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https://github.com/getzep/graphiti.git
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d6add504bd
* benchmark logging * load schema updates * add extract bulk nodes and edges * updated bulk calls * compression updates * bulk updates * bulk logic first pass * updated bulk process * debug * remove exact names first * cleaned up prompt * fix bad merge * update * fix merge issues
107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
from abc import ABC, abstractmethod
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from time import time
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from datetime import datetime
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from uuid import uuid4
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from openai import OpenAI
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from pydantic import BaseModel, Field
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from neo4j import AsyncDriver
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import logging
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from core.llm_client.config import EMBEDDING_DIM
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logger = logging.getLogger(__name__)
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class Node(BaseModel, ABC):
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uuid: str = Field(default_factory=lambda: uuid4().hex)
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name: str
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labels: list[str] = Field(default_factory=list)
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created_at: datetime
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@abstractmethod
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async def save(self, driver: AsyncDriver): ...
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def __hash__(self):
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return hash(self.uuid)
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def __eq__(self, other):
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if isinstance(other, Node):
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return self.uuid == other.uuid
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return False
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class EpisodicNode(Node):
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source: str = Field(description="source type")
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source_description: str = Field(description="description of the data source")
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content: str = Field(description="raw episode data")
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valid_at: datetime = Field(
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description="datetime of when the original document was created",
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)
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entity_edges: list[str] = Field(
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description="list of entity edges referenced in this episode",
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default_factory=list,
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)
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async def save(self, driver: AsyncDriver):
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result = await driver.execute_query(
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"""
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MERGE (n:Episodic {uuid: $uuid})
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SET n = {uuid: $uuid, name: $name, source_description: $source_description, source: $source, content: $content,
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entity_edges: $entity_edges, created_at: $created_at, valid_at: $valid_at}
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RETURN n.uuid AS uuid""",
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uuid=self.uuid,
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name=self.name,
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source_description=self.source_description,
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content=self.content,
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entity_edges=self.entity_edges,
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created_at=self.created_at,
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valid_at=self.valid_at,
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source=self.source,
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_database="neo4j",
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)
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logger.info(f"Saved Node to neo4j: {self.uuid}")
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return result
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class EntityNode(Node):
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name_embedding: list[float] | None = Field(
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default=None, description="embedding of the name"
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)
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summary: str = Field(
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description="regional summary of surrounding edges", default_factory=str
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)
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async def update_summary(self, driver: AsyncDriver): ...
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async def refresh_summary(self, driver: AsyncDriver, llm_client: OpenAI): ...
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async def generate_name_embedding(self, embedder, model="text-embedding-3-small"):
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start = time()
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text = self.name.replace("\n", " ")
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embedding = (await embedder.create(input=[text], model=model)).data[0].embedding
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self.name_embedding = embedding[:EMBEDDING_DIM]
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end = time()
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logger.info(f"embedded {text} in {end-start} ms")
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return embedding
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async def save(self, driver: AsyncDriver):
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result = await driver.execute_query(
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"""
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MERGE (n:Entity {uuid: $uuid})
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SET n = {uuid: $uuid, name: $name, name_embedding: $name_embedding, summary: $summary, created_at: $created_at}
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RETURN n.uuid AS uuid""",
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uuid=self.uuid,
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name=self.name,
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summary=self.summary,
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name_embedding=self.name_embedding,
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created_at=self.created_at,
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)
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logger.info(f"Saved Node to neo4j: {self.uuid}")
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return result
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