sample_id stringlengths 21 196 | text stringlengths 105 936k | metadata dict | category stringclasses 6
values |
|---|---|---|---|
mlflow/mlflow:tests/genai/judges/test_alignment_optimizer.py | from unittest.mock import Mock, patch
import pytest
from mlflow.entities.trace import Trace
from mlflow.genai.judges import AlignmentOptimizer, Judge, make_judge
from mlflow.genai.judges.base import JudgeField
from mlflow.genai.scorers import UserFrustration
class MockJudge(Judge):
"""Mock Judge implementation ... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_alignment_optimizer.py",
"license": "Apache License 2.0",
"lines": 114,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_judge_base.py | from typing import Any
import pytest
from mlflow.entities.assessment import Feedback
from mlflow.entities.trace import Trace
from mlflow.genai.judges import Judge
from mlflow.genai.judges.base import JudgeField
from mlflow.genai.scorers.base import Scorer
class MockJudgeImplementation(Judge):
def __init__(self,... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_judge_base.py",
"license": "Apache License 2.0",
"lines": 88,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_judge_tool_get_root_span.py | import pytest
from mlflow.entities.span import Span
from mlflow.entities.trace import Trace
from mlflow.entities.trace_data import TraceData
from mlflow.entities.trace_info import TraceInfo
from mlflow.entities.trace_location import TraceLocation
from mlflow.entities.trace_state import TraceState
from mlflow.genai.jud... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_judge_tool_get_root_span.py",
"license": "Apache License 2.0",
"lines": 208,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_judge_tool_get_span.py | import pytest
from mlflow.entities.span import Span
from mlflow.entities.trace import Trace
from mlflow.entities.trace_data import TraceData
from mlflow.entities.trace_info import TraceInfo
from mlflow.entities.trace_location import TraceLocation
from mlflow.entities.trace_state import TraceState
from mlflow.genai.jud... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_judge_tool_get_span.py",
"license": "Apache License 2.0",
"lines": 185,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_judge_tool_get_trace_info.py | from mlflow.entities.trace import Trace
from mlflow.entities.trace_info import TraceInfo
from mlflow.entities.trace_location import TraceLocation
from mlflow.entities.trace_state import TraceState
from mlflow.genai.judges.tools.get_trace_info import GetTraceInfoTool
from mlflow.types.llm import ToolDefinition
def tes... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_judge_tool_get_trace_info.py",
"license": "Apache License 2.0",
"lines": 61,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_judge_tool_list_spans.py | from unittest import mock
import pytest
from mlflow.entities.span import Span
from mlflow.entities.span_status import SpanStatus, SpanStatusCode
from mlflow.entities.trace import Trace
from mlflow.entities.trace_data import TraceData
from mlflow.entities.trace_info import TraceInfo
from mlflow.entities.trace_location... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_judge_tool_list_spans.py",
"license": "Apache License 2.0",
"lines": 123,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_judge_tool_registry.py | import inspect
import json
import pytest
import mlflow
from mlflow.entities.span import SpanType
from mlflow.entities.trace import Trace
from mlflow.entities.trace_info import TraceInfo
from mlflow.entities.trace_location import TraceLocation
from mlflow.entities.trace_state import TraceState
from mlflow.exceptions i... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_judge_tool_registry.py",
"license": "Apache License 2.0",
"lines": 166,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_make_judge.py | import json
import sys
import types
import typing
from dataclasses import asdict
from typing import Any, Literal
from unittest import mock
from unittest.mock import patch
import litellm
import pandas as pd
import pydantic
import pytest
from opentelemetry.sdk.trace import ReadableSpan as OTelReadableSpan
import mlflow... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_make_judge.py",
"license": "Apache License 2.0",
"lines": 3006,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_search_trace_regex_tool.py | import json
import pytest
from mlflow.entities.trace import Trace
from mlflow.entities.trace_data import TraceData
from mlflow.entities.trace_info import TraceInfo
from mlflow.entities.trace_location import TraceLocation
from mlflow.entities.trace_state import TraceState
from mlflow.genai.judges.tools.search_trace_re... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_search_trace_regex_tool.py",
"license": "Apache License 2.0",
"lines": 254,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/prompts/utils.py | import re
from typing import Any
def format_prompt(prompt: str, **values: Any) -> str:
"""Format double-curly variables in the prompt template."""
for key, value in values.items():
# Escape backslashes in the replacement string to prevent re.sub from interpreting
# them as escape sequences (e.... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/prompts/utils.py",
"license": "Apache License 2.0",
"lines": 10,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:dev/clint/src/clint/rules/no_shebang.py | from clint.rules.base import Rule
class NoShebang(Rule):
def _message(self) -> str:
return "Python scripts should not contain shebang lines"
@staticmethod
def check(file_content: str) -> bool:
"""
Returns True if the file contains a shebang line at the beginning.
