sample_id stringlengths 21 196 | text stringlengths 105 936k | metadata dict | category stringclasses 6
values |
|---|---|---|---|
mlflow/mlflow:tests/gateway/providers/test_litellm.py | from unittest import mock
import pytest
from mlflow.gateway.config import EndpointConfig
from mlflow.gateway.providers.base import PassthroughAction
from mlflow.gateway.providers.litellm import LiteLLMAdapter, LiteLLMProvider
from mlflow.gateway.schemas import chat, embeddings
TEST_MESSAGE = "This is a test"
def c... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/gateway/providers/test_litellm.py",
"license": "Apache License 2.0",
"lines": 728,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/scorers/ragas/scorers/comparison_metrics.py | from __future__ import annotations
from typing import ClassVar
from mlflow.genai.judges.builtin import _MODEL_API_DOC
from mlflow.genai.scorers.ragas import RagasScorer
from mlflow.utils.annotations import experimental
from mlflow.utils.docstring_utils import format_docstring
@experimental(version="3.8.0")
@format_... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/ragas/scorers/comparison_metrics.py",
"license": "Apache License 2.0",
"lines": 124,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/scorers/ragas/scorers/rag_metrics.py | from __future__ import annotations
from typing import ClassVar
from ragas.embeddings.base import Embeddings
from mlflow.genai.judges.builtin import _MODEL_API_DOC
from mlflow.genai.scorers.ragas import RagasScorer
from mlflow.utils.annotations import experimental
from mlflow.utils.docstring_utils import format_docst... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/ragas/scorers/rag_metrics.py",
"license": "Apache License 2.0",
"lines": 165,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:tests/genai/judges/adapters/test_utils.py | from unittest import mock
import pytest
from mlflow.exceptions import MlflowException
from mlflow.genai.judges.adapters.databricks_managed_judge_adapter import (
DatabricksManagedJudgeAdapter,
)
from mlflow.genai.judges.adapters.gateway_adapter import GatewayAdapter
from mlflow.genai.judges.adapters.litellm_adapt... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/adapters/test_utils.py",
"license": "Apache License 2.0",
"lines": 94,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/judges/prompts/fluency.py | # NB: User-facing name for the fluency assessment.
FLUENCY_ASSESSMENT_NAME = "fluency"
FLUENCY_PROMPT = """\
You are a linguistic expert evaluating the Fluency of AI-generated text in {{ outputs }}.
Definition: Fluency measures the grammatical correctness, natural flow, and linguistic quality
of the text, regardless ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/fluency.py",
"license": "Apache License 2.0",
"lines": 12,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/scorers/ragas/models.py | from __future__ import annotations
import json
import typing as t
import instructor
import litellm
from openai import AsyncOpenAI
from pydantic import BaseModel
from ragas.embeddings import OpenAIEmbeddings
from ragas.llms import InstructorBaseRagasLLM
from ragas.llms.litellm_llm import LiteLLMStructuredLLM
from mlf... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/ragas/models.py",
"license": "Apache License 2.0",
"lines": 80,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/genai/scorers/ragas/registry.py | from __future__ import annotations
from dataclasses import dataclass
from mlflow.exceptions import MlflowException
@dataclass(frozen=True)
class MetricConfig:
classpath: str
is_agentic_or_multiturn: bool = False
requires_embeddings: bool = False
requires_llm_in_constructor: bool = True
requires_... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/ragas/registry.py",
"license": "Apache License 2.0",
"lines": 133,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/genai/scorers/ragas/utils.py | from __future__ import annotations
from typing import Any
from mlflow.entities.trace import Trace
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.scorer_utils import parse_tool_call_expectations
from mlflow.genai.utils.trace_utils import (
extract_retrieval_context_from_trace,
extract_... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/ragas/utils.py",
"license": "Apache License 2.0",
"lines": 251,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/genai/scorers/ragas/test_models.py | from unittest.mock import Mock, patch
import pytest
from pydantic import BaseModel
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.ragas.models import DatabricksRagasLLM, create_ragas_model
class DummyResponseModel(BaseModel):
answer: str
score: int
@pytest.fixture
def mock_call_ch... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/ragas/test_models.py",
"license": "Apache License 2.0",
"lines": 44,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/scorers/ragas/test_ragas_scorer.py | from unittest.mock import MagicMock, patch
import pytest
from ragas.embeddings.base import BaseRagasEmbedding
import mlflow
from mlflow.entities.assessment import Feedback
from mlflow.entities.assessment_source import AssessmentSourceType
from mlflow.exceptions import MlflowException
from mlflow.genai.judges.utils im... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/ragas/test_ragas_scorer.py",
"license": "Apache License 2.0",
"lines": 306,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/scorers/ragas/test_registry.py | from unittest import mock
import pytest
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.ragas.registry import (
get_metric_class,
is_agentic_or_multiturn_metric,
requires_args_from_placeholders,
requires_embeddings,
requires_llm_at_score_time,
requires_llm_in_constructo... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/ragas/test_registry.py",
"license": "Apache License 2.0",
"lines": 84,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/scorers/ragas/test_utils.py | import pytest
from langchain_core.documents import Document
from ragas.dataset_schema import MultiTurnSample, SingleTurnSample
from ragas.messages import AIMessage, HumanMessage, ToolCall
import mlflow
from mlflow.entities.span import SpanType
from mlflow.genai.scorers.ragas.utils import (
create_mlflow_error_mess... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/ragas/test_utils.py",
"license": "Apache License 2.0",
"lines": 212,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/judges/prompts/conversational_role_adherence.py | # NB: User-facing name for the conversational role adherence assessment.
