Quick Start

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import asyncio
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import json
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import logging
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import os
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from datetime import datetime, timezone
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from logging import INFO
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from dotenv import load_dotenv
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from graphiti_core import Graphiti
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from graphiti_core.nodes import EpisodeType
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from graphiti_core.search.search_config_recipes import NODE_HYBRID_SEARCH_RRF

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# Configure logging
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logging.basicConfig(
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    level=INFO,
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    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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    datefmt='%Y-%m-%d %H:%M:%S',
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)
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logger = logging.getLogger(__name__)
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load_dotenv()
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# Neo4j connection parameters
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# Make sure Neo4j Desktop is running with a local DBMS started
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neo4j_uri = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
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neo4j_user = os.environ.get('NEO4J_USER', 'neo4j')
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neo4j_password = os.environ.get('NEO4J_PASSWORD', 'password')
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if not neo4j_uri or not neo4j_user or not neo4j_password:
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    raise ValueError('NEO4J_URI, NEO4J_USER, and NEO4J_PASSWORD must be set')

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async def main():
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    # Main function implementation will go here
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    pass
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if __name__ == '__main__':
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    asyncio.run(main())

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# Initialize Graphiti with Neo4j connection
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graphiti = Graphiti(neo4j_uri, neo4j_user, neo4j_password)
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try:
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    # Initialize the graph database with graphiti's indices. This only needs to be done once.
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    await graphiti.build_indices_and_constraints()
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    # Additional code will go here
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finally:
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    # Close the connection
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    await graphiti.close()
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    print('\nConnection closed')

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# Episodes list containing both text and JSON episodes
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episodes = [
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    {
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        'content': 'Kamala Harris is the Attorney General of California. She was previously '
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        'the district attorney for San Francisco.',
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        'type': EpisodeType.text,
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        'description': 'podcast transcript',
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    },
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    {
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        'content': 'As AG, Harris was in office from January 3, 2011 – January 3, 2017',
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        'type': EpisodeType.text,
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        'description': 'podcast transcript',
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    },
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    {
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        'content': {
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            'name': 'Gavin Newsom',
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            'position': 'Governor',
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            'state': 'California',
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            'previous_role': 'Lieutenant Governor',
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            'previous_location': 'San Francisco',
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        },
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        'type': EpisodeType.json,
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        'description': 'podcast metadata',
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    },
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    {
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        'content': {
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            'name': 'Gavin Newsom',
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            'position': 'Governor',
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            'term_start': 'January 7, 2019',
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            'term_end': 'Present',
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        },
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        'type': EpisodeType.json,
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        'description': 'podcast metadata',
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    },
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]
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# Add episodes to the graph
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for i, episode in enumerate(episodes):
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    await graphiti.add_episode(
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        name=f'Freakonomics Radio {i}',
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        episode_body=episode['content']
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        if isinstance(episode['content'], str)
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        else json.dumps(episode['content']),
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        source=episode['type'],
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        source_description=episode['description'],
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        reference_time=datetime.now(timezone.utc),
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    )
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    print(f'Added episode: Freakonomics Radio {i} ({episode["type"].value})')

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# Perform a hybrid search combining semantic similarity and BM25 retrieval
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print("\nSearching for: 'Who was the California Attorney General?'")
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results = await graphiti.search('Who was the California Attorney General?')
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# Print search results
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print('\nSearch Results:')
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for result in results:
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    print(f'UUID: {result.uuid}')
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    print(f'Fact: {result.fact}')
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    if hasattr(result, 'valid_at') and result.valid_at:
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        print(f'Valid from: {result.valid_at}')
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    if hasattr(result, 'invalid_at') and result.invalid_at:
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        print(f'Valid until: {result.invalid_at}')
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    print('---')

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# Use the top search result's UUID as the center node for reranking
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if results and len(results) > 0:
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    # Get the source node UUID from the top result
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    center_node_uuid = results[0].source_node_uuid
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    print('\nReranking search results based on graph distance:')
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    print(f'Using center node UUID: {center_node_uuid}')
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    reranked_results = await graphiti.search(
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        'Who was the California Attorney General?', center_node_uuid=center_node_uuid
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    )
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    # Print reranked search results
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    print('\nReranked Search Results:')
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    for result in reranked_results:
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        print(f'UUID: {result.uuid}')
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        print(f'Fact: {result.fact}')
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        if hasattr(result, 'valid_at') and result.valid_at:
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            print(f'Valid from: {result.valid_at}')
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        if hasattr(result, 'invalid_at') and result.invalid_at:
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            print(f'Valid until: {result.invalid_at}')
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        print('---')
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else:
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    print('No results found in the initial search to use as center node.')

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# Example: Perform a node search using _search method with standard recipes
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print(
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    '\nPerforming node search using _search method with standard recipe NODE_HYBRID_SEARCH_RRF:'
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)
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# Use a predefined search configuration recipe and modify its limit
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node_search_config = NODE_HYBRID_SEARCH_RRF.model_copy(deep=True)
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node_search_config.limit = 5  # Limit to 5 results
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# Execute the node search
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node_search_results = await graphiti._search(
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    query='California Governor',
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    config=node_search_config,
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)
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# Print node search results
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print('\nNode Search Results:')
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for node in node_search_results.nodes:
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    print(f'Node UUID: {node.uuid}')
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    print(f'Node Name: {node.name}')
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    node_summary = node.summary[:100] + '...' if len(node.summary) > 100 else node.summary
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    print(f'Content Summary: {node_summary}')
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    print(f"Node Labels: {', '.join(node.labels)}")
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    print(f'Created At: {node.created_at}')
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    if hasattr(node, 'attributes') and node.attributes:
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        print('Attributes:')
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        for key, value in node.attributes.items():
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            print(f'  {key}: {value}')
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    print('---')