Cross encoder reranker in search query (#202)

* cross encoder reranker

* update reranker

* add openai reranker

* format

* mypy

* update

* updates

* MyPy typing

* bump version

graphiti_core/cross_encoder/openai_reranker_client.py

@@ -0,0 +1,113 @@
+"""
+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 asyncio
+import logging
+from typing import Any
+
+import openai
+from openai import AsyncOpenAI
+from pydantic import BaseModel
+
+from ..llm_client import LLMConfig, RateLimitError
+from ..prompts import Message
+from .client import CrossEncoderClient
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MODEL = 'gpt-4o-mini'
+
+
+class BooleanClassifier(BaseModel):
+    isTrue: bool
+
+
+class OpenAIRerankerClient(CrossEncoderClient):
+    def __init__(self, config: LLMConfig | None = None):
+        """
+        Initialize the OpenAIClient with the provided configuration, cache setting, and client.
+
+        Args:
+            config (LLMConfig | None): The configuration for the LLM client, including API key, model, base URL, temperature, and max tokens.
+            cache (bool): Whether to use caching for responses. Defaults to False.
+            client (Any | None): An optional async client instance to use. If not provided, a new AsyncOpenAI client is created.
+
+        """
+        if config is None:
+            config = LLMConfig()
+
+        self.config = config
+        self.client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+
+    async def rank(self, query: str, passages: list[str]) -> list[tuple[str, float]]:
+        openai_messages_list: Any = [
+            [
+                Message(
+                    role='system',
+                    content='You are an expert tasked with determining whether the passage is relevant to the query',
+                ),
+                Message(
+                    role='user',
+                    content=f"""
+                           Respond with "True" if PASSAGE is relevant to QUERY and "False" otherwise. 
+                           <PASSAGE>
+                           {query}
+                           </PASSAGE>
+                           {passage}
+                           <QUERY>
+                           </QUERY>
+                           """,
+                ),
+            ]
+            for passage in passages
+        ]
+        try:
+            responses = await asyncio.gather(
+                *[
+                    self.client.chat.completions.create(
+                        model=DEFAULT_MODEL,
+                        messages=openai_messages,
+                        temperature=0,
+                        max_tokens=1,
+                        logit_bias={'6432': 1, '7983': 1},
+                        logprobs=True,
+                        top_logprobs=2,
+                    )
+                    for openai_messages in openai_messages_list
+                ]
+            )
+
+            responses_top_logprobs = [
+                response.choices[0].logprobs.content[0].top_logprobs
+                if response.choices[0].logprobs is not None
+                and response.choices[0].logprobs.content is not None
+                else []
+                for response in responses
+            ]
+            scores: list[float] = []
+            for top_logprobs in responses_top_logprobs:
+                for logprob in top_logprobs:
+                    if bool(logprob.token):
+                        scores.append(logprob.logprob)
+
+            results = [(passage, score) for passage, score in zip(passages, scores)]
+            results.sort(reverse=True, key=lambda x: x[1])
+            return results
+        except openai.RateLimitError as e:
+            raise RateLimitError from e
+        except Exception as e:
+            logger.error(f'Error in generating LLM response: {e}')
+            raise

graphiti_core/graphiti.py

@@ -23,8 +23,11 @@ from dotenv import load_dotenv
 from neo4j import AsyncGraphDatabase
 from pydantic import BaseModel
 
+from graphiti_core.cross_encoder.client import CrossEncoderClient
+from graphiti_core.cross_encoder.openai_reranker_client import OpenAIRerankerClient
 from graphiti_core.edges import EntityEdge, EpisodicEdge
 from graphiti_core.embedder import EmbedderClient, OpenAIEmbedder
+from graphiti_core.helpers import DEFAULT_DATABASE
 from graphiti_core.llm_client import LLMClient, OpenAIClient
 from graphiti_core.nodes import CommunityNode, EntityNode, EpisodeType, EpisodicNode
 from graphiti_core.search.search import SearchConfig, search
@@ -92,6 +95,7 @@ class Graphiti:
         password: str,
         llm_client: LLMClient | None = None,
         embedder: EmbedderClient | None = None,
+        cross_encoder: CrossEncoderClient | None = None,
         store_raw_episode_content: bool = True,
     ):
         """
@@ -131,7 +135,7 @@ class Graphiti:
         Graphiti if you're using the default OpenAIClient.
         """
         self.driver = AsyncGraphDatabase.driver(uri, auth=(user, password))
-        self.database = 'neo4j'
+        self.database = DEFAULT_DATABASE
         self.store_raw_episode_content = store_raw_episode_content
         if llm_client:
             self.llm_client = llm_client
@@ -141,6 +145,10 @@ class Graphiti:
             self.embedder = embedder
         else:
             self.embedder = OpenAIEmbedder()
+        if cross_encoder:
+            self.cross_encoder = cross_encoder
+        else:
+            self.cross_encoder = OpenAIRerankerClient()
 
