search_utils.py•71.8 kB
"""
Copyright 2024, Zep Software, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import logging
from collections import defaultdict
from time import time
from typing import Any
import numpy as np
from numpy._typing import NDArray
from typing_extensions import LiteralString
from graphiti_core.driver.driver import (
GraphDriver,
GraphProvider,
)
from graphiti_core.edges import EntityEdge, get_entity_edge_from_record
from graphiti_core.graph_queries import (
get_nodes_query,
get_relationships_query,
get_vector_cosine_func_query,
)
from graphiti_core.helpers import (
lucene_sanitize,
normalize_l2,
semaphore_gather,
)
from graphiti_core.models.edges.edge_db_queries import get_entity_edge_return_query
from graphiti_core.models.nodes.node_db_queries import (
COMMUNITY_NODE_RETURN,
EPISODIC_NODE_RETURN,
get_entity_node_return_query,
)
from graphiti_core.nodes import (
CommunityNode,
EntityNode,
EpisodicNode,
get_community_node_from_record,
get_entity_node_from_record,
get_episodic_node_from_record,
)
from graphiti_core.search.search_filters import (
SearchFilters,
edge_search_filter_query_constructor,
node_search_filter_query_constructor,
)
logger = logging.getLogger(__name__)
RELEVANT_SCHEMA_LIMIT = 10
DEFAULT_MIN_SCORE = 0.6
DEFAULT_MMR_LAMBDA = 0.5
MAX_SEARCH_DEPTH = 3
MAX_QUERY_LENGTH = 128
def calculate_cosine_similarity(vector1: list[float], vector2: list[float]) -> float:
"""
Calculates the cosine similarity between two vectors using NumPy.
"""
dot_product = np.dot(vector1, vector2)
norm_vector1 = np.linalg.norm(vector1)
norm_vector2 = np.linalg.norm(vector2)
if norm_vector1 == 0 or norm_vector2 == 0:
return 0 # Handle cases where one or both vectors are zero vectors
return dot_product / (norm_vector1 * norm_vector2)
def fulltext_query(query: str, group_ids: list[str] | None, driver: GraphDriver):
if driver.provider == GraphProvider.KUZU:
# Kuzu only supports simple queries.
if len(query.split(' ')) > MAX_QUERY_LENGTH:
return ''
return query
elif driver.provider == GraphProvider.FALKORDB:
return driver.build_fulltext_query(query, group_ids, MAX_QUERY_LENGTH)
group_ids_filter_list = (
[driver.fulltext_syntax + f'group_id:"{g}"' for g in group_ids]
if group_ids is not None
else []
)
group_ids_filter = ''
for f in group_ids_filter_list:
group_ids_filter += f if not group_ids_filter else f' OR {f}'
group_ids_filter += ' AND ' if group_ids_filter else ''
lucene_query = lucene_sanitize(query)
# If the lucene query is too long return no query
if len(lucene_query.split(' ')) + len(group_ids or '') >= MAX_QUERY_LENGTH:
return ''
full_query = group_ids_filter + '(' + lucene_query + ')'
return full_query
async def get_episodes_by_mentions(
driver: GraphDriver,
nodes: list[EntityNode],
edges: list[EntityEdge],
limit: int = RELEVANT_SCHEMA_LIMIT,
) -> list[EpisodicNode]:
episode_uuids: list[str] = []
for edge in edges:
episode_uuids.extend(edge.episodes)
episodes = await EpisodicNode.get_by_uuids(driver, episode_uuids[:limit])
return episodes
async def get_mentioned_nodes(
driver: GraphDriver, episodes: list[EpisodicNode]
) -> list[EntityNode]:
episode_uuids = [episode.uuid for episode in episodes]
records, _, _ = await driver.execute_query(
"""
MATCH (episode:Episodic)-[:MENTIONS]->(n:Entity)
WHERE episode.uuid IN $uuids
RETURN DISTINCT
"""
+ get_entity_node_return_query(driver.provider),
uuids=episode_uuids,
routing_='r',
)
nodes = [get_entity_node_from_record(record, driver.provider) for record in records]
return nodes
async def get_communities_by_nodes(
driver: GraphDriver, nodes: list[EntityNode]
) -> list[CommunityNode]:
node_uuids = [node.uuid for node in nodes]
records, _, _ = await driver.execute_query(
"""
MATCH (c:Community)-[:HAS_MEMBER]->(m:Entity)
WHERE m.uuid IN $uuids
RETURN DISTINCT
"""
+ COMMUNITY_NODE_RETURN,
uuids=node_uuids,
routing_='r',
)
communities = [get_community_node_from_record(record) for record in records]
return communities
async def edge_fulltext_search(
driver: GraphDriver,
query: str,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit=RELEVANT_SCHEMA_LIMIT,
) -> list[EntityEdge]:
if driver.search_interface:
return await driver.search_interface.edge_fulltext_search(
driver, query, search_filter, group_ids, limit
)
# fulltext search over facts
fuzzy_query = fulltext_query(query, group_ids, driver)
if fuzzy_query == '':
return []
match_query = """
YIELD relationship AS rel, score
MATCH (n:Entity)-[e:RELATES_TO {uuid: rel.uuid}]->(m:Entity)
"""
if driver.provider == GraphProvider.KUZU:
match_query = """
YIELD node, score
MATCH (n:Entity)-[:RELATES_TO]->(e:RelatesToNode_ {uuid: node.uuid})-[:RELATES_TO]->(m:Entity)
"""
filter_queries, filter_params = edge_search_filter_query_constructor(
search_filter, driver.provider
)
if group_ids is not None:
filter_queries.append('e.group_id IN $group_ids')
filter_params['group_ids'] = group_ids
filter_query = ''
if filter_queries:
filter_query = ' WHERE ' + (' AND '.join(filter_queries))
if driver.provider == GraphProvider.NEPTUNE:
res = driver.run_aoss_query('edge_name_and_fact', query) # pyright: ignore reportAttributeAccessIssue
if res['hits']['total']['value'] > 0:
input_ids = []
for r in res['hits']['hits']:
input_ids.append({'id': r['_source']['uuid'], 'score': r['_score']})
# Match the edge ids and return the values
query = (
"""
UNWIND $ids as id
MATCH (n:Entity)-[e:RELATES_TO]->(m:Entity)
WHERE e.group_id IN $group_ids
AND id(e)=id
"""
+ filter_query
+ """
AND id(e)=id
WITH e, id.score as score, startNode(e) AS n, endNode(e) AS m
RETURN
e.uuid AS uuid,
e.group_id AS group_id,
n.uuid AS source_node_uuid,
m.uuid AS target_node_uuid,
e.created_at AS created_at,
e.name AS name,
e.fact AS fact,
split(e.episodes, ",") AS episodes,
e.expired_at AS expired_at,
e.valid_at AS valid_at,
e.invalid_at AS invalid_at,
properties(e) AS attributes
ORDER BY score DESC LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query,
query=fuzzy_query,
ids=input_ids,
limit=limit,
routing_='r',
**filter_params,
)
else:
return []
else:
query = (
get_relationships_query('edge_name_and_fact', limit=limit, provider=driver.provider)
+ match_query
+ filter_query
+ """
WITH e, score, n, m
RETURN
"""
+ get_entity_edge_return_query(driver.provider)
+ """
ORDER BY score DESC
LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query,
query=fuzzy_query,
limit=limit,
routing_='r',
**filter_params,
)
edges = [get_entity_edge_from_record(record, driver.provider) for record in records]
return edges
async def edge_similarity_search(
driver: GraphDriver,
search_vector: list[float],
source_node_uuid: str | None,
target_node_uuid: str | None,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = RELEVANT_SCHEMA_LIMIT,
min_score: float = DEFAULT_MIN_SCORE,
) -> list[EntityEdge]:
if driver.search_interface:
return await driver.search_interface.edge_similarity_search(
driver,
search_vector,
source_node_uuid,
target_node_uuid,
search_filter,
group_ids,
limit,
min_score,
)
match_query = """
MATCH (n:Entity)-[e:RELATES_TO]->(m:Entity)
"""
if driver.provider == GraphProvider.KUZU:
match_query = """
MATCH (n:Entity)-[:RELATES_TO]->(e:RelatesToNode_)-[:RELATES_TO]->(m:Entity)
"""
filter_queries, filter_params = edge_search_filter_query_constructor(
search_filter, driver.provider
)
if group_ids is not None:
filter_queries.append('e.group_id IN $group_ids')
filter_params['group_ids'] = group_ids
if source_node_uuid is not None:
filter_params['source_uuid'] = source_node_uuid
filter_queries.append('n.uuid = $source_uuid')
if target_node_uuid is not None:
filter_params['target_uuid'] = target_node_uuid
filter_queries.append('m.uuid = $target_uuid')
filter_query = ''
if filter_queries:
filter_query = ' WHERE ' + (' AND '.join(filter_queries))
search_vector_var = '$search_vector'
if driver.provider == GraphProvider.KUZU:
search_vector_var = f'CAST($search_vector AS FLOAT[{len(search_vector)}])'
if driver.provider == GraphProvider.NEPTUNE:
query = (
"""
MATCH (n:Entity)-[e:RELATES_TO]->(m:Entity)
"""
+ filter_query
+ """
RETURN DISTINCT id(e) as id, e.fact_embedding as embedding
"""
)
resp, header, _ = await driver.execute_query(
query,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
if len(resp) > 0:
# Calculate Cosine similarity then return the edge ids
input_ids = []
for r in resp:
if r['embedding']:
score = calculate_cosine_similarity(
search_vector, list(map(float, r['embedding'].split(',')))
)
if score > min_score:
input_ids.append({'id': r['id'], 'score': score})
# Match the edge ides and return the values
query = """
UNWIND $ids as i
MATCH ()-[r]->()
WHERE id(r) = i.id
RETURN
r.uuid AS uuid,
r.group_id AS group_id,
startNode(r).uuid AS source_node_uuid,
endNode(r).uuid AS target_node_uuid,
r.created_at AS created_at,
r.name AS name,
r.fact AS fact,
split(r.episodes, ",") AS episodes,
r.expired_at AS expired_at,
r.valid_at AS valid_at,
r.invalid_at AS invalid_at,
properties(r) AS attributes
ORDER BY i.score DESC
LIMIT $limit
"""
records, _, _ = await driver.execute_query(
query,
ids=input_ids,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
else:
return []
else:
query = (
match_query
+ filter_query
+ """
WITH DISTINCT e, n, m, """
+ get_vector_cosine_func_query('e.fact_embedding', search_vector_var, driver.provider)
+ """ AS score
WHERE score > $min_score
RETURN
"""
+ get_entity_edge_return_query(driver.provider)
+ """
ORDER BY score DESC
LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
edges = [get_entity_edge_from_record(record, driver.provider) for record in records]
return edges
async def edge_bfs_search(
driver: GraphDriver,
bfs_origin_node_uuids: list[str] | None,
bfs_max_depth: int,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = RELEVANT_SCHEMA_LIMIT,
) -> list[EntityEdge]:
# vector similarity search over embedded facts
if bfs_origin_node_uuids is None or len(bfs_origin_node_uuids) == 0:
return []
filter_queries, filter_params = edge_search_filter_query_constructor(
search_filter, driver.provider
)
if group_ids is not None:
filter_queries.append('e.group_id IN $group_ids')
filter_params['group_ids'] = group_ids
filter_query = ''
if filter_queries:
filter_query = ' WHERE ' + (' AND '.join(filter_queries))
if driver.provider == GraphProvider.KUZU:
