import logging from .base import NeptuneBase try: from langchain_aws import NeptuneAnalyticsGraph except ImportError: raise ImportError("langchain_aws is not installed. Please install it using 'make install_all'.") logger = logging.getLogger(__name__) class MemoryGraph(NeptuneBase): def __init__(self, config): self.config = config self.graph = None endpoint = self.config.graph_store.config.endpoint if endpoint and endpoint.startswith("neptune-graph://"): graph_identifier = endpoint.replace("neptune-graph://", "") self.graph = NeptuneAnalyticsGraph(graph_identifier) if not self.graph: raise ValueError("Unable to create a Neptune client: missing 'endpoint' in config") self.node_label = ":`__Entity__`" if self.config.graph_store.config.base_label else "" self.embedding_model = NeptuneBase._create_embedding_model(self.config) self.llm_provider = "openai_structured" if self.config.llm.provider: self.llm_provider = self.config.llm.provider if self.config.graph_store.llm: self.llm_provider = self.config.graph_store.llm.provider self.llm = NeptuneBase._create_llm(self.config, self.llm_provider) self.user_id = None self.threshold = 0.7 def _delete_entities_cypher(self, source, destination, relationship, user_id): """ Returns the OpenCypher query and parameters for deleting entities in the graph DB :param source: source node :param destination: destination node :param relationship: relationship label :param user_id: user_id to use :return: str, dict """ cypher = f""" MATCH (n {self.node_label} {{name: $source_name, user_id: $user_id}}) -[r:{relationship}]-> (m {self.node_label} {{name: $dest_name, user_id: $user_id}}) DELETE r RETURN n.name AS source, m.name AS target, type(r) AS relationship """ params = { "source_name": source, "dest_name": destination, "user_id": user_id, } logger.debug(f"_delete_entities\n query={cypher}") return cypher, params def _add_entities_cypher( self, source_node_list, source, source_embedding, source_type, destination_node_list, destination, dest_embedding, destination_type, relationship, user_id, ): """ Returns the OpenCypher query and parameters for adding entities in the graph DB :param source_node_list: list of source nodes :param source: source node name :param source_embedding: source node embedding :param source_type: source node label :param destination_node_list: list of dest nodes :param destination: destination name :param dest_embedding: destination embedding :param destination_type: destination node label :param relationship: relationship label :param user_id: user id to use :return: str, dict """ source_label = self.node_label if self.node_label else f":`{source_type}`" source_extra_set = f", source:`{source_type}`" if self.node_label else "" destination_label = self.node_label if self.node_label else f":`{destination_type}`" destination_extra_set = f", destination:`{destination_type}`" if self.node_label else "" # Refactor this code with the graph_memory.py implementation if not destination_node_list and source_node_list: cypher = f""" MATCH (source) WHERE id(source) = $source_id SET source.mentions = coalesce(source.mentions, 0) + 1 WITH source MERGE (destination {destination_label} {{name: $destination_name, user_id: $user_id}}) ON CREATE SET destination.created = timestamp(), destination.mentions = 1 {destination_extra_set} ON MATCH SET destination.mentions = coalesce(destination.mentions, 0) + 1 WITH source, destination, $dest_embedding as dest_embedding CALL neptune.algo.vectors.upsert(destination, dest_embedding) WITH source, destination MERGE (source)-[r:{relationship}]->(destination) ON CREATE SET r.created = timestamp(), r.mentions = 1 ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1 RETURN source.name AS source, type(r) AS relationship, destination.name AS target """ params = { "source_id": source_node_list[0]["id(source_candidate)"], "destination_name": destination, "dest_embedding": dest_embedding, "user_id": user_id, } elif destination_node_list and not source_node_list: cypher = f""" MATCH (destination) WHERE id(destination) = $destination_id SET destination.mentions = coalesce(destination.mentions, 0) + 1 WITH destination MERGE (source {source_label} {{name: $source_name, user_id: $user_id}}) ON CREATE SET source.created = timestamp(), source.mentions = 1 {source_extra_set} ON MATCH SET source.mentions = coalesce(source.mentions, 0) + 1 WITH source, destination, $source_embedding as source_embedding CALL neptune.algo.vectors.upsert(source, source_embedding) WITH source, destination MERGE (source)-[r:{relationship}]->(destination) ON CREATE SET r.created = timestamp(), r.mentions = 1 ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1 RETURN source.name AS source, type(r) AS relationship, destination.name AS target """ params = { "destination_id": destination_node_list[0]["id(destination_candidate)"], "source_name": source, "source_embedding": source_embedding, "user_id": user_id, } elif source_node_list and destination_node_list: cypher = f""" MATCH (source) WHERE id(source) = $source_id SET source.mentions = coalesce(source.mentions, 0) + 1 WITH source MATCH (destination) WHERE id(destination) = $destination_id SET destination.mentions = coalesce(destination.mentions) + 1 MERGE (source)-[r:{relationship}]->(destination) ON CREATE SET r.created_at = timestamp(), r.updated_at = timestamp(), r.mentions = 1 ON MATCH SET r.mentions = coalesce(r.mentions, 0) + 1 RETURN source.name AS source, type(r) AS relationship, destination.name AS target """ params = { "source_id": source_node_list[0]["id(source_candidate)"], "destination_id": destination_node_list[0]["id(destination_candidate)"], "user_id": user_id, } else: cypher = f""" MERGE (n {source_label} {{name: $source_name, user_id: $user_id}}) ON CREATE SET n.created = timestamp(), n.mentions = 1 {source_extra_set} ON MATCH SET n.mentions = coalesce(n.mentions, 0) + 1 WITH n, $source_embedding as source_embedding CALL neptune.algo.vectors.upsert(n, source_embedding) WITH n MERGE (m {destination_label} {{name: $dest_name, user_id: $user_id}}) ON CREATE SET m.created = timestamp(), m.mentions = 1 {destination_extra_set} ON MATCH SET m.mentions = coalesce(m.mentions, 0) + 1 WITH n, m, $dest_embedding as dest_embedding CALL neptune.algo.vectors.upsert(m, dest_embedding) WITH n, m MERGE (n)-[rel:{relationship}]->(m) ON CREATE SET rel.created = timestamp(), rel.mentions = 1 ON MATCH SET rel.mentions = coalesce(rel.mentions, 0) + 1 RETURN n.name AS source, type(rel) AS relationship, m.name AS target """ params = { "source_name": source, "dest_name": destination, "source_embedding": source_embedding, "dest_embedding": dest_embedding, "user_id": user_id, } logger.debug( f"_add_entities:\n destination_node_search_result={destination_node_list}\n source_node_search_result={source_node_list}\n query={cypher}" ) return cypher, params def _search_source_node_cypher(self, source_embedding, user_id, threshold): """ Returns the OpenCypher query and parameters to search for source nodes :param source_embedding: source vector :param user_id: user_id to use :param threshold: the threshold for similarity :return: str, dict """ cypher = f""" MATCH (source_candidate {self.node_label}) WHERE source_candidate.user_id = $user_id WITH source_candidate, $source_embedding as v_embedding CALL neptune.algo.vectors.distanceByEmbedding( v_embedding, source_candidate, {{metric:"CosineSimilarity"}} ) YIELD distance WITH source_candidate, distance AS cosine_similarity WHERE cosine_similarity >= $threshold WITH source_candidate, cosine_similarity ORDER BY cosine_similarity DESC LIMIT 1 RETURN id(source_candidate), cosine_similarity """ params = { "source_embedding": source_embedding, "user_id": user_id, "threshold": threshold, } logger.debug(f"_search_source_node\n query={cypher}") return cypher, params def _search_destination_node_cypher(self, destination_embedding, user_id, threshold): """ Returns the OpenCypher query and parameters to search for destination nodes :param source_embedding: source vector :param user_id: user_id to use :param threshold: the threshold for similarity :return: str, dict """ cypher = f""" MATCH (destination_candidate {self.node_label}) WHERE destination_candidate.user_id = $user_id WITH destination_candidate, $destination_embedding as v_embedding CALL neptune.algo.vectors.distanceByEmbedding( v_embedding, destination_candidate, {{metric:"CosineSimilarity"}} ) YIELD distance WITH destination_candidate, distance AS cosine_similarity WHERE cosine_similarity >= $threshold WITH destination_candidate, cosine_similarity ORDER BY cosine_similarity DESC LIMIT 1 RETURN id(destination_candidate), cosine_similarity """ params = { "destination_embedding": destination_embedding, "user_id": user_id, "threshold": threshold, } logger.debug(f"_search_destination_node\n query={cypher}") return cypher, params def _delete_all_cypher(self, filters): """ Returns the OpenCypher query and parameters to delete all edges/nodes in the memory store :param filters: search filters :return: str, dict """ cypher = f""" MATCH (n {self.node_label} {{user_id: $user_id}}) DETACH DELETE n """ params = {"user_id": filters["user_id"]} logger.debug(f"delete_all query={cypher}") return cypher, params def _get_all_cypher(self, filters, limit): """ Returns the OpenCypher query and parameters to get all edges/nodes in the memory store :param filters: search filters :param limit: return limit :return: str, dict """ cypher = f""" MATCH (n {self.node_label} {{user_id: $user_id}})-[r]->(m {self.node_label} {{user_id: $user_id}}) RETURN n.name AS source, type(r) AS relationship, m.name AS target LIMIT $limit """ params = {"user_id": filters["user_id"], "limit": limit} return cypher, params def _search_graph_db_cypher(self, n_embedding, filters, limit): """ Returns the OpenCypher query and parameters to search for similar nodes in the memory store :param n_embedding: node vector :param filters: search filters :param limit: return limit :return: str, dict """ cypher_query = f""" MATCH (n {self.node_label}) WHERE n.user_id = $user_id WITH n, $n_embedding as n_embedding CALL neptune.algo.vectors.distanceByEmbedding( n_embedding, n, {{metric:"CosineSimilarity"}} ) YIELD distance WITH n, distance as similarity WHERE similarity >= $threshold CALL {{ WITH n MATCH (n)-[r]->(m) RETURN n.name AS source, id(n) AS source_id, type(r) AS relationship, id(r) AS relation_id, m.name AS destination, id(m) AS destination_id UNION ALL WITH n MATCH (m)-[r]->(n) RETURN m.name AS source, id(m) AS source_id, type(r) AS relationship, id(r) AS relation_id, n.name AS destination, id(n) AS destination_id }} WITH distinct source, source_id, relationship, relation_id, destination, destination_id, similarity RETURN source, source_id, relationship, relation_id, destination, destination_id, similarity ORDER BY similarity DESC LIMIT $limit """ params = { "n_embedding": n_embedding, "threshold": self.threshold, "user_id": filters["user_id"], "limit": limit, } logger.debug(f"_search_graph_db\n query={cypher_query}") return cypher_query, params