A sheban... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/src/clint/rules/no_shebang.py",
"license": "Apache License 2.0",
"lines": 11,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:dev/clint/tests/rules/test_no_shebang.py | from pathlib import Path
import pytest
from clint.config import Config
from clint.linter import Position, Range, lint_file
from clint.rules import NoShebang
def test_no_shebang(index_path: Path) -> None:
config = Config(select={NoShebang.name})
# Test file with shebang - should trigger violation
code = ... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/tests/rules/test_no_shebang.py",
"license": "Apache License 2.0",
"lines": 53,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/scorers/test_builtin_scorers_registration.py | from pathlib import Path
from typing import Iterator
from unittest import mock
import pytest
import mlflow
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers import RetrievalRelevance, Safety, Scorer
from mlflow.genai.scorers.base import ScorerSamplingConfig
@pytest.fixture
def mock_databricks_... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/test_builtin_scorers_registration.py",
"license": "Apache License 2.0",
"lines": 108,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/judges/prompts/retrieval_relevance.py | from mlflow.genai.prompts.utils import format_prompt
RETRIEVAL_RELEVANCE_PROMPT = """\
Consider the following question and document. You must determine whether the document provides information that is (fully or partially) relevant to the question. Do not focus on the correctness or completeness of the document. Do no... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/retrieval_relevance.py",
"license": "Apache License 2.0",
"lines": 17,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/prompts/safety.py | from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the safety assessment.
SAFETY_ASSESSMENT_NAME = "safety"
SAFETY_PROMPT = """\
You are a content safety classifier. Analyze the given text against policies prohibiting hate speech, harassment, incitement of violence, and the promotion of ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/safety.py",
"license": "Apache License 2.0",
"lines": 14,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/data/evaluation_dataset_source.py | from typing import Any
from mlflow.data.dataset_source import DatasetSource
class EvaluationDatasetSource(DatasetSource):
"""
Represents the source of an evaluation dataset stored in MLflow's tracking store.
"""
def __init__(self, dataset_id: str):
"""
Args:
dataset_id: T... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/data/evaluation_dataset_source.py",
"license": "Apache License 2.0",
"lines": 49,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/entities/dataset_record.py | from __future__ import annotations
import json
from dataclasses import dataclass
from typing import Any
from google.protobuf.json_format import MessageToDict
from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.entities.dataset_record_source import DatasetRecordSource, DatasetRecordSourceType
from ml... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/entities/dataset_record.py",
"license": "Apache License 2.0",
"lines": 157,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/entities/dataset_record_source.py | from __future__ import annotations
import json
from dataclasses import asdict, dataclass
from enum import Enum
from typing import Any
from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
from mlflow.prot... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/entities/dataset_record_source.py",
"license": "Apache License 2.0",
"lines": 99,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/entities/evaluation_dataset.py | from __future__ import annotations
import json
from enum import Enum
from typing import TYPE_CHECKING, Any
from mlflow.data import Dataset
from mlflow.data.evaluation_dataset_source import EvaluationDatasetSource
from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin
from mlflow.entities._mlflow_o... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/entities/evaluation_dataset.py",
"license": "Apache License 2.0",
"lines": 519,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/store/tracking/_sql_backend_utils.py | from functools import wraps
from typing import Any, Callable, TypeVar, cast
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import FEATURE_DISABLED
F = TypeVar("F", bound=Callable[..., Any])
def filestore_not_supported(func: F) -> F:
"""
Decorator for FileStore methods that a... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/tracking/_sql_backend_utils.py",
"license": "Apache License 2.0",
"lines": 25,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:tests/entities/test_dataset_record.py | import json
import pytest
from mlflow.entities.dataset_record import DatasetRecord
from mlflow.entities.dataset_record_source import DatasetRecordSource
from mlflow.protos.datasets_pb2 import DatasetRecord as ProtoDatasetRecord
from mlflow.protos.datasets_pb2 import DatasetRecordSource as ProtoDatasetRecordSource
d... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/entities/test_dataset_record.py",
"license": "Apache License 2.0",
"lines": 428,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/entities/test_dataset_record_source.py | import json
import pytest
from mlflow.entities.dataset_record_source import DatasetRecordSource, DatasetRecordSourceType
from mlflow.exceptions import MlflowException
from mlflow.protos.datasets_pb2 import DatasetRecordSource as ProtoDatasetRecordSource
def test_dataset_record_source_type_constants():
assert Da... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/entities/test_dataset_record_source.py",
"license": "Apache License 2.0",
"lines": 168,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/entities/test_evaluation_dataset.py | import json
from unittest.mock import Mock, patch
import pandas as pd
import pytest
from opentelemetry.sdk.trace import ReadableSpan as OTelReadableSpan
from mlflow.entities.dataset_record import DatasetRecord
from mlflow.entities.dataset_record_source import DatasetRecordSourceType
from mlflow.entities.evaluation_da... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/entities/test_evaluation_dataset.py",
"license": "Apache License 2.0",
"lines": 619,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/datasets/test_fluent.py | import json
import os
import sys
import warnings
from unittest import mock
import pandas as pd
import pytest
import mlflow
from mlflow.data import Dataset
from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin
from mlflow.entities.dataset_record_source import DatasetRecordSourceType
from mlflow.en... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/datasets/test_fluent.py",
"license": "Apache License 2.0",
"lines": 1832,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/ai_commands/ai_command_utils.py | """Core module for managing MLflow commands."""
import os
import re
from pathlib import Path
from typing import Any
import yaml
def parse_frontmatter(content: str) -> tuple[dict[str, Any], str]:
"""Parse frontmatter from markdown content.
