ZmeiGorynych

Run coderabbit review --plain to get comprehensive code analysis and improvement suggestions. Apply the feedback to write cleaner, more maintainable code.

Only run this from a git repository root directory.

Before running the command, check whether the codebase has any changes since the last commit, if yes just call the command normally, if not first ask the user what they want to use as a baseline: previous commit, the moment when the branch was first created, the codebase as a whole, etc, and use the corresponding option from the below.

ALWAYS use the --config command and attach ALL CLAUDE.md files that you yourself would have used to write code in this repo (so global-level, repository-level, and any contained deeper down in the repo).

Available options for the coderabbit review command:

ZmeiGorynych

Add to the sqlmodels_to_cubes function in @storyline/storyline/local/sql_to_cube a new parameter rollup_references: bool = True, and an optional parameter additional_joins: List[JoinSpec] = None Here JoinSpec is

class JoinSpec(BaseModel):
    left_column: Tuple[str, str]  # (table_name, column_name)
    right_column: Tuple[str, str]  # (table_name, column_name)
    join_type: str = "left"
 relationship: str = "many_to_one"

ZmeiGorynych

import pandas as pd
from pydantic import BaseModel, field_validator, field_serializer, ConfigDict
from typing import Any, Union


class PydanticSerializableDataFrame(BaseModel):
    model_config = ConfigDict(arbitrary_types_allowed=True)
    df: pd.DataFrame

    @field_serializer("df")

ZmeiGorynych

import os
import logging
from typing import Callable, Any

import cloudpickle

logger = logging.getLogger(__name__)


def cached_call(func: Callable, filename: str, force: bool = False) -> Any:

ZmeiGorynych

#!/bin/bash

# Function to display usage information
usage() {
    echo "Usage: $0 env_file [command...]"
    echo "The conda environment name will be read from CONDA_ENV_NAME in the env file"
    echo "env_file is required and must be provided as the first argument"
    echo "Any additional arguments will be executed as a command after activating the environment"
    exit 1
}

ZmeiGorynych

from typing import Union

from llama_index.core.schema import BaseNode
from llama_index.core.vector_stores import (
    MetadataFilter,
    MetadataFilters,
    FilterOperator,
    FilterCondition,
)

ZmeiGorynych

from typing import Callable, Iterable
import logging
import concurrent.futures

logger = logging.getLogger(__name__)


def process_batch_parallel(
    function: Callable,
    batched_args: Iterable,