fix: run cognee distributed with modal
This commit is contained in:
Boris Arzentar
parent
611df1e9b9
commit
00dd3b8d97
27 changed files with 431 additions and 609 deletions
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@ -17,7 +17,7 @@ async def add(
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dataset_id: UUID = None,
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):
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tasks = [
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Task(resolve_data_directories),
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Task(resolve_data_directories, include_subdirectories=True),
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Task(ingest_data, dataset_name, user, node_set, dataset_id),
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]
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@ -1,21 +1,27 @@
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"""Neo4j Adapter for Graph Database"""
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import json
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from cognee.shared.logging_utils import get_logger, ERROR
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import asyncio
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from textwrap import dedent
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from typing import Optional, Any, List, Dict, Type, Tuple
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from contextlib import asynccontextmanager
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from uuid import UUID
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from textwrap import dedent
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from neo4j import AsyncSession
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from neo4j import AsyncGraphDatabase
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from neo4j.exceptions import Neo4jError
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from contextlib import asynccontextmanager
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from typing import Optional, Any, List, Dict, Type, Tuple
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from cognee.infrastructure.engine import DataPoint
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from cognee.shared.logging_utils import get_logger, ERROR
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from cognee.infrastructure.databases.graph.utils import override_distributed
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from cognee.infrastructure.databases.graph.graph_db_interface import (
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GraphDBInterface,
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record_graph_changes,
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)
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from cognee.modules.storage.utils import JSONEncoder
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from distributed.tasks.queued_add_nodes import queued_add_nodes
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from distributed.tasks.queued_add_edges import queued_add_edges
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from .neo4j_metrics_utils import (
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get_avg_clustering,
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get_edge_density,
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@ -166,6 +172,7 @@ class Neo4jAdapter(GraphDBInterface):
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return await self.query(query, params)
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@record_graph_changes
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@override_distributed(queued_add_nodes)
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async def add_nodes(self, nodes: list[DataPoint]) -> None:
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"""
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Add multiple nodes to the database in a single query.
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@ -404,6 +411,7 @@ class Neo4jAdapter(GraphDBInterface):
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return await self.query(query, params)
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@record_graph_changes
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@override_distributed(queued_add_edges)
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async def add_edges(self, edges: list[tuple[str, str, str, dict[str, Any]]]) -> None:
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"""
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Add multiple edges between nodes in a single query.
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19
cognee/infrastructure/databases/graph/utils.py
Normal file
19
cognee/infrastructure/databases/graph/utils.py
Normal file
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@ -0,0 +1,19 @@
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import os
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from functools import wraps
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def override_distributed(new_func):
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def decorator(func):
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@wraps(func)
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async def wrapper(self, *args, distributed=None, **kwargs):
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default_distributed_value = os.getenv("COGNEE_DISTRIBUTED", "False").lower() == "true"
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distributed = default_distributed_value if distributed is None else distributed
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if distributed:
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return await new_func(*args, **kwargs)
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else:
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return await func(self, *args, **kwargs)
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return wrapper
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return decorator
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@ -1,10 +1,14 @@
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import asyncio
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from uuid import UUID, uuid4
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from typing import List, Optional, get_type_hints
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from sqlalchemy.inspection import inspect
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from typing import List, Optional, Union, get_type_hints
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from sqlalchemy.orm import Mapped, mapped_column
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from sqlalchemy import JSON, Column, Table, select, delete, MetaData, text
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from sqlalchemy.dialects.postgresql import insert
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from asyncpg import DeadlockDetectedError, DuplicateTableError, UniqueViolationError
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from sqlalchemy import JSON, Column, Table, select, delete, MetaData
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from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker
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from tenacity import retry, retry_if_exception_type, stop_after_attempt
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from cognee.exceptions import InvalidValueError
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from cognee.shared.logging_utils import get_logger
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@ -107,6 +111,11 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
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else:
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return False
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@retry(
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retry=retry_if_exception_type(Union[DuplicateTableError, UniqueViolationError]),
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stop=stop_after_attempt(3),
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sleep=1,
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)
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async def create_collection(self, collection_name: str, payload_schema=None):
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data_point_types = get_type_hints(DataPoint)
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vector_size = self.embedding_engine.get_vector_size()
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@ -123,7 +132,6 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
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- __init__(self, id, payload, vector): Initializes a new PGVectorDataPoint instance.
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Instance variables:
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- primary_key: Unique identifier for the data point, generated by uuid4.
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- id: Identifier for the data point, defined by data_point_types.
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- payload: JSON data associated with the data point.
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- vector: Vector representation of the data point, with size defined by vector_size.
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@ -132,8 +140,7 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
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__tablename__ = collection_name
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__table_args__ = {"extend_existing": True}
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# PGVector requires one column to be the primary key
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primary_key: Mapped[UUID] = mapped_column(primary_key=True, default=uuid4)
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id: Mapped[data_point_types["id"]]
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id: Mapped[data_point_types["id"]] = mapped_column(primary_key=True)
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payload = Column(JSON)
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vector = Column(self.Vector(vector_size))
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@ -148,6 +155,11 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
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Base.metadata.create_all, tables=[PGVectorDataPoint.__table__]
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)
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@retry(
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retry=retry_if_exception_type(DeadlockDetectedError),
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stop=stop_after_attempt(3),
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sleep=1,
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)
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async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
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data_point_types = get_type_hints(DataPoint)
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if not await self.has_collection(collection_name):
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@ -168,7 +180,6 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
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the SQLAlchemy ORM.
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It contains the following public instance variables:
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- primary_key: The primary key of the data point, generated automatically.
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- id: An identifier for the data point.
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- payload: A JSON object containing additional data related to the data point.
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- vector: A vector representation of the data point, configured to the specified size.
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@ -177,8 +188,7 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
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__tablename__ = collection_name
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__table_args__ = {"extend_existing": True}
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# PGVector requires one column to be the primary key
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primary_key: Mapped[UUID] = mapped_column(primary_key=True, default=uuid4)
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id: Mapped[data_point_types["id"]]
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id: Mapped[data_point_types["id"]] = mapped_column(primary_key=True)
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payload = Column(JSON)
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vector = Column(self.Vector(vector_size))
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@ -213,7 +223,18 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
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)
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)
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session.add_all(pgvector_data_points)
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def to_dict(obj):
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return {
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column.key: getattr(obj, column.key)
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for column in inspect(obj).mapper.column_attrs
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}
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# session.add_all(pgvector_data_points)
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insert_statement = insert(PGVectorDataPoint).values(
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[to_dict(data_point) for data_point in pgvector_data_points]
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)
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insert_statement = insert_statement.on_conflict_do_nothing(index_elements=["id"])
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await session.execute(insert_statement)
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await session.commit()
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async def create_vector_index(self, index_name: str, index_property_name: str):
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@ -1,3 +1,4 @@
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import io
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from typing import BinaryIO, TypedDict
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from .guess_file_type import guess_file_type
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from cognee.shared.utils import get_file_content_hash
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@ -38,9 +39,14 @@ def get_file_metadata(file: BinaryIO) -> FileMetadata:
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- FileMetadata: A dictionary containing the file's name, path, MIME type, file
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extension, and content hash.