CONVERSATIONAL_ROLE_ADHERENCE_ASSESSMENT_NAME = "conversational_role_adherence"
CONVERSATIONAL_ROLE_ADHERENCE_PROMPT = """\
Consider the following conversation history between a user and an assistant. \
Your task is to evaluate whether the assist... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/conversational_role_adherence.py",
"license": "Apache License 2.0",
"lines": 24,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/server/gateway_api.py | """
Database-backed Gateway API endpoints for MLflow Server.
This module provides dynamic gateway endpoints that are configured from the database
rather than from a static YAML configuration file. It integrates the AI Gateway
functionality directly into the MLflow tracking server.
"""
import functools
import logging
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/server/gateway_api.py",
"license": "Apache License 2.0",
"lines": 792,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/server/test_gateway_api.py | import json
from pathlib import Path
from typing import Any
from unittest import mock
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from fastapi import HTTPException
from fastapi.responses import StreamingResponse
import mlflow
from mlflow.entities import (
FallbackConfig,
FallbackStrate... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/server/test_gateway_api.py",
"license": "Apache License 2.0",
"lines": 2487,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/tracing/export/span_batcher.py | import atexit
import logging
import threading
from collections import defaultdict
from queue import Queue
from typing import Callable
from mlflow.entities.span import Span
from mlflow.environment_variables import (
MLFLOW_ASYNC_TRACE_LOGGING_MAX_INTERVAL_MILLIS,
MLFLOW_ASYNC_TRACE_LOGGING_MAX_SPAN_BATCH_SIZE,
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/export/span_batcher.py",
"license": "Apache License 2.0",
"lines": 102,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/scorers/deepeval/scorers/agentic_metrics.py | """Agentic metrics for evaluating AI agent performance."""
from __future__ import annotations
from typing import ClassVar
from mlflow.genai.judges.builtin import _MODEL_API_DOC
from mlflow.genai.scorers.deepeval import DeepEvalScorer
from mlflow.utils.annotations import experimental
from mlflow.utils.docstring_utils... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/deepeval/scorers/agentic_metrics.py",
"license": "Apache License 2.0",
"lines": 123,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/scorers/deepeval/scorers/conversational_metrics.py | """Conversational metrics for evaluating multi-turn dialogue performance."""
from __future__ import annotations
from typing import ClassVar
from mlflow.genai.judges.builtin import _MODEL_API_DOC
from mlflow.genai.scorers.deepeval import DeepEvalScorer
from mlflow.utils.annotations import experimental
from mlflow.uti... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/deepeval/scorers/conversational_metrics.py",
"license": "Apache License 2.0",
"lines": 152,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/scorers/deepeval/scorers/rag_metrics.py | """RAG (Retrieval-Augmented Generation) metrics for DeepEval integration."""
from __future__ import annotations
from typing import ClassVar
from mlflow.genai.judges.builtin import _MODEL_API_DOC
from mlflow.genai.scorers.deepeval import DeepEvalScorer
from mlflow.utils.annotations import experimental
from mlflow.uti... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/deepeval/scorers/rag_metrics.py",
"license": "Apache License 2.0",
"lines": 109,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/scorers/deepeval/scorers/safety_metrics.py | """Safety and responsible AI metrics for content evaluation."""
from __future__ import annotations
from typing import ClassVar
from mlflow.genai.judges.builtin import _MODEL_API_DOC
from mlflow.genai.scorers.deepeval import DeepEvalScorer
from mlflow.utils.annotations import experimental
from mlflow.utils.docstring_... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/deepeval/scorers/safety_metrics.py",
"license": "Apache License 2.0",
"lines": 134,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/utils/providers.py | import importlib.util
from typing import Any, TypedDict
from typing_extensions import NotRequired
_PROVIDER_BACKEND_AVAILABLE = importlib.util.find_spec("litellm") is not None
_SUPPORTED_MODEL_MODES = ("chat", "completion", "embedding", None)
class FieldDict(TypedDict):
name: str
description: str
secre... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/utils/providers.py",
"license": "Apache License 2.0",
"lines": 517,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/judges/prompts/conversational_tool_call_efficiency.py | # NB: User-facing name for the conversational tool call efficiency assessment.
CONVERSATIONAL_TOOL_CALL_EFFICIENCY_ASSESSMENT_NAME = "conversational_tool_call_efficiency"
CONVERSATIONAL_TOOL_CALL_EFFICIENCY_PROMPT = """\
Consider the following conversation history between a user and an assistant, including tool calls ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/conversational_tool_call_efficiency.py",
"license": "Apache License 2.0",
"lines": 21,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/server/constants.py | """
Constants used for internal server-to-worker communication.
These are internal environment variables (prefixed with _MLFLOW_SERVER_) used for
communication between the MLflow CLI and forked server processes (gunicorn/uvicorn workers).
They are set by the server and read by workers, and should not be set by end use... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/server/constants.py",
"license": "Apache License 2.0",
"lines": 57,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/store/tracking/_secret_cache.py | """
Server-side encrypted cache for secrets management.
Implements time-bucketed ephemeral encryption for cached secrets to provide defense-in-depth
and satisfy CWE-316 (https://cwe.mitre.org/data/definitions/316.html).
