graphiti/graphiti_core/utils/maintenance/node_operations.py

"""
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 contextlib import suppress
from time import time
from typing import Any
from uuid import uuid4

import pydantic
from pydantic import BaseModel, Field

from graphiti_core.graphiti_types import GraphitiClients
from graphiti_core.helpers import MAX_REFLEXION_ITERATIONS, semaphore_gather
from graphiti_core.llm_client import LLMClient
from graphiti_core.llm_client.config import ModelSize
from graphiti_core.nodes import EntityNode, EpisodeType, EpisodicNode, create_entity_node_embeddings
from graphiti_core.prompts import prompt_library
from graphiti_core.prompts.dedupe_nodes import NodeResolutions
from graphiti_core.prompts.extract_nodes import (
    ExtractedEntities,
    ExtractedEntity,
    MissedEntities,
)
from graphiti_core.search.search import search
from graphiti_core.search.search_config import SearchResults
from graphiti_core.search.search_config_recipes import NODE_HYBRID_SEARCH_RRF
from graphiti_core.search.search_filters import SearchFilters
from graphiti_core.utils.datetime_utils import utc_now
from graphiti_core.utils.maintenance.edge_operations import filter_existing_duplicate_of_edges

logger = logging.getLogger(__name__)


async def extract_nodes_reflexion(
    llm_client: LLMClient,
    episode: EpisodicNode,
    previous_episodes: list[EpisodicNode],
    node_names: list[str],
) -> list[str]:
    # Prepare context for LLM
    context = {
        'episode_content': episode.content,
        'previous_episodes': [ep.content for ep in previous_episodes],
        'extracted_entities': node_names,
    }

    llm_response = await llm_client.generate_response(
        prompt_library.extract_nodes.reflexion(context), MissedEntities
    )
    missed_entities = llm_response.get('missed_entities', [])

    return missed_entities


async def extract_nodes(
    clients: GraphitiClients,
    episode: EpisodicNode,
    previous_episodes: list[EpisodicNode],
    entity_types: dict[str, BaseModel] | None = None,
    excluded_entity_types: list[str] | None = None,
) -> list[EntityNode]:
    start = time()
    llm_client = clients.llm_client
    llm_response = {}
    custom_prompt = ''
    entities_missed = True
    reflexion_iterations = 0

    entity_types_context = [
        {
            'entity_type_id': 0,
            'entity_type_name': 'Entity',
            'entity_type_description': 'Default entity classification. Use this entity type if the entity is not one of the other listed types.',
        }
    ]

    entity_types_context += (
        [
            {
                'entity_type_id': i + 1,
                'entity_type_name': type_name,
                'entity_type_description': type_model.__doc__,
            }
            for i, (type_name, type_model) in enumerate(entity_types.items())
        ]
        if entity_types is not None
        else []
    )

    context = {
        'episode_content': episode.content,
        'episode_timestamp': episode.valid_at.isoformat(),
        'previous_episodes': [ep.content for ep in previous_episodes],
        'custom_prompt': custom_prompt,
        'entity_types': entity_types_context,
        'source_description': episode.source_description,
    }

    while entities_missed and reflexion_iterations <= MAX_REFLEXION_ITERATIONS:
        if episode.source == EpisodeType.message:
            llm_response = await llm_client.generate_response(
                prompt_library.extract_nodes.extract_message(context),
                response_model=ExtractedEntities,
            )
        elif episode.source == EpisodeType.text:
            llm_response = await llm_client.generate_response(
                prompt_library.extract_nodes.extract_text(context), response_model=ExtractedEntities
            )
        elif episode.source == EpisodeType.json:
            llm_response = await llm_client.generate_response(
                prompt_library.extract_nodes.extract_json(context), response_model=ExtractedEntities
            )

        extracted_entities: list[ExtractedEntity] = [
            ExtractedEntity(**entity_types_context)
            for entity_types_context in llm_response.get('extracted_entities', [])
        ]

        reflexion_iterations += 1
        if reflexion_iterations < MAX_REFLEXION_ITERATIONS:
            missing_entities = await extract_nodes_reflexion(
                llm_client,
                episode,
                previous_episodes,
                [entity.name for entity in extracted_entities],
            )

            entities_missed = len(missing_entities) != 0

            custom_prompt = 'Make sure that the following entities are extracted: '
            for entity in missing_entities:
                custom_prompt += f'\n{entity},'

    filtered_extracted_entities = [entity for entity in extracted_entities if entity.name.strip()]
    end = time()
    logger.debug(f'Extracted new nodes: {filtered_extracted_entities} in {(end - start) * 1000} ms')
    # Convert the extracted data into EntityNode objects
    extracted_nodes = []
    for extracted_entity in filtered_extracted_entities:
        entity_type_name = entity_types_context[extracted_entity.entity_type_id].get(
            'entity_type_name'
        )

