""" 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 datetime import datetime from time import time from dotenv import load_dotenv from neo4j import AsyncGraphDatabase from pydantic import BaseModel from typing_extensions import LiteralString 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.graphiti_types import GraphitiClients from graphiti_core.helpers import DEFAULT_DATABASE, semaphore_gather 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 from graphiti_core.search.search_config import DEFAULT_SEARCH_LIMIT, SearchResults from graphiti_core.search.search_config_recipes import ( COMBINED_HYBRID_SEARCH_CROSS_ENCODER, EDGE_HYBRID_SEARCH_NODE_DISTANCE, EDGE_HYBRID_SEARCH_RRF, ) from graphiti_core.search.search_filters import SearchFilters from graphiti_core.search.search_utils import ( RELEVANT_SCHEMA_LIMIT, get_mentioned_nodes, get_relevant_edges, ) from graphiti_core.utils.bulk_utils import ( RawEpisode, add_nodes_and_edges_bulk, dedupe_edges_bulk, dedupe_nodes_bulk, extract_edge_dates_bulk, extract_nodes_and_edges_bulk, resolve_edge_pointers, retrieve_previous_episodes_bulk, ) from graphiti_core.utils.datetime_utils import utc_now from graphiti_core.utils.maintenance.community_operations import ( build_communities, remove_communities, update_community, ) from graphiti_core.utils.maintenance.edge_operations import ( build_episodic_edges, dedupe_extracted_edge, extract_edges, resolve_edge_contradictions, resolve_extracted_edges, ) from graphiti_core.utils.maintenance.graph_data_operations import ( EPISODE_WINDOW_LEN, build_indices_and_constraints, retrieve_episodes, ) from graphiti_core.utils.maintenance.node_operations import ( extract_attributes_from_nodes, extract_nodes, resolve_extracted_nodes, ) from graphiti_core.utils.maintenance.temporal_operations import get_edge_contradictions from graphiti_core.utils.ontology_utils.entity_types_utils import validate_entity_types logger = logging.getLogger(__name__) load_dotenv() class AddEpisodeResults(BaseModel): episode: EpisodicNode nodes: list[EntityNode] edges: list[EntityEdge] class Graphiti: def __init__( self, uri: str, user: str, password: str, llm_client: LLMClient | None = None, embedder: EmbedderClient | None = None, cross_encoder: CrossEncoderClient | None = None, store_raw_episode_content: bool = True, ): """ Initialize a Graphiti instance. This constructor sets up a connection to the Neo4j database and initializes the LLM client for natural language processing tasks. Parameters ---------- uri : str The URI of the Neo4j database. user : str The username for authenticating with the Neo4j database. password : str The password for authenticating with the Neo4j database. llm_client : LLMClient | None, optional An instance of LLMClient for natural language processing tasks. If not provided, a default OpenAIClient will be initialized. Returns ------- None Notes ----- This method establishes a connection to the Neo4j database using the provided credentials. It also sets up the LLM client, either using the provided client or by creating a default OpenAIClient. The default database name is set to 'neo4j'. If a different database name is required, it should be specified in the URI or set separately after initialization. The OpenAI API key is expected to be set in the environment variables. Make sure to set the OPENAI_API_KEY environment variable before initializing Graphiti if you're using the default OpenAIClient. """ self.driver = AsyncGraphDatabase.driver(uri, auth=(user, password)) self.database = DEFAULT_DATABASE self.store_raw_episode_content = store_raw_episode_content if llm_client: self.llm_client = llm_client else: self.llm_client = OpenAIClient() if embedder: self.embedder = embedder else: self.embedder = OpenAIEmbedder() if cross_encoder: self.cross_encoder = cross_encoder else: self.cross_encoder = OpenAIRerankerClient() self.clients = GraphitiClients( driver=self.driver, llm_client=self.llm_client, embedder=self.embedder, cross_encoder=self.cross_encoder, ) async def close(self): """ Close the connection to the Neo4j database. This method safely closes the driver connection to the Neo4j database. It should be called when the Graphiti instance is no longer needed or when the application is shutting down. Parameters ---------- self Returns ------- None Notes ----- It's important to close the driver connection to release system resources and ensure that all pending transactions are completed or rolled back. This method should be called as part of a cleanup process, potentially in a context manager or a shutdown hook. Example: graphiti = Graphiti(uri, user, password) try: # Use graphiti... finally: graphiti.close() """ await self.driver.close() async def build_indices_and_constraints(self, delete_existing: bool = False): """ Build indices and constraints in the Neo4j database. This method sets up the necessary indices and constraints in the Neo4j database to optimize query performance and ensure data integrity for the knowledge graph. Parameters ---------- self delete_existing : bool, optional Whether to clear existing indices before