Security Model and Limitations:
This cache protects against accidental exposure of secrets in log... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/tracking/_secret_cache.py",
"license": "Apache License 2.0",
"lines": 210,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/store/tracking/test_secret_cache.py | import json
import time
from concurrent.futures import ThreadPoolExecutor
import pytest
# Commented out pending integration with rest branch:
# from mlflow.entities import SecretResourceType
from mlflow.store.tracking._secret_cache import (
_MAX_TTL,
_MIN_TTL,
EphemeralCacheEncryption,
SecretCache,
)
... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/store/tracking/test_secret_cache.py",
"license": "Apache License 2.0",
"lines": 381,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/pyfunc/utils.py | import json
import os
from typing import TYPE_CHECKING
from fastapi.testclient import TestClient
import mlflow
from mlflow.pyfunc import scoring_server
if TYPE_CHECKING:
import httpx
def score_model_in_process(model_uri: str, data: str, content_type: str) -> "httpx.Response":
"""Score a model using in-proc... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/pyfunc/utils.py",
"license": "Apache License 2.0",
"lines": 31,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/scorers/deepeval/models.py | from __future__ import annotations
import json
from deepeval.models import LiteLLMModel
from deepeval.models.base_model import DeepEvalBaseLLM
from pydantic import ValidationError
from mlflow.genai.judges.adapters.databricks_managed_judge_adapter import (
call_chat_completions,
)
from mlflow.genai.judges.constan... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/deepeval/models.py",
"license": "Apache License 2.0",
"lines": 59,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/scorers/deepeval/registry.py | from __future__ import annotations
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.deepeval.utils import DEEPEVAL_NOT_INSTALLED_ERROR_MESSAGE
# Registry format: metric_name -> (classpath, is_deterministic)
_METRIC_REGISTRY = {
# RAG Metrics
"AnswerRelevancy": ("deepeval.metrics.AnswerR... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/deepeval/registry.py",
"license": "Apache License 2.0",
"lines": 77,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/genai/scorers/deepeval/utils.py | """Utility functions and constants for DeepEval integration."""
from __future__ import annotations
from typing import Any
from mlflow.entities.span import SpanAttributeKey, SpanType
from mlflow.entities.trace import Trace
from mlflow.exceptions import MlflowException
from mlflow.genai.utils.trace_utils import (
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/scorers/deepeval/utils.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/genai/scorers/deepeval/test_deepeval_scorer.py | from unittest.mock import Mock, patch
import pytest
import mlflow
from mlflow.entities.assessment import Feedback
from mlflow.entities.assessment_source import AssessmentSourceType
from mlflow.genai.judges.utils import CategoricalRating
from mlflow.genai.scorers import FRAMEWORK_METADATA_KEY
from mlflow.genai.scorers... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/deepeval/test_deepeval_scorer.py",
"license": "Apache License 2.0",
"lines": 278,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/scorers/deepeval/test_models.py | from unittest.mock import Mock, patch
import pytest
from mlflow.genai.scorers.deepeval.models import DatabricksDeepEvalLLM
@pytest.fixture
def mock_call_chat_completions():
with patch("mlflow.genai.scorers.deepeval.models.call_chat_completions") as mock:
result = Mock()
result.output = "Test out... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/deepeval/test_models.py",
"license": "Apache License 2.0",
"lines": 18,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/scorers/deepeval/test_registry.py | from unittest import mock
import pytest
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.deepeval.registry import get_metric_class, is_deterministic_metric
def test_get_metric_class_returns_valid_class():
metric_class = get_metric_class("AnswerRelevancy")
assert metric_class.__name__ ... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/deepeval/test_registry.py",
"license": "Apache License 2.0",
"lines": 27,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/scorers/deepeval/test_utils.py | from unittest.mock import Mock
import pytest
from mlflow.entities.span import Span, SpanAttributeKey, SpanType
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.deepeval.models import create_deepeval_model
from mlflow.genai.scorers.deepeval.utils import (
_convert_to_deepeval_tool_calls,
... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/scorers/deepeval/test_utils.py",
"license": "Apache License 2.0",
"lines": 107,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/judges/adapters/base_adapter.py | from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any
import pydantic
if TYPE_CHECKING:
from mlflow.entities.trace import Trace
from mlflow.types.llm import ChatMessage
from mlflow.entities.assessment import Feedback
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/adapters/base_adapter.py",
"license": "Apache License 2.0",
"lines": 94,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/adapters/utils.py | """Utility functions for judge adapters."""
from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from mlflow.genai.judges.adapters.base_adapter import BaseJudgeAdapter
from mlflow.types.llm import ChatMessage
from mlflow.exceptions import MlflowException
from mlflow.genai.ju... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/adapters/utils.py",
"license": "Apache License 2.0",
"lines": 42,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/genai/judges/prompts/summarization.py | # NB: User-facing name for the summarization assessment.
SUMMARIZATION_ASSESSMENT_NAME = "summarization"
SUMMARIZATION_PROMPT = """\
Consider the following source document and candidate summary.
You must decide whether the summary is an acceptable summary of the document.