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"""
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file.seek(0)
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content_hash = get_file_content_hash(file)
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file.seek(0)
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try:
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file.seek(0)
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content_hash = get_file_content_hash(file)
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file.seek(0)
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except io.UnsupportedOperation as error:
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raise Exception(
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f"Error retrieving metadata from file: {file.name} \n{str(error)}\n\n"
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) from error
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file_type = guess_file_type(file)
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@ -125,6 +125,6 @@ def guess_file_type(file: BinaryIO) -> filetype.Type:
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file_type = filetype.guess(file)
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if file_type is None:
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raise FileTypeException("Unknown file detected.")
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raise FileTypeException(f"Unknown file detected: {file.name}.")
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return file_type
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25
cognee/infrastructure/utils/run_sync.py
Normal file
25
cognee/infrastructure/utils/run_sync.py
Normal file
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@ -0,0 +1,25 @@
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import asyncio
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import threading
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def run_sync(coro, timeout=None):
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result = None
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exception = None
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def runner():
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nonlocal result, exception
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try:
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result = asyncio.run(coro)
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except Exception as e:
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exception = e
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thread = threading.Thread(target=runner)
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thread.start()
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thread.join(timeout)
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if thread.is_alive():
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raise asyncio.TimeoutError("Coroutine execution timed out.")
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if exception:
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raise exception
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return result
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@ -20,17 +20,7 @@ def classify(data: Union[str, BinaryIO], filename: str = None, s3fs: Optional[An
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if isinstance(data, BufferedReader) or isinstance(data, SpooledTemporaryFile):
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return BinaryData(data, str(data.name).split("/")[-1] if data.name else filename)
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try:
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from importlib import import_module
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s3core = import_module("s3fs.core")
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S3File = s3core.S3File
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except ImportError:
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S3File = None
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if S3File is not None:
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from cognee.modules.ingestion.data_types.S3BinaryData import S3BinaryData
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if isinstance(data, S3File):
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derived_filename = str(data.full_name).split("/")[-1] if data.full_name else filename
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return S3BinaryData(s3_path=data.full_name, name=derived_filename, s3=s3fs)
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@ -184,7 +184,7 @@ async def run_pipeline(
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if not isinstance(task, Task):
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raise ValueError(f"Task {task} is not an instance of Task")
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pipeline_run = run_tasks(tasks, dataset_id, data, user, pipeline_name, context=context)
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pipeline_run = run_tasks(tasks, dataset_id, data, user, pipeline_name, context)
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async for pipeline_run_info in pipeline_run:
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yield pipeline_run_info
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@ -1,8 +1,11 @@
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import json
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import os
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from uuid import UUID
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from typing import Any
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from uuid import UUID, uuid4
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from functools import wraps
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from cognee.infrastructure.databases.relational import get_relational_engine
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from cognee.modules.pipelines.operations.run_tasks_distributed import run_tasks_distributed
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from cognee.modules.users.models import User
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from cognee.shared.logging_utils import get_logger
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from cognee.modules.users.methods import get_default_user
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from cognee.modules.pipelines.utils import generate_pipeline_id
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@ -18,64 +21,34 @@ from cognee.modules.pipelines.operations import (
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log_pipeline_run_complete,
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log_pipeline_run_error,
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)
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from cognee.modules.settings import get_current_settings
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from cognee.modules.users.models import User
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from cognee.shared.utils import send_telemetry
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from .run_tasks_base import run_tasks_base
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from .run_tasks_with_telemetry import run_tasks_with_telemetry
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from ..tasks.task import Task
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logger = get_logger("run_tasks(tasks: [Task], data)")
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async def run_tasks_with_telemetry(
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tasks: list[Task], data, user: User, pipeline_name: str, context: dict = None
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):
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config = get_current_settings()
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def override_run_tasks(new_gen):
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def decorator(original_gen):
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@wraps(original_gen)
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async def wrapper(*args, distributed=None, **kwargs):
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default_distributed_value = os.getenv("COGNEE_DISTRIBUTED", "False").lower() == "true"