Args:
content: Markdown content with optional YAML frontmatt... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/ai_commands/ai_command_utils.py",
"license": "Apache License 2.0",
"lines": 88,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/ai_commands/test_ai_command_utils.py | import platform
from unittest import mock
import pytest
from mlflow.ai_commands import get_command, get_command_body, list_commands, parse_frontmatter
def test_parse_frontmatter_with_metadata():
content = """---
namespace: genai
description: Test command
---
# Command content
This is the body."""
metadata... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/ai_commands/test_ai_command_utils.py",
"license": "Apache License 2.0",
"lines": 200,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/cli/test_ai_commands.py | from unittest import mock
from click.testing import CliRunner
from mlflow.cli import cli
def test_list_commands_cli():
mock_commands = [
{
"key": "genai/analyze_experiment",
"namespace": "genai",
"description": "Analyzes an MLflow experiment",
},
{
... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/cli/test_ai_commands.py",
"license": "Apache License 2.0",
"lines": 152,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:bin/install.py | """
Install binary tools for MLflow development.
"""
# ruff: noqa: T201
import argparse
import gzip
import http.client
import json
import platform
import subprocess
import tarfile
import time
import urllib.request
from dataclasses import dataclass
from pathlib import Path
from typing import Literal
from urllib.error i... | {
"repo_id": "mlflow/mlflow",
"file_path": "bin/install.py",
"license": "Apache License 2.0",
"lines": 296,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/tracing/processor/otel_metrics_mixin.py | """
Mixin class for OpenTelemetry span processors that provides metrics recording functionality.
This mixin allows different span processor implementations to share common metrics logic
while maintaining their own inheritance hierarchies (BatchSpanProcessor, SimpleSpanProcessor).
"""
import logging
from typing import... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/processor/otel_metrics_mixin.py",
"license": "Apache License 2.0",
"lines": 100,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/tracing/processor/test_otel_metrics.py | import time
import pytest
from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import InMemoryMetricReader
import mlflow
@pytest.fixture
def metric_reader() -> InMemoryMetricReader:
"""Create an in-memory metric reader for testing."""
re... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/tracing/processor/test_otel_metrics.py",
"license": "Apache License 2.0",
"lines": 84,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:dev/clint/src/clint/rules/forbidden_deprecation_warning.py | import ast
from clint.resolver import Resolver
from clint.rules.base import Rule
def _is_deprecation_warning(expr: ast.expr) -> bool:
return isinstance(expr, ast.Name) and expr.id == "DeprecationWarning"
class ForbiddenDeprecationWarning(Rule):
def _message(self) -> str:
return (
"Do no... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/src/clint/rules/forbidden_deprecation_warning.py",
"license": "Apache License 2.0",
"lines": 27,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:dev/clint/tests/rules/test_forbidden_deprecation_warning.py | from pathlib import Path
from clint.config import Config
from clint.linter import Position, Range, lint_file
from clint.rules import ForbiddenDeprecationWarning
def test_forbidden_deprecation_warning(index_path: Path) -> None:
code = """
import warnings
# Bad - should be flagged
warnings.warn("message", categor... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/tests/rules/test_forbidden_deprecation_warning.py",
"license": "Apache License 2.0",
"lines": 66,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/mcp/cli.py | import click
from mlflow.mcp.server import run_server
from mlflow.telemetry.events import McpRunEvent
from mlflow.telemetry.track import record_usage_event
@click.group(
"mcp",
help=(
"Model Context Protocol (MCP) server for MLflow. "
"MCP enables LLM applications to interact with MLflow trac... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/mcp/cli.py",
"license": "Apache License 2.0",
"lines": 27,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/mcp/server.py | import contextlib
import io
import os
from typing import TYPE_CHECKING, Any, Callable
import click
from click.types import BOOL, FLOAT, INT, STRING, UUID
import mlflow.deployments.cli as deployments_cli
import mlflow.experiments
import mlflow.models.cli as models_cli
import mlflow.runs
from mlflow.ai_commands.ai_comm... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/mcp/server.py",
"license": "Apache License 2.0",
"lines": 183,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/mcp/test_cli.py | import sys
import pytest
from fastmcp import Client
from fastmcp.client.transports import StdioTransport
import mlflow
@pytest.mark.asyncio
async def test_cli():
transport = StdioTransport(
command=sys.executable,
args=[
"-m",
"mlflow",
"mcp",
"run... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/mcp/test_cli.py",
"license": "Apache License 2.0",
"lines": 23,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/mcp/test_mcp.py | import sys
from collections.abc import AsyncIterator
import pytest
import pytest_asyncio
from fastmcp import Client
from fastmcp.client.transports import StdioTransport
import mlflow
from mlflow.mcp import server
@pytest_asyncio.fixture()
async def client() -> AsyncIterator[Client]:
transport = StdioTransport(
... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/mcp/test_mcp.py",
"license": "Apache License 2.0",
"lines": 139,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/git_versioning/git_info.py | import logging
from dataclasses import dataclass
from typing_extensions import Self
from mlflow.utils.mlflow_tags import (
MLFLOW_GIT_BRANCH,
MLFLOW_GIT_COMMIT,
MLFLOW_GIT_DIFF,
MLFLOW_GIT_DIRTY,
MLFLOW_GIT_REPO_URL,
)
_logger = logging.getLogger(__name__)
class GitOperationError(Exception):
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/git_versioning/git_info.py",
"license": "Apache License 2.0",
"lines": 82,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/genai/test_git_versioning.py | import subprocess
from pathlib import Path
from unittest import mock
import pytest
import mlflow
from mlflow.genai import disable_git_model_versioning, enable_git_model_versioning
from mlflow.genai.git_versioning import _get_active_git_context
from mlflow.utils.mlflow_tags import MLFLOW_GIT_DIFF
@pytest.fixture(aut... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/test_git_versioning.py",
"license": "Apache License 2.0",
"lines": 207,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/store/artifact/databricks_run_artifact_repo.py | import re
from mlflow.store.artifact.databricks_tracking_artifact_repo import (
DatabricksTrackingArtifactRepository,
)
class DatabricksRunArtifactRepository(DatabricksTrackingArtifactRepository):
"""
Artifact repository for interacting with run artifacts in a Databricks workspace.
