repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
mlflow | mlflow/genai/judges/prompts/summarization.py | .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... | 27 | 1,969 |
mlflow | mlflow/genai/judges/prompts/equivalence.py | .py | from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the equivalence assessment.
EQUIVALENCE_FEEDBACK_NAME = "equivalence"
EQUIVALENCE_PROMPT_INSTRUCTIONS = """\
Compare the following actual output against the expected output. You must determine whether they \
are semantically equivalent o... | 46 | 1,512 |
mlflow | mlflow/genai/judges/prompts/retrieval_relevance.py | .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... | 23 | 1,107 |
mlflow | mlflow/genai/judges/prompts/conversation_completeness.py | .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... | 20 | 1,527 |
mlflow | mlflow/genai/judges/prompts/correctness.py | .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... | 70 | 2,729 |
mlflow | mlflow/genai/judges/prompts/tool_call_efficiency.py | .py | from typing import TYPE_CHECKING
from mlflow.genai.judges.utils.formatting_utils import (
format_available_tools,
format_tools_called,
)
from mlflow.genai.prompts.utils import format_prompt
if TYPE_CHECKING:
from mlflow.genai.utils.type import FunctionCall
from mlflow.types.chat import ChatTool
# NB:... | 84 | 2,804 |
mlflow | mlflow/genai/judges/instructions_judge/__init__.py | .py | import json
import logging
from dataclasses import asdict
from typing import Any, Literal
from urllib.parse import urlparse, urlunparse
import pydantic
from pydantic import PrivateAttr
import mlflow
from mlflow.entities.assessment import Feedback
from mlflow.entities.model_registry.prompt_version import PromptVersion... | 910 | 39,049 |
mlflow | mlflow/genai/judges/instructions_judge/constants.py | .py | """
Constants for the InstructionsJudge module.
This module contains constant values used by the InstructionsJudge class,
including the augmented prompt template for trace-based evaluation.
"""
# Common base prompt for all judge evaluations
JUDGE_BASE_PROMPT = """You are an expert judge tasked with evaluating the per... | 68 | 3,384 |
mlflow | mlflow/genai/judges/utils/parsing_utils.py | .py | """Response parsing utilities for judge models."""
import re
def _strip_markdown_code_blocks(response: str) -> str:
"""
Strip markdown code blocks from LLM responses.
Some legacy models wrap responses in markdown code blocks (```json...``` or
unlabeled fences). This function removes those wrappers t... | 45 | 1,428 |
mlflow | mlflow/genai/judges/utils/prompt_utils.py | .py | """Prompt formatting and manipulation utilities for judge models."""
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any, Literal, NamedTuple, get_origin
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import BAD_REQUEST
if TYPE_CHECKING:
from mlflo... | 115 | 4,073 |
mlflow | mlflow/genai/judges/utils/tool_calling_utils.py | .py | """Tool calling support for judge models."""
from __future__ import annotations
import json
import logging
from dataclasses import asdict, is_dataclass
from typing import TYPE_CHECKING, Any, NoReturn
if TYPE_CHECKING:
from mlflow.entities.trace import Trace
from mlflow.types.llm import ChatMessage, ToolCall
... | 249 | 9,302 |
mlflow | mlflow/genai/judges/utils/invocation_utils.py | .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... | 273 | 10,994 |
mlflow | mlflow/genai/judges/utils/__init__.py | .py | """Main utilities module for judges. Maintains backwards compatibility."""
from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from mlflow.genai.judges.base import AlignmentOptimizer
import mlflow
from mlflow.environment_variables import MLFLOW_GENAI_JUDGE_DEFAULT_MODEL
from ml... | 134 | 3,918 |
mlflow | mlflow/genai/judges/utils/formatting_utils.py | .py | import logging
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from mlflow.genai.utils.type import FunctionCall
from mlflow.types.chat import ChatTool
_logger = logging.getLogger(__name__)
def format_available_tools(available_tools: list["ChatTool"]) -> str:
"""Format available tools with description... | 97 | 3,548 |
mlflow | mlflow/genai/judges/utils/telemetry_utils.py | .py | from __future__ import annotations
import logging
_logger = logging.getLogger(__name__)
def _record_judge_model_usage_success_databricks_telemetry(
*,
request_id: str | None,
model_provider: str,
endpoint_name: str,
num_prompt_tokens: int | None,
num_completion_tokens: int | None,
) -> None:... | 70 | 2,158 |
mlflow | mlflow/genai/judges/adapters/utils.py | .py | """Shared utilities for judge adapters."""
from __future__ import annotations
import time
from typing import TYPE_CHECKING, Any
import requests
if TYPE_CHECKING:
from mlflow.genai.judges.adapters.base_adapter import BaseJudