multimedia_example.py•1.67 kB
import os
import asyncio
import pathlib
from cognee.shared.logging_utils import setup_logging, ERROR
import cognee
from cognee.api.v1.search import SearchType
# Prerequisites:
# 1. Copy `.env.template` and rename it to `.env`.
# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
# LLM_API_KEY = "your_key_here"
async def main():
# Create a clean slate for cognee -- reset data and system state
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
# cognee knowledge graph will be created based on the text
# and description of these files
mp3_file_path = os.path.join(
pathlib.Path(__file__).parent.parent.parent,
"examples/data/multimedia/text_to_speech.mp3",
)
png_file_path = os.path.join(
pathlib.Path(__file__).parent.parent.parent,
"examples/data/multimedia/example.png",
)
# Add the files, and make it available for cognify
await cognee.add([mp3_file_path, png_file_path])
# Use LLMs and cognee to create knowledge graph
await cognee.cognify()
# Query cognee for summaries of the data in the multimedia files
search_results = await cognee.search(
query_type=SearchType.SUMMARIES,
query_text="What is in the multimedia files?",
)
# Display search results
for result_text in search_results:
print(result_text)
if __name__ == "__main__":
logger = setup_logging(log_level=ERROR)
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
loop.run_until_complete(main())
finally:
loop.run_until_complete(loop.shutdown_asyncgens())