creating new ones. Returns ------- None Notes ----- This method should typically be called once during the initial setup of the knowledge graph or when updating the database schema. It uses the `build_indices_and_constraints` function from the `graphiti_core.utils.maintenance.graph_data_operations` module to perform the actual database operations. The specific indices and constraints created depend on the implementation of the `build_indices_and_constraints` function. Refer to that function's documentation for details on the exact database schema modifications. Caution: Running this method on a large existing database may take some time and could impact database performance during execution. """ await build_indices_and_constraints(self.driver, delete_existing) async def retrieve_episodes( self, reference_time: datetime, last_n: int = EPISODE_WINDOW_LEN, group_ids: list[str] | None = None, source: EpisodeType | None = None, ) -> list[EpisodicNode]: """ Retrieve the last n episodic nodes from the graph. This method fetches a specified number of the most recent episodic nodes from the graph, relative to the given reference time. Parameters ---------- reference_time : datetime The reference time to retrieve episodes before. last_n : int, optional The number of episodes to retrieve. Defaults to EPISODE_WINDOW_LEN. group_ids : list[str | None], optional The group ids to return data from. Returns ------- list[EpisodicNode] A list of the most recent EpisodicNode objects. Notes ----- The actual retrieval is performed by the `retrieve_episodes` function from the `graphiti_core.utils` module. """ return await retrieve_episodes(self.driver, reference_time, last_n, group_ids, source) async def add_episode( self, name: str, episode_body: str, source_description: str, reference_time: datetime, source: EpisodeType = EpisodeType.message, group_id: str = '', uuid: str | None = None, update_communities: bool = False, entity_types: dict[str, BaseModel] | None = None, previous_episode_uuids: list[str] | None = None, ) -> AddEpisodeResults: """ Process an episode and update the graph. This method extracts information from the episode, creates nodes and edges, and updates the graph database accordingly. Parameters ---------- name : str The name of the episode. episode_body : str The content of the episode. source_description : str A description of the episode's source. reference_time : datetime The reference time for the episode. source : EpisodeType, optional The type of the episode. Defaults to EpisodeType.message. group_id : str | None An id for the graph partition the episode is a part of. uuid : str | None Optional uuid of the episode. update_communities : bool Optional. Whether to update communities with new node information previous_episode_uuids : list[str] | None Optional. list of episode uuids to use as the previous episodes. If this is not provided, the most recent episodes by created_at date will be used. Returns ------- None Notes ----- This method performs several steps including node extraction, edge extraction, deduplication, and database updates. It also handles embedding generation and edge invalidation. It is recommended to run this method as a background process, such as in a queue. It's important that each episode is added sequentially and awaited before adding the next one. For web applications, consider using FastAPI's background tasks or a dedicated task queue like Celery for this purpose. Example using FastAPI background tasks: @app.post("/add_episode") async def add_episode_endpoint(episode_data: EpisodeData): background_tasks.add_task(graphiti.add_episode, **episode_data.dict()) return {"message": "Episode processing started"} """ try: start = time() now = utc_now() validate_entity_types(entity_types) previous_episodes = ( await self.retrieve_episodes( reference_time, last_n=RELEVANT_SCHEMA_LIMIT, group_ids=[group_id], source=source, ) if previous_episode_uuids is None else await EpisodicNode.get_by_uuids(self.driver, previous_episode_uuids) ) episode = ( await EpisodicNode.get_by_uuid(self.driver, uuid) if uuid is not None else EpisodicNode( name=name, group_id=group_id, labels=[], source=source, content=episode_body, source_description=source_description, created_at=now, valid_at=reference_time, ) ) # Extract entities as nodes extracted_nodes = await extract_nodes( self.clients, episode, previous_episodes, entity_types ) # Extract edges and resolve nodes (nodes, uuid_map), extracted_edges = await semaphore_gather( resolve_extracted_nodes( self.clients, extracted_nodes, episode, previous_episodes, entity_types, ), extract_edges(self.clients, episode, extracted_nodes, previous_episodes, group_id), ) edges = resolve_edge_pointers(extracted_edges, uuid_map) (resolved_edges, invalidated_edges), hydrated_nodes = await semaphore_gather( resolve_extracted_edges( self.clients, edges, ), extract_attributes_from_nodes( self.clients, nodes, episode, previous_episodes, entity_types ), ) entity_edges = resolved_edges + invalidated_edges episodic_edges = build_episodic_edges(nodes, episode, now) episode.entity_edges = [edge.uuid for edge in entity_edges] if not self.store_raw_episode_content: episode.content = '' await add_nodes_and_edges_bulk( self.driver, [episode], episodic_edges, nodes, entity_edges ) # Update any communities if update_communities: await semaphore_gather( *[ update_community(self.driver, self.llm_client, self.embedder, node) for node in nodes ] ) end = time() logger.info(f'Completed add_episode in {(end - start) * 1000} ms') return AddEpisodeResults(episode=episode, nodes=nodes, edges=entity_edges) except Exception as e: raise e #### WIP: USE AT YOUR OWN RISK #### async def add_episode_bulk(self, bulk_episodes: list[RawEpisode], group_id: str = ''): """ Process multiple episodes in bulk and update the graph. This method extracts information from multiple episodes, creates nodes and edges, and updates the graph database accordingly, all in a single batch operation. Parameters ---------- bulk_episodes : list[RawEpisode] A list of RawEpisode objects to be processed and added to the graph. group_id : str | None An id for the graph partition the episode is a part of. Returns ------- None Notes ----- This method performs several steps including: - Saving all episodes to the database - Retrieving previous episode context for each new episode - Extracting nodes and edges from all episodes - Generating embeddings for nodes and edges - Deduplicating nodes and edges - Saving nodes, episodic edges, and entity edges to the knowledge graph This bulk operation is designed for efficiency when processing multiple episodes at once. However, it's important to ensure that the bulk operation doesn't overwhelm system resources. Consider implementing rate limiting or chunking for very large batches of episodes. Important: This method does not perform edge invalidation or date extraction steps. If these operations are required, use the `add_episode` method instead for each individual episode. """ try: start = time() now = utc_now() episodes = [ EpisodicNode( name=episode.name, labels=[], source=episode.source, content=episode.content, source_description=episode.source_description, group_id=group_id, created_at=now, valid_at=episode.reference_time, ) for episode in bulk_episodes ] # Save all the episodes await semaphore_gather(*[episode.save(self.driver) for episode in episodes]) # Get previous episode context for each episode episode_pairs = await retrieve_previous_episodes_bulk(self.driver, episodes) # Extract all nodes and edges ( extracted_nodes, extracted_edges, episodic_edges, ) = await extract_nodes_and_edges_bulk(self.clients, episode_pairs) # Generate embeddings await semaphore_gather( *[node.generate_name_embedding(self.embedder) for node in extracted_nodes], *[edge.generate_embedding(self.embedder) for edge in extracted_edges], ) # Dedupe extracted nodes, compress extracted edges (nodes, uuid_map), extracted_edges_timestamped = await semaphore_gather( dedupe_nodes_bulk(self.driver, self.llm_client, extracted_nodes), extract_edge_dates_bulk(self.llm_client, extracted_edges, episode_pairs), ) # save nodes to KG await semaphore_gather(*[node.save(self.driver) for node in nodes]) # re-map edge pointers so that they don't point to discard dupe nodes extracted_edges_with_resolved_pointers: list[EntityEdge] = resolve_edge_pointers( extracted_edges_timestamped, uuid_map ) episodic_edges_with_resolved_pointers: list[EpisodicEdge] = resolve_edge_pointers( episodic_edges, uuid_map ) # save episodic edges to KG await semaphore_gather( *[edge.save(self.driver) for edge in episodic_edges_with_resolved_pointers] ) # Dedupe extracted edges edges = await dedupe_edges_bulk( self.driver, self.llm_client, extracted_edges_with_resolved_pointers ) logger.debug(f'extracted edge length: {len(edges)}') # invalidate edges # save edges to KG await semaphore_gather(*[edge.save(self.driver) for edge in edges]) end = time() logger.info(f'Completed add_episode_bulk in {(end - start) * 1000} ms') except Exception as e: raise e async def build_communities(self, group_ids: list[str] | None = None) -> list[CommunityNode]: """ Use a community clustering algorithm to find communities of nodes. Create community nodes summarising the content of these communities. ---------- query : list[str] | None Optional. Create communities only for the listed group_ids. If blank the entire graph will be used. """ # Clear existing communities await remove_communities(self.driver) community_nodes, community_edges = await build_communities( self.driver, self.llm_client, group_ids ) await semaphore_gather( *[node.generate_name_embedding(self.embedder) for node in community_nodes] ) await semaphore_gather(*[node.save(self.driver) for node in community_nodes]) await semaphore_gather(*[edge.save(self.driver) for edge in community_edges]) return community_nodes async def search( self, query: str, center_node_uuid: str | None = None, group_ids: list[str] | None = None, num_results=DEFAULT_SEARCH_LIMIT, search_filter: SearchFilters | None = None, ) -> list[EntityEdge]: """ Perform a hybrid search on the knowledge graph. This method executes a search query on the