# Kuzu stores entity edges twice with an intermediate node, so we need to match them
# separately for the correct BFS depth.
depth = bfs_max_depth * 2 - 1
match_queries = [
f"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH path = (origin:Entity {{uuid: origin_uuid}})-[:RELATES_TO*1..{depth}]->(:RelatesToNode_)
UNWIND nodes(path) AS relNode
MATCH (n:Entity)-[:RELATES_TO]->(e:RelatesToNode_ {{uuid: relNode.uuid}})-[:RELATES_TO]->(m:Entity)
""",
]
if bfs_max_depth > 1:
depth = (bfs_max_depth - 1) * 2 - 1
match_queries.append(f"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH path = (origin:Episodic {{uuid: origin_uuid}})-[:MENTIONS]->(:Entity)-[:RELATES_TO*1..{depth}]->(:RelatesToNode_)
UNWIND nodes(path) AS relNode
MATCH (n:Entity)-[:RELATES_TO]->(e:RelatesToNode_ {{uuid: relNode.uuid}})-[:RELATES_TO]->(m:Entity)
""")
records = []
for match_query in match_queries:
sub_records, _, _ = await driver.execute_query(
match_query
+ filter_query
+ """
RETURN DISTINCT
"""
+ get_entity_edge_return_query(driver.provider)
+ """
LIMIT $limit
""",
bfs_origin_node_uuids=bfs_origin_node_uuids,
limit=limit,
routing_='r',
**filter_params,
)
records.extend(sub_records)
else:
if driver.provider == GraphProvider.NEPTUNE:
query = (
f"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH path = (origin {{uuid: origin_uuid}})-[:RELATES_TO|MENTIONS *1..{bfs_max_depth}]->(n:Entity)
WHERE origin:Entity OR origin:Episodic
UNWIND relationships(path) AS rel
MATCH (n:Entity)-[e:RELATES_TO {{uuid: rel.uuid}}]-(m:Entity)
"""
+ filter_query
+ """
RETURN DISTINCT
e.uuid AS uuid,
e.group_id AS group_id,
startNode(e).uuid AS source_node_uuid,
endNode(e).uuid AS target_node_uuid,
e.created_at AS created_at,
e.name AS name,
e.fact AS fact,
split(e.episodes, ',') AS episodes,
e.expired_at AS expired_at,
e.valid_at AS valid_at,
e.invalid_at AS invalid_at,
properties(e) AS attributes
LIMIT $limit
"""
)
else:
query = (
f"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH path = (origin {{uuid: origin_uuid}})-[:RELATES_TO|MENTIONS*1..{bfs_max_depth}]->(:Entity)
UNWIND relationships(path) AS rel
MATCH (n:Entity)-[e:RELATES_TO {{uuid: rel.uuid}}]-(m:Entity)
"""
+ filter_query
+ """
RETURN DISTINCT
"""
+ get_entity_edge_return_query(driver.provider)
+ """
LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query,
bfs_origin_node_uuids=bfs_origin_node_uuids,
depth=bfs_max_depth,
limit=limit,
routing_='r',
**filter_params,
)
edges = [get_entity_edge_from_record(record, driver.provider) for record in records]
return edges
async def node_fulltext_search(
driver: GraphDriver,
query: str,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit=RELEVANT_SCHEMA_LIMIT,
) -> list[EntityNode]:
if driver.search_interface:
return await driver.search_interface.node_fulltext_search(
driver, query, search_filter, group_ids, limit
)
# BM25 search to get top nodes
fuzzy_query = fulltext_query(query, group_ids, driver)
if fuzzy_query == '':
return []
filter_queries, filter_params = node_search_filter_query_constructor(
search_filter, driver.provider
)
if group_ids is not None:
filter_queries.append('n.group_id IN $group_ids')
filter_params['group_ids'] = group_ids
filter_query = ''
if filter_queries:
filter_query = ' WHERE ' + (' AND '.join(filter_queries))
yield_query = 'YIELD node AS n, score'
if driver.provider == GraphProvider.KUZU:
yield_query = 'WITH node AS n, score'
if driver.provider == GraphProvider.NEPTUNE:
res = driver.run_aoss_query('node_name_and_summary', query, limit=limit) # pyright: ignore reportAttributeAccessIssue
if res['hits']['total']['value'] > 0:
input_ids = []
for r in res['hits']['hits']:
input_ids.append({'id': r['_source']['uuid'], 'score': r['_score']})
# Match the edge ides and return the values
query = (
"""
UNWIND $ids as i
MATCH (n:Entity)
WHERE n.uuid=i.id
RETURN
"""
+ get_entity_node_return_query(driver.provider)
+ """
ORDER BY i.score DESC
LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query,
ids=input_ids,
query=fuzzy_query,
limit=limit,
routing_='r',
**filter_params,
)
else:
return []
else:
query = (
get_nodes_query(
'node_name_and_summary', '$query', limit=limit, provider=driver.provider
)
+ yield_query
+ filter_query
+ """
WITH n, score
ORDER BY score DESC
LIMIT $limit
RETURN
"""
+ get_entity_node_return_query(driver.provider)
)
records, _, _ = await driver.execute_query(
query,
query=fuzzy_query,
limit=limit,
routing_='r',
**filter_params,
)
nodes = [get_entity_node_from_record(record, driver.provider) for record in records]
return nodes
async def node_similarity_search(
driver: GraphDriver,
search_vector: list[float],
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit=RELEVANT_SCHEMA_LIMIT,
min_score: float = DEFAULT_MIN_SCORE,
) -> list[EntityNode]:
if driver.search_interface:
return await driver.search_interface.node_similarity_search(
driver, search_vector, search_filter, group_ids, limit, min_score
)
filter_queries, filter_params = node_search_filter_query_constructor(
search_filter, driver.provider
)
if group_ids is not None:
filter_queries.append('n.group_id IN $group_ids')
filter_params['group_ids'] = group_ids
filter_query = ''
if filter_queries:
filter_query = ' WHERE ' + (' AND '.join(filter_queries))
search_vector_var = '$search_vector'
if driver.provider == GraphProvider.KUZU:
search_vector_var = f'CAST($search_vector AS FLOAT[{len(search_vector)}])'
if driver.provider == GraphProvider.NEPTUNE:
query = (
"""
MATCH (n:Entity)
"""
+ filter_query
+ """
RETURN DISTINCT id(n) as id, n.name_embedding as embedding
"""
)
resp, header, _ = await driver.execute_query(
query,
params=filter_params,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
)
if len(resp) > 0:
# Calculate Cosine similarity then return the edge ids
input_ids = []
for r in resp:
if r['embedding']:
score = calculate_cosine_similarity(
search_vector, list(map(float, r['embedding'].split(',')))
)
if score > min_score:
input_ids.append({'id': r['id'], 'score': score})
# Match the edge ides and return the values
query = (
"""
UNWIND $ids as i
MATCH (n:Entity)
WHERE id(n)=i.id
RETURN
"""
+ get_entity_node_return_query(driver.provider)
+ """
ORDER BY i.score DESC
LIMIT $limit
"""
)
records, header, _ = await driver.execute_query(
query,
ids=input_ids,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
else:
return []
else:
query = (
"""
MATCH (n:Entity)
"""
+ filter_query
+ """
WITH n, """
+ get_vector_cosine_func_query('n.name_embedding', search_vector_var, driver.provider)
+ """ AS score
WHERE score > $min_score
RETURN
"""
+ get_entity_node_return_query(driver.provider)
+ """
ORDER BY score DESC
LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
nodes = [get_entity_node_from_record(record, driver.provider) for record in records]
return nodes
async def node_bfs_search(
driver: GraphDriver,
bfs_origin_node_uuids: list[str] | None,
search_filter: SearchFilters,
bfs_max_depth: int,
group_ids: list[str] | None = None,
limit: int = RELEVANT_SCHEMA_LIMIT,
) -> list[EntityNode]:
if bfs_origin_node_uuids is None or len(bfs_origin_node_uuids) == 0 or bfs_max_depth < 1:
return []
filter_queries, filter_params = node_search_filter_query_constructor(
search_filter, driver.provider
)
if group_ids is not None:
filter_queries.append('n.group_id IN $group_ids')
filter_queries.append('origin.group_id IN $group_ids')
filter_params['group_ids'] = group_ids
filter_query = ''
if filter_queries:
filter_query = ' AND ' + (' AND '.join(filter_queries))
match_queries = [
f"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH (origin {{uuid: origin_uuid}})-[:RELATES_TO|MENTIONS*1..{bfs_max_depth}]->(n:Entity)
WHERE n.group_id = origin.group_id
"""
]
if driver.provider == GraphProvider.NEPTUNE:
match_queries = [