     async def close(self):
         """
@@ -648,6 +656,7 @@ class Graphiti:
             await search(
                 self.driver,
                 self.embedder,
+                self.cross_encoder,
                 query,
                 group_ids,
                 search_config,
@@ -663,8 +672,18 @@ class Graphiti:
         config: SearchConfig,
         group_ids: list[str] | None = None,
         center_node_uuid: str | None = None,
+        bfs_origin_node_uuids: list[str] | None = None,
     ) -> SearchResults:
-        return await search(self.driver, self.embedder, query, group_ids, config, center_node_uuid)
+        return await search(
+            self.driver,
+            self.embedder,
+            self.cross_encoder,
+            query,
+            group_ids,
+            config,
+            center_node_uuid,
+            bfs_origin_node_uuids,
+        )
 
     async def get_nodes_by_query(
         self,
@@ -716,7 +735,13 @@ class Graphiti:
 
         nodes = (
             await search(
-                self.driver, self.embedder, query, group_ids, search_config, center_node_uuid
+                self.driver,
+                self.embedder,
+                self.cross_encoder,
+                query,
+                group_ids,
+                search_config,
+                center_node_uuid,
             )
         ).nodes
         return nodes

graphiti_core/search/search.py

@@ -21,6 +21,7 @@ from time import time
 
 from neo4j import AsyncDriver
 
+from graphiti_core.cross_encoder.client import CrossEncoderClient
 from graphiti_core.edges import EntityEdge
 from graphiti_core.embedder import EmbedderClient
 from graphiti_core.errors import SearchRerankerError
@@ -39,6 +40,7 @@ from graphiti_core.search.search_config import (
 from graphiti_core.search.search_utils import (
     community_fulltext_search,
     community_similarity_search,
+    edge_bfs_search,
     edge_fulltext_search,
     edge_similarity_search,
     episode_mentions_reranker,
@@ -55,10 +57,12 @@ logger = logging.getLogger(__name__)
 async def search(
     driver: AsyncDriver,
     embedder: EmbedderClient,
+    cross_encoder: CrossEncoderClient,
     query: str,
     group_ids: list[str] | None,
     config: SearchConfig,
     center_node_uuid: str | None = None,
+    bfs_origin_node_uuids: list[str] | None = None,
 ) -> SearchResults:
     start = time()
     query_vector = await embedder.create(input=[query.replace('\n', ' ')])
@@ -68,28 +72,34 @@ async def search(
     edges, nodes, communities = await asyncio.gather(
         edge_search(
             driver,
+            cross_encoder,
             query,
             query_vector,
             group_ids,
             config.edge_config,
             center_node_uuid,
+            bfs_origin_node_uuids,
             config.limit,
         ),
         node_search(
             driver,
+            cross_encoder,
             query,
             query_vector,
             group_ids,
             config.node_config,
             center_node_uuid,
+            bfs_origin_node_uuids,
             config.limit,
         ),
         community_search(
             driver,
+            cross_encoder,
             query,
             query_vector,
             group_ids,
             config.community_config,
+            bfs_origin_node_uuids,
             config.limit,
         ),
     )
@@ -109,11 +119,13 @@ async def search(
 
 async def edge_search(
     driver: AsyncDriver,
+    cross_encoder: CrossEncoderClient,
     query: str,
     query_vector: list[float],
     group_ids: list[str] | None,
     config: EdgeSearchConfig | None,
     center_node_uuid: str | None = None,
+    bfs_origin_node_uuids: list[str] | None = None,
     limit=DEFAULT_SEARCH_LIMIT,
 ) -> list[EntityEdge]:
     if config is None:
@@ -126,6 +138,7 @@ async def edge_search(
                 edge_similarity_search(
                     driver, query_vector, None, None, group_ids, 2 * limit, config.sim_min_score
                 ),
+                edge_bfs_search(driver, bfs_origin_node_uuids, config.bfs_max_depth),
             ]
         )
     )
@@ -146,6 +159,10 @@ async def edge_search(
         reranked_uuids = maximal_marginal_relevance(
             query_vector, search_result_uuids_and_vectors, config.mmr_lambda
         )
+    elif config.reranker == EdgeReranker.cross_encoder:
+        fact_to_uuid_map = {edge.fact: edge.uuid for result in search_results for edge in result}
+        reranked_facts = await cross_encoder.rank(query, list(fact_to_uuid_map.keys()))
+        reranked_uuids = [fact_to_uuid_map[fact] for fact, _ in reranked_facts]
     elif config.reranker == EdgeReranker.node_distance:
         if center_node_uuid is None:
             raise SearchRerankerError('No center node provided for Node Distance reranker')
@@ -176,11 +193,13 @@ async def edge_search(
 