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distributed = default_distributed_value if distributed is None else distributed
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logger.debug("\nRunning pipeline with configuration:\n%s\n", json.dumps(config, indent=1))
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print(f"run_tasks_distributed: {distributed}")
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if distributed:
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async for run_info in new_gen(*args, **kwargs):
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yield run_info
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else:
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async for run_info in original_gen(*args, **kwargs):
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yield run_info
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try:
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logger.info("Pipeline run started: `%s`", pipeline_name)
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send_telemetry(
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"Pipeline Run Started",
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user.id,
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additional_properties={
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"pipeline_name": str(pipeline_name),
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}
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| config,
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)
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return wrapper
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async for result in run_tasks_base(tasks, data, user, context):
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yield result
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logger.info("Pipeline run completed: `%s`", pipeline_name)
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send_telemetry(
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"Pipeline Run Completed",
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user.id,
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additional_properties={
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"pipeline_name": str(pipeline_name),
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},
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)
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except Exception as error:
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logger.error(
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"Pipeline run errored: `%s`\n%s\n",
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pipeline_name,
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str(error),
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exc_info=True,
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)
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send_telemetry(
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"Pipeline Run Errored",
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user.id,
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additional_properties={
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"pipeline_name": str(pipeline_name),
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}
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| config,
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)
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raise error
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return decorator
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@override_run_tasks(run_tasks_distributed)
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async def run_tasks(
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tasks: list[Task],
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dataset_id: UUID,
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|
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119
cognee/modules/pipelines/operations/run_tasks_distributed.py
Normal file
119
cognee/modules/pipelines/operations/run_tasks_distributed.py
Normal file
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@ -0,0 +1,119 @@
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try:
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import modal
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except ModuleNotFoundError:
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modal = None
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from cognee.infrastructure.databases.relational import get_relational_engine
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from cognee.modules.pipelines.models import (
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PipelineRunStarted,
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PipelineRunYield,
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PipelineRunCompleted,
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)
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from cognee.modules.pipelines.operations import log_pipeline_run_start, log_pipeline_run_complete
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from cognee.modules.pipelines.utils.generate_pipeline_id import generate_pipeline_id
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from cognee.modules.users.methods import get_default_user
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from cognee.shared.logging_utils import get_logger
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from .run_tasks_with_telemetry import run_tasks_with_telemetry
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logger = get_logger("run_tasks_distributed()")
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if modal:
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from distributed.app import app
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from distributed.modal_image import image
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@app.function(
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image=image,
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timeout=86400,
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max_containers=100,
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secrets=[modal.Secret.from_name("distributed_cognee")],
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)
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async def run_tasks_on_modal(tasks, data_item, user, pipeline_name, context):
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pipeline_run = run_tasks_with_telemetry(tasks, data_item, user, pipeline_name, context)
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run_info = None
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async for pipeline_run_info in pipeline_run:
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run_info = pipeline_run_info
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return run_info
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async def run_tasks_distributed(tasks, dataset_id, data, user, pipeline_name, context):
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if not user:
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user = get_default_user()
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db_engine = get_relational_engine()
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async with db_engine.get_async_session() as session:
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from cognee.modules.data.models import Dataset
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dataset = await session.get(Dataset, dataset_id)
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pipeline_id = generate_pipeline_id(user.id, dataset.id, pipeline_name)
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pipeline_run = await log_pipeline_run_start(pipeline_id, pipeline_name, dataset_id, data)
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pipeline_run_id = pipeline_run.pipeline_run_id
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yield PipelineRunStarted(
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pipeline_run_id=pipeline_run_id,
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dataset_id=dataset.id,
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dataset_name=dataset.name,
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payload=data,
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)