f"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH (origin {{uuid: origin_uuid}})-[e:RELATES_TO|MENTIONS*1..{bfs_max_depth}]->(n:Entity)
WHERE origin:Entity OR origin.Episode
AND n.group_id = origin.group_id
"""
]
if driver.provider == GraphProvider.KUZU:
depth = bfs_max_depth * 2
match_queries = [
"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH (origin:Episodic {uuid: origin_uuid})-[:MENTIONS]->(n:Entity)
WHERE n.group_id = origin.group_id
""",
f"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH (origin:Entity {{uuid: origin_uuid}})-[:RELATES_TO*2..{depth}]->(n:Entity)
WHERE n.group_id = origin.group_id
""",
]
if bfs_max_depth > 1:
depth = (bfs_max_depth - 1) * 2
match_queries.append(f"""
UNWIND $bfs_origin_node_uuids AS origin_uuid
MATCH (origin:Episodic {{uuid: origin_uuid}})-[:MENTIONS]->(:Entity)-[:RELATES_TO*2..{depth}]->(n:Entity)
WHERE n.group_id = origin.group_id
""")
records = []
for match_query in match_queries:
sub_records, _, _ = await driver.execute_query(
match_query
+ filter_query
+ """
RETURN
"""
+ get_entity_node_return_query(driver.provider)
+ """
LIMIT $limit
""",
bfs_origin_node_uuids=bfs_origin_node_uuids,
limit=limit,
routing_='r',
**filter_params,
)
records.extend(sub_records)
nodes = [get_entity_node_from_record(record, driver.provider) for record in records]
return nodes
async def episode_fulltext_search(
driver: GraphDriver,
query: str,
_search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit=RELEVANT_SCHEMA_LIMIT,
) -> list[EpisodicNode]:
if driver.search_interface:
return await driver.search_interface.episode_fulltext_search(
driver, query, _search_filter, group_ids, limit
)
# BM25 search to get top episodes
fuzzy_query = fulltext_query(query, group_ids, driver)
if fuzzy_query == '':
return []
filter_params: dict[str, Any] = {}
group_filter_query: LiteralString = ''
if group_ids is not None:
group_filter_query += '\nAND e.group_id IN $group_ids'
filter_params['group_ids'] = group_ids
if driver.provider == GraphProvider.NEPTUNE:
res = driver.run_aoss_query('episode_content', query, limit=limit) # pyright: ignore reportAttributeAccessIssue
if res['hits']['total']['value'] > 0:
input_ids = []
for r in res['hits']['hits']:
input_ids.append({'id': r['_source']['uuid'], 'score': r['_score']})
# Match the edge ides and return the values
query = """
UNWIND $ids as i
MATCH (e:Episodic)
WHERE e.uuid=i.uuid
RETURN
e.content AS content,
e.created_at AS created_at,
e.valid_at AS valid_at,
e.uuid AS uuid,
e.name AS name,
e.group_id AS group_id,
e.source_description AS source_description,
e.source AS source,
e.entity_edges AS entity_edges
ORDER BY i.score DESC
LIMIT $limit
"""
records, _, _ = await driver.execute_query(
query,
ids=input_ids,
query=fuzzy_query,
limit=limit,
routing_='r',
**filter_params,
)
else:
return []
else:
query = (
get_nodes_query('episode_content', '$query', limit=limit, provider=driver.provider)
+ """
YIELD node AS episode, score
MATCH (e:Episodic)
WHERE e.uuid = episode.uuid
"""
+ group_filter_query
+ """
RETURN
"""
+ EPISODIC_NODE_RETURN
+ """
ORDER BY score DESC
LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query, query=fuzzy_query, limit=limit, routing_='r', **filter_params
)
episodes = [get_episodic_node_from_record(record) for record in records]
return episodes
async def community_fulltext_search(
driver: GraphDriver,
query: str,
group_ids: list[str] | None = None,
limit=RELEVANT_SCHEMA_LIMIT,
) -> list[CommunityNode]:
# BM25 search to get top communities
fuzzy_query = fulltext_query(query, group_ids, driver)
if fuzzy_query == '':
return []
filter_params: dict[str, Any] = {}
group_filter_query: LiteralString = ''
if group_ids is not None:
group_filter_query = 'WHERE c.group_id IN $group_ids'
filter_params['group_ids'] = group_ids
yield_query = 'YIELD node AS c, score'
if driver.provider == GraphProvider.KUZU:
yield_query = 'WITH node AS c, score'
if driver.provider == GraphProvider.NEPTUNE:
res = driver.run_aoss_query('community_name', query, limit=limit) # pyright: ignore reportAttributeAccessIssue
if res['hits']['total']['value'] > 0:
# Calculate Cosine similarity then return the edge ids
input_ids = []
for r in res['hits']['hits']:
input_ids.append({'id': r['_source']['uuid'], 'score': r['_score']})
# Match the edge ides and return the values
query = """
UNWIND $ids as i
MATCH (comm:Community)
WHERE comm.uuid=i.id
RETURN
comm.uuid AS uuid,
comm.group_id AS group_id,
comm.name AS name,
comm.created_at AS created_at,
comm.summary AS summary,
[x IN split(comm.name_embedding, ",") | toFloat(x)]AS name_embedding
ORDER BY i.score DESC
LIMIT $limit
"""
records, _, _ = await driver.execute_query(
query,
ids=input_ids,
query=fuzzy_query,
limit=limit,
routing_='r',
**filter_params,
)
else:
return []
else:
query = (
get_nodes_query('community_name', '$query', limit=limit, provider=driver.provider)
+ yield_query
+ """
WITH c, score
"""
+ group_filter_query
+ """
RETURN
"""
+ COMMUNITY_NODE_RETURN
+ """
ORDER BY score DESC
LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query, query=fuzzy_query, limit=limit, routing_='r', **filter_params
)
communities = [get_community_node_from_record(record) for record in records]
return communities
async def community_similarity_search(
driver: GraphDriver,
search_vector: list[float],
group_ids: list[str] | None = None,
limit=RELEVANT_SCHEMA_LIMIT,
min_score=DEFAULT_MIN_SCORE,
) -> list[CommunityNode]:
# vector similarity search over entity names
query_params: dict[str, Any] = {}
group_filter_query: LiteralString = ''
if group_ids is not None:
group_filter_query += ' WHERE c.group_id IN $group_ids'
query_params['group_ids'] = group_ids
if driver.provider == GraphProvider.NEPTUNE:
query = (
"""
MATCH (n:Community)
"""
+ group_filter_query
+ """
RETURN DISTINCT id(n) as id, n.name_embedding as embedding
"""
)
resp, header, _ = await driver.execute_query(
query,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
**query_params,
)
if len(resp) > 0:
# Calculate Cosine similarity then return the edge ids
input_ids = []
for r in resp:
if r['embedding']:
score = calculate_cosine_similarity(
search_vector, list(map(float, r['embedding'].split(',')))
)
if score > min_score:
input_ids.append({'id': r['id'], 'score': score})
# Match the edge ides and return the values
query = """
UNWIND $ids as i
MATCH (comm:Community)
WHERE id(comm)=i.id
RETURN
comm.uuid As uuid,
comm.group_id AS group_id,
comm.name AS name,
comm.created_at AS created_at,
comm.summary AS summary,
comm.name_embedding AS name_embedding
ORDER BY i.score DESC
LIMIT $limit
"""
records, header, _ = await driver.execute_query(
query,
ids=input_ids,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
**query_params,
)
else:
return []
else:
search_vector_var = '$search_vector'
if driver.provider == GraphProvider.KUZU:
search_vector_var = f'CAST($search_vector AS FLOAT[{len(search_vector)}])'
query = (
"""
MATCH (c:Community)
"""
+ group_filter_query
+ """
WITH c,
"""
+ get_vector_cosine_func_query('c.name_embedding', search_vector_var, driver.provider)
+ """ AS score
WHERE score > $min_score
RETURN
"""
+ COMMUNITY_NODE_RETURN
+ """
ORDER BY score DESC
LIMIT $limit
"""
)
records, _, _ = await driver.execute_query(
query,
search_vector=search_vector,
limit=limit,
min_score=min_score,
routing_='r',
**query_params,
)
communities = [get_community_node_from_record(record) for record in records]
return communities
async def hybrid_node_search(
queries: list[str],
embeddings: list[list[float]],
driver: GraphDriver,
search_filter: SearchFilters,
group_ids: list[str] | None = None,
limit: int = RELEVANT_SCHEMA_LIMIT,
) -> list[EntityNode]:
"""
Perform a hybrid search for nodes using both text queries and embeddings.