 async def node_search(
     driver: AsyncDriver,
+    cross_encoder: CrossEncoderClient,
     query: str,
     query_vector: list[float],
     group_ids: list[str] | None,
     config: NodeSearchConfig | None,
     center_node_uuid: str | None = None,
+    bfs_origin_node_uuids: list[str] | None = None,
     limit=DEFAULT_SEARCH_LIMIT,
 ) -> list[EntityNode]:
     if config is None:
@@ -212,6 +231,12 @@ async def node_search(
         reranked_uuids = maximal_marginal_relevance(
             query_vector, search_result_uuids_and_vectors, config.mmr_lambda
         )
+    elif config.reranker == NodeReranker.cross_encoder:
+        summary_to_uuid_map = {
+            node.summary: node.uuid for result in search_results for node in result
+        }
+        reranked_summaries = await cross_encoder.rank(query, list(summary_to_uuid_map.keys()))
+        reranked_uuids = [summary_to_uuid_map[fact] for fact, _ in reranked_summaries]
     elif config.reranker == NodeReranker.episode_mentions:
         reranked_uuids = await episode_mentions_reranker(driver, search_result_uuids)
     elif config.reranker == NodeReranker.node_distance:
@@ -228,10 +253,12 @@ async def node_search(
 
 async def community_search(
     driver: AsyncDriver,
+    cross_encoder: CrossEncoderClient,
     query: str,
     query_vector: list[float],
     group_ids: list[str] | None,
     config: CommunitySearchConfig | None,
+    bfs_origin_node_uuids: list[str] | None = None,
     limit=DEFAULT_SEARCH_LIMIT,
 ) -> list[CommunityNode]:
     if config is None:
@@ -268,6 +295,12 @@ async def community_search(
         reranked_uuids = maximal_marginal_relevance(
             query_vector, search_result_uuids_and_vectors, config.mmr_lambda
         )
+    elif config.reranker == CommunityReranker.cross_encoder:
+        summary_to_uuid_map = {
+            node.summary: node.uuid for result in search_results for node in result
+        }
+        reranked_summaries = await cross_encoder.rank(query, list(summary_to_uuid_map.keys()))
+        reranked_uuids = [summary_to_uuid_map[fact] for fact, _ in reranked_summaries]
 
     reranked_communities = [community_uuid_map[uuid] for uuid in reranked_uuids]
 

graphiti_core/search/search_config.py

@@ -20,7 +20,11 @@ from pydantic import BaseModel, Field
 
 from graphiti_core.edges import EntityEdge
 from graphiti_core.nodes import CommunityNode, EntityNode
-from graphiti_core.search.search_utils import DEFAULT_MIN_SCORE, DEFAULT_MMR_LAMBDA
+from graphiti_core.search.search_utils import (
+    DEFAULT_MIN_SCORE,
+    DEFAULT_MMR_LAMBDA,
+    MAX_SEARCH_DEPTH,
+)
 
 DEFAULT_SEARCH_LIMIT = 10
 
@@ -28,11 +32,13 @@ DEFAULT_SEARCH_LIMIT = 10
 class EdgeSearchMethod(Enum):
     cosine_similarity = 'cosine_similarity'
     bm25 = 'bm25'
+    bfs = 'breadth_first_search'
 
 
 class NodeSearchMethod(Enum):
     cosine_similarity = 'cosine_similarity'
     bm25 = 'bm25'
+    bfs = 'breadth_first_search'
 
 
 class CommunitySearchMethod(Enum):
@@ -45,6 +51,7 @@ class EdgeReranker(Enum):
     node_distance = 'node_distance'
     episode_mentions = 'episode_mentions'
     mmr = 'mmr'
+    cross_encoder = 'cross_encoder'
 
