default.py•2.23 kB
import os
import asyncio
import pathlib
from cognee import config, add, cognify, search, SearchType, prune, visualize_graph
async def main():
data_directory_path = str(
pathlib.Path(os.path.join(pathlib.Path(__file__).parent, ".data_storage")).resolve()
)
# Set up the data directory. Cognee will store files here.
config.data_root_directory(data_directory_path)
cognee_directory_path = str(
pathlib.Path(os.path.join(pathlib.Path(__file__).parent, ".cognee_system")).resolve()
)
# Set up the Cognee system directory. Cognee will store system files and databases here.
config.system_root_directory(cognee_directory_path)
# Prune data and system metadata before running, only if we want "fresh" state.
await prune.prune_data()
await prune.prune_system(metadata=True)
text = "The Python programming language is widely used in data analysis, web development, and machine learning."
# Add the text data to Cognee.
await add(text)
# Cognify the text data.
await cognify()
# Or use our simple graph preview
graph_file_path = str(
pathlib.Path(
os.path.join(pathlib.Path(__file__).parent, ".artifacts/graph_visualization.html")
).resolve()
)
await visualize_graph(graph_file_path)
# Completion query that uses graph data to form context.
graph_completion = await search(
query_text="What is python?", query_type=SearchType.GRAPH_COMPLETION
)
print("Graph completion result is:")
print(graph_completion)
# Completion query that uses document chunks to form context.
rag_completion = await search(
query_text="What is Python?", query_type=SearchType.RAG_COMPLETION
)
print("Completion result is:")
print(rag_completion)
# Query all summaries related to query.
summaries = await search(query_text="Python", query_type=SearchType.SUMMARIES)
print("Summary results are:")
for summary in summaries:
print(summary)
chunks = await search(query_text="Python", query_type=SearchType.CHUNKS)
print("Chunk results are:")
for chunk in chunks:
print(chunk)
if __name__ == "__main__":
asyncio.run(main())