If operations usin... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/artifact/databricks_run_artifact_repo.py",
"license": "Apache License 2.0",
"lines": 26,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/store/artifact/databricks_tracking_artifact_repo.py | import logging
import re
from abc import ABC, abstractmethod
from mlflow.entities import FileInfo
from mlflow.exceptions import MlflowException
from mlflow.store.artifact.artifact_repo import ArtifactRepository
from mlflow.store.artifact.databricks_artifact_repo import DatabricksArtifactRepository
from mlflow.store.ar... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/artifact/databricks_tracking_artifact_repo.py",
"license": "Apache License 2.0",
"lines": 81,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/store/artifact/test_databricks_run_artifact_repo.py | from pathlib import Path
from unittest import mock
import pytest
from databricks.sdk.service.files import DirectoryEntry
from mlflow.entities.file_info import FileInfo
from mlflow.store.artifact.databricks_run_artifact_repo import DatabricksRunArtifactRepository
@pytest.fixture(autouse=True)
def set_fake_databricks... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/store/artifact/test_databricks_run_artifact_repo.py",
"license": "Apache License 2.0",
"lines": 239,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/store/analytics/trace_correlation.py | import math
from dataclasses import dataclass
# Recommended smoothing parameter for NPMI calculation
# Using Jeffreys prior (alpha=0.5) to minimize bias while providing robust estimates
JEFFREYS_PRIOR = 0.5
@dataclass
class TraceCorrelationCounts:
"""
Count statistics for trace correlation analysis.
Thi... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/analytics/trace_correlation.py",
"license": "Apache License 2.0",
"lines": 163,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/tracing/analysis.py | import math
from dataclasses import dataclass
from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.protos.service_pb2 import CalculateTraceFilterCorrelation
@dataclass
class TraceFilterCorrelationResult(_MlflowObject):
"""
Result of calculating correlation between two trace filter conditions.... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/analysis.py",
"license": "Apache License 2.0",
"lines": 71,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/cli/traces.py | """
Comprehensive MLflow Traces CLI for managing trace data, assessments, and metadata.
This module provides a complete command-line interface for working with MLflow traces,
including search, retrieval, deletion, tagging, and assessment management. It supports
both table and JSON output formats with flexible field se... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/cli/traces.py",
"license": "Apache License 2.0",
"lines": 780,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/utils/jsonpath_utils.py | """
JSONPath utilities for navigating and manipulating nested JSON structures.
This module provides a simplified JSONPath-like implementation without adding
external dependencies to MLflow. Instead of using a full JSONPath library,
we implement a lightweight subset focused on trace data navigation using
dot notation w... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/utils/jsonpath_utils.py",
"license": "Apache License 2.0",
"lines": 271,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/cli/test_traces.py | import json
import logging
from unittest import mock
import pytest
from click.testing import CliRunner
from mlflow.cli.traces import commands
from mlflow.entities import (
AssessmentSourceType,
MlflowExperimentLocation,
Trace,
TraceData,
TraceInfo,
TraceLocation,
TraceLocationType,
Tra... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/cli/test_traces.py",
"license": "Apache License 2.0",
"lines": 188,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/utils/test_jsonpath_utils.py | import pytest
from mlflow.utils.jsonpath_utils import (
filter_json_by_fields,
jsonpath_extract_values,
split_path_respecting_backticks,
validate_field_paths,
)
def test_jsonpath_extract_values_simple():
data = {"info": {"trace_id": "tr-123", "state": "OK"}}
values = jsonpath_extract_values(d... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/utils/test_jsonpath_utils.py",
"license": "Apache License 2.0",
"lines": 196,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/utils/display_utils.py | import sys
from mlflow.entities import Run
from mlflow.store.tracking.rest_store import RestStore
from mlflow.tracing.display.display_handler import _is_jupyter
from mlflow.tracking._tracking_service.utils import _get_store, get_tracking_uri
from mlflow.utils.mlflow_tags import MLFLOW_DATABRICKS_WORKSPACE_URL
from mlf... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/utils/display_utils.py",
"license": "Apache License 2.0",
"lines": 134,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:tests/genai/utils/test_display_utils.py | from unittest import mock
import mlflow
from mlflow.genai.utils import display_utils
from mlflow.store.tracking.rest_store import RestStore
from mlflow.tracking.client import MlflowClient
from mlflow.utils.mlflow_tags import MLFLOW_DATABRICKS_WORKSPACE_URL
def test_display_outputs_jupyter(monkeypatch):
mock_stor... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/utils/test_display_utils.py",
"license": "Apache License 2.0",
"lines": 62,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/tracing/archival.py | _ERROR_MSG = (
"The `databricks-agents` package is required to use databricks trace archival. "
"Please install it with `pip install databricks-agents`."
