.
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Milvus supports hybrid searches combining vector similarity with scalar filtering.
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The system can be deployed standalone, in clusters, or through a cloud service."""
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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 "Milvus"
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insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Milvus")
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print("\nInsights about Milvus:")
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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 "vector similarity"
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chunks_results = await cognee.search(
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query_type=SearchType.CHUNKS, query_text="vector similarity", datasets=[dataset_name]
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)
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print("\nChunks about vector similarity:")
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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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