Output only "yes" or "no" based on whether the... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/summarization.py",
"license": "Apache License 2.0",
"lines": 20,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/store/tracking/gateway/rest_mixin.py | """REST Gateway Store Mixin - Gateway API implementation for REST-based tracking stores."""
from __future__ import annotations
from typing import Any
from mlflow.entities import (
GatewayEndpoint,
GatewayEndpointBinding,
GatewayEndpointModelConfig,
GatewayEndpointModelMapping,
GatewayEndpointTag,... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/tracking/gateway/rest_mixin.py",
"license": "Apache License 2.0",
"lines": 624,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/entities/test_gateway_endpoint.py | from mlflow.entities import (
GatewayEndpoint,
GatewayEndpointBinding,
GatewayEndpointModelMapping,
GatewayModelDefinition,
GatewayModelLinkageType,
GatewayResourceType,
)
def test_model_definition_creation_full():
model_def = GatewayModelDefinition(
model_definition_id="model-def-... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/entities/test_gateway_endpoint.py",
"license": "Apache License 2.0",
"lines": 347,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/entities/test_gateway_secrets.py | from mlflow.entities import GatewaySecretInfo
def test_secret_creation_full():
secret = GatewaySecretInfo(
secret_id="test-secret-id",
secret_name="my_api_key",
masked_values={"api_key": "sk-...abc123"},
created_at=1234567890000,
last_updated_at=1234567890000,
provi... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/entities/test_gateway_secrets.py",
"license": "Apache License 2.0",
"lines": 168,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/store/tracking/gateway/config_resolver.py | """
Server-side only configuration resolver for Gateway endpoints.
This module provides functions to retrieve decrypted endpoint configurations
for resources. These functions are privileged operations that should only be
called server-side and never exposed to clients via MlflowClient.
"""
import json
from mlflow.ex... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/tracking/gateway/config_resolver.py",
"license": "Apache License 2.0",
"lines": 203,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/store/tracking/gateway/sqlalchemy_mixin.py | from __future__ import annotations
import json
import os
import uuid
from typing import Any
from sqlalchemy import func
from sqlalchemy.exc import IntegrityError
from sqlalchemy.orm import joinedload
from mlflow.entities import (
FallbackConfig,
GatewayEndpoint,
GatewayEndpointBinding,
GatewayEndpoin... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/tracking/gateway/sqlalchemy_mixin.py",
"license": "Apache License 2.0",
"lines": 1179,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/store/tracking/test_gateway_sql_store.py | import json
import uuid
from pathlib import Path
import pytest
from mlflow.entities import (
BudgetAction,
BudgetDurationUnit,
BudgetTargetScope,
BudgetUnit,
FallbackConfig,
FallbackStrategy,
GatewayBudgetPolicy,
GatewayEndpoint,
GatewayEndpointBinding,
GatewayEndpointModelConf... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/store/tracking/test_gateway_sql_store.py",
"license": "Apache License 2.0",
"lines": 1999,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/store/tracking/gateway/abstract_mixin.py | from typing import Any
from mlflow.entities import (
FallbackConfig,
GatewayEndpoint,
GatewayEndpointBinding,
GatewayEndpointModelConfig,
GatewayEndpointModelMapping,
GatewayEndpointTag,
GatewayModelDefinition,
GatewaySecretInfo,
RoutingStrategy,
)
from mlflow.entities.gateway_budge... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/tracking/gateway/abstract_mixin.py",
"license": "Apache License 2.0",
"lines": 467,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/prompts/conversational_safety.py | # NB: User-facing name for the conversational safety assessment.
CONVERSATIONAL_SAFETY_ASSESSMENT_NAME = "conversational_safety"
CONVERSATIONAL_SAFETY_PROMPT = """\
Consider the following conversation history between a user and an assistant. Your task is to \
evaluate the safety of the assistant's responses only and o... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/conversational_safety.py",
"license": "Apache License 2.0",
"lines": 21,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/entities/gateway_endpoint.py | from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum
from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.protos.service_pb2 import FallbackConfig as ProtoFallbackConfig
from mlflow.protos.service_pb2 import FallbackStrategy as ProtoFallbackStrategy
from ml... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/entities/gateway_endpoint.py",
"license": "Apache License 2.0",
"lines": 402,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/entities/gateway_secrets.py | from dataclasses import dataclass
from typing import Any
from mlflow.entities._mlflow_object import _MlflowObject
from mlflow.protos.service_pb2 import GatewaySecretInfo as ProtoGatewaySecretInfo
from mlflow.utils.workspace_utils import resolve_entity_workspace_name
@dataclass(frozen=True)
class GatewaySecretInfo(_M... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/entities/gateway_secrets.py",
"license": "Apache License 2.0",
"lines": 78,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/store/tracking/gateway/entities.py | from dataclasses import dataclass, field
from typing import Any
from mlflow.entities.gateway_endpoint import (
FallbackConfig,
GatewayModelLinkageType,
RoutingStrategy,
)
@dataclass
class GatewayModelConfig:
"""
Model configuration with decrypted credentials for runtime use.
This entity cont... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/store/tracking/gateway/entities.py",
"license": "Apache License 2.0",
"lines": 57,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:tests/genai/utils/test_prompt_cache.py | import threading
import time
import pytest
from mlflow.prompt.registry_utils import PromptCache, PromptCacheKey
@pytest.fixture(autouse=True)
def reset_cache():
"""Reset the prompt cache before and after each test."""
PromptCache._reset_instance()
yield
PromptCache._reset_instance()
def test_singl... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/utils/test_prompt_cache.py",
"license": "Apache License 2.0",
"lines": 146,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/tracing/otel/translation/voltagent.py | import json
from typing import Any
from mlflow.entities.span import SpanType
from mlflow.tracing.otel.translation.base import OtelSchemaTranslator
class VoltAgentTranslator(OtelSchemaTranslator):
"""
Translator for VoltAgent semantic conventions.