)
def enable_databricks_trace_archival(
*,
delta_table_fullname: str,
experiment_id: str | None = None,
) -> None:
"""
Enable archiving ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/archival.py",
"license": "Apache License 2.0",
"lines": 50,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:tests/tracing/test_archival.py | from unittest import mock
import pytest
from mlflow.tracing.archival import (
disable_databricks_trace_archival,
enable_databricks_trace_archival,
)
from mlflow.version import IS_TRACING_SDK_ONLY
if IS_TRACING_SDK_ONLY:
pytest.skip("Databricks archival enablement requires skinny", allow_module_level=True... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/tracing/test_archival.py",
"license": "Apache License 2.0",
"lines": 63,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:dev/clint/src/clint/rules/no_class_based_tests.py | import ast
from typing_extensions import Self
from clint.rules.base import Rule
class NoClassBasedTests(Rule):
def __init__(self, class_name: str) -> None:
self.class_name = class_name
@classmethod
def check(cls, node: ast.ClassDef, path_name: str) -> Self | None:
# Only check in test f... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/src/clint/rules/no_class_based_tests.py",
"license": "Apache License 2.0",
"lines": 26,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:dev/clint/tests/rules/test_no_class_based_tests.py | from pathlib import Path
from clint.config import Config
from clint.linter import Position, Range, lint_file
from clint.rules.no_class_based_tests import NoClassBasedTests
def test_no_class_based_tests(index_path: Path) -> None:
code = """import pytest
# Bad - class-based test with test methods
class TestSometh... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/tests/rules/test_no_class_based_tests.py",
"license": "Apache License 2.0",
"lines": 49,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/claude_code/cli.py | """MLflow CLI commands for Claude Code integration."""
from pathlib import Path
import click
from mlflow.claude_code.config import get_tracing_status, setup_environment_config
from mlflow.claude_code.hooks import disable_tracing_hooks, setup_hooks_config
@click.group("autolog")
def commands():
"""Commands for ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/claude_code/cli.py",
"license": "Apache License 2.0",
"lines": 122,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/claude_code/config.py | """Configuration management for Claude Code integration with MLflow."""
import json
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any
from mlflow.environment_variables import (
MLFLOW_EXPERIMENT_ID,
MLFLOW_EXPERIMENT_NAME,
MLFLOW_TRACKING_URI,
)
# Configuration f... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/claude_code/config.py",
"license": "Apache License 2.0",
"lines": 122,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/claude_code/hooks.py | """Hook management for Claude Code integration with MLflow."""
import json
import os
import sys
from pathlib import Path
from typing import Any
from mlflow.claude_code.config import (
ENVIRONMENT_FIELD,
HOOK_FIELD_COMMAND,
HOOK_FIELD_HOOKS,
MLFLOW_EXPERIMENT_ID,
MLFLOW_EXPERIMENT_NAME,
MLFLOW_... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/claude_code/hooks.py",
"license": "Apache License 2.0",
"lines": 193,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/claude_code/tracing.py | """MLflow tracing integration for Claude Code interactions."""
import dataclasses
import json
import logging
import os
import sys
from datetime import datetime
from pathlib import Path
from typing import Any
import dateutil.parser
import mlflow
from mlflow.claude_code.config import (
MLFLOW_TRACING_ENABLED,
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/claude_code/tracing.py",
"license": "Apache License 2.0",
"lines": 700,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/claude_code/test_cli.py | import json
from pathlib import Path
import pytest
from click.testing import CliRunner
from mlflow.claude_code.cli import commands
from mlflow.claude_code.config import HOOK_FIELD_COMMAND, HOOK_FIELD_HOOKS
from mlflow.claude_code.hooks import upsert_hook
@pytest.fixture
def runner():
"""Provide a CLI runner for... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/claude_code/test_cli.py",
"license": "Apache License 2.0",
"lines": 69,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/claude_code/test_config.py | import json
import pytest
from mlflow.claude_code.config import (
MLFLOW_TRACING_ENABLED,
get_env_var,
get_tracing_status,
load_claude_config,
save_claude_config,
setup_environment_config,
)
@pytest.fixture
def temp_settings_path(tmp_path):
"""Provide a temporary settings.json path for t... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/claude_code/test_config.py",
"license": "Apache License 2.0",
"lines": 131,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/claude_code/test_tracing.py | import importlib
import json
import logging
from pathlib import Path
import pytest
from claude_agent_sdk.types import (
AssistantMessage,
ResultMessage,
TextBlock,
ToolResultBlock,
ToolUseBlock,
UserMessage,
)
import mlflow
import mlflow.claude_code.tracing as tracing_module
from mlflow.claude... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/claude_code/test_tracing.py",
"license": "Apache License 2.0",
"lines": 717,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/evaluate/test_context.py | import threading
from unittest import mock
import pytest
import mlflow
from mlflow.environment_variables import MLFLOW_TRACKING_USERNAME
from mlflow.genai.evaluation.context import NoneContext, _set_context, eval_context, get_context
@pytest.fixture(autouse=True)
def reset_context():
yield
_set_context(None... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/evaluate/test_context.py",
"license": "Apache License 2.0",
"lines": 51,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/entities/scorer.py | import json
from functools import lru_cache
from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.protos.service_pb2 import Scorer as ProtoScorer
class ScorerVersion(_MlflowObject):
"""
A versioned scorer entity that represents a specific version of a scorer within an MLflow
experiment.
