mirror of
https://github.com/getzep/graphiti.git
synced 2025-06-27 02:00:02 +00:00
a6fd0ddb75
* feat: Initial version of temporal invalidation + tests * fix: dont run int tests on CI * fix: dont run int tests on CI * fix: dont run int tests on CI * fix: time of day issue * fix: running non int tests in ci * fix: running non int tests in ci * fix: running non int tests in ci * fix: running non int tests in ci * fix: running non int tests in ci * fix: running non int tests in ci * fix: running non int tests in ci * revert: Tests structural changes * chore: Remove idea file * chore: Get rid of NodesWithEdges class and define a triplet type instead
105 lines
3.3 KiB
Python
105 lines
3.3 KiB
Python
from abc import ABC, abstractmethod
|
|
from pydantic import Field
|
|
from datetime import datetime
|
|
from uuid import uuid4
|
|
|
|
from openai import OpenAI
|
|
from pydantic import BaseModel, Field
|
|
from neo4j import AsyncDriver
|
|
import logging
|
|
|
|
from core.llm_client.config import EMBEDDING_DIM
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class Node(BaseModel, ABC):
|
|
uuid: str = Field(default_factory=lambda: uuid4().hex)
|
|
name: str
|
|
labels: list[str] = Field(default_factory=list)
|
|
created_at: datetime
|
|
|
|
@abstractmethod
|
|
async def save(self, driver: AsyncDriver): ...
|
|
|
|
def __hash__(self):
|
|
return hash(self.uuid)
|
|
|
|
def __eq__(self, other):
|
|
if isinstance(other, Node):
|
|
return self.uuid == other.uuid
|
|
return False
|
|
|
|
|
|
class EpisodicNode(Node):
|
|
source: str = Field(description="source type")
|
|
source_description: str = Field(description="description of the data source")
|
|
content: str = Field(description="raw episode data")
|
|
entity_edges: list[str] = Field(
|
|
description="list of entity edges referenced in this episode",
|
|
default_factory=list,
|
|
)
|
|
valid_at: datetime | None = Field(
|
|
description="datetime of when the original document was created",
|
|
default=None,
|
|
)
|
|
|
|
async def save(self, driver: AsyncDriver):
|
|
result = await driver.execute_query(
|
|
"""
|
|
MERGE (n:Episodic {uuid: $uuid})
|
|
SET n = {uuid: $uuid, name: $name, source_description: $source_description, source: $source, content: $content,
|
|
entity_edges: $entity_edges, created_at: $created_at, valid_at: $valid_at}
|
|
RETURN n.uuid AS uuid""",
|
|
uuid=self.uuid,
|
|
name=self.name,
|
|
source_description=self.source_description,
|
|
content=self.content,
|
|
entity_edges=self.entity_edges,
|
|
created_at=self.created_at,
|
|
valid_at=self.valid_at,
|
|
source=self.source,
|
|
_database="neo4j",
|
|
)
|
|
|
|
logger.info(f"Saved Node to neo4j: {self.uuid}")
|
|
|
|
return result
|
|
|
|
|
|
class EntityNode(Node):
|
|
name_embedding: list[float] | None = Field(
|
|
default=None, description="embedding of the name"
|
|
)
|
|
summary: str = Field(
|
|
description="regional summary of surrounding edges", default_factory=str
|
|
)
|
|
|
|
async def update_summary(self, driver: AsyncDriver): ...
|
|
|
|
async def refresh_summary(self, driver: AsyncDriver, llm_client: OpenAI): ...
|
|
|
|
async def generate_name_embedding(self, embedder, model="text-embedding-3-small"):
|
|
text = self.name.replace("\n", " ")
|
|
embedding = (await embedder.create(input=[text], model=model)).data[0].embedding
|
|
self.name_embedding = embedding[:EMBEDDING_DIM]
|
|
|
|
return embedding
|
|
|
|
async def save(self, driver: AsyncDriver):
|
|
result = await driver.execute_query(
|
|
"""
|
|
MERGE (n:Entity {uuid: $uuid})
|
|
SET n = {uuid: $uuid, name: $name, name_embedding: $name_embedding, summary: $summary, created_at: $created_at}
|
|
RETURN n.uuid AS uuid""",
|
|
uuid=self.uuid,
|
|
name=self.name,
|
|
summary=self.summary,
|
|
name_embedding=self.name_embedding,
|
|
created_at=self.created_at,
|
|
)
|
|
|
|
logger.info(f"Saved Node to neo4j: {self.uuid}")
|
|
|
|
return result
|