This method combines fulltext search and vector similarity search to find
relevant nodes in the graph database. It uses a rrf reranker.
Parameters
----------
queries : list[str]
A list of text queries to search for.
embeddings : list[list[float]]
A list of embedding vectors corresponding to the queries. If empty only fulltext search is performed.
driver : GraphDriver
The Neo4j driver instance for database operations.
group_ids : list[str] | None, optional
The list of group ids to retrieve nodes from.
limit : int | None, optional
The maximum number of results to return per search method. If None, a default limit will be applied.
Returns
-------
list[EntityNode]
A list of unique EntityNode objects that match the search criteria.
Notes
-----
This method performs the following steps:
1. Executes fulltext searches for each query.
2. Executes vector similarity searches for each embedding.
3. Combines and deduplicates the results from both search types.
4. Logs the performance metrics of the search operation.
The search results are deduplicated based on the node UUIDs to ensure
uniqueness in the returned list. The 'limit' parameter is applied to each
individual search method before deduplication. If not specified, a default
limit (defined in the individual search functions) will be used.
"""
start = time()
results: list[list[EntityNode]] = list(
await semaphore_gather(
*[
node_fulltext_search(driver, q, search_filter, group_ids, 2 * limit)
for q in queries
],
*[
node_similarity_search(driver, e, search_filter, group_ids, 2 * limit)
for e in embeddings
],
)
)
node_uuid_map: dict[str, EntityNode] = {
node.uuid: node for result in results for node in result
}
result_uuids = [[node.uuid for node in result] for result in results]
ranked_uuids, _ = rrf(result_uuids)
relevant_nodes: list[EntityNode] = [node_uuid_map[uuid] for uuid in ranked_uuids]
end = time()
logger.debug(f'Found relevant nodes: {ranked_uuids} in {(end - start) * 1000} ms')
return relevant_nodes
async def get_relevant_nodes(
driver: GraphDriver,
nodes: list[EntityNode],
search_filter: SearchFilters,
min_score: float = DEFAULT_MIN_SCORE,
limit: int = RELEVANT_SCHEMA_LIMIT,
) -> list[list[EntityNode]]:
if len(nodes) == 0:
return []
group_id = nodes[0].group_id
query_nodes = [
{
'uuid': node.uuid,
'name': node.name,
'name_embedding': node.name_embedding,
'fulltext_query': fulltext_query(node.name, [node.group_id], driver),
}
for node in nodes
]
filter_queries, filter_params = node_search_filter_query_constructor(
search_filter, driver.provider
)
filter_query = ''
if filter_queries:
filter_query = 'WHERE ' + (' AND '.join(filter_queries))
if driver.provider == GraphProvider.KUZU:
embedding_size = len(nodes[0].name_embedding) if nodes[0].name_embedding is not None else 0
if embedding_size == 0:
return []
# FIXME: Kuzu currently does not support using variables such as `node.fulltext_query` as an input to FTS, which means `get_relevant_nodes()` won't work with Kuzu as the graph driver.
query = (
"""
UNWIND $nodes AS node
MATCH (n:Entity {group_id: $group_id})
"""
+ filter_query
+ """
WITH node, n, """
+ get_vector_cosine_func_query(
'n.name_embedding',
f'CAST(node.name_embedding AS FLOAT[{embedding_size}])',
driver.provider,
)
+ """ AS score
WHERE score > $min_score
WITH node, collect(n)[:$limit] AS top_vector_nodes, collect(n.uuid) AS vector_node_uuids
"""
+ get_nodes_query(
'node_name_and_summary',
'node.fulltext_query',
limit=limit,
provider=driver.provider,
)
+ """
WITH node AS m
WHERE m.group_id = $group_id AND NOT m.uuid IN vector_node_uuids
WITH node, top_vector_nodes, collect(m) AS fulltext_nodes
WITH node, list_concat(top_vector_nodes, fulltext_nodes) AS combined_nodes
UNWIND combined_nodes AS x
WITH node, collect(DISTINCT {
uuid: x.uuid,
name: x.name,
name_embedding: x.name_embedding,
group_id: x.group_id,
created_at: x.created_at,
summary: x.summary,
labels: x.labels,
attributes: x.attributes
}) AS matches
RETURN
node.uuid AS search_node_uuid, matches
"""
)
else:
query = (
"""
UNWIND $nodes AS node
MATCH (n:Entity {group_id: $group_id})
"""
+ filter_query
+ """
WITH node, n, """
+ get_vector_cosine_func_query(
'n.name_embedding', 'node.name_embedding', driver.provider
)
+ """ AS score
WHERE score > $min_score
WITH node, collect(n)[..$limit] AS top_vector_nodes, collect(n.uuid) AS vector_node_uuids
"""
+ get_nodes_query(
'node_name_and_summary',
'node.fulltext_query',
limit=limit,
provider=driver.provider,
)
+ """
YIELD node AS m
WHERE m.group_id = $group_id
WITH node, top_vector_nodes, vector_node_uuids, collect(m) AS fulltext_nodes
WITH node,
top_vector_nodes,
[m IN fulltext_nodes WHERE NOT m.uuid IN vector_node_uuids] AS filtered_fulltext_nodes
WITH node, top_vector_nodes + filtered_fulltext_nodes AS combined_nodes
UNWIND combined_nodes AS combined_node
WITH node, collect(DISTINCT combined_node) AS deduped_nodes
RETURN
node.uuid AS search_node_uuid,
[x IN deduped_nodes | {
uuid: x.uuid,
name: x.name,
name_embedding: x.name_embedding,
group_id: x.group_id,
created_at: x.created_at,
summary: x.summary,
labels: labels(x),
attributes: properties(x)
}] AS matches
"""
)
results, _, _ = await driver.execute_query(
query,
nodes=query_nodes,
group_id=group_id,
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
relevant_nodes_dict: dict[str, list[EntityNode]] = {
result['search_node_uuid']: [
get_entity_node_from_record(record, driver.provider) for record in result['matches']