        # Check if this entity type should be excluded
        if excluded_entity_types and entity_type_name in excluded_entity_types:
            logger.debug(f'Excluding entity "{extracted_entity.name}" of type "{entity_type_name}"')
            continue

        labels: list[str] = list({'Entity', str(entity_type_name)})

        new_node = EntityNode(
            name=extracted_entity.name,
            group_id=episode.group_id,
            labels=labels,
            summary='',
            created_at=utc_now(),
        )
        extracted_nodes.append(new_node)
        logger.debug(f'Created new node: {new_node.name} (UUID: {new_node.uuid})')

    logger.debug(f'Extracted nodes: {[(n.name, n.uuid) for n in extracted_nodes]}')
    return extracted_nodes


async def resolve_extracted_nodes(
    clients: GraphitiClients,
    extracted_nodes: list[EntityNode],
    episode: EpisodicNode | None = None,
    previous_episodes: list[EpisodicNode] | None = None,
    entity_types: dict[str, BaseModel] | None = None,
    existing_nodes_override: list[EntityNode] | None = None,
) -> tuple[list[EntityNode], dict[str, str], list[tuple[EntityNode, EntityNode]]]:
    llm_client = clients.llm_client
    driver = clients.driver

    search_results: list[SearchResults] = await semaphore_gather(
        *[
            search(
                clients=clients,
                query=node.name,
                group_ids=[node.group_id],
                search_filter=SearchFilters(),
                config=NODE_HYBRID_SEARCH_RRF,
            )
            for node in extracted_nodes
        ]
    )

    candidate_nodes: list[EntityNode] = (
        [node for result in search_results for node in result.nodes]
        if existing_nodes_override is None
        else existing_nodes_override
    )

    existing_nodes_dict: dict[str, EntityNode] = {node.uuid: node for node in candidate_nodes}

    existing_nodes: list[EntityNode] = list(existing_nodes_dict.values())

    existing_nodes_context = (
        [
            {
                **{
                    'idx': i,
                    'name': candidate.name,
                    'entity_types': candidate.labels,
                },
                **candidate.attributes,
            }
            for i, candidate in enumerate(existing_nodes)
        ],
    )

    entity_types_dict: dict[str, BaseModel] = entity_types if entity_types is not None else {}

    # Prepare context for LLM
    extracted_nodes_context = [
        {
            'id': i,
            'name': node.name,
            'entity_type': node.labels,
            'entity_type_description': entity_types_dict.get(
                next((item for item in node.labels if item != 'Entity'), '')
            ).__doc__
            or 'Default Entity Type',
        }
        for i, node in enumerate(extracted_nodes)
    ]

    context = {
        'extracted_nodes': extracted_nodes_context,
        'existing_nodes': existing_nodes_context,
        'episode_content': episode.content if episode is not None else '',
        'previous_episodes': [ep.content for ep in previous_episodes]
        if previous_episodes is not None
        else [],
    }

    llm_response = await llm_client.generate_response(
        prompt_library.dedupe_nodes.nodes(context),
        response_model=NodeResolutions,
    )

    node_resolutions: list = llm_response.get('entity_resolutions', [])

    resolved_nodes: list[EntityNode] = []
    uuid_map: dict[str, str] = {}
    node_duplicates: list[tuple[EntityNode, EntityNode]] = []
    for resolution in node_resolutions:
        resolution_id: int = resolution.get('id', -1)
        duplicate_idx: int = resolution.get('duplicate_idx', -1)

        extracted_node = extracted_nodes[resolution_id]

        resolved_node = (
            existing_nodes[duplicate_idx]
            if 0 <= duplicate_idx < len(existing_nodes)
            else extracted_node
        )