 
 class NodeReranker(Enum):
@@ -52,11 +59,13 @@ class NodeReranker(Enum):
     node_distance = 'node_distance'
     episode_mentions = 'episode_mentions'
     mmr = 'mmr'
+    cross_encoder = 'cross_encoder'
 
 
 class CommunityReranker(Enum):
     rrf = 'reciprocal_rank_fusion'
     mmr = 'mmr'
+    cross_encoder = 'cross_encoder'
 
 
 class EdgeSearchConfig(BaseModel):
@@ -64,6 +73,7 @@ class EdgeSearchConfig(BaseModel):
     reranker: EdgeReranker = Field(default=EdgeReranker.rrf)
     sim_min_score: float = Field(default=DEFAULT_MIN_SCORE)
     mmr_lambda: float = Field(default=DEFAULT_MMR_LAMBDA)
+    bfs_max_depth: int = Field(default=MAX_SEARCH_DEPTH)
 
 
 class NodeSearchConfig(BaseModel):
@@ -71,6 +81,7 @@ class NodeSearchConfig(BaseModel):
     reranker: NodeReranker = Field(default=NodeReranker.rrf)
     sim_min_score: float = Field(default=DEFAULT_MIN_SCORE)
     mmr_lambda: float = Field(default=DEFAULT_MMR_LAMBDA)
+    bfs_max_depth: int = Field(default=MAX_SEARCH_DEPTH)
 
 
 class CommunitySearchConfig(BaseModel):
@@ -78,6 +89,7 @@ class CommunitySearchConfig(BaseModel):
     reranker: CommunityReranker = Field(default=CommunityReranker.rrf)
     sim_min_score: float = Field(default=DEFAULT_MIN_SCORE)
     mmr_lambda: float = Field(default=DEFAULT_MMR_LAMBDA)
+    bfs_max_depth: int = Field(default=MAX_SEARCH_DEPTH)
 
 
 class SearchConfig(BaseModel):

graphiti_core/search/search_config_recipes.py

@@ -48,14 +48,41 @@ COMBINED_HYBRID_SEARCH_MMR = SearchConfig(
     edge_config=EdgeSearchConfig(
         search_methods=[EdgeSearchMethod.bm25, EdgeSearchMethod.cosine_similarity],
         reranker=EdgeReranker.mmr,
+        mmr_lambda=1,
     ),
     node_config=NodeSearchConfig(
         search_methods=[NodeSearchMethod.bm25, NodeSearchMethod.cosine_similarity],
         reranker=NodeReranker.mmr,
+        mmr_lambda=1,
     ),
     community_config=CommunitySearchConfig(
         search_methods=[CommunitySearchMethod.bm25, CommunitySearchMethod.cosine_similarity],
         reranker=CommunityReranker.mmr,
+        mmr_lambda=1,
+    ),
+)
+
+# Performs a full-text search, similarity search, and bfs with cross_encoder reranking over edges, nodes, and communities
+COMBINED_HYBRID_SEARCH_CROSS_ENCODER = SearchConfig(
+    edge_config=EdgeSearchConfig(
+        search_methods=[
+            EdgeSearchMethod.bm25,
+            EdgeSearchMethod.cosine_similarity,
+            EdgeSearchMethod.bfs,
+        ],
+        reranker=EdgeReranker.cross_encoder,
+    ),
+    node_config=NodeSearchConfig(
+        search_methods=[
+            NodeSearchMethod.bm25,
+            NodeSearchMethod.cosine_similarity,
+            NodeSearchMethod.bfs,
+        ],
+        reranker=NodeReranker.cross_encoder,
+    ),
+    community_config=CommunitySearchConfig(
+        search_methods=[CommunitySearchMethod.bm25, CommunitySearchMethod.cosine_similarity],
+        reranker=CommunityReranker.cross_encoder,
     ),
 )
 
@@ -81,7 +108,6 @@ EDGE_HYBRID_SEARCH_NODE_DISTANCE = SearchConfig(
         search_methods=[EdgeSearchMethod.bm25, EdgeSearchMethod.cosine_similarity],
         reranker=EdgeReranker.node_distance,
     ),
-    limit=30,
 )
 
 # performs a hybrid search over edges with episode mention reranking

graphiti_core/search/search_utils.py

@@ -19,7 +19,6 @@ import logging
 from collections import defaultdict
 from time import time
 
-import neo4j
 import numpy as np
 from neo4j import AsyncDriver, Query
 
@@ -38,6 +37,7 @@ logger = logging.getLogger(__name__)
 RELEVANT_SCHEMA_LIMIT = 3
 DEFAULT_MIN_SCORE = 0.6
 DEFAULT_MMR_LAMBDA = 0.5
+MAX_SEARCH_DEPTH = 3
 MAX_QUERY_LENGTH = 128
 