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/entities/scorer.py",
"license": "Apache License 2.0",
"lines": 169,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:tests/genai/scorers/test_scorer_CRUD.py | from unittest.mock import ANY, Mock, patch
import mlflow
import mlflow.genai
from mlflow.entities import GatewayEndpointModelConfig, GatewayModelLinkageType
from mlflow.entities.gateway_endpoint import GatewayEndpoint
from mlflow.genai.scorers import Guidelines, Scorer, scorer
from mlflow.genai.scorers.base import Sco... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/test_scorer_CRUD.py",
"license": "Apache License 2.0",
"lines": 305,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/entities/webhook.py | from enum import Enum
from typing import Literal, TypeAlias
from typing_extensions import Self
from mlflow.exceptions import MlflowException
from mlflow.protos.webhooks_pb2 import Webhook as ProtoWebhook
from mlflow.protos.webhooks_pb2 import WebhookAction as ProtoWebhookAction
from mlflow.protos.webhooks_pb2 import ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/entities/webhook.py",
"license": "Apache License 2.0",
"lines": 383,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/webhooks/constants.py | # MLflow webhook headers
WEBHOOK_SIGNATURE_HEADER = "X-MLflow-Signature"
WEBHOOK_TIMESTAMP_HEADER = "X-MLflow-Timestamp"
WEBHOOK_DELIVERY_ID_HEADER = "X-MLflow-Delivery-Id"
# Webhook signature version
WEBHOOK_SIGNATURE_VERSION = "v1"
| {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/webhooks/constants.py",
"license": "Apache License 2.0",
"lines": 6,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/webhooks/delivery.py | """Webhook delivery implementation following Standard Webhooks conventions.
This module implements webhook delivery patterns similar to the Standard Webhooks
specification (https://www.standardwebhooks.com), providing consistent and secure
webhook delivery with HMAC signature verification and timestamp-based replay pr... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/webhooks/delivery.py",
"license": "Apache License 2.0",
"lines": 286,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/webhooks/types.py | """Type definitions for MLflow webhook payloads.
This module contains class definitions for all webhook event payloads
that are sent when various model registry events occur.
"""
from typing import Literal, TypeAlias, TypedDict
from mlflow.entities.webhook import WebhookAction, WebhookEntity, WebhookEvent
class Re... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/webhooks/types.py",
"license": "Apache License 2.0",
"lines": 448,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:tests/entities/test_webhook.py | import pytest
from mlflow.entities.webhook import (
Webhook,
WebhookAction,
WebhookEntity,
WebhookEvent,
WebhookStatus,
WebhookTestResult,
)
from mlflow.exceptions import MlflowException
from mlflow.protos.webhooks_pb2 import WebhookAction as ProtoWebhookAction
from mlflow.protos.webhooks_pb2 i... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/entities/test_webhook.py",
"license": "Apache License 2.0",
"lines": 186,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/store/model_registry/test_rest_store_webhooks.py | """
This test file verifies webhook CRUD operations with the REST client,
testing both server handlers and the REST client together.
"""
from pathlib import Path
from typing import Iterator
import pytest
from cryptography.fernet import Fernet
from mlflow.entities.webhook import WebhookAction, WebhookEntity, WebhookE... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/store/model_registry/test_rest_store_webhooks.py",
"license": "Apache License 2.0",
"lines": 188,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/tracking/test_client_webhooks.py | from pathlib import Path
from typing import Iterator
import pytest
from cryptography.fernet import Fernet
from mlflow.entities.webhook import WebhookAction, WebhookEntity, WebhookEvent, WebhookStatus
from mlflow.environment_variables import MLFLOW_WEBHOOK_SECRET_ENCRYPTION_KEY
from mlflow.exceptions import MlflowExce... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/tracking/test_client_webhooks.py",
"license": "Apache License 2.0",
"lines": 186,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/webhooks/app.py | import base64
import hashlib
import hmac
import itertools
import json
import sys
from pathlib import Path
import fastapi
import uvicorn
from fastapi import HTTPException, Request
from mlflow.webhooks.constants import (
WEBHOOK_DELIVERY_ID_HEADER,
WEBHOOK_SIGNATURE_HEADER,
WEBHOOK_SIGNATURE_VERSION,
WE... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/webhooks/app.py",
"license": "Apache License 2.0",
"lines": 191,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/webhooks/test_e2e.py | import contextlib
import os
import subprocess
import sys
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Generator
import psutil
import pytest
import requests
from cryptography.fernet import Fernet
from mlflow import MlflowClient
from mlflow.entities.webhook import Webho... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/webhooks/test_e2e.py",
"license": "Apache License 2.0",
"lines": 814,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:dev/normalize_chars.py | import sys
from pathlib import Path
# Mapping of characters to normalize. Start with quotes; extend as needed.
CHAR_MAP = {
"\u2018": "'", # left single quotation mark
"\u2019": "'", # right single quotation mark
"\u201c": '"', # left double quotation mark
"\u201d": '"', # right double quotation ma... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/normalize_chars.py",
"license": "Apache License 2.0",
"lines": 34,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:examples/agno/tracing.py | import mlflow
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("AGNO Reasoning Finance Team")
mlflow.agno.autolog()
mlflow.anthropic.autolog()
mlflow.openai.autolog()
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.team.t... | {
"repo_id": "mlflow/mlflow",
"file_path": "examples/agno/tracing.py",
"license": "Apache License 2.0",
"lines": 69,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/agno/utils.py | import importlib
import logging
import pkgutil
from agno.models.base import Model
from agno.storage.base import Storage
_logger = logging.getLogger(__name__)
def discover_storage_backends():
# 1. Import all storage modules
import agno.storage as pkg
for _, modname, _ in pkgutil.iter_modules(pkg.__path_... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/agno/utils.py",
"license": "Apache License 2.0",
"lines": 39,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:tests/agno/test_agno_tracing.py | import sys
from unittest.mock import MagicMock, patch
import agno
import pytest
from agno.agent import Agent
from agno.exceptions import ModelProviderError
from agno.models.anthropic import Claude
from agno.tools.function import Function, FunctionCall
from anthropic.types import Message, TextBlock, Usage
from packagin... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/agno/test_agno_tracing.py",
"license": "Apache License 2.0",
"lines": 292,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/entities/entity_type.py | """
Entity type constants for MLflow's entity_association table.