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data_count = len(data) if isinstance(data, list) else 1
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print(f"Data count: {data_count}")
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print(f"Data item to process: {type(data)} - {data}")
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arguments = [
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[tasks] * data_count,
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[[data_item] for data_item in data[:data_count]] if data_count > 1 else [data],
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[user] * data_count,
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[pipeline_name] * data_count,
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[context] * data_count,
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]
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async for result in run_tasks_on_modal.map.aio(*arguments):
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logger.info(f"Received result: {result}")
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|
||||
yield PipelineRunYield(
|
||||
pipeline_run_id=pipeline_run_id,
|
||||
dataset_id=dataset.id,
|
||||
dataset_name=dataset.name,
|
||||
payload=result,
|
||||
)
|
||||
|
||||
# producer_futures = []
|
||||
|
||||
# for data_item in data[:5]:
|
||||
# producer_future = run_tasks_distributed(
|
||||
# run_tasks_with_telemetry, tasks, [data_item], user, pipeline_name, context
|
||||
# )
|
||||
# producer_futures.append(producer_future)
|
||||
|
||||
# batch_results = []
|
||||
# for producer_future in producer_futures:
|
||||
# try:
|
||||
# result = producer_future.get()
|
||||
# except Exception as e:
|
||||
# result = e
|
||||
# batch_results.append(result)
|
||||
|
||||
# yield PipelineRunYield(
|
||||
# pipeline_run_id=pipeline_run_id,
|
||||
# dataset_id=dataset.id,
|
||||
# dataset_name=dataset.name,
|
||||
# payload=result,
|
||||
# )
|
||||
|
||||
await log_pipeline_run_complete(pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data)
|
||||
|
||||
yield PipelineRunCompleted(
|
||||
pipeline_run_id=pipeline_run_id,
|
||||
dataset_id=dataset.id,
|
||||
dataset_name=dataset.name,
|
||||
)
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
import json
|
||||
|
||||
from cognee.modules.settings import get_current_settings
|
||||
from cognee.modules.users.models import User
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.shared.utils import send_telemetry
|
||||
|
||||
from .run_tasks_base import run_tasks_base
|
||||
from ..tasks.task import Task
|
||||
|
||||
|
||||
logger = get_logger("run_tasks_with_telemetry()")
|
||||
|
||||
|
||||
async def run_tasks_with_telemetry(
|
||||
tasks: list[Task], data, user: User, pipeline_name: str, context: dict = None
|
||||
):
|
||||
config = get_current_settings()
|
||||
|
||||
logger.debug("\nRunning pipeline with configuration:\n%s\n", json.dumps(config, indent=1))
|
||||
|
||||
try:
|
||||
logger.info("Pipeline run started: `%s`", pipeline_name)
|
||||
send_telemetry(
|
||||
"Pipeline Run Started",
|
||||
user.id,
|
||||
additional_properties={
|
||||
"pipeline_name": str(pipeline_name),
|
||||
}
|
||||
| config,
|
||||
)
|
||||
|
||||
async for result in run_tasks_base(tasks, data, user, context):
|
||||
yield result
|
||||
|
||||
logger.info("Pipeline run completed: `%s`", pipeline_name)
|
||||
send_telemetry(
|
||||
"Pipeline Run Completed",
|
||||
user.id,
|
||||
additional_properties={
|
||||
"pipeline_name": str(pipeline_name),
|
||||
},
|
||||
)
|
||||
except Exception as error:
|
||||
logger.error(
|
||||
"Pipeline run errored: `%s`\n%s\n",
|
||||
pipeline_name,
|
||||
str(error),
|
||||
exc_info=True,
|
||||
)
|
||||
send_telemetry(
|
||||
"Pipeline Run Errored",
|
||||
user.id,
|
||||
additional_properties={
|
||||
"pipeline_name": str(pipeline_name),
|
||||
}
|
||||
| config,
|
||||
)
|
||||
|
||||
raise error
|
||||
|
|
@ -1,11 +1,20 @@
|
|||
from sqlalchemy.future import select
|
||||
from cognee.infrastructure.databases.relational import get_relational_engine
|
||||
from ...models import Principal, ACL, Permission
|
||||
from uuid import UUID
|
||||
from sqlalchemy.future import select
|
||||
from asyncpg import UniqueViolationError
|
||||
from tenacity import retry, retry_if_exception_type, stop_after_attempt
|
||||
|
||||
from cognee.infrastructure.databases.relational import get_relational_engine
|
||||
from cognee.modules.users.permissions import PERMISSION_TYPES
|
||||
from cognee.modules.users.exceptions import PermissionNotFoundError
|
||||
|
||||
from ...models import Principal, ACL, Permission
|
||||
|
||||
|
||||
@retry(
|
||||
retry=retry_if_exception_type(UniqueViolationError),
|
||||
stop=stop_after_attempt(3),
|
||||
sleep=1,
|
||||
)
|
||||
async def give_permission_on_dataset(
|
||||
principal: Principal,
|
||||
dataset_id: UUID,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
import dlt
|
||||
import json
|
||||
import inspect
|
||||
from uuid import UUID
|
||||
|
|
@ -11,7 +10,6 @@ from cognee.modules.data.models.DatasetData import DatasetData
|
|||
from cognee.modules.users.models import User
|
||||
from cognee.modules.users.permissions.methods import give_permission_on_dataset
|
||||
from cognee.modules.users.permissions.methods import get_specific_user_permission_datasets
|
||||
from .get_dlt_destination import get_dlt_destination
|
||||
from .save_data_item_to_storage import save_data_item_to_storage
|
||||
|
||||
|
||||
|
|
@ -25,16 +23,9 @@ async def ingest_data(
|
|||
node_set: Optional[List[str]] = None,
|
||||
dataset_id: UUID = None,
|
||||
):
|
||||
destination = get_dlt_destination()
|
||||
|
||||
if not user:
|
||||
user = await get_default_user()
|
||||
|
||||
pipeline = dlt.pipeline(
|
||||
pipeline_name="metadata_extraction_pipeline",
|
||||
destination=destination,
|
||||
)
|
||||
|
||||
s3_config = get_s3_config()
|
||||
|
||||
fs = None
|
||||
|
|
@ -58,27 +49,6 @@ async def ingest_data(
|
|||
else:
|
||||
return {}
|
||||
|
||||
@dlt.resource(standalone=True, primary_key="id", merge_key="id")
|
||||
async def data_resources(file_paths: List[str], user: User):
|
||||
for file_path in file_paths:
|
||||
with open_data_file(file_path) as file:
|
||||
if file_path.startswith("s3://"):
|
||||
classified_data = ingestion.classify(file, s3fs=fs)
|
||||
else:
|
||||
classified_data = ingestion.classify(file)
|
||||
data_id = ingestion.identify(classified_data, user)
|
||||
file_metadata = classified_data.get_metadata()
|
||||
yield {
|
||||
"id": data_id,
|
||||
"name": file_metadata["name"],
|
||||
"file_path": file_metadata["file_path"],
|
||||
"extension": file_metadata["extension"],
|
||||
"mime_type": file_metadata["mime_type"],
|
||||
"content_hash": file_metadata["content_hash"],
|
||||
"owner_id": str(user.id),
|
||||
"node_set": json.dumps(node_set) if node_set else None,
|
||||
}
|
||||
|
||||
async def store_data_to_dataset(
|
||||
data: Any,
|
||||
dataset_name: str,
|
||||
|
|
@ -187,29 +157,7 @@ async def ingest_data(
|
|||
|
||||
return file_paths
|
||||
|
||||
db_engine = get_relational_engine()
|
||||
|
||||
file_paths = await store_data_to_dataset(data, dataset_name, user, node_set, dataset_id)
|
||||
|
||||
# Note: DLT pipeline has its own event loop, therefore objects created in another event loop
|
||||
# can't be used inside the pipeline
|
||||
if db_engine.engine.dialect.name == "sqlite":
|
||||
# To use sqlite with dlt dataset_name must be set to "main".
|
||||
# Sqlite doesn't support schemas
|
||||
pipeline.run(
|
||||
data_resources(file_paths, user),
|
||||
table_name="file_metadata",
|
||||
dataset_name="main",
|
||||
write_disposition="merge",
|
||||
)
|
||||
else:
|
||||
# Data should be stored in the same schema to allow deduplication
|
||||
pipeline.run(
|
||||
data_resources(file_paths, user),
|
||||
table_name="file_metadata",
|
||||
dataset_name="public",
|
||||
write_disposition="merge",
|
||||
)
|
||||
await store_data_to_dataset(data, dataset_name, user, node_set, dataset_id)
|
||||
|
||||
datasets = await get_datasets_by_name(dataset_name, user.id)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
FROM python:3.12-bookworm-slim
|
||||
FROM python:3.11-slim
|
||||
|
||||
# Set environment variables
|
||||
ENV PIP_NO_CACHE_DIR=true
|
||||
|
|
@ -6,6 +6,7 @@ ENV PATH="${PATH}:/root/.poetry/bin"
|
|||
ENV PYTHONPATH=/app
|
||||
ENV RUN_MODE=modal
|
||||
ENV SKIP_MIGRATIONS=true
|
||||