VoltAgent provides clean chat-formatted messages in `agen... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/otel/translation/voltagent.py",
"license": "Apache License 2.0",
"lines": 102,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/tracing/otel/test_voltagent_translator.py | import json
from unittest import mock
import pytest
from mlflow.entities.span import Span, SpanType
from mlflow.tracing.constant import SpanAttributeKey
from mlflow.tracing.otel.translation import (
translate_span_type_from_otel,
translate_span_when_storing,
)
from mlflow.tracing.otel.translation.voltagent im... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/tracing/otel/test_voltagent_translator.py",
"license": "Apache License 2.0",
"lines": 200,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:dev/clint/src/clint/rules/use_walrus_operator.py | import ast
from clint.rules.base import Rule
class UseWalrusOperator(Rule):
def _message(self) -> str:
return (
"Use the walrus operator `:=` when a variable is assigned and only used "
"within an `if` block that tests its truthiness. "
"For example, replace `a = ...; ... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/src/clint/rules/use_walrus_operator.py",
"license": "Apache License 2.0",
"lines": 136,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:dev/clint/tests/rules/test_use_walrus_operator.py | from pathlib import Path
from clint.config import Config
from clint.linter import Position, Range, lint_file
from clint.rules import UseWalrusOperator
def test_basic_walrus_pattern(index_path: Path) -> None:
code = """
def f():
a = func()
if a:
use(a)
"""
config = Config(select={UseWalrusOper... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/tests/rules/test_use_walrus_operator.py",
"license": "Apache License 2.0",
"lines": 354,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:dev/clint/src/clint/rules/assign_before_append.py | import ast
from clint.rules.base import Rule
class AssignBeforeAppend(Rule):
def _message(self) -> str:
return (
"Avoid unnecessary assignment before appending to a list. "
"Use a list comprehension instead."
)
@staticmethod
def check(node: ast.For, prev_stmt: ast... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/src/clint/rules/assign_before_append.py",
"license": "Apache License 2.0",
"lines": 56,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:dev/clint/tests/rules/test_assign_before_append.py | from pathlib import Path
from clint.config import Config
from clint.linter import Position, Range, lint_file
from clint.rules import AssignBeforeAppend
def test_assign_before_append_basic(index_path: Path) -> None:
code = """
items = []
for x in data:
item = transform(x)
items.append(item)
"""
config... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/tests/rules/test_assign_before_append.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/evaluate/test_entities.py | from mlflow.entities.dataset_record_source import DatasetRecordSource, DatasetRecordSourceType
from mlflow.genai.evaluation.entities import EvalItem
def test_eval_item_from_dataset_row_extracts_source():
source = DatasetRecordSource(
source_type=DatasetRecordSourceType.TRACE,
source_data={"trace_i... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/evaluate/test_entities.py",
"license": "Apache License 2.0",
"lines": 28,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/judges/prompts/completeness.py | # NB: User-facing name for the completeness assessment.
COMPLETENESS_ASSESSMENT_NAME = "completeness"
COMPLETENESS_PROMPT = """\
Consider the following user prompt and assistant response.
You must decide whether the assistant successfully addressed all explicit requests in the user's prompt.
Output only "yes" or "no" ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/completeness.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/conversation_completeness.py | # NB: User-facing name for the conversation completeness assessment.
CONVERSATION_COMPLETENESS_ASSESSMENT_NAME = "conversation_completeness"
CONVERSATION_COMPLETENESS_PROMPT = """\
Consider the following conversation history between a user and an assistant.
Your task is to output exactly one label: "yes" or "no" based... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/conversation_completeness.py",
"license": "Apache License 2.0",
"lines": 16,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/prompts/user_frustration.py | # NB: User-facing name for the user frustration assessment.
USER_FRUSTRATION_ASSESSMENT_NAME = "user_frustration"
USER_FRUSTRATION_PROMPT = """\
Consider the following conversation history between a user and an assistant. Your task is to
determine the user's emotional trajectory and output exactly one of the following... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/prompts/user_frustration.py",
"license": "Apache License 2.0",
"lines": 15,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/evaluation/session_utils.py | """Utilities for session-level (multi-turn) evaluation."""
import traceback
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import TYPE_CHECKING, Any
from mlflow.entities.assessment import Feedback
from mlflow.entities.assessment_error import AssessmentE... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/evaluation/session_utils.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:tests/genai/evaluate/test_session_utils.py | from unittest.mock import Mock, patch
import pytest
import mlflow
from mlflow.entities import TraceData, TraceInfo, TraceLocation, TraceState
from mlflow.entities.assessment import Feedback
from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType
from mlflow.entities.trace import Trace
fro... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/evaluate/test_session_utils.py",
"license": "Apache License 2.0",
"lines": 423,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/tracing/utils/test_copy.py | import time
import pytest
import mlflow
from mlflow.tracing.utils.copy import copy_trace_to_experiment
from tests.tracing.helper import purge_traces
def _create_test_span_dict(request_id="test-trace", parent_id=None):
"""Helper to create a minimal valid span dict for testing"""
return {
"name": "ro... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/tracing/utils/test_copy.py",
"license": "Apache License 2.0",
"lines": 97,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:.claude/hooks/lint.py | """
Lightweight hook for validating code written by Claude Code.
"""
import ast
import json
import os
import re
import subprocess
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Literal
KILL_SWITCH_ENV_VAR = "CLAUDE_LINT_HOOK_DISABLED"
@dataclass
class LintError:
file: P... | {
"repo_id": "mlflow/mlflow",
"file_path": ".claude/hooks/lint.py",
"license": "Apache License 2.0",
"lines": 123,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/agno/autolog_v2.py | """
Autologging logic for Agno V2 (>= 2.0.0) using OpenTelemetry instrumentation.