The entity_association table enables many-to-many relationships between different
MLflow entities. It uses source and destination type/id pairs to create flexible
associations without requiring dedicated junction tables for each relationship type.
"""
c... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/entities/entity_type.py",
"license": "Apache License 2.0",
"lines": 14,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/evaluation/context.py | """
Introduces main Context class and the framework to specify different specialized
contexts.
"""
import functools
from abc import ABC, abstractmethod
from typing import Callable, ParamSpec, TypeVar
import mlflow
from mlflow.tracking.context import registry as context_registry
from mlflow.utils.mlflow_tags import ML... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/evaluation/context.py",
"license": "Apache License 2.0",
"lines": 106,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/evaluation/entities.py | """Entities for evaluation."""
import hashlib
import json
from dataclasses import dataclass, field
from typing import Any
import pandas as pd
from mlflow.entities.assessment import Expectation, Feedback
from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType
from mlflow.entities.dataset_... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/evaluation/entities.py",
"license": "Apache License 2.0",
"lines": 183,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/evaluation/harness.py | """Entry point to the evaluation harness"""
from __future__ import annotations
import logging
import time
import traceback
import uuid
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Any, Callable
import pandas as pd
from mlflow.exceptions import MlflowException
try:
from tqd... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/evaluation/harness.py",
"license": "Apache License 2.0",
"lines": 429,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/judges/custom_prompt_judge.py | import re
from difflib import unified_diff
from typing import Callable
from mlflow.entities.assessment import Feedback
from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType
from mlflow.genai.judges.builtin import _MODEL_API_DOC
from mlflow.genai.judges.constants import USE_CASE_CUSTOM_PR... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/custom_prompt_judge.py",
"license": "Apache License 2.0",
"lines": 138,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/judges/prompts/context_sufficiency.py | from typing import Any
from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the is_context_sufficient assessment.
CONTEXT_SUFFICIENCY_FEEDBACK_NAME = "context_sufficiency"
CONTEXT_SUFFICIENCY_PROMPT_INSTRUCTIONS = """\
Consider the following claim and document. You must determine whether ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/context_sufficiency.py",
"license": "Apache License 2.0",
"lines": 56,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/prompts/correctness.py | from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the is_correct assessment.
CORRECTNESS_FEEDBACK_NAME = "correctness"
CORRECTNESS_PROMPT_INSTRUCTIONS = """\
Consider the following question, claim and document. You must determine whether the claim is \
supported by the document in the c... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/correctness.py",
"license": "Apache License 2.0",
"lines": 53,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/prompts/groundedness.py | from typing import Any
from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the is_grounded assessment.
GROUNDEDNESS_FEEDBACK_NAME = "groundedness"
GROUNDEDNESS_PROMPT_INSTRUCTIONS = """\
Consider the following claim and document. You must determine whether claim is supported by the \
doc... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/groundedness.py",
"license": "Apache License 2.0",
"lines": 37,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/prompts/guidelines.py | from mlflow.genai.prompts.utils import format_prompt
GUIDELINES_FEEDBACK_NAME = "guidelines"
GUIDELINES_PROMPT_INSTRUCTIONS = """\
Given the following set of guidelines and some inputs, please assess whether the inputs fully \
comply with all the provided guidelines. Only focus on the provided guidelines and not the... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/guidelines.py",
"license": "Apache License 2.0",
"lines": 36,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/prompts/relevance_to_query.py | from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the is_context_relevant assessment.
RELEVANCE_TO_QUERY_ASSESSMENT_NAME = "relevance_to_context"
RELEVANCE_TO_QUERY_PROMPT_INSTRUCTIONS = """\
Consider the following question and answer. You must determine whether the answer provides \
in... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/relevance_to_query.py",
"license": "Apache License 2.0",
"lines": 27,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/scorers/aggregation.py | """Generate the metrics logged into MLflow."""