        # resolved_node.name = resolution.get('name')

        resolved_nodes.append(resolved_node)
        uuid_map[extracted_node.uuid] = resolved_node.uuid

        duplicates: list[int] = resolution.get('duplicates', [])
        if duplicate_idx not in duplicates and duplicate_idx > -1:
            duplicates.append(duplicate_idx)
        for idx in duplicates:
            existing_node = existing_nodes[idx] if idx < len(existing_nodes) else resolved_node

            node_duplicates.append((extracted_node, existing_node))

    logger.debug(f'Resolved nodes: {[(n.name, n.uuid) for n in resolved_nodes]}')

    new_node_duplicates: list[
        tuple[EntityNode, EntityNode]
    ] = await filter_existing_duplicate_of_edges(driver, node_duplicates)

    return resolved_nodes, uuid_map, new_node_duplicates


async def extract_attributes_from_nodes(
    clients: GraphitiClients,
    nodes: list[EntityNode],
    episode: EpisodicNode | None = None,
    previous_episodes: list[EpisodicNode] | None = None,
    entity_types: dict[str, BaseModel] | None = None,
) -> list[EntityNode]:
    llm_client = clients.llm_client
    embedder = clients.embedder
    updated_nodes: list[EntityNode] = await semaphore_gather(
        *[
            extract_attributes_from_node(
                llm_client,
                node,
                episode,
                previous_episodes,
                entity_types.get(next((item for item in node.labels if item != 'Entity'), ''))
                if entity_types is not None
                else None,
            )
            for node in nodes
        ]
    )

    await create_entity_node_embeddings(embedder, updated_nodes)

    return updated_nodes


async def extract_attributes_from_node(
    llm_client: LLMClient,
    node: EntityNode,
    episode: EpisodicNode | None = None,
    previous_episodes: list[EpisodicNode] | None = None,
    entity_type: BaseModel | None = None,
) -> EntityNode:
    node_context: dict[str, Any] = {
        'name': node.name,
        'summary': node.summary,
        'entity_types': node.labels,
        'attributes': node.attributes,
    }

    attributes_definitions: dict[str, Any] = {
        'summary': (
            str,
            Field(
                description='Summary containing the important information about the entity. Under 250 words',
            ),
        )
    }

    if entity_type is not None:
        for field_name, field_info in entity_type.model_fields.items():
            attributes_definitions[field_name] = (
                field_info.annotation,
                Field(description=field_info.description),
            )

    unique_model_name = f'EntityAttributes_{uuid4().hex}'
    entity_attributes_model = pydantic.create_model(unique_model_name, **attributes_definitions)

    summary_context: dict[str, Any] = {
        'node': node_context,
        'episode_content': episode.content if episode is not None else '',
        'previous_episodes': [ep.content for ep in previous_episodes]
        if previous_episodes is not None
        else [],
    }

    llm_response = await llm_client.generate_response(
        prompt_library.extract_nodes.extract_attributes(summary_context),
        response_model=entity_attributes_model,
        model_size=ModelSize.small,
    )

    node.summary = llm_response.get('summary', node.summary)
    node_attributes = {key: value for key, value in llm_response.items()}

    with suppress(KeyError):
        del node_attributes['summary']

    node.attributes.update(node_attributes)

    return node


async def dedupe_node_list(
    llm_client: LLMClient,
    nodes: list[EntityNode],
) -> tuple[list[EntityNode], dict[str, str]]:
    start = time()

    # build node map
    node_map = {}
    for node in nodes:
        node_map[node.uuid] = node

    # Prepare context for LLM
    nodes_context = [{'uuid': node.uuid, 'name': node.name, **node.attributes} for node in nodes]

    context = {
        'nodes': nodes_context,
    }

    llm_response = await llm_client.generate_response(
        prompt_library.dedupe_nodes.node_list(context)
    )

    nodes_data = llm_response.get('nodes', [])

    end = time()
    logger.debug(f'Deduplicated nodes: {nodes_data} in {(end - start) * 1000} ms')

    # Get full node data
    unique_nodes = []
    uuid_map: dict[str, str] = {}
    for node_data in nodes_data:
        node_instance: EntityNode | None = node_map.get(node_data['uuids'][0])
        if node_instance is None:
            logger.warning(f'Node {node_data["uuids"][0]} not found in node map')
            continue
        node_instance.summary = node_data['summary']
        unique_nodes.append(node_instance)

        for uuid in node_data['uuids'][1:]:
            uuid_value = node_map[node_data['uuids'][0]].uuid
            uuid_map[uuid] = uuid_value

    return unique_nodes, uuid_map