ENV COGNEE_DISTRIBUTED=true
|
||||
|
||||
# System dependencies
|
||||
RUN apt-get update && apt-get install -y \
|
||||
|
|
@ -24,7 +25,7 @@ RUN pip install poetry
|
|||
|
||||
RUN poetry config virtualenvs.create false
|
||||
|
||||
RUN poetry install --extras neo4j --extras postgres --no-root
|
||||
RUN poetry install --extras neo4j --extras postgres --extras aws --extras distributed --no-root
|
||||
|
||||
COPY cognee/ /app/cognee
|
||||
COPY distributed/ /app/distributed
|
||||
|
|
|
|||
|
|
@ -1,162 +1,50 @@
|
|||
import pathlib
|
||||
from os import path
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
# from cognee.api.v1.add import add
|
||||
import cognee
|
||||
from cognee.api.v1.prune import prune
|
||||
from cognee.infrastructure.databases.relational import get_relational_engine
|
||||
from cognee.infrastructure.llm.utils import get_max_chunk_tokens
|
||||
from cognee.modules.chunking.TextChunker import TextChunker
|
||||
from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
|
||||
from cognee.modules.data.models import Data
|
||||
from cognee.modules.data.processing.document_types import Document
|
||||
from cognee.modules.engine.operations.setup import setup
|
||||
from cognee.modules.ingestion.get_text_content_hash import get_text_content_hash
|
||||
from cognee.modules.pipelines.operations.run_tasks import run_tasks
|
||||
from cognee.modules.pipelines.tasks.task import Task
|
||||
from cognee.modules.users.methods.get_default_user import get_default_user
|
||||
# from cognee.modules.data.methods.get_dataset_data import get_dataset_data
|
||||
# from cognee.modules.data.methods.get_datasets_by_name import get_datasets_by_name
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.tasks.documents.extract_chunks_from_documents import extract_chunks_from_documents
|
||||
from cognee.modules.engine.operations.setup import setup
|
||||
|
||||
from distributed.app import app
|
||||
from distributed.models.TextDocument import TextDocument
|
||||
from distributed.queues import save_data_points_queue
|
||||
from distributed.workers.data_point_saver_worker import data_point_saver_worker
|
||||
from distributed.workers.graph_extraction_worker import graph_extraction_worker
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
os.environ["COGNEE_DISTRIBUTED"] = "True"
|
||||
|
||||
|
||||
@app.local_entrypoint()
|
||||
async def main():
|
||||
# Clear queues
|
||||
save_data_points_queue.clear()
|
||||
await save_data_points_queue.clear.aio()
|
||||
|
||||
# dataset_name = "main"
|
||||
data_directory_name = ".data"
|
||||
data_directory_path = path.join(pathlib.Path(__file__).parent, data_directory_name)
|
||||
|
||||
number_of_data_saving_workers = 1 # Total number of graph_extraction_worker functions to spawn
|
||||
document_batch_size = 50 # Batch size for producers
|
||||
number_of_data_saving_workers = 1 # Total number of data_point_saver_worker functions to spawn
|
||||
|
||||
results = []
|
||||
consumer_futures = []
|
||||
|
||||
# await prune.prune_data() # We don't want to delete files on s3
|
||||
# Delete DBs and saved files from metastore
|
||||
await prune.prune_data()
|
||||
await prune.prune_system(metadata=True)
|
||||
|
||||
await setup()
|
||||
|
||||
# Add files to the metastore
|
||||
# await add(data=data_directory_path, dataset_name=dataset_name)
|
||||
|
||||
user = await get_default_user()
|
||||
# datasets = await get_datasets_by_name(dataset_name, user.id)
|
||||
# documents = await get_dataset_data(dataset_id=datasets[0].id)
|
||||
|
||||
import duckdb
|
||||
|
||||
connection = duckdb.connect()
|
||||
dataset_file_name = "de-00000-of-00003-f8e581c008ccc7f2.parquet"
|
||||
dataset_file_path = path.join(data_directory_path, dataset_file_name)
|
||||
df = connection.execute(f"SELECT * FROM '{dataset_file_path}'").fetchdf()
|
||||
|
||||
documents = []
|
||||
|
||||
for _, row in df.iterrows():
|
||||
file_id = str(row["id"])
|
||||
content = row["text"]
|
||||
|
||||
documents.append(
|
||||
TextDocument(
|
||||
name=file_id,
|
||||
content=content,
|
||||
raw_data_location=f"{dataset_file_name}_{file_id}",
|
||||
external_metadata="",
|
||||
)
|
||||
)
|
||||
|
||||
documents: list[TextDocument] = documents[0:100]
|
||||
print(f"We have {len(documents)} documents in the dataset.")
|
||||
|
||||
data_documents = [
|
||||
Data(
|
||||
id=document.id,
|
||||
name=document.name,
|
||||
raw_data_location=document.raw_data_location,
|
||||
extension="txt",
|
||||
mime_type=document.mime_type,
|
||||
owner_id=user.id,
|
||||
content_hash=get_text_content_hash(document.content),
|
||||
external_metadata=document.external_metadata,
|
||||
node_set=None,
|
||||
token_count=-1,
|
||||
)
|
||||
for document in documents
|
||||
]
|
||||
|
||||
db_engine = get_relational_engine()
|
||||
|
||||
async with db_engine.get_async_session() as session:
|
||||
session.add_all(data_documents)
|
||||
await session.commit()
|
||||
|
||||
# Start data_point_saver_worker functions
|
||||
for _ in range(number_of_data_saving_workers):
|
||||
worker_future = data_point_saver_worker.spawn()
|
||||
consumer_futures.append(worker_future)
|
||||
|
||||
producer_futures = []
|
||||
s3_bucket_name = "s3://s3-test-laszlo/Database for KG v1"
|
||||
|
||||
def process_chunks_remotely(document_chunks: list[DocumentChunk], document: Document):
|
||||
producer_future = graph_extraction_worker.spawn(
|
||||
user=user, document_name=document.name, document_chunks=document_chunks
|
||||
)
|
||||
producer_futures.append(producer_future)
|
||||
return producer_future
|
||||
await cognee.add(s3_bucket_name, dataset_name="s3-files")
|
||||
|
||||
# Produce chunks and spawn a graph_extraction_worker job for each batch of chunks
|
||||
for i in range(0, len(documents), document_batch_size):
|
||||
batch = documents[i : i + document_batch_size]
|
||||
|
||||
for item in batch:
|
||||
async for worker_feature in run_tasks(
|
||||
[
|
||||
Task(
|
||||
extract_chunks_from_documents,
|
||||
max_chunk_size=2000,
|
||||
chunker=TextChunker,
|
||||
),
|
||||
Task(
|
||||
process_chunks_remotely,
|
||||
document=item,
|
||||
task_config={"batch_size": 10},
|
||||
),
|
||||
],
|
||||
data=[item],
|
||||
user=user,
|
||||
pipeline_name="chunk_processing",
|
||||
):
|
||||
pass
|
||||
|
||||
batch_results = []
|
||||
|
||||
for producer_future in producer_futures:
|
||||
try:
|
||||
result = producer_future.get()
|
||||
except Exception as e:
|
||||
result = e
|
||||
|
||||
batch_results.append(result)
|
||||
|
||||
print(f"Number of documents processed: {len(results)}")
|
||||
results.extend(batch_results)
|
||||
await cognee.cognify(datasets=["s3-files"])
|
||||
|
||||
# Push empty tuple into the queue to signal the end of data.
|
||||
save_data_points_queue.put(())
|
||||
await save_data_points_queue.put.aio(())
|
||||
|
||||
for consumer_future in consumer_futures:
|
||||
try:
|
||||
|
|
@ -167,3 +55,7 @@ async def main():
|
|||
logger.error(e)
|
||||
|
||||
print(results)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
|
|||
|
|
@ -1,15 +0,0 @@
|
|||
from cognee.modules.chunking.Chunker import Chunker
|
||||
from cognee.modules.data.processing.document_types import Document
|
||||
|
||||
|
||||
class TextDocument(Document):
|
||||
type: str = "text"
|
||||
mime_type: str = "text/plain"
|
||||
content: str
|
||||
|
||||
def read(self, chunker_cls: Chunker, max_chunk_size: int):
|
||||
def get_text():
|
||||
yield self.content
|
||||
|
||||
chunker: Chunker = chunker_cls(self, max_chunk_size=max_chunk_size, get_text=get_text)
|
||||
yield from chunker.read()
|
||||
|
|
@ -1,41 +0,0 @@
|
|||
import asyncio
|
||||
from typing import Type, List
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from cognee.modules.graph.utils import (
|
||||
expand_with_nodes_and_edges,
|
||||
retrieve_existing_edges,
|
||||
)
|
||||
from cognee.shared.data_models import KnowledgeGraph
|
||||
from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
|
||||
from cognee.modules.ontology.rdf_xml.OntologyResolver import OntologyResolver
|
||||
from cognee.modules.data.extraction.knowledge_graph import extract_content_graph
|
||||
|
||||
|
||||
async def extract_graph_from_data(
|
||||
data_chunks: list[DocumentChunk],
|
||||
graph_model: Type[BaseModel],
|
||||
ontology_adapter: OntologyResolver = OntologyResolver(),
|
||||
) -> List[DocumentChunk]:
|
||||
"""Extracts and integrates a knowledge graph from the text content of document chunks using a specified graph model."""