]
for result in results
}
relevant_nodes = [relevant_nodes_dict.get(node.uuid, []) for node in nodes]
return relevant_nodes
async def get_relevant_edges(
driver: GraphDriver,
edges: list[EntityEdge],
search_filter: SearchFilters,
min_score: float = DEFAULT_MIN_SCORE,
limit: int = RELEVANT_SCHEMA_LIMIT,
) -> list[list[EntityEdge]]:
if len(edges) == 0:
return []
filter_queries, filter_params = edge_search_filter_query_constructor(
search_filter, driver.provider
)
filter_query = ''
if filter_queries:
filter_query = ' WHERE ' + (' AND '.join(filter_queries))
if driver.provider == GraphProvider.NEPTUNE:
query = (
"""
UNWIND $edges AS edge
MATCH (n:Entity {uuid: edge.source_node_uuid})-[e:RELATES_TO {group_id: edge.group_id}]-(m:Entity {uuid: edge.target_node_uuid})
"""
+ filter_query
+ """
WITH e, edge
RETURN DISTINCT id(e) as id, e.fact_embedding as source_embedding, edge.uuid as search_edge_uuid,
edge.fact_embedding as target_embedding
"""
)
resp, _, _ = await driver.execute_query(
query,
edges=[edge.model_dump() for edge in edges],
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
# Calculate Cosine similarity then return the edge ids
input_ids = []
for r in resp:
score = calculate_cosine_similarity(
list(map(float, r['source_embedding'].split(','))), r['target_embedding']
)
if score > min_score:
input_ids.append({'id': r['id'], 'score': score, 'uuid': r['search_edge_uuid']})
# Match the edge ides and return the values
query = """
UNWIND $ids AS edge
MATCH ()-[e]->()
WHERE id(e) = edge.id
WITH edge, e
ORDER BY edge.score DESC
RETURN edge.uuid AS search_edge_uuid,
collect({
uuid: e.uuid,
source_node_uuid: startNode(e).uuid,
target_node_uuid: endNode(e).uuid,
created_at: e.created_at,
name: e.name,
group_id: e.group_id,
fact: e.fact,
fact_embedding: [x IN split(e.fact_embedding, ",") | toFloat(x)],
episodes: split(e.episodes, ","),
expired_at: e.expired_at,
valid_at: e.valid_at,
invalid_at: e.invalid_at,
attributes: properties(e)
})[..$limit] AS matches
"""
results, _, _ = await driver.execute_query(
query,
ids=input_ids,
edges=[edge.model_dump() for edge in edges],
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
else:
if driver.provider == GraphProvider.KUZU:
embedding_size = (
len(edges[0].fact_embedding) if edges[0].fact_embedding is not None else 0
)
if embedding_size == 0:
return []
query = (
"""
UNWIND $edges AS edge
MATCH (n:Entity {uuid: edge.source_node_uuid})-[:RELATES_TO]-(e:RelatesToNode_ {group_id: edge.group_id})-[:RELATES_TO]-(m:Entity {uuid: edge.target_node_uuid})
"""
+ filter_query
+ """
WITH e, edge, n, m, """
+ get_vector_cosine_func_query(
'e.fact_embedding',
f'CAST(edge.fact_embedding AS FLOAT[{embedding_size}])',
driver.provider,
)
+ """ AS score
WHERE score > $min_score
WITH e, edge, n, m, score
ORDER BY score DESC
LIMIT $limit
RETURN
edge.uuid AS search_edge_uuid,
collect({
uuid: e.uuid,
source_node_uuid: n.uuid,
target_node_uuid: m.uuid,
created_at: e.created_at,
name: e.name,
group_id: e.group_id,
fact: e.fact,
fact_embedding: e.fact_embedding,
episodes: e.episodes,
expired_at: e.expired_at,
valid_at: e.valid_at,
invalid_at: e.invalid_at,
attributes: e.attributes
}) AS matches
"""
)
else:
query = (
"""
UNWIND $edges AS edge
MATCH (n:Entity {uuid: edge.source_node_uuid})-[e:RELATES_TO {group_id: edge.group_id}]-(m:Entity {uuid: edge.target_node_uuid})
"""
+ filter_query
+ """
WITH e, edge, """
+ get_vector_cosine_func_query(
'e.fact_embedding', 'edge.fact_embedding', driver.provider
)
+ """ AS score
WHERE score > $min_score
WITH edge, e, score
ORDER BY score DESC
RETURN
edge.uuid AS search_edge_uuid,
collect({
uuid: e.uuid,
source_node_uuid: startNode(e).uuid,
target_node_uuid: endNode(e).uuid,
created_at: e.created_at,
name: e.name,
group_id: e.group_id,
fact: e.fact,
fact_embedding: e.fact_embedding,
episodes: e.episodes,
expired_at: e.expired_at,
valid_at: e.valid_at,
invalid_at: e.invalid_at,
attributes: properties(e)
})[..$limit] AS matches
"""
)
results, _, _ = await driver.execute_query(
query,
edges=[edge.model_dump() for edge in edges],
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
relevant_edges_dict: dict[str, list[EntityEdge]] = {
result['search_edge_uuid']: [
get_entity_edge_from_record(record, driver.provider) for record in result['matches']
]
for result in results
}
relevant_edges = [relevant_edges_dict.get(edge.uuid, []) for edge in edges]
return relevant_edges
async def get_edge_invalidation_candidates(
driver: GraphDriver,
edges: list[EntityEdge],
search_filter: SearchFilters,
min_score: float = DEFAULT_MIN_SCORE,
limit: int = RELEVANT_SCHEMA_LIMIT,
) -> list[list[EntityEdge]]:
if len(edges) == 0:
return []
filter_queries, filter_params = edge_search_filter_query_constructor(
search_filter, driver.provider
)
filter_query = ''
if filter_queries:
filter_query = ' AND ' + (' AND '.join(filter_queries))
if driver.provider == GraphProvider.NEPTUNE:
query = (
"""
UNWIND $edges AS edge
MATCH (n:Entity)-[e:RELATES_TO {group_id: edge.group_id}]->(m:Entity)
WHERE n.uuid IN [edge.source_node_uuid, edge.target_node_uuid] OR m.uuid IN [edge.target_node_uuid, edge.source_node_uuid]
"""
+ filter_query
+ """
WITH e, edge
RETURN DISTINCT id(e) as id, e.fact_embedding as source_embedding,
edge.fact_embedding as target_embedding,
edge.uuid as search_edge_uuid
"""
)
resp, _, _ = await driver.execute_query(
query,
