relational_database_migration_example.py•4.62 kB
from pathlib import Path
import asyncio
import os
import cognee
from cognee.infrastructure.databases.relational.config import get_migration_config
from cognee.infrastructure.databases.graph import get_graph_engine
from cognee.api.v1.visualize.visualize import visualize_graph
from cognee.infrastructure.databases.relational import (
get_migration_relational_engine,
)
from cognee.modules.search.types import SearchType
from cognee.infrastructure.databases.relational import (
create_db_and_tables as create_relational_db_and_tables,
)
from cognee.infrastructure.databases.vector.pgvector import (
create_db_and_tables as create_vector_db_and_tables,
)
# 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"
# 3. Fill all relevant MIGRATION_DB information for the database you want to migrate to graph / Cognee
# NOTE: If you don't have a DB you want to migrate you can try it out with our
# test database at the following location:
# MIGRATION_DB_PATH="/{path_to_your_local_cognee}/cognee/tests/test_data"
# MIGRATION_DB_NAME="migration_database.sqlite"
# MIGRATION_DB_PROVIDER="sqlite"
async def main():
# Clean all data stored in Cognee
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
# Needed to create appropriate database tables only on the Cognee side
await create_relational_db_and_tables()
await create_vector_db_and_tables()
# In case environment variables are not set use the example database from the Cognee repo
migration_db_provider = os.environ.get("MIGRATION_DB_PROVIDER", "sqlite")
migration_db_path = os.environ.get(
"MIGRATION_DB_PATH",
os.path.join(Path(__file__).resolve().parent.parent.parent, "cognee/tests/test_data"),
)
migration_db_name = os.environ.get("MIGRATION_DB_NAME", "migration_database.sqlite")
migration_config = get_migration_config()
migration_config.migration_db_provider = migration_db_provider
migration_config.migration_db_path = migration_db_path
migration_config.migration_db_name = migration_db_name
engine = get_migration_relational_engine()
print("\nExtracting schema of database to migrate.")
schema = await engine.extract_schema()
print(f"Migrated database schema:\n{schema}")
graph = await get_graph_engine()
print("Migrating relational database to graph database based on schema.")
from cognee.tasks.ingestion import migrate_relational_database
await migrate_relational_database(graph, schema=schema)
print("Relational database migration complete.")
# Make sure to set top_k at a high value for a broader search, the default value is only 10!
# top_k represent the number of graph tripplets to supply to the LLM to answer your question
search_results = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text="What kind of data do you contain?",
top_k=200,
)
print(f"Search results: {search_results}")
# Having a top_k value set to too high might overwhelm the LLM context when specific questions need to be answered.
# For this kind of question we've set the top_k to 50
search_results = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text="What invoices are related to Leonie Köhler?",
top_k=50,
)
print(f"Search results: {search_results}")
search_results = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text="What invoices are related to Luís Gonçalves?",
top_k=50,
)
print(f"Search results: {search_results}")
# If you check the relational database for this example you can see that the search results successfully found all
# the invoices related to the two customers, without any hallucinations or additional information
# Define location where to store html visualization of graph of the migrated database
home_dir = os.path.expanduser("~")
destination_file_path = os.path.join(home_dir, "graph_visualization.html")
print("Adding html visualization of graph database after migration.")
await visualize_graph(destination_file_path)
print(f"Visualization can be found at: {destination_file_path}")
if __name__ == "__main__":
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
loop.run_until_complete(main())
finally:
loop.run_until_complete(loop.shutdown_asyncgens())