cognee-mcp

import asyncio from pprint import pprint import cognee from cognee.shared.logging_utils import setup_logging, ERROR 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 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...\n") print("Cognify process steps:") print("1. Classifying the document: Determining the type and category of the input text.") print( "2. Extracting text chunks: Breaking down the text into sentences or phrases for analysis." ) print( "3. Generating knowledge graph: Extracting entities and relationships to form a knowledge graph." ) print("4. Summarizing text: Creating concise summaries of the content for quick insights.") print("5. Adding data points: Storing the extracted chunks for processing.\n") # 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( query_type=SearchType.GRAPH_COMPLETION, query_text=query_text ) print("Search results:") # Display results for result_text in search_results: pprint(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())