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docs: added cognify steps in the print statement and commented example output

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hande-k 2024-11-21 13:57:42 +01:00
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commit 157d7d217d
2 changed files with 46 additions and 14 deletions
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42
README.md
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@ -116,34 +116,52 @@ async def main():
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("Adding text to cognee:")
print(text.strip()) 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("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
print("Running cognify to create knowledge graph...")
await cognee.cognify() await cognee.cognify()
print("Cognify process complete.\n") print("Cognify process complete.\n")
# Query cognee for insights on the added text
query_text = 'Tell me about NLP' query_text = 'Tell me about NLP'
print(f"Searching cognee for insights with query: '{query_text}'") 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
query_text=query_text,
) )
# Display search results
print("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)
# 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()) # 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. 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`

18
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
@ -29,7 +28,15 @@ async def main():
print("Text added successfully.\n") print("Text added successfully.\n")
print("Running cognify to create knowledge graph...") 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") print("Cognify process complete.\n")
@ -47,6 +54,13 @@ async def main():
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())