edges=[edge.model_dump() for edge in edges],
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
# Calculate Cosine similarity then return the edge ids
input_ids = []
for r in resp:
score = calculate_cosine_similarity(
list(map(float, r['source_embedding'].split(','))), r['target_embedding']
)
if score > min_score:
input_ids.append({'id': r['id'], 'score': score, 'uuid': r['search_edge_uuid']})
# Match the edge ides and return the values
query = """
UNWIND $ids AS edge
MATCH ()-[e]->()
WHERE id(e) = edge.id
WITH edge, e
ORDER BY edge.score DESC
RETURN edge.uuid AS search_edge_uuid,
collect({
uuid: e.uuid,
source_node_uuid: startNode(e).uuid,
target_node_uuid: endNode(e).uuid,
created_at: e.created_at,
name: e.name,
group_id: e.group_id,
fact: e.fact,
fact_embedding: [x IN split(e.fact_embedding, ",") | toFloat(x)],
episodes: split(e.episodes, ","),
expired_at: e.expired_at,
valid_at: e.valid_at,
invalid_at: e.invalid_at,
attributes: properties(e)
})[..$limit] AS matches
"""
results, _, _ = await driver.execute_query(
query,
ids=input_ids,
edges=[edge.model_dump() for edge in edges],
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
else:
if driver.provider == GraphProvider.KUZU:
embedding_size = (
len(edges[0].fact_embedding) if edges[0].fact_embedding is not None else 0
)
if embedding_size == 0:
return []
query = (
"""
UNWIND $edges AS edge
MATCH (n:Entity)-[:RELATES_TO]->(e:RelatesToNode_ {group_id: edge.group_id})-[:RELATES_TO]->(m:Entity)
WHERE (n.uuid IN [edge.source_node_uuid, edge.target_node_uuid] OR m.uuid IN [edge.target_node_uuid, edge.source_node_uuid])
"""
+ filter_query
+ """
WITH edge, e, n, m, """
+ get_vector_cosine_func_query(
'e.fact_embedding',
f'CAST(edge.fact_embedding AS FLOAT[{embedding_size}])',
driver.provider,
)
+ """ AS score
WHERE score > $min_score
WITH edge, e, n, m, score
ORDER BY score DESC
LIMIT $limit
RETURN
edge.uuid AS search_edge_uuid,
collect({
uuid: e.uuid,
source_node_uuid: n.uuid,
target_node_uuid: m.uuid,
created_at: e.created_at,
name: e.name,
group_id: e.group_id,
fact: e.fact,
fact_embedding: e.fact_embedding,
episodes: e.episodes,
expired_at: e.expired_at,
valid_at: e.valid_at,
invalid_at: e.invalid_at,
attributes: e.attributes
}) AS matches
"""
)
else:
query = (
"""
UNWIND $edges AS edge
MATCH (n:Entity)-[e:RELATES_TO {group_id: edge.group_id}]->(m:Entity)
WHERE n.uuid IN [edge.source_node_uuid, edge.target_node_uuid] OR m.uuid IN [edge.target_node_uuid, edge.source_node_uuid]
"""
+ filter_query
+ """
WITH edge, e, """
+ get_vector_cosine_func_query(
'e.fact_embedding', 'edge.fact_embedding', driver.provider
)
+ """ AS score
WHERE score > $min_score
WITH edge, e, score
ORDER BY score DESC
RETURN
edge.uuid AS search_edge_uuid,
collect({
uuid: e.uuid,
source_node_uuid: startNode(e).uuid,
target_node_uuid: endNode(e).uuid,
created_at: e.created_at,
name: e.name,
group_id: e.group_id,
fact: e.fact,
fact_embedding: e.fact_embedding,
episodes: e.episodes,
expired_at: e.expired_at,
valid_at: e.valid_at,
invalid_at: e.invalid_at,
attributes: properties(e)
})[..$limit] AS matches
"""
)
results, _, _ = await driver.execute_query(
query,
edges=[edge.model_dump() for edge in edges],
limit=limit,
min_score=min_score,
routing_='r',
**filter_params,
)
invalidation_edges_dict: dict[str, list[EntityEdge]] = {
result['search_edge_uuid']: [
get_entity_edge_from_record(record, driver.provider) for record in result['matches']
]
for result in results
}
invalidation_edges = [invalidation_edges_dict.get(edge.uuid, []) for edge in edges]
return invalidation_edges
# takes in a list of rankings of uuids
def rrf(
results: list[list[str]], rank_const=1, min_score: float = 0
) -> tuple[list[str], list[float]]:
scores: dict[str, float] = defaultdict(float)
for result in results:
for i, uuid in enumerate(result):
scores[uuid] += 1 / (i + rank_const)
scored_uuids = [term for term in scores.items()]
scored_uuids.sort(reverse=True, key=lambda term: term[1])
sorted_uuids = [term[0] for term in scored_uuids]
return [uuid for uuid in sorted_uuids if scores[uuid] >= min_score], [
scores[uuid] for uuid in sorted_uuids if scores[uuid] >= min_score
]
async def node_distance_reranker(
driver: GraphDriver,
node_uuids: list[str],
center_node_uuid: str,
min_score: float = 0,
) -> tuple[list[str], list[float]]:
# filter out node_uuid center node node uuid
filtered_uuids = list(filter(lambda node_uuid: node_uuid != center_node_uuid, node_uuids))
scores: dict[str, float] = {center_node_uuid: 0.0}
query = """
UNWIND $node_uuids AS node_uuid
MATCH (center:Entity {uuid: $center_uuid})-[:RELATES_TO]-(n:Entity {uuid: node_uuid})
RETURN 1 AS score, node_uuid AS uuid
"""
if driver.provider == GraphProvider.KUZU:
query = """
UNWIND $node_uuids AS node_uuid
MATCH (center:Entity {uuid: $center_uuid})-[:RELATES_TO]->(e:RelatesToNode_)-[:RELATES_TO]->(n:Entity {uuid: node_uuid})
RETURN 1 AS score, node_uuid AS uuid
"""
# Find the shortest path to center node
results, header, _ = await driver.execute_query(
query,
node_uuids=filtered_uuids,
center_uuid=center_node_uuid,
routing_='r',
)
if driver.provider == GraphProvider.FALKORDB:
results = [dict(zip(header, row, strict=True)) for row in results]
for result in results:
uuid = result['uuid']
score = result['score']
scores[uuid] = score
for uuid in filtered_uuids:
if uuid not in scores:
scores[uuid] = float('inf')
# rerank on shortest distance
filtered_uuids.sort(key=lambda cur_uuid: scores[cur_uuid])
