86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
import asyncio
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import pathlib
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import os
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import cognee
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from cognee.api.v1.visualize.visualize import visualize_graph
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from cognee.shared.logging_utils import setup_logging, ERROR
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from cognee.api.v1.cognify.memify import memify
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from cognee.modules.pipelines.tasks.task import Task
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from cognee.tasks.memify.extract_subgraph import extract_subgraph
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from cognee.modules.graph.utils import resolve_edges_to_text
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from cognee.tasks.codingagents.coding_rule_associations import (
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add_rule_associations,
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get_existing_rules,
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)
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# Prerequisites:
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# 1. Copy `.env.template` and rename it to `.env`.
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# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
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# LLM_API_KEY = "your_key_here"
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async def main():
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# Create a clean slate for cognee -- reset data and system state
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print("Resetting cognee data...")
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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print("Data reset complete.\n")
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# cognee knowledge graph will be created based on this text
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text = """
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Natural language processing (NLP) is an interdisciplinary
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subfield of computer science and information retrieval.
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"""
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coding_rules_text = """
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Code must be formatted by PEP8 standards.
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Typing and Docstrings must be added.
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"""
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print("Adding text to cognee:")
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print(text.strip())
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# Add the text, and make it available for cognify
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await cognee.add(text)
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await cognee.add(coding_rules_text, node_set=["coding_rules"])
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print("Text added successfully.\n")
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# Use LLMs and cognee to create knowledge graph
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await cognee.cognify()
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print("Cognify process complete.\n")
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subgraph_extraction_tasks = [Task(extract_subgraph)]
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rule_association_tasks = [
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Task(resolve_edges_to_text, task_config={"batch_size": 10}),
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Task(
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add_rule_associations,
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rules_nodeset_name="coding_agent_rules",
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user_prompt_location="memify_coding_rule_association_agent_user.txt",
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system_prompt_location="memify_coding_rule_association_agent_system.txt",
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),
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]
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await memify(
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preprocessing_tasks=subgraph_extraction_tasks,
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processing_tasks=rule_association_tasks,
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node_name=["coding_rules"],
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)
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developer_rules = await get_existing_rules(rules_nodeset_name="coding_agent_rules")
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print(developer_rules)
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file_path = os.path.join(
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pathlib.Path(__file__).parent, ".artifacts", "graph_visualization.html"
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)
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await visualize_graph(file_path)
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if __name__ == "__main__":
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logger = setup_logging(log_level=ERROR)
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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loop.run_until_complete(main())
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finally:
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loop.run_until_complete(loop.shutdown_asyncgens())
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