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docs: add print statements to the simple example, update readme (#9)

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Vasilije 2024-11-22 11:33:21 +01:00 • committed by GitHub
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52
README.md
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@ -105,37 +105,65 @@ import asyncio
from cognee.api.v1.search import SearchType from cognee.api.v1.search import SearchType
async def main(): 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() await cognee.prune.prune_data()
# Reset cognee system state
await cognee.prune.prune_system(metadata=True) await cognee.prune.prune_system(metadata=True)
print("Data reset complete.\n")
# cognee knowledge graph will be created based on this text
text = """ text = """
Natural language processing (NLP) is an interdisciplinary Natural language processing (NLP) is an interdisciplinary
subfield of computer science and information retrieval. subfield of computer science and information retrieval.
""" """
# Add text to cognee print("Adding text to cognee:")
print(text.strip())
# Add the text, and make it available for cognify
await cognee.add(text) await cognee.add(text)
print("Text added successfully.\n")
print("Running cognify to create knowledge graph...\n")
print("Cognify process steps:")
print("1. Classifying the document: Determining the type and category of the input text.")
print("2. Checking permissions: Ensuring the user has the necessary rights to process the text.")
print("3. Extracting text chunks: Breaking down the text into sentences or phrases for analysis.")
print("4. Adding data points: Storing the extracted chunks for processing.")
print("5. Generating knowledge graph: Extracting entities and relationships to form a knowledge graph.")
print("6. Summarizing text: Creating concise summaries of the content for quick insights.\n")
# Use LLMs and cognee to create knowledge graph # Use LLMs and cognee to create knowledge graph
await cognee.cognify() await cognee.cognify()
print("Cognify process complete.\n")
# Search cognee for insights
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( search_results = await cognee.search(
SearchType.INSIGHTS, SearchType.INSIGHTS, query_text=query_text
"Tell me about NLP",
) )
print("Search results:")
# Display results # Display results
for result_text in search_results: for result_text in search_results:
print(result_text) print(result_text)
# 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()) # Example output:
# ({'id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'target_node_id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 15, 473137, tzinfo=datetime.timezone.utc)}, {'id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'computer science', 'description': 'The study of computation and information processing.'})
# (...)
#
# It represents nodes and relationships in the knowledge graph:
# - The first element is the source node (e.g., 'natural language processing').
# - The second element is the relationship between nodes (e.g., 'is_a_subfield_of').
# - The third element is the target node (e.g., 'computer science').
if __name__ == '__main__':
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` A version of this example is here: `examples/python/simple_example.py`
### Create your own memory store ### Create your own memory store

37
examples/python/simple_example.py
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@ -1,5 +1,4 @@
import asyncio import asyncio
import cognee import cognee
from cognee.api.v1.search import SearchType from cognee.api.v1.search import SearchType
@ -11,29 +10,57 @@ from cognee.api.v1.search import SearchType
async def main(): async def main():
# Create a clean slate for cognee -- reset data and system state # Create a clean slate for cognee -- reset data and system state
print("Resetting cognee data...")
await cognee.prune.prune_data() await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True) await cognee.prune.prune_system(metadata=True)
print("Data reset complete.\n")
# cognee knowledge graph will be created based on this text # cognee knowledge graph will be created based on this text
text = """ text = """
Natural language processing (NLP) is an interdisciplinary Natural language processing (NLP) is an interdisciplinary
subfield of computer science and information retrieval. subfield of computer science and information retrieval.
""" """
print("Adding text to cognee:")
print(text.strip())
# Add the text, and make it available for cognify # Add the text, and make it available for cognify
await cognee.add(text) await cognee.add(text)
print("Text added successfully.\n")
print("Running cognify to create knowledge graph...\n")
print("Cognify process steps:")
print("1. Classifying the document: Determining the type and category of the input text.")
print("2. Checking permissions: Ensuring the user has the necessary rights to process the text.")
print("3. Extracting text chunks: Breaking down the text into sentences or phrases for analysis.")
print("4. Adding data points: Storing the extracted chunks for processing.")
print("5. Generating knowledge graph: Extracting entities and relationships to form a knowledge graph.")
print("6. Summarizing text: Creating concise summaries of the content for quick insights.\n")
# Use LLMs and cognee to create knowledge graph # Use LLMs and cognee to create knowledge graph
await cognee.cognify() 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 # Query cognee for insights on the added text
search_results = await cognee.search( 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: for result_text in search_results:
print(result_text) print(result_text)
# Example output:
# ({'id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'target_node_id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 15, 473137, tzinfo=datetime.timezone.utc)}, {'id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'computer science', 'description': 'The study of computation and information processing.'})
# (...)
# It represents nodes and relationships in the knowledge graph:
# - The first element is the source node (e.g., 'natural language processing').
# - The second element is the relationship between nodes (e.g., 'is_a_subfield_of').
# - The third element is the target node (e.g., 'computer science').
if __name__ == '__main__': if __name__ == '__main__':
asyncio.run(main()) asyncio.run(main())