import collections
import logging
import numpy as np
from mlflow.entities.assessment import Feedback
from mlflow.genai.evaluation.entities import EvalResult
from mlflow.genai.judges.builtin import CategoricalRating
from mlflow.genai.scorers.base import AggregationFunc, ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/aggregation.py",
"license": "Apache License 2.0",
"lines": 92,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/genai/judges/test_builtin.py | import json
from unittest import mock
import pytest
from litellm.types.utils import ModelResponse
from mlflow.entities.assessment import (
AssessmentError,
AssessmentSource,
AssessmentSourceType,
Feedback,
)
from mlflow.exceptions import MlflowException
from mlflow.genai import judges
from mlflow.gena... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_builtin.py",
"license": "Apache License 2.0",
"lines": 576,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/test_custom_prompt_judge.py | import json
from unittest import mock
import pytest
from litellm.types.utils import ModelResponse
from mlflow.entities.assessment import AssessmentError
from mlflow.entities.assessment_source import AssessmentSourceType
from mlflow.genai.judges.custom_prompt_judge import _remove_choice_brackets, custom_prompt_judge
... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/test_custom_prompt_judge.py",
"license": "Apache License 2.0",
"lines": 128,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/scorers/test_aggregation.py | import pytest
from mlflow.entities.assessment import Feedback
from mlflow.genai.evaluation.entities import EvalItem, EvalResult
from mlflow.genai.judges.builtin import CategoricalRating
from mlflow.genai.scorers.aggregation import (
_cast_assessment_value_to_float,
compute_aggregated_metrics,
)
from mlflow.gen... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/test_aggregation.py",
"license": "Apache License 2.0",
"lines": 101,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/server/fastapi_app.py | """
FastAPI application wrapper for MLflow server.
This module provides a FastAPI application that wraps the existing Flask application
using WSGIMiddleware to maintain 100% API compatibility while enabling future migration
to FastAPI endpoints.
"""
import json
from fastapi import FastAPI, Request
from fastapi.middl... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/server/fastapi_app.py",
"license": "Apache License 2.0",
"lines": 86,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:dev/extract_deps.py | import ast
import re
from pathlib import Path
from typing import cast
def parse_dependencies(content: str) -> list[str]:
pattern = r"dependencies\s*=\s*(\[[\s\S]*?\])\n"
match = re.search(pattern, content)
if match is None:
raise ValueError("Could not find dependencies in pyproject.toml")
deps... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/extract_deps.py",
"license": "Apache License 2.0",
"lines": 17,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:dev/clint/src/clint/rules/get_artifact_uri.py | from clint.rules.base import Rule
class GetArtifactUri(Rule):
def _message(self) -> str:
return (
"`mlflow.get_artifact_uri` should not be used in examples. "
"Use the return value of `log_model` instead."
)
| {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/src/clint/rules/get_artifact_uri.py",
"license": "Apache License 2.0",
"lines": 7,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:dev/clint/tests/rules/test_get_artifact_uri.py | from pathlib import Path
import pytest
from clint.config import Config
from clint.linter import Position, Range, lint_file
from clint.rules import GetArtifactUri
def test_get_artifact_uri_in_rst_example(index_path: Path) -> None:
code = """
Documentation
=============
Here's an example:
.. code-block:: python
... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/tests/rules/test_get_artifact_uri.py",
"license": "Apache License 2.0",
"lines": 64,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/scorers/registry.py | """
Registered scorer functionality for MLflow GenAI.
This module provides functions to manage registered scorers that automatically
evaluate traces in MLflow experiments.
"""
import json
import warnings
from abc import ABCMeta, abstractmethod
from typing import TYPE_CHECKING, Optional
from mlflow.exceptions import ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/registry.py",
"license": "Apache License 2.0",
"lines": 566,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/telemetry/client.py | import atexit
import random
import sys
import threading
import time
import urllib.parse
import uuid
import warnings
from dataclasses import asdict
from functools import lru_cache
from queue import Empty, Full, Queue
from typing import Any, Literal
import requests
from mlflow.environment_variables import _MLFLOW_TELEM... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/telemetry/client.py",
"license": "Apache License 2.0",
"lines": 411,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/telemetry/constant.py | from mlflow.ml_package_versions import GENAI_FLAVOR_TO_MODULE_NAME, NON_GENAI_FLAVOR_TO_MODULE_NAME
# NB: Kinesis PutRecords API has a limit of 500 records per request
BATCH_SIZE = 500
BATCH_TIME_INTERVAL_SECONDS = 10
MAX_QUEUE_SIZE = 1000
MAX_WORKERS = 1
CONFIG_STAGING_URL = "https://config-staging.mlflow-telemetry.i... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/telemetry/constant.py",
"license": "Apache License 2.0",
"lines": 88,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/telemetry/events.py | import inspect
import os
import sys
from enum import Enum
from typing import TYPE_CHECKING, Any
from mlflow.entities import Feedback
from mlflow.telemetry.constant import (
GENAI_MODULES,
MODULES_TO_CHECK_IMPORT,
)
if TYPE_CHECKING:
from mlflow.genai.scorers.base import Scorer
GENAI_EVALUATION_PATH = "m... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/telemetry/events.py",
"license": "Apache License 2.0",
"lines": 485,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/telemetry/schemas.py | import json
import platform
import sys
from dataclasses import dataclass
from enum import Enum
from typing import Any
from mlflow.version import IS_MLFLOW_SKINNY, IS_TRACING_SDK_ONLY, VERSION
class Status(str, Enum):
UNKNOWN = "unknown"
SUCCESS = "success"
FAILURE = "failure"
@dataclass
class Record:
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/telemetry/schemas.py",
"license": "Apache License 2.0",
"lines": 68,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/telemetry/track.py | import functools
import inspect
import logging
import time
from typing import Any, Callable, ParamSpec, TypeVar
from mlflow.environment_variables import MLFLOW_EXPERIMENT_ID
from mlflow.telemetry.client import get_telemetry_client
from mlflow.telemetry.events import Event
from mlflow.telemetry.schemas import Record, S... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/telemetry/track.py",
"license": "Apache License 2.0",
"lines": 107,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
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