 
@@ -80,23 +80,21 @@ async def get_mentioned_nodes(
     driver: AsyncDriver, episodes: list[EpisodicNode]
 ) -> list[EntityNode]:
     episode_uuids = [episode.uuid for episode in episodes]
-    async with driver.session(
+    records, _, _ = await driver.execute_query(
-        database=DEFAULT_DATABASE, default_access_mode=neo4j.READ_ACCESS
+        """
-    ) as session:
+        MATCH (episode:Episodic)-[:MENTIONS]->(n:Entity) WHERE episode.uuid IN $uuids
-        result = await session.run(
+        RETURN DISTINCT
-            """
+            n.uuid As uuid, 
-            MATCH (episode:Episodic)-[:MENTIONS]->(n:Entity) WHERE episode.uuid IN $uuids
+            n.group_id AS group_id,
-            RETURN DISTINCT
+            n.name AS name,
-                n.uuid As uuid, 
+            n.name_embedding AS name_embedding,
-                n.group_id AS group_id,
+            n.created_at AS created_at, 
-                n.name AS name,
+            n.summary AS summary
-                n.name_embedding AS name_embedding,
+        """,
-                n.created_at AS created_at, 
+        uuids=episode_uuids,
-                n.summary AS summary
+        database_=DEFAULT_DATABASE,
-            """,
+        routing_='r',
-            {'uuids': episode_uuids},
+    )
-        )
-        records = [record async for record in result]
 
     nodes = [get_entity_node_from_record(record) for record in records]
 
@@ -107,23 +105,21 @@ async def get_communities_by_nodes(
     driver: AsyncDriver, nodes: list[EntityNode]
 ) -> list[CommunityNode]:
     node_uuids = [node.uuid for node in nodes]
-    async with driver.session(
+    records, _, _ = await driver.execute_query(
-        database=DEFAULT_DATABASE, default_access_mode=neo4j.READ_ACCESS
+        """
-    ) as session:
+    MATCH (c:Community)-[:HAS_MEMBER]->(n:Entity) WHERE n.uuid IN $uuids
-        result = await session.run(
+    RETURN DISTINCT
-            """
+        c.uuid As uuid, 
-        MATCH (c:Community)-[:HAS_MEMBER]->(n:Entity) WHERE n.uuid IN $uuids
+        c.group_id AS group_id,
-        RETURN DISTINCT
+        c.name AS name,
-            c.uuid As uuid, 
+        c.name_embedding AS name_embedding
-            c.group_id AS group_id,
+        c.created_at AS created_at, 
-            c.name AS name,
+        c.summary AS summary
-            c.name_embedding AS name_embedding
+    """,
-            c.created_at AS created_at, 
+        uuids=node_uuids,
-            c.summary AS summary
+        database_=DEFAULT_DATABASE,
-        """,
+        routing_='r',
-            {'uuids': node_uuids},
+    )
-        )
-        records = [record async for record in result]
 
     communities = [get_community_node_from_record(record) for record in records]
 
@@ -165,20 +161,16 @@ async def edge_fulltext_search(
                 ORDER BY score DESC LIMIT $limit
                 """)
 
-    async with driver.session(
+    records, _, _ = await driver.execute_query(
-        database=DEFAULT_DATABASE, default_access_mode=neo4j.READ_ACCESS
+        cypher_query,
-    ) as session:
+        query=fuzzy_query,
-        result = await session.run(
+        source_uuid=source_node_uuid,
-            cypher_query,
+        target_uuid=target_node_uuid,
-            {
+        group_ids=group_ids,
-                'query': fuzzy_query,
+        limit=limit,
-                'source_uuid': source_node_uuid,
+        database_=DEFAULT_DATABASE,
-                'target_uuid': target_node_uuid,
+        routing_='r',
-                'group_ids': group_ids,
+    )
-                'limit': limit,
-            },
-        )
-        records = [record async for record in result]
 
     edges = [get_entity_edge_from_record(record) for record in records]
 