graph, combining vector and text-based search techniques to retrieve relevant facts, returning the edges as a string. This is our basic out-of-the-box search, for more robust results we recommend using our more advanced search method graphiti.search_(). Parameters ---------- query : str The search query string. center_node_uuid: str, optional Facts will be reranked based on proximity to this node group_ids : list[str | None] | None, optional The graph partitions to return data from. num_results : int, optional The maximum number of results to return. Defaults to 10. Returns ------- list A list of EntityEdge objects that are relevant to the search query. Notes ----- This method uses a SearchConfig with num_episodes set to 0 and num_results set to the provided num_results parameter. The search is performed using the current date and time as the reference point for temporal relevance. """ search_config = ( EDGE_HYBRID_SEARCH_RRF if center_node_uuid is None else EDGE_HYBRID_SEARCH_NODE_DISTANCE ) search_config.limit = num_results edges = ( await search( self.clients, query, group_ids, search_config, search_filter if search_filter is not None else SearchFilters(), center_node_uuid, ) ).edges return edges async def _search( self, query: str, config: SearchConfig, group_ids: list[str] | None = None, center_node_uuid: str | None = None, bfs_origin_node_uuids: list[str] | None = None, search_filter: SearchFilters | None = None, ) -> SearchResults: """DEPRECATED""" return await self.search_( query, config, group_ids, center_node_uuid, bfs_origin_node_uuids, search_filter ) async def search_( self, query: str, config: SearchConfig = COMBINED_HYBRID_SEARCH_CROSS_ENCODER, group_ids: list[str] | None = None, center_node_uuid: str | None = None, bfs_origin_node_uuids: list[str] | None = None, search_filter: SearchFilters | None = None, ) -> SearchResults: """search_ (replaces _search) is our advanced search method that returns Graph objects (nodes and edges) rather than a list of facts. This endpoint allows the end user to utilize more advanced features such as filters and different search and reranker methodologies across different layers in the graph. For different config recipes refer to search/search_config_recipes. """ return await search( self.clients, query, group_ids, config, search_filter if search_filter is not None else SearchFilters(), center_node_uuid, bfs_origin_node_uuids, ) async def get_nodes_and_edges_by_episode(self, episode_uuids: list[str]) -> SearchResults: episodes = await EpisodicNode.get_by_uuids(self.driver, episode_uuids) edges_list = await semaphore_gather( *[EntityEdge.get_by_uuids(self.driver, episode.entity_edges) for episode in episodes] ) edges: list[EntityEdge] = [edge for lst in edges_list for edge in lst] nodes = await get_mentioned_nodes(self.driver, episodes) return SearchResults(edges=edges, nodes=nodes, episodes=[], communities=[]) async def add_triplet(self, source_node: EntityNode, edge: EntityEdge, target_node: EntityNode): if source_node.name_embedding is None: await source_node.generate_name_embedding(self.embedder) if target_node.name_embedding is None: await target_node.generate_name_embedding(self.embedder) if edge.fact_embedding is None: await edge.generate_embedding(self.embedder) resolved_nodes, uuid_map = await resolve_extracted_nodes( self.clients, [source_node, target_node], ) updated_edge = resolve_edge_pointers([edge], uuid_map)[0] related_edges = await get_relevant_edges(self.driver, [updated_edge], SearchFilters(), 0.8) resolved_edge = await dedupe_extracted_edge(self.llm_client, updated_edge, related_edges[0]) contradicting_edges = await get_edge_contradictions(self.llm_client, edge, related_edges[0]) invalidated_edges = resolve_edge_contradictions(resolved_edge, contradicting_edges) await add_nodes_and_edges_bulk( self.driver, [], [], resolved_nodes, [resolved_edge] + invalidated_edges ) async def remove_episode(self, episode_uuid: str): # Find the episode to be deleted episode = await EpisodicNode.get_by_uuid(self.driver, episode_uuid) # Find edges mentioned by the episode edges = await EntityEdge.get_by_uuids(self.driver, episode.entity_edges) # We should only delete edges created by the episode edges_to_delete: list[EntityEdge] = [] for edge in edges: if edge.episodes and edge.episodes[0] == episode.uuid: edges_to_delete.append(edge) # Find nodes mentioned by the episode nodes = await get_mentioned_nodes(self.driver, [episode]) # We should delete all nodes that are only mentioned in the deleted episode nodes_to_delete: list[EntityNode] = [] for node in nodes: query: LiteralString = 'MATCH (e:Episodic)-[:MENTIONS]->(n:Entity {uuid: $uuid}) RETURN count(*) AS episode_count' records, _, _ = await self.driver.execute_query( query, uuid=node.uuid, database_=DEFAULT_DATABASE, routing_='r' ) for record in records: if record['episode_count'] == 1: nodes_to_delete.append(node) await semaphore_gather(*[node.delete(self.driver) for node in nodes_to_delete]) await semaphore_gather(*[edge.delete(self.driver) for edge in edges_to_delete]) await episode.delete(self.driver)