"""
import importlib.metadata as _meta
import logging
from packaging.version import Version
import mlflow
from mlflow.exceptions import MlflowException
from mlflow.tracing.utils.otlp import build_otlp_headers
_logger = logging.getLogg... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/agno/autolog_v2.py",
"license": "Apache License 2.0",
"lines": 74,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/langchain/sample_code/simple_runnable.py | from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
import mlflow
prompt = PromptTemplate(
input_variables=["product"],
template="What is {product}?",
)
llm = ChatOpenAI(temperature=0.1, stream_usage=True)
chain = ... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/langchain/sample_code/simple_runnable.py",
"license": "Apache License 2.0",
"lines": 11,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/telemetry/installation_id.py | import json
import os
import threading
import uuid
from datetime import datetime, timezone
from pathlib import Path
from mlflow.utils.os import is_windows
from mlflow.version import VERSION
_KEY_INSTALLATION_ID = "installation_id"
_CACHE_LOCK = threading.RLock()
_INSTALLATION_ID_CACHE: str | None = None
def get_or_... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/telemetry/installation_id.py",
"license": "Apache License 2.0",
"lines": 72,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/telemetry/test_installation_id.py | import json
import uuid
from unittest import mock
import pytest
import mlflow
from mlflow.telemetry.client import get_telemetry_client, set_telemetry_client
from mlflow.telemetry.installation_id import get_or_create_installation_id
from mlflow.utils.os import is_windows
from mlflow.version import VERSION
@pytest.fi... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/telemetry/test_installation_id.py",
"license": "Apache License 2.0",
"lines": 71,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:dev/clint/src/clint/rules/subprocess_check_call.py | import ast
from clint.resolver import Resolver
from clint.rules.base import Rule
class SubprocessCheckCall(Rule):
def _message(self) -> str:
return (
"Use `subprocess.check_call(...)` instead of `subprocess.run(..., check=True)` "
"for better readability. Only applies when check=T... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/src/clint/rules/subprocess_check_call.py",
"license": "Apache License 2.0",
"lines": 32,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:dev/clint/tests/rules/test_subprocess_check_call.py | from pathlib import Path
from clint.config import Config
from clint.linter import Position, Range, lint_file
from clint.rules import SubprocessCheckCall
def test_subprocess_check_call(index_path: Path) -> None:
code = """
import subprocess
# Bad
subprocess.run(["echo", "hello"], check=True)
# Good - has other ... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/tests/rules/test_subprocess_check_call.py",
"license": "Apache License 2.0",
"lines": 21,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/tracing/otel/translation/vercel_ai.py | import json
from typing import Any
from mlflow.entities.span import SpanType
from mlflow.tracing.constant import SpanAttributeKey
from mlflow.tracing.otel.translation.base import OtelSchemaTranslator
class VercelAITranslator(OtelSchemaTranslator):
"""Translator for Vercel AI SDK spans."""
# https://ai-sdk.d... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/otel/translation/vercel_ai.py",
"license": "Apache License 2.0",
"lines": 80,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/tracing/otel/test_vercel_ai_translator.py | import json
from unittest import mock
import pytest
from mlflow.entities.span import Span
from mlflow.tracing.constant import SpanAttributeKey
from mlflow.tracing.otel.translation import translate_span_when_storing
@pytest.mark.parametrize(
("attributes", "expected_inputs", "expected_outputs"),
[
# ... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/tracing/otel/test_vercel_ai_translator.py",
"license": "Apache License 2.0",
"lines": 194,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:dev/clint/src/clint/rules/version_major_check.py | import ast
import re
from typing import TYPE_CHECKING
from clint.rules.base import Rule
if TYPE_CHECKING:
from clint.resolver import Resolver
class MajorVersionCheck(Rule):
def _message(self) -> str:
return (
"Use `.major` field for major version comparisons instead of full version strin... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/src/clint/rules/version_major_check.py",
"license": "Apache License 2.0",
"lines": 45,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:dev/clint/tests/rules/test_version_major_check.py | from pathlib import Path
from clint.config import Config
from clint.linter import lint_file
from clint.rules.version_major_check import MajorVersionCheck
def test_version_major_check(index_path: Path) -> None:
code = """
from packaging.version import Version
Version("0.9.0") >= Version("1.0.0")
Version("1.2.3")... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/clint/tests/rules/test_version_major_check.py",
"license": "Apache License 2.0",
"lines": 34,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:dev/check_whitespace_only.py | """
Detect files where all changes are whitespace-only.
This helps avoid unnecessary commit history noise from whitespace-only changes.
"""
import argparse
import json
import os
import sys
import urllib.request
from typing import cast
BYPASS_LABEL = "allow-whitespace-only"
def github_api_request(url: str, accept: ... | {
"repo_id": "mlflow/mlflow",
"file_path": "dev/check_whitespace_only.py",
"license": "Apache License 2.0",
"lines": 93,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/tracing/otel/translation/google_adk.py | from mlflow.tracing.otel.translation.base import OtelSchemaTranslator
class GoogleADKTranslator(OtelSchemaTranslator):
"""
Translator for Google ADK semantic conventions.
Google ADK mostly uses OpenTelemetry semantic conventions, but with some custom
inputs and outputs attributes.
"""
# Input... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/otel/translation/google_adk.py",
"license": "Apache License 2.0",
"lines": 11,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:tests/store/tracking/test_plugin_validation.py | import subprocess
import sys
from mlflow.store.tracking.sqlalchemy_store import SqlAlchemyStore
def test_sqlalchemy_store_import_does_not_cause_circular_import():
"""
Regression test for circular import issue (https://github.com/mlflow/mlflow/issues/18386).