@@ -201,13 +193,13 @@ async def edge_similarity_search(
                 WHERE ($group_ids IS NULL OR r.group_id IN $group_ids)
                 AND ($source_uuid IS NULL OR n.uuid IN [$source_uuid, $target_uuid])
                 AND ($target_uuid IS NULL OR m.uuid IN [$source_uuid, $target_uuid])
-                WITH DISTINCT n, r, m, vector.similarity.cosine(r.fact_embedding, $search_vector) AS score
+                WITH DISTINCT r, vector.similarity.cosine(r.fact_embedding, $search_vector) AS score
                 WHERE score > $min_score
                 RETURN
                     r.uuid AS uuid,
                     r.group_id AS group_id,
-                    n.uuid AS source_node_uuid,
+                    startNode(r).uuid AS source_node_uuid,
-                    m.uuid AS target_node_uuid,
+                    endNode(r).uuid AS target_node_uuid,
                     r.created_at AS created_at,
                     r.name AS name,
                     r.fact AS fact,
@@ -220,21 +212,59 @@ async def edge_similarity_search(
                 LIMIT $limit
         """)
 
-    async with driver.session(
+    records, _, _ = await driver.execute_query(
-        database=DEFAULT_DATABASE, default_access_mode=neo4j.READ_ACCESS
+        query,
-    ) as session:
+        search_vector=search_vector,
-        result = await session.run(
+        source_uuid=source_node_uuid,
-            query,
+        target_uuid=target_node_uuid,
-            {
+        group_ids=group_ids,
-                'search_vector': search_vector,
+        limit=limit,
-                'source_uuid': source_node_uuid,
+        min_score=min_score,
-                'target_uuid': target_node_uuid,
+        database_=DEFAULT_DATABASE,
-                'group_ids': group_ids,
+        routing_='r',
-                'limit': limit,
+    )
-                'min_score': min_score,
+
-            },
+    edges = [get_entity_edge_from_record(record) for record in records]
-        )
+
-        records = [record async for record in result]
+    return edges
+
+
+async def edge_bfs_search(
+    driver: AsyncDriver,
+    bfs_origin_node_uuids: list[str] | None,
+    bfs_max_depth: int,
+) -> list[EntityEdge]:
+    # vector similarity search over embedded facts
+    if bfs_origin_node_uuids is None:
+        return []
+
+    query = Query("""
+                UNWIND $bfs_origin_node_uuids AS origin_uuid
+                MATCH path = (origin:Entity|Episodic {uuid: origin_uuid})-[:RELATES_TO|MENTIONS]->{1,3}(n:Entity)
+                UNWIND relationships(path) AS rel
+                MATCH ()-[r:RELATES_TO {uuid: rel.uuid}]-()
+                RETURN DISTINCT
+                    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,
+                    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
+        """)
+
+    records, _, _ = await driver.execute_query(
+        query,
+        bfs_origin_node_uuids=bfs_origin_node_uuids,
+        depth=bfs_max_depth,
+        database_=DEFAULT_DATABASE,
+        routing_='r',
+    )
 
     edges = [get_entity_edge_from_record(record) for record in records]
 
@@ -252,30 +282,26 @@ async def node_fulltext_search(
     if fuzzy_query == '':
         return []
 
-    async with driver.session(
+    records, _, _ = await driver.execute_query(
-        database=DEFAULT_DATABASE, default_access_mode=neo4j.READ_ACCESS
+        """
-    ) as session:
+        CALL db.index.fulltext.queryNodes("node_name_and_summary", $query) 
-        result = await session.run(
+        YIELD node AS n, score
-            """
+        RETURN
-    CALL db.index.fulltext.queryNodes("node_name_and_summary", $query) 
+            n.uuid AS uuid,
-    YIELD node AS n, score
+            n.group_id AS group_id, 
-    RETURN
+            n.name AS name, 
-        n.uuid AS uuid,
+            n.name_embedding AS name_embedding,
-        n.group_id AS group_id, 
+            n.created_at AS created_at, 
-        n.name AS name, 
+            n.summary AS summary
-        n.name_embedding AS name_embedding,
+        ORDER BY score DESC
-        n.created_at AS created_at, 
+        LIMIT $limit
-        n.summary AS summary
+        """,
-    ORDER BY score DESC
+        query=fuzzy_query,
-    LIMIT $limit
+        group_ids=group_ids,
-    """,
+        limit=limit,
-            {
+        database_=DEFAULT_DATABASE,
-                'query': fuzzy_query,
+        routing_='r',
-                'group_ids': group_ids,
+    )
-                'limit': limit,
-            },
-        )
-        records = [record async for record in result]
     nodes = [get_entity_node_from_record(record) for record in records]
 