|
||||
chunk_graphs = await asyncio.gather(
|
||||
*[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
|
||||
)
|
||||
|
||||
if graph_model is not KnowledgeGraph:
|
||||
for chunk_index, chunk_graph in enumerate(chunk_graphs):
|
||||
data_chunks[chunk_index].contains = chunk_graph
|
||||
|
||||
return data_chunks
|
||||
|
||||
existing_edges_map = await retrieve_existing_edges(
|
||||
data_chunks,
|
||||
chunk_graphs,
|
||||
)
|
||||
|
||||
graph_nodes, graph_edges = expand_with_nodes_and_edges(
|
||||
data_chunks, chunk_graphs, ontology_adapter, existing_edges_map
|
||||
)
|
||||
|
||||
return data_chunks, graph_nodes, graph_edges
|
||||
12
distributed/tasks/queued_add_edges.py
Normal file
12
distributed/tasks/queued_add_edges.py
Normal file
|
|
@ -0,0 +1,12 @@
|
|||
async def queued_add_edges(edge_batch):
|
||||
from ..queues import save_data_points_queue
|
||||
|
||||
try:
|
||||
await save_data_points_queue.put.aio(([], edge_batch))
|
||||
except Exception:
|
||||
first_half, second_half = (
|
||||
edge_batch[: len(edge_batch) // 2],
|
||||
edge_batch[len(edge_batch) // 2 :],
|
||||
)
|
||||
await save_data_points_queue.put.aio(([], first_half))
|
||||
await save_data_points_queue.put.aio(([], second_half))
|
||||
12
distributed/tasks/queued_add_nodes.py
Normal file
12
distributed/tasks/queued_add_nodes.py
Normal file
|
|
@ -0,0 +1,12 @@
|
|||
async def queued_add_nodes(node_batch):
|
||||
from ..queues import save_data_points_queue
|
||||
|
||||
try:
|
||||
await save_data_points_queue.put.aio((node_batch, []))
|
||||
except Exception:
|
||||
first_half, second_half = (
|
||||
node_batch[: len(node_batch) // 2],
|
||||
node_batch[len(node_batch) // 2 :],
|
||||
)
|
||||
await save_data_points_queue.put.aio((first_half, []))
|
||||
await save_data_points_queue.put.aio((second_half, []))
|
||||
|
|
@ -1,116 +0,0 @@
|
|||
# import json
|
||||
# import asyncio
|
||||
from pympler import asizeof
|
||||
|
||||
# from cognee.modules.storage.utils import JSONEncoder
|
||||
from distributed.queues import save_data_points_queue
|
||||
# from cognee.modules.graph.utils import get_graph_from_model
|
||||
|
||||
|
||||
async def save_data_points(data_points_and_relationships: tuple[list, list]):
|
||||
# data_points = data_points_and_relationships[0]
|
||||
# data_point_connections = data_points_and_relationships[1]
|
||||
|
||||
# added_nodes = {}
|
||||
# added_edges = {}
|
||||
# visited_properties = {}
|
||||
|
||||
# nodes_and_edges: list[tuple] = await asyncio.gather(
|
||||
# *[
|
||||
# get_graph_from_model(
|
||||
# data_point,
|
||||
# added_nodes=added_nodes,
|
||||
# added_edges=added_edges,
|
||||
# visited_properties=visited_properties,
|
||||
# )
|
||||
# for data_point in data_points
|
||||
# ]
|
||||
# )
|
||||
|
||||
# graph_data_deduplication = GraphDataDeduplication()
|
||||
# deduplicated_nodes_and_edges = [graph_data_deduplication.deduplicate_nodes_and_edges(nodes, edges + data_point_connections) for nodes, edges in nodes_and_edges]
|
||||
|
||||
node_batch = []
|
||||
edge_batch = []
|
||||
|
||||
for nodes, edges in data_points_and_relationships:
|
||||
for node in nodes:
|
||||
if asizeof.asizeof(node) >= 500000:
|
||||
try_pushing_nodes_to_queue([node])
|
||||
continue
|
||||
# print(f"Node too large:\n{node.id}\n")
|
||||
|
||||
node_batch.append(node)
|
||||
|
||||
if asizeof.asizeof(node_batch) >= 500000:
|
||||
try_pushing_nodes_to_queue(node_batch)
|
||||
node_batch = []
|
||||
|
||||
if len(node_batch) > 0:
|
||||
try_pushing_nodes_to_queue(node_batch)
|
||||
node_batch = []
|
||||
|
||||
for edge in edges:
|
||||
edge_batch.append(edge)
|
||||
|
||||
if asizeof.asizeof(edge_batch) >= 500000:
|
||||
try_pushing_edges_to_queue(edge_batch)
|
||||
edge_batch = []
|
||||
|
||||
if len(edge_batch) > 0:
|
||||
try_pushing_edges_to_queue(edge_batch)
|
||||
edge_batch = []
|
||||
|
||||
# graph_data_deduplication.reset()
|
||||
|
||||
|
||||
class GraphDataDeduplication:
|
||||
nodes_and_edges_map: dict
|
||||
|
||||
def __init__(self):
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.nodes_and_edges_map = {}
|
||||
|
||||
def deduplicate_nodes_and_edges(self, nodes: list, edges: list):
|
||||
final_nodes = []
|
||||
final_edges = []
|
||||
|
||||
for node in nodes:
|
||||
node_key = str(node.id)
|
||||
if node_key not in self.nodes_and_edges_map:
|
||||
self.nodes_and_edges_map[node_key] = True
|
||||
final_nodes.append(node)
|
||||
|
||||
for edge in edges:
|
||||
edge_key = str(edge[0]) + str(edge[2]) + str(edge[1])
|
||||
if edge_key not in self.nodes_and_edges_map:
|
||||
self.nodes_and_edges_map[edge_key] = True
|
||||
final_edges.append(edge)
|
||||
|
||||
return final_nodes, final_edges
|
||||
|
||||
|
||||
def try_pushing_nodes_to_queue(node_batch):
|
||||
try:
|
||||
save_data_points_queue.put((node_batch, []))
|
||||
except Exception:
|
||||
first_half, second_half = (
|
||||
node_batch[: len(node_batch) // 2],
|
||||
node_batch[len(node_batch) // 2 :],
|
||||
)
|
||||
save_data_points_queue.put((first_half, []))
|
||||
