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docs: add print statements to the simple example, update README

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hande-k 2024-11-20 08:47:02 +01:00
parent 2331739e07
commit c6e447f28c
2 changed files with 34 additions and 11 deletions
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24
README.md
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@ -105,37 +105,47 @@ import asyncio
from cognee.api.v1.search import SearchType
async def main():
# Reset cognee data
# Create a clean slate for cognee -- reset data and system state
print("Resetting cognee data...")
await cognee.prune.prune_data()
# Reset cognee system state
await cognee.prune.prune_system(metadata=True)
print("Data reset complete.\n")
# cognee knowledge graph will be created based on this text
text = """
Natural language processing (NLP) is an interdisciplinary
subfield of computer science and information retrieval.
"""
# Add text to cognee
print("Adding text to cognee:")
print(text.strip())
await cognee.add(text)
print("Text added successfully.\n")
# Use LLMs and cognee to create knowledge graph
print("Running cognify to create knowledge graph...")
await cognee.cognify()
print("Cognify process complete.\n")
# Search cognee for insights
# Query cognee for insights on the added text
query_text = 'Tell me about NLP'
print(f"Searching cognee for insights with query: '{query_text}'")
search_results = await cognee.search(
SearchType.INSIGHTS,
"Tell me about NLP",
query_text=query_text,
)
# Display results
# Display search results
print("Search results:")
for result_text in search_results:
print(result_text)
# Expected output:
# natural_language_processing is_a field
# natural_language_processing is_subfield_of computer_science
# natural_language_processing is_subfield_of information_retrieval
asyncio.run(main())
```
When you run this script, you will see step-by-step messages in the console that help you trace the execution flow and understand what the script is doing at each stage.
A version of this example is here: `examples/python/simple_example.py`
### Create your own memory store

21
examples/python/simple_example.py
View file

@ -11,29 +11,42 @@ from cognee.api.v1.search import SearchType
async def main():
# Create a clean slate for cognee -- reset data and system state
print("Resetting cognee data...")
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
print("Data reset complete.\n")
# cognee knowledge graph will be created based on this text
text = """
Natural language processing (NLP) is an interdisciplinary
subfield of computer science and information retrieval.
"""
print("Adding text to cognee:")
print(text.strip())
# Add the text, and make it available for cognify
await cognee.add(text)
print("Text added successfully.\n")
print("Running cognify to create knowledge graph...")
# Use LLMs and cognee to create knowledge graph
await cognee.cognify()
print("Cognify process complete.\n")
query_text = 'Tell me about NLP'
print(f"Searching cognee for insights with query: '{query_text}'")
# Query cognee for insights on the added text
search_results = await cognee.search(
SearchType.INSIGHTS, query_text='Tell me about NLP'
SearchType.INSIGHTS, query_text=query_text
)
# Display search results
print("Search results:")
# Display results
for result_text in search_results:
print(result_text)
if __name__ == '__main__':
asyncio.run(main())