     return nodes
@@ -289,34 +315,62 @@ async def node_similarity_search(
     min_score: float = DEFAULT_MIN_SCORE,
 ) -> list[EntityNode]:
     # vector similarity search over entity names
-    async with driver.session(
+    records, _, _ = await driver.execute_query(
-        database=DEFAULT_DATABASE, default_access_mode=neo4j.READ_ACCESS
+        """
-    ) as session:
+            CYPHER runtime = parallel parallelRuntimeSupport=all
-        result = await session.run(
+            MATCH (n:Entity)
-            """
+            WHERE $group_ids IS NULL OR n.group_id IN $group_ids
-                CYPHER runtime = parallel parallelRuntimeSupport=all
+            WITH n, vector.similarity.cosine(n.name_embedding, $search_vector) AS score
-                MATCH (n:Entity)
+            WHERE score > $min_score
-                WHERE $group_ids IS NULL OR n.group_id IN $group_ids
+            RETURN
-                WITH n, vector.similarity.cosine(n.name_embedding, $search_vector) AS score
+                n.uuid As uuid,
-                WHERE score > $min_score
+                n.group_id AS group_id,
-                RETURN
+                n.name AS name, 
-                    n.uuid As uuid,
+                n.name_embedding AS name_embedding,
-                    n.group_id AS group_id,
+                n.created_at AS created_at, 
-                    n.name AS name, 
+                n.summary AS summary
-                    n.name_embedding AS name_embedding,
+            ORDER BY score DESC
-                    n.created_at AS created_at, 
+            LIMIT $limit
-                    n.summary AS summary
+            """,
-                ORDER BY score DESC
+        search_vector=search_vector,
-                LIMIT $limit
+        group_ids=group_ids,
-                """,
+        limit=limit,
-            {
+        min_score=min_score,
-                'search_vector': search_vector,
+        database_=DEFAULT_DATABASE,
-                'group_ids': group_ids,
+        routing_='r',
-                'limit': limit,
+    )
-                'min_score': min_score,
+    nodes = [get_entity_node_from_record(record) for record in records]
-            },
+
-        )
+    return nodes
-        records = [record async for record in result]
+
+
+async def node_bfs_search(
+    driver: AsyncDriver,
+    bfs_origin_node_uuids: list[str] | None,
+    bfs_max_depth: int,
+) -> list[EntityNode]:
+    # vector similarity search over entity names
+    if bfs_origin_node_uuids is None:
+        return []
+
+    records, _, _ = await driver.execute_query(
+        """
+            UNWIND $bfs_origin_node_uuids AS origin_uuid
+            MATCH (origin:Entity|Episodic {uuid: origin_uuid})-[:RELATES_TO|MENTIONS]->{1,3}(n:Entity)
+            RETURN DISTINCT
+                n.uuid As uuid,
+                n.group_id AS group_id,
+                n.name AS name, 
+                n.name_embedding AS name_embedding,
+                n.created_at AS created_at, 
+                n.summary AS summary
+            LIMIT $limit
+            """,
+        bfs_origin_node_uuids=bfs_origin_node_uuids,
+        depth=bfs_max_depth,
+        database_=DEFAULT_DATABASE,
+        routing_='r',
+    )
     nodes = [get_entity_node_from_record(record) for record in records]
 
     return nodes
@@ -333,30 +387,26 @@ async def community_fulltext_search(
     if fuzzy_query == '':
         return []
 
-    async with driver.session(
+    records, _, _ = await driver.execute_query(
-        database=DEFAULT_DATABASE, default_access_mode=neo4j.READ_ACCESS
+        """
-    ) as session:
+        CALL db.index.fulltext.queryNodes("community_name", $query) 
-        result = await session.run(
+        YIELD node AS comm, score
-            """
+        RETURN
-    CALL db.index.fulltext.queryNodes("community_name", $query) 
+            comm.uuid AS uuid,
-    YIELD node AS comm, score
+            comm.group_id AS group_id, 
-    RETURN
+            comm.name AS name, 
-        comm.uuid AS uuid,
+            comm.name_embedding AS name_embedding,
-        comm.group_id AS group_id, 
+            comm.created_at AS created_at, 
-        comm.name AS name, 
+            comm.summary AS summary
-        comm.name_embedding AS name_embedding,
+        ORDER BY score DESC
-        comm.created_at AS created_at, 
+        LIMIT $limit
-        comm.summary AS summary
+        """,
-    ORDER BY score DESC
+        query=fuzzy_query,
-    LIMIT $limit
+        group_ids=group_ids,
-    """,
+        limit=limit,
-            {
+        database_=DEFAULT_DATABASE,
-                'query': fuzzy_query,
+        routing_='r',
-                'group_ids': group_ids,
+    )
-                'limit': limit,
-            },
-        )
-        records = [record async for record in result]
     communities = [get_community_node_from_record(record) for record in records]
 