Store plugins that inherit from SqlAlchemyStor... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/store/tracking/test_plugin_validation.py",
"license": "Apache License 2.0",
"lines": 75,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/judges/adapters/databricks_managed_judge_adapter.py | from __future__ import annotations
import inspect
import json
import logging
from typing import TYPE_CHECKING, Any, Callable, TypeVar
if TYPE_CHECKING:
import litellm
from mlflow.entities.trace import Trace
from mlflow.types.llm import ChatMessage, ToolDefinition
T = TypeVar("T") # Generic type for age... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/adapters/databricks_managed_judge_adapter.py",
"license": "Apache License 2.0",
"lines": 324,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/judges/adapters/gateway_adapter.py | from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from mlflow.types.llm import ChatMessage
from mlflow.entities.assessment import Feedback
from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType
from mlflow.exceptions import Mlflow... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/adapters/gateway_adapter.py",
"license": "Apache License 2.0",
"lines": 98,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/judges/adapters/litellm_adapter.py | from __future__ import annotations
import json
import logging
import re
import threading
from contextlib import ContextDecorator
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any
import pydantic
if TYPE_CHECKING:
import litellm
from mlflow.entities.trace import Trace
from mlflow.ty... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/adapters/litellm_adapter.py",
"license": "Apache License 2.0",
"lines": 511,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/judges/utils/invocation_utils.py | """Main invocation utilities for judge models."""
from __future__ import annotations
import json
import logging
from typing import TYPE_CHECKING, Any
import pydantic
if TYPE_CHECKING:
from mlflow.entities.trace import Trace
from mlflow.types.llm import ChatMessage
from mlflow.entities.assessment import Fee... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/utils/invocation_utils.py",
"license": "Apache License 2.0",
"lines": 202,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/genai/judges/utils/parsing_utils.py | """Response parsing utilities for judge models."""
def _strip_markdown_code_blocks(response: str) -> str:
"""
Strip markdown code blocks from LLM responses.
Some legacy models wrap JSON responses in markdown code blocks (```json...```).
This function removes those wrappers to extract the raw JSON con... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/utils/parsing_utils.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/genai/judges/utils/prompt_utils.py | """Prompt formatting and manipulation utilities for judge models."""
from __future__ import annotations
import re
from typing import TYPE_CHECKING, NamedTuple
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import BAD_REQUEST
if TYPE_CHECKING:
from mlflow.genai.judges.base import... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/utils/prompt_utils.py",
"license": "Apache License 2.0",
"lines": 74,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/judges/utils/tool_calling_utils.py | """Tool calling support for judge models."""
from __future__ import annotations
import json
from dataclasses import asdict, is_dataclass
from typing import TYPE_CHECKING, NoReturn
if TYPE_CHECKING:
import litellm
from mlflow.entities.trace import Trace
from mlflow.types.llm import ToolCall
from mlflow.... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/judges/utils/tool_calling_utils.py",
"license": "Apache License 2.0",
"lines": 103,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:tests/genai/judges/adapters/test_litellm_adapter.py | from unittest import mock
import litellm
import pytest
from litellm import RetryPolicy
from litellm.types.utils import ModelResponse
from pydantic import BaseModel, Field
from mlflow.entities.trace import Trace
from mlflow.entities.trace_info import TraceInfo
from mlflow.entities.trace_location import TraceLocation
f... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/adapters/test_litellm_adapter.py",
"license": "Apache License 2.0",
"lines": 505,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/utils/test_invocation_utils.py | import json
from unittest import mock
import litellm
import pytest
from litellm import RetryPolicy
from litellm.types.utils import ModelResponse, Usage
from pydantic import BaseModel, Field
from mlflow.entities.assessment import AssessmentSourceType
from mlflow.entities.trace import Trace
from mlflow.entities.trace_i... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/utils/test_invocation_utils.py",
"license": "Apache License 2.0",
"lines": 946,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/utils/test_parsing_utils.py | from mlflow.genai.judges.utils.parsing_utils import (
_sanitize_justification,
_strip_markdown_code_blocks,
)
def test_strip_markdown_no_markdown_returns_unchanged():
response = '{"result": "yes", "rationale": "looks good"}'
assert _strip_markdown_code_blocks(response) == response
def test_strip_mar... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/utils/test_parsing_utils.py",
"license": "Apache License 2.0",
"lines": 105,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/utils/test_prompt_utils.py | import pytest
from mlflow.genai.judges.base import Judge
from mlflow.genai.judges.utils.prompt_utils import add_output_format_instructions
from mlflow.genai.prompts.utils import format_prompt
def test_add_output_format_instructions():
output_fields = Judge.get_output_fields()
simple_prompt = "Evaluate this ... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/utils/test_prompt_utils.py",
"license": "Apache License 2.0",
"lines": 51,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/genai/judges/utils/test_tool_calling_utils.py | import json
from dataclasses import dataclass
from unittest import mock
import litellm
import pytest
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.ju... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/judges/utils/test_tool_calling_utils.py",
"license": "Apache License 2.0",
"lines": 122,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/genai/agent_server/server.py | import argparse
import functools
import inspect
import json
import logging
import os
import posixpath
from typing import Any, AsyncGenerator, Callable, Literal, ParamSpec, TypeVar
import httpx
import uvicorn
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import Response, StreamingResponse
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/agent_server/server.py",
"license": "Apache License 2.0",
"lines": 353,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/agent_server/utils.py | import logging
import os
import subprocess
from contextvars import ContextVar
from mlflow.tracking.fluent import _set_active_model
# Context-isolated storage for request headers
# ensuring thread-safe access across async execution contexts
_request_headers: ContextVar[dict[str, str]] = ContextVar[dict[str, str]](
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/agent_server/utils.py",
"license": "Apache License 2.0",
"lines": 36,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/genai/agent_server/validator.py | from dataclasses import asdict, is_dataclass
from typing import Any
from pydantic import BaseModel
from mlflow.types.responses import (
ResponsesAgentRequest,
ResponsesAgentResponse,
ResponsesAgentStreamEvent,
)
class BaseAgentValidator:
"""Base validator class with common validation methods"""
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/agent_server/validator.py",
"license": "Apache License 2.0",
"lines": 54,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/genai/test_agent_server.py | import contextvars
from typing import Any, AsyncGenerator
from unittest.mock import AsyncMock, Mock, patch
import httpx
import pytest
from fastapi.testclient import TestClient
from mlflow.genai.agent_server import (
AgentServer,
get_invoke_function,
get_request_headers,
get_stream_function,
invoke... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/test_agent_server.py",
"license": "Apache License 2.0",
"lines": 1076,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:tests/tracing/opentelemetry/test_integration.py | import pytest
from opentelemetry import trace as otel_trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
import mlflow
from mlflow.entities.span import SpanStatus... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/tracing/opentelemetry/test_integration.py",
"license": "Apache License 2.0",
"lines": 241,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/tracing/otel/translation/base.py | """
Base class for OTEL semantic convention translators.
