graphiti/core/utils/search/search_utils.py

import asyncio
import logging
from datetime import datetime
from time import time

from neo4j import AsyncDriver
from neo4j import time as neo4j_time

from core.edges import EntityEdge
from core.nodes import EntityNode

logger = logging.getLogger(__name__)

RELEVANT_SCHEMA_LIMIT = 3


async def bfs(node_ids: list[str], driver: AsyncDriver):
	records, _, _ = await driver.execute_query(
		"""
        MATCH (n WHERE n.uuid in $node_ids)-[r]->(m)
        RETURN
            n.uuid AS source_node_uuid,
            n.name AS source_name, 
            n.summary AS source_summary,
            m.uuid AS target_node_uuid,
            m.name AS target_name, 
            m.summary AS target_summary,
            r.uuid AS uuid,
            r.created_at AS created_at,
            r.name AS name,
            r.fact AS fact,
            r.fact_embedding AS fact_embedding,
            r.episodes AS episodes,
            r.expired_at AS expired_at,
            r.valid_at AS valid_at,
            r.invalid_at AS invalid_at
        
    """,
		node_ids=node_ids,
	)

	context = {}

	for record in records:
		n_uuid = record['source_node_uuid']
		if n_uuid in context:
			context[n_uuid]['facts'].append(record['fact'])
		else:
			context[n_uuid] = {
				'name': record['source_name'],
				'summary': record['source_summary'],
				'facts': [record['fact']],
			}

		m_uuid = record['target_node_uuid']
		if m_uuid not in context:
			context[m_uuid] = {
				'name': record['target_name'],
				'summary': record['target_summary'],
				'facts': [],
			}
	logger.info(f'bfs search returned context: {context}')
	return context


async def edge_similarity_search(
	search_vector: list[float], driver: AsyncDriver, limit=RELEVANT_SCHEMA_LIMIT
) -> list[EntityEdge]:
	# vector similarity search over embedded facts
	records, _, _ = await driver.execute_query(
		"""
                CALL db.index.vector.queryRelationships("fact_embedding", 5, $search_vector)
                YIELD relationship AS r, score
                MATCH (n)-[r:RELATES_TO]->(m)
                RETURN
                    r.uuid AS uuid,
                    n.uuid AS source_node_uuid,
                    m.uuid AS target_node_uuid,
                    r.created_at AS created_at,
                    r.name AS name,
                    r.fact AS fact,
                    r.fact_embedding AS fact_embedding,
                    r.episodes AS episodes,
                    r.expired_at AS expired_at,
                    r.valid_at AS valid_at,
                    r.invalid_at AS invalid_at
                ORDER BY score DESC LIMIT $limit
                """,
		search_vector=search_vector,
		limit=limit,
	)

	edges: list[EntityEdge] = []

	for record in records:
		edge = EntityEdge(
			uuid=record['uuid'],
			source_node_uuid=record['source_node_uuid'],
			target_node_uuid=record['target_node_uuid'],
			fact=record['fact'],
			name=record['name'],
			episodes=record['episodes'],
			fact_embedding=record['fact_embedding'],
			created_at=safely_parse_db_date(record['created_at']),
			expired_at=safely_parse_db_date(record['expired_at']),
			valid_at=safely_parse_db_date(record['valid_at']),
			invalid_At=safely_parse_db_date(record['invalid_at']),
		)

		edges.append(edge)

	return edges


async def entity_similarity_search(
	search_vector: list[float], driver: AsyncDriver, limit=RELEVANT_SCHEMA_LIMIT
) -> list[EntityNode]:
	# vector similarity search over entity names
	records, _, _ = await driver.execute_query(
		"""
                CALL db.index.vector.queryNodes("name_embedding", $limit, $search_vector)
                YIELD node AS n, score
                RETURN
                    n.uuid As uuid, 
                    n.name AS name, 
                    n.created_at AS created_at, 
                    n.summary AS summary
                ORDER BY score DESC
                """,
		search_vector=search_vector,
		limit=limit,
	)
	nodes: list[EntityNode] = []

	for record in records:
		nodes.append(
			EntityNode(
				uuid=record['uuid'],
				name=record['name'],
				labels=[],
				created_at=safely_parse_db_date(record['created_at']),
				summary=record['summary'],
			)
		)