save_data_points_queue.put((second_half, []))
|
||||
|
||||
|
||||
def try_pushing_edges_to_queue(edge_batch):
|
||||
try:
|
||||
save_data_points_queue.put(([], edge_batch))
|
||||
except Exception:
|
||||
first_half, second_half = (
|
||||
edge_batch[: len(edge_batch) // 2],
|
||||
edge_batch[len(edge_batch) // 2 :],
|
||||
)
|
||||
save_data_points_queue.put(([], first_half))
|
||||
save_data_points_queue.put(([], second_half))
|
||||
|
|
@ -1,88 +0,0 @@
|
|||
import asyncio
|
||||
from typing import Type
|
||||
from uuid import uuid5
|
||||
from pydantic import BaseModel
|
||||
from cognee.modules.graph.utils import get_graph_from_model
|
||||
from cognee.tasks.summarization.models import TextSummary
|
||||
from cognee.infrastructure.engine.models.DataPoint import DataPoint
|
||||
from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
|
||||
from cognee.modules.data.extraction.extract_summary import extract_summary
|
||||
|
||||
|
||||
async def summarize_text(
|
||||
data_points_and_relationships: tuple[list[DocumentChunk], list[DataPoint], list],
|
||||
summarization_model: Type[BaseModel],
|
||||
):
|
||||
document_chunks = data_points_and_relationships[0]
|
||||
nodes = data_points_and_relationships[1]
|
||||
relationships = data_points_and_relationships[2]
|
||||
|
||||
if len(document_chunks) == 0:
|
||||
return document_chunks
|
||||
|
||||
chunk_summaries = await asyncio.gather(
|
||||
*[extract_summary(chunk.text, summarization_model) for chunk in document_chunks]
|
||||
)
|
||||
|
||||
summaries = [
|
||||
TextSummary(
|
||||
id=uuid5(chunk.id, "TextSummary"),
|
||||
made_from=chunk,
|
||||
text=chunk_summaries[chunk_index].summary,
|
||||
)
|
||||
for (chunk_index, chunk) in enumerate(document_chunks)
|
||||
]
|
||||
|
||||
data_points = summaries + nodes
|
||||
|
||||
added_nodes = {}
|
||||
added_edges = {}
|
||||
visited_properties = {}
|
||||
|
||||
nodes_and_edges: list[tuple] = await asyncio.gather(
|
||||
*[
|
||||
get_graph_from_model(
|
||||
data_point,
|
||||
added_nodes=added_nodes,
|
||||
added_edges=added_edges,
|
||||
visited_properties=visited_properties,
|
||||
)
|
||||
for data_point in data_points
|
||||
]
|
||||
)
|
||||
|
||||
graph_data_deduplication = GraphDataDeduplication()
|
||||
deduplicated_nodes_and_edges = [
|
||||
graph_data_deduplication.deduplicate_nodes_and_edges(nodes, edges + relationships)
|
||||
for nodes, edges in nodes_and_edges
|
||||
]
|
||||
|
||||
return deduplicated_nodes_and_edges
|
||||
|
||||
|
||||
class GraphDataDeduplication:
|
||||
nodes_and_edges_map: dict
|
||||
|
||||
def __init__(self):
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.nodes_and_edges_map = {}
|
||||
|
||||
def deduplicate_nodes_and_edges(self, nodes: list, edges: list):
|
||||
final_nodes = []
|
||||
final_edges = []
|
||||
|
||||
for node in nodes:
|
||||
node_key = str(node.id)
|
||||
if node_key not in self.nodes_and_edges_map:
|
||||
self.nodes_and_edges_map[node_key] = True
|
||||
final_nodes.append(node)
|
||||
|
||||
for edge in edges:
|
||||
edge_key = str(edge[0]) + str(edge[2]) + str(edge[1])
|
||||
if edge_key not in self.nodes_and_edges_map:
|
||||
self.nodes_and_edges_map[edge_key] = True
|
||||
final_edges.append(edge)
|
||||
|
||||
return final_nodes, final_edges
|
||||
|
|
@ -1,20 +1,35 @@
|
|||
import modal
|
||||
import asyncio
|
||||
|
||||
|
||||
from distributed.app import app
|
||||
from distributed.modal_image import image
|
||||
from distributed.queues import save_data_points_queue
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.infrastructure.databases.graph import get_graph_engine
|
||||
|
||||
|
||||
@app.function(image=image, timeout=86400, max_containers=100)
|
||||
logger = get_logger("data_point_saver_worker")
|
||||
|
||||
|
||||
@app.function(
|
||||
image=image,
|
||||
timeout=86400,
|
||||
max_containers=100,
|
||||
secrets=[modal.Secret.from_name("distributed_cognee")],
|
||||
)
|
||||
async def data_point_saver_worker():
|
||||
print("Started processing of nodes and edges; starting graph engine queue.")
|
||||
graph_engine = await get_graph_engine()
|
||||
|
||||
while True:
|
||||
if save_data_points_queue.len() != 0:
|
||||
nodes_and_edges = save_data_points_queue.get(block=False)
|
||||
if await save_data_points_queue.len.aio() != 0:
|
||||
try:
|
||||
nodes_and_edges = await save_data_points_queue.get.aio(block=False)
|
||||
except modal.exception.DeserializationError as error:
|
||||
logger.error(f"Deserialization error: {str(error)}")
|
||||
continue
|
||||
|
||||
if len(nodes_and_edges) == 0:
|
||||
print("Finished processing all nodes and edges; stopping graph engine queue.")
|
||||
|
|
@ -28,10 +43,10 @@ async def data_point_saver_worker():
|
|||
edges = nodes_and_edges[1]
|
||||
|
||||
if nodes:
|
||||
await graph_engine.add_nodes(nodes)
|
||||
await graph_engine.add_nodes(nodes, distributed=False)
|
||||
|
||||
if edges:
|
||||
await graph_engine.add_edges(edges)
|
||||
await graph_engine.add_edges(edges, distributed=False)
|
||||
print("Finished processing nodes and edges.")