This module provides a base class that implements common translation logic.
Subclasses only need to define the attribute keys and mappings as class attributes.
"""
import json
import logging
from typing import Any
_logger = logging.getLogger(__name__)
class ... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/otel/translation/base.py",
"license": "Apache License 2.0",
"lines": 171,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | documentation |
mlflow/mlflow:mlflow/tracing/otel/translation/genai_semconv.py | """
Translation utilities for GenAI (Generic AI) semantic conventions.
Reference: https://opentelemetry.io/docs/specs/semconv/registry/attributes/gen-ai/
"""
import json
from typing import Any
from mlflow.entities.span import SpanType
from mlflow.tracing.otel.translation.base import OtelSchemaTranslator
class GenA... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/otel/translation/genai_semconv.py",
"license": "Apache License 2.0",
"lines": 101,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/tracing/otel/translation/open_inference.py | """
Translation utilities for OpenInference semantic conventions.
Reference: https://github.com/Arize-ai/openinference/blob/main/python/openinference-semantic-conventions/
"""
from mlflow.entities.span import SpanType
from mlflow.tracing.otel.translation.base import OtelSchemaTranslator
class OpenInferenceTranslato... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/otel/translation/open_inference.py",
"license": "Apache License 2.0",
"lines": 42,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_simple |
mlflow/mlflow:mlflow/tracing/otel/translation/traceloop.py | """
Translation utilities for Traceloop/OpenLLMetry semantic conventions.
Reference: https://github.com/traceloop/openllmetry/
"""
import re
from typing import Any
from mlflow.entities.span import SpanType
from mlflow.tracing.otel.translation.base import OtelSchemaTranslator
class TraceloopTranslator(OtelSchemaTra... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/tracing/otel/translation/traceloop.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:tests/tracing/otel/test_span_translation.py | import json
from typing import Any
from unittest import mock
import pytest
from mlflow.entities.span import Span, SpanType
from mlflow.tracing.constant import SpanAttributeKey, TokenUsageKey
from mlflow.tracing.otel.translation import (
sanitize_attributes,
translate_loaded_span,
translate_span_type_from_... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/tracing/otel/test_span_translation.py",
"license": "Apache License 2.0",
"lines": 596,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
mlflow/mlflow:mlflow/langchain/_compat.py | def import_base_retriever():
try:
from langchain.schema import BaseRetriever
return BaseRetriever
except ImportError:
from langchain_core.retrievers import BaseRetriever
return BaseRetriever
def import_document():
try:
from langchain.schema import Document
... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/langchain/_compat.py",
"license": "Apache License 2.0",
"lines": 145,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:mlflow/genai/evaluation/telemetry.py | import hashlib
import threading
import uuid
import mlflow
from mlflow.genai.scorers.base import Scorer
from mlflow.genai.scorers.builtin_scorers import BuiltInScorer
from mlflow.utils.databricks_utils import get_databricks_host_creds
from mlflow.utils.rest_utils import _REST_API_PATH_PREFIX, http_request
from mlflow.u... | {
"repo_id": "mlflow/mlflow",
"file_path": "mlflow/genai/evaluation/telemetry.py",
"license": "Apache License 2.0",
"lines": 115,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | function_complex |
mlflow/mlflow:tests/genai/evaluate/test_telemetry.py | from unittest import mock
import pytest
from mlflow.genai import Scorer, scorer
from mlflow.genai.evaluation.telemetry import (
_BATCH_SIZE_HEADER,
_CLIENT_NAME_HEADER,
_CLIENT_VERSION_HEADER,
_SESSION_ID_HEADER,
emit_metric_usage_event,
)
from mlflow.genai.judges import make_judge
from mlflow.gen... | {
"repo_id": "mlflow/mlflow",
"file_path": "tests/genai/evaluate/test_telemetry.py",
"license": "Apache License 2.0",
"lines": 247,
"canary_id": -1,
"canary_value": "",
"pii_type": "",
"provider": "",
"regex_pattern": "",
"repetition": -1,
"template": ""
} | test |
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