	return nodes


async def entity_fulltext_search(
	query: str, driver: AsyncDriver, limit=RELEVANT_SCHEMA_LIMIT
) -> list[EntityNode]:
	# BM25 search to get top nodes
	fuzzy_query = query + '~'
	records, _, _ = await driver.execute_query(
		"""
    CALL db.index.fulltext.queryNodes("name_and_summary", $query) YIELD node, score
    RETURN 
        node.uuid As uuid, 
        node.name AS name, 
        node.created_at AS created_at, 
        node.summary AS summary
    ORDER BY score DESC
    LIMIT $limit
    """,
		query=fuzzy_query,
		limit=limit,
	)
	nodes: list[EntityNode] = []

	for record in records:
		nodes.append(
			EntityNode(
				uuid=record['uuid'],
				name=record['name'],
				labels=[],
				created_at=safely_parse_db_date(record['created_at']),
				summary=record['summary'],
			)
		)

	return nodes


async def edge_fulltext_search(
	query: str, driver: AsyncDriver, limit=RELEVANT_SCHEMA_LIMIT
) -> list[EntityEdge]:
	# fulltext search over facts
	fuzzy_query = query + '~'

	records, _, _ = await driver.execute_query(
		"""
                CALL db.index.fulltext.queryRelationships("name_and_fact", $query) 
                YIELD relationship AS r, score
                MATCH (n:Entity)-[r]->(m:Entity)
                RETURN
                    r.uuid AS uuid,
                    n.uuid AS source_node_uuid,
                    m.uuid AS target_node_uuid,
                    r.created_at AS created_at,
                    r.name AS name,
                    r.fact AS fact,
                    r.fact_embedding AS fact_embedding,
                    r.episodes AS episodes,
                    r.expired_at AS expired_at,
                    r.valid_at AS valid_at,
                    r.invalid_at AS invalid_at
                ORDER BY score DESC LIMIT $limit
                """,
		query=fuzzy_query,
		limit=limit,
	)

	edges: list[EntityEdge] = []

	for record in records:
		edge = EntityEdge(
			uuid=record['uuid'],
			source_node_uuid=record['source_node_uuid'],
			target_node_uuid=record['target_node_uuid'],
			fact=record['fact'],
			name=record['name'],
			episodes=record['episodes'],
			fact_embedding=record['fact_embedding'],
			created_at=safely_parse_db_date(record['created_at']),
			expired_at=safely_parse_db_date(record['expired_at']),
			valid_at=safely_parse_db_date(record['valid_at']),
			invalid_At=safely_parse_db_date(record['invalid_at']),
		)

		edges.append(edge)

	return edges


def safely_parse_db_date(date_str: neo4j_time.Date) -> datetime:
	if date_str:
		return datetime.fromisoformat(date_str.iso_format())
	return None


async def get_relevant_nodes(
	nodes: list[EntityNode],
	driver: AsyncDriver,
) -> list[EntityNode]:
	start = time()
	relevant_nodes: list[EntityNode] = []
	relevant_node_uuids = set()

	results = await asyncio.gather(
		*[entity_fulltext_search(node.name, driver) for node in nodes],
		*[entity_similarity_search(node.name_embedding, driver) for node in nodes],
	)

	for result in results:
		for node in result:
			if node.uuid in relevant_node_uuids:
				continue

			relevant_node_uuids.add(node.uuid)
			relevant_nodes.append(node)

	end = time()
	logger.info(f'Found relevant nodes: {relevant_node_uuids} in {(end - start) * 1000} ms')

	return relevant_nodes


async def get_relevant_edges(
	edges: list[EntityEdge],
	driver: AsyncDriver,
) -> list[EntityEdge]:
	start = time()
	relevant_edges: list[EntityEdge] = []
	relevant_edge_uuids = set()

	results = await asyncio.gather(
		*[edge_similarity_search(edge.fact_embedding, driver) for edge in edges],
		*[edge_fulltext_search(edge.fact, driver) for edge in edges],
	)

	for result in results:
		for edge in result:
			if edge.uuid in relevant_edge_uuids:
				continue

			relevant_edge_uuids.add(edge.uuid)
			relevant_edges.append(edge)

	end = time()
	logger.info(f'Found relevant edges: {relevant_edge_uuids} in {(end - start) * 1000} ms')

	return relevant_edges