|
||||
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -1,42 +0,0 @@
|
|||
# ------------------------------------------------------------------------------
|
||||
# Producer function that produces data points from documents and pushes them into the queue.
|
||||
# ------------------------------------------------------------------------------
|
||||
from cognee.modules.cognify.config import get_cognify_config
|
||||
from cognee.modules.pipelines.operations.run_tasks import run_tasks
|
||||
from cognee.modules.pipelines.tasks.task import Task
|
||||
from cognee.shared.data_models import KnowledgeGraph
|
||||
|
||||
from distributed.app import app
|
||||
from distributed.modal_image import image
|
||||
from distributed.tasks.summarize_text import summarize_text
|
||||
from distributed.tasks.extract_graph_from_data import extract_graph_from_data
|
||||
from distributed.tasks.save_data_points import save_data_points
|
||||
|
||||
|
||||
@app.function(image=image, timeout=86400, max_containers=100)
|
||||
async def graph_extraction_worker(user, document_name: str, document_chunks: list):
|
||||
cognee_config = get_cognify_config()
|
||||
|
||||
tasks = [
|
||||
Task(
|
||||
extract_graph_from_data,
|
||||
graph_model=KnowledgeGraph,
|
||||
),
|
||||
Task(
|
||||
summarize_text,
|
||||
summarization_model=cognee_config.summarization_model,
|
||||
),
|
||||
Task(save_data_points),
|
||||
]
|
||||
|
||||
async for _ in run_tasks(
|
||||
tasks,
|
||||
data=document_chunks,
|
||||
pipeline_name=f"modal_execution_file_{document_name}",
|
||||
user=user,
|
||||
):
|
||||
pass
|
||||
|
||||
print(f"File execution finished: {document_name}")
|
||||
|
||||
return document_name
|
||||
31
poetry.lock
generated
31
poetry.lock
generated
|
|
@ -1114,14 +1114,14 @@ uvicorn = {version = ">=0.18.3", extras = ["standard"]}
|
|||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.2.1"
|
||||
version = "8.1.8"
|
||||
description = "Composable command line interface toolkit"
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "click-8.2.1-py3-none-any.whl", hash = "sha256:61a3265b914e850b85317d0b3109c7f8cd35a670f963866005d6ef1d5175a12b"},
|
||||
{file = "click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202"},
|
||||
{file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
|
||||
{file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5841,26 +5841,25 @@ type = ["mypy (==1.14.1)"]
|
|||
|
||||
[[package]]
|
||||
name = "modal"
|
||||
version = "0.74.15"
|
||||
version = "1.0.5"
|
||||
description = "Python client library for Modal"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"distributed\""
|
||||
files = [
|
||||
{file = "modal-0.74.15-py3-none-any.whl", hash = "sha256:084e898ab202ccd698fd277d9dc9e9cec8d4b0954a1c09d4ba529f0446ab3526"},
|
||||
{file = "modal-0.74.15.tar.gz", hash = "sha256:95512811ebd42a52fa03724f60d0d1c32259788351e798d0d695974d94b2e49c"},
|
||||
{file = "modal-1.0.5-py3-none-any.whl", hash = "sha256:e575414b7e10c5ad97ae0a77149456f021e9f2c457559bb4a5af70771f481c09"},
|
||||
{file = "modal-1.0.5.tar.gz", hash = "sha256:1c6927dd5eb9bd8d86d83ee499b87d64366525f1dd3e5b31d1ac1ed62090f72a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = "*"
|
||||
certifi = "*"
|
||||
click = ">=8.1.0"
|
||||
fastapi = "*"
|
||||
grpclib = "0.4.7"
|
||||
protobuf = ">=3.19,<4.24.0 || >4.24.0,<6.0"
|
||||
click = ">=8.1.0,<8.2.0"
|
||||
grpclib = ">=0.4.7,<0.4.9"
|
||||
protobuf = ">=3.19,<4.24.0 || >4.24.0,<7.0"
|
||||
rich = ">=12.0.0"
|
||||
synchronicity = ">=0.9.10,<0.10.0"
|
||||
synchronicity = ">=0.9.15,<0.10.0"
|
||||
toml = "*"
|
||||
typer = ">=0.9"
|
||||
types-certifi = "*"
|
||||
|
|
@ -10492,15 +10491,15 @@ dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "synchronicity"
|
||||
version = "0.9.12"
|
||||
version = "0.9.16"
|
||||
description = "Export blocking and async library versions from a single async implementation"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"distributed\""
|
||||
files = [
|
||||
{file = "synchronicity-0.9.12-py3-none-any.whl", hash = "sha256:b006f57bd216d55e578316096a11b6dc16016d6b48e2766bcffabe40c88f9793"},
|
||||
{file = "synchronicity-0.9.12.tar.gz", hash = "sha256:977f3ed8f6e35de4d1a3f0aeee4937143ba8d913f531d33e8df7c539b2792fb8"},
|
||||
{file = "synchronicity-0.9.16-py3-none-any.whl", hash = "sha256:79dd3451c063a06489d466b0585808e1d5d257fc3c2304f5f733a142ef269e97"},
|
||||
{file = "synchronicity-0.9.16.tar.gz", hash = "sha256:bc174b756d12330b1e6027f8293c3b944cf3684dec241af009cb7bb169a0072f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -12237,4 +12236,4 @@ weaviate = ["weaviate-client"]
|
|||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.10,<=3.13"
|
||||
content-hash = "963b6d6541a74c8d063e8acc529489f3876c38cab3616a12a17cbe4e48fa40b6"
|
||||
content-hash = "ca2a3e8260092933419793efe202d50ae7b1c6ce738750876fd5f64a31718790"
|
||||
|
|
|
|||
|
|
@ -69,7 +69,7 @@ api = [
|
|||
"websockets>=15.0.1"
|
||||
]
|
||||
distributed = [
|
||||
"modal==0.74.15",
|
||||
"modal==1.0.5",
|
||||
]
|
||||
weaviate = ["weaviate-client==4.9.6"]
|
||||
qdrant = ["qdrant-client>=1.14.2,<2"]
|
||||
|
|
|
|||
59
uv.lock
generated
59
uv.lock
generated
|
|
@ -58,7 +58,6 @@ dependencies = [
|
|||
{ name = "aiohappyeyeballs" },
|
||||
{ name = "aiosignal" },
|
||||
{ name = "async-timeout", version = "4.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
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{ url = "https://files.pythonhosted.org/packages/f2/6d/17f80f4e1f0761f02160fc433abd4109fa1548dcfdca46cfdadaf9efa565/pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fe27fb049cdcca11f11a7bfda64043c37b30e6b91f10cb5bab275806c32f6ab3", size = 4874248, upload-time = "2025-07-03T13:11:20.738Z" },
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{ url = "https://files.pythonhosted.org/packages/34/e7/ae39f538fd6844e982063c3a5e4598b8ced43b9633baa3a85ef33af8c05c/pillow-11.3.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c84d689db21a1c397d001aa08241044aa2069e7587b398c8cc63020390b1c1b8", size = 6984598, upload-time = "2025-07-01T09:16:27.732Z" },
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||||
|
|
|
|||
Loading…
Add table
Reference in a new issue