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A focused walkthrough for converting an existing relational database (SQLite or Postgres) into a knowledge graph that can be queried with natural language. Before you start:
  • Complete Quickstart to understand basic operations
  • Ensure you have LLM Providers configured
  • Have connection details for the database you want to migrate

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What This Does

Cognee reads your relational database schema (tables, columns, primary keys, foreign keys) and can map it into graph nodes and edges:
  • In full migration mode, each table becomes a TableType node
  • Each row becomes a TableRow node linked to its table
  • Non-key column values can become ColumnValue nodes linked from each row
  • Foreign key relationships become edges between row nodes
  • Migrated nodes and edges are embedded and indexed for semantic search
After migration, you can query your relational data using cognee.recall() with natural language.

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1. Configure the Migration Database

Set the source database connection in your .env file: 
MIGRATION_DB_PROVIDER="sqlite"
MIGRATION_DB_PATH="/path/to/db/directory"
MIGRATION_DB_NAME="my_database.sqlite"

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2. Run the Migration

import asyncio
import os
import tempfile
from pathlib import Path

import cognee
from cognee.api.v1.visualize.visualize import visualize_graph
from cognee.infrastructure.databases.graph import get_graph_engine
from cognee.infrastructure.databases.relational import get_migration_relational_engine
from cognee.infrastructure.databases.relational.config import get_migration_config
from cognee.tasks.ingestion import migrate_relational_database

# Isolate this example from any pre-existing local Cognee state so old SQLite
# metadata files do not interfere with the run.
example_root = Path(tempfile.gettempdir()) / "cognee-relational-migration-example"
os.environ.setdefault("SYSTEM_ROOT_DIRECTORY", str(example_root / "system"))
os.environ.setdefault("DATA_ROOT_DIRECTORY", str(example_root / "data"))
os.environ.setdefault("CACHE_ROOT_DIRECTORY", str(example_root / "cache"))
os.environ.setdefault("ENABLE_BACKEND_ACCESS_CONTROL", "false")

async def main():
    migration_config = get_migration_config()
    migration_config.migration_db_provider = os.environ.get("MIGRATION_DB_PROVIDER", "sqlite")
    migration_config.migration_db_path = os.environ.get("MIGRATION_DB_PATH", "/path/to/db")
    migration_config.migration_db_name = os.environ.get(
        "MIGRATION_DB_NAME", "my_database.sqlite"
    )

    # Start from a clean local state for repeatable runs.
    await cognee.forget(everything=True)

    # Extract the schema from the source database
    engine = get_migration_relational_engine()
    schema = await engine.extract_schema()
    print(f"Loaded schema with {len(schema)} tables: {sorted(schema)}")

    # Migrate schema + all rows into the graph
    graph = await get_graph_engine()
    nodes, edges = await migrate_relational_database(graph, schema=schema)
    print(f"Migrated graph: {len(nodes)} nodes / {len(edges)} edges")

    # Query the migrated data
    results = await cognee.recall(
        query_text="What data does this database contain?",
        top_k=100,
    )
    print("Recall results:")
    print(results)

    # Visualize the migrated graph
    graph_path = Path(__file__).resolve().parent / "relational_migration_graph.html"
    graph_html = await visualize_graph(str(graph_path))
    graph_path.write_text(graph_html, encoding="utf-8")
    print(f"Graph visualization saved at: {graph_path}")

asyncio.run(main())
The migration pipeline itself uses lower-level APIs because it is importing an external relational schema directly into the graph. Once the graph is built, prefer cognee.recall() as the default query interface in v1.0. Set top_k higher (100-200) for broad exploratory queries over large databases, and lower (20-50) for specific lookups to keep LLM context manageable. The example above also isolates Cognee’s local state in a temp directory so repeated runs do not pick up stale metadata from earlier experiments.

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Additional information

migrate_relational_database has two modes controlled by the schema_only flag:
ModeFlagWhat is migrated
Full (default)schema_only=FalseTableType, TableRow, optional ColumnValue, and foreign-key edges
Schema onlyschema_only=TrueDatabaseSchema, SchemaTable, and SchemaRelationship datapoints for structural understanding
Schema-only mode does not create TableRow nodes for every record. Instead, it ingests schema-level datapoints and attaches up to a few sample rows as metadata on each SchemaTable node. Use it when you want to explore table structure and relationships without migrating the full dataset:
await migrate_relational_database(graph, schema=schema, schema_only=True)
This is especially useful for large databases when your questions are structural, for example:
  • “What tables exist in this database?”
  • “Which tables are connected by foreign keys?”
  • “What kind of data does the invoices table contain?”
from cognee.api.v1.visualize.visualize import visualize_graph
from pathlib import Path

graph_path = Path("./migration_graph.html")
graph_html = await visualize_graph(str(graph_path))
graph_path.write_text(graph_html, encoding="utf-8")
This generates an HTML file you can open in a browser to explore the graph structure. See Graph Visualization for more options.

Relational Databases

Configure SQLite and Postgres connections

Recall and Search

Learn the current recall flow and lower-level search options

Graph Visualization

Explore your knowledge graph visually