     return communities
@@ -370,34 +420,30 @@ async def community_similarity_search(
     min_score=DEFAULT_MIN_SCORE,
 ) -> list[CommunityNode]:
     # vector similarity search over entity names
-    async with driver.session(
+    records, _, _ = await driver.execute_query(
-        database=DEFAULT_DATABASE, default_access_mode=neo4j.READ_ACCESS
+        """
-    ) as session:
+            CYPHER runtime = parallel parallelRuntimeSupport=all
-        result = await session.run(
+            MATCH (comm:Community)
-            """
+            WHERE ($group_ids IS NULL OR comm.group_id IN $group_ids)
-                CYPHER runtime = parallel parallelRuntimeSupport=all
+            WITH comm, vector.similarity.cosine(comm.name_embedding, $search_vector) AS score
-                MATCH (comm:Community)
+            WHERE score > $min_score
-                WHERE ($group_ids IS NULL OR comm.group_id IN $group_ids)
+            RETURN
-                WITH comm, vector.similarity.cosine(comm.name_embedding, $search_vector) AS score
+                comm.uuid As uuid,
-                WHERE score > $min_score
+                comm.group_id AS group_id,
-                RETURN
+                comm.name AS name, 
-                    comm.uuid As uuid,
+                comm.name_embedding AS name_embedding,
-                    comm.group_id AS group_id,
+                comm.created_at AS created_at, 
-                    comm.name AS name, 
+                comm.summary AS summary
-                    comm.name_embedding AS name_embedding,
+            ORDER BY score DESC
-                    comm.created_at AS created_at, 
+            LIMIT $limit
-                    comm.summary AS summary
+            """,
-                ORDER BY score DESC
+        search_vector=search_vector,
-                LIMIT $limit
+        group_ids=group_ids,
-                """,
+        limit=limit,
-            {
+        min_score=min_score,
-                'search_vector': search_vector,
+        database_=DEFAULT_DATABASE,
-                'group_ids': group_ids,
+        routing_='r',
-                'limit': limit,
+    )
-                'min_score': min_score,
-            },
-        )
-        records = [record async for record in result]
     communities = [get_community_node_from_record(record) for record in records]
 
     return communities

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "graphiti-core"
-version = "0.3.16"
+version = "0.3.17"
 description = "A temporal graph building library"
 authors = [
     "Paul Paliychuk <paul@getzep.com>",

tests/test_graphiti_int.py

@@ -26,7 +26,9 @@ from dotenv import load_dotenv
 from graphiti_core.edges import EntityEdge, EpisodicEdge
 from graphiti_core.graphiti import Graphiti
 from graphiti_core.nodes import EntityNode, EpisodicNode
-from graphiti_core.search.search_config_recipes import COMBINED_HYBRID_SEARCH_RRF
+from graphiti_core.search.search_config_recipes import (
+    COMBINED_HYBRID_SEARCH_CROSS_ENCODER,
+)
 
 pytestmark = pytest.mark.integration
 
@@ -60,22 +62,19 @@ def setup_logging():
     return logger
 
 
-def format_context(facts):
-    formatted_string = ''
-    formatted_string += 'FACTS:\n'
-    for fact in facts:
-        formatted_string += f'  - {fact}\n'
-    formatted_string += '\n'
-
-    return formatted_string.strip()
-
-
 @pytest.mark.asyncio
 async def test_graphiti_init():
     logger = setup_logging()
     graphiti = Graphiti(NEO4J_URI, NEO4j_USER, NEO4j_PASSWORD)
+    episodes = await graphiti.retrieve_episodes(datetime.now(), group_ids=None)
+    episode_uuids = [episode.uuid for episode in episodes]
 
-    results = await graphiti._search('new house', COMBINED_HYBRID_SEARCH_RRF, group_ids=None)
+    results = await graphiti._search(
+        "Emily: I can't log in",
+        COMBINED_HYBRID_SEARCH_CROSS_ENCODER,
+        bfs_origin_node_uuids=episode_uuids,
+        group_ids=None,
+    )
     pretty_results = {
         'edges': [edge.fact for edge in results.edges],
         'nodes': [node.name for node in results.nodes],
@@ -83,6 +82,7 @@ async def test_graphiti_init():
     }
 
     logger.info(pretty_results)
+
     await graphiti.close()