# add back in filtered center uuid if it was filtered out
if center_node_uuid in node_uuids:
scores[center_node_uuid] = 0.1
filtered_uuids = [center_node_uuid] + filtered_uuids
return [uuid for uuid in filtered_uuids if (1 / scores[uuid]) >= min_score], [
1 / scores[uuid] for uuid in filtered_uuids if (1 / scores[uuid]) >= min_score
]
async def episode_mentions_reranker(
driver: GraphDriver, node_uuids: list[list[str]], min_score: float = 0
) -> tuple[list[str], list[float]]:
# use rrf as a preliminary ranker
sorted_uuids, _ = rrf(node_uuids)
scores: dict[str, float] = {}
# Find the shortest path to center node
results, _, _ = await driver.execute_query(
"""
UNWIND $node_uuids AS node_uuid
MATCH (episode:Episodic)-[r:MENTIONS]->(n:Entity {uuid: node_uuid})
RETURN count(*) AS score, n.uuid AS uuid
""",
node_uuids=sorted_uuids,
routing_='r',
)
for result in results:
scores[result['uuid']] = result['score']
for uuid in sorted_uuids:
if uuid not in scores:
scores[uuid] = float('inf')
# rerank on shortest distance
sorted_uuids.sort(key=lambda cur_uuid: scores[cur_uuid])
return [uuid for uuid in sorted_uuids if scores[uuid] >= min_score], [
scores[uuid] for uuid in sorted_uuids if scores[uuid] >= min_score
]
def maximal_marginal_relevance(
query_vector: list[float],
candidates: dict[str, list[float]],
mmr_lambda: float = DEFAULT_MMR_LAMBDA,
min_score: float = -2.0,
) -> tuple[list[str], list[float]]:
start = time()
query_array = np.array(query_vector)
candidate_arrays: dict[str, NDArray] = {}
for uuid, embedding in candidates.items():
candidate_arrays[uuid] = normalize_l2(embedding)
uuids: list[str] = list(candidate_arrays.keys())
similarity_matrix = np.zeros((len(uuids), len(uuids)))
for i, uuid_1 in enumerate(uuids):
for j, uuid_2 in enumerate(uuids[:i]):
u = candidate_arrays[uuid_1]
v = candidate_arrays[uuid_2]
similarity = np.dot(u, v)
similarity_matrix[i, j] = similarity
similarity_matrix[j, i] = similarity
mmr_scores: dict[str, float] = {}
for i, uuid in enumerate(uuids):
max_sim = np.max(similarity_matrix[i, :])
mmr = mmr_lambda * np.dot(query_array, candidate_arrays[uuid]) + (mmr_lambda - 1) * max_sim
mmr_scores[uuid] = mmr
uuids.sort(reverse=True, key=lambda c: mmr_scores[c])
end = time()
logger.debug(f'Completed MMR reranking in {(end - start) * 1000} ms')
return [uuid for uuid in uuids if mmr_scores[uuid] >= min_score], [
mmr_scores[uuid] for uuid in uuids if mmr_scores[uuid] >= min_score
]
async def get_embeddings_for_nodes(
driver: GraphDriver, nodes: list[EntityNode]
) -> dict[str, list[float]]:
if driver.graph_operations_interface:
return await driver.graph_operations_interface.node_load_embeddings_bulk(driver, nodes)
elif driver.provider == GraphProvider.NEPTUNE:
query = """
MATCH (n:Entity)
WHERE n.uuid IN $node_uuids
RETURN DISTINCT
n.uuid AS uuid,
split(n.name_embedding, ",") AS name_embedding
"""
else:
query = """
MATCH (n:Entity)
WHERE n.uuid IN $node_uuids
RETURN DISTINCT
n.uuid AS uuid,
n.name_embedding AS name_embedding
"""
results, _, _ = await driver.execute_query(
query,
node_uuids=[node.uuid for node in nodes],
routing_='r',
)
embeddings_dict: dict[str, list[float]] = {}
for result in results:
uuid: str = result.get('uuid')
embedding: list[float] = result.get('name_embedding')
if uuid is not None and embedding is not None:
embeddings_dict[uuid] = embedding
return embeddings_dict
async def get_embeddings_for_communities(
driver: GraphDriver, communities: list[CommunityNode]
) -> dict[str, list[float]]:
if driver.provider == GraphProvider.NEPTUNE:
query = """
MATCH (c:Community)
WHERE c.uuid IN $community_uuids
RETURN DISTINCT
c.uuid AS uuid,
split(c.name_embedding, ",") AS name_embedding
"""
else:
query = """
MATCH (c:Community)
WHERE c.uuid IN $community_uuids
RETURN DISTINCT
c.uuid AS uuid,
c.name_embedding AS name_embedding
"""
results, _, _ = await driver.execute_query(
query,
community_uuids=[community.uuid for community in communities],
routing_='r',
)
embeddings_dict: dict[str, list[float]] = {}
for result in results:
uuid: str = result.get('uuid')
embedding: list[float] = result.get('name_embedding')
if uuid is not None and embedding is not None:
embeddings_dict[uuid] = embedding
return embeddings_dict
async def get_embeddings_for_edges(
driver: GraphDriver, edges: list[EntityEdge]
) -> dict[str, list[float]]:
if driver.graph_operations_interface:
return await driver.graph_operations_interface.edge_load_embeddings_bulk(driver, edges)
elif driver.provider == GraphProvider.NEPTUNE:
query = """
MATCH (n:Entity)-[e:RELATES_TO]-(m:Entity)
WHERE e.uuid IN $edge_uuids
RETURN DISTINCT
e.uuid AS uuid,
split(e.fact_embedding, ",") AS fact_embedding
"""
else:
match_query = """
MATCH (n:Entity)-[e:RELATES_TO]-(m:Entity)
"""
if driver.provider == GraphProvider.KUZU:
match_query = """
MATCH (n:Entity)-[:RELATES_TO]-(e:RelatesToNode_)-[:RELATES_TO]-(m:Entity)
"""
query = (
match_query
+ """
WHERE e.uuid IN $edge_uuids
RETURN DISTINCT
e.uuid AS uuid,
e.fact_embedding AS fact_embedding
"""
)
results, _, _ = await driver.execute_query(
query,
edge_uuids=[edge.uuid for edge in edges],
routing_='r',
)
embeddings_dict: dict[str, list[float]] = {}
for result in results:
uuid: str = result.get('uuid')
embedding: list[float] = result.get('fact_embedding')
if uuid is not None and embedding is not None:
embeddings_dict[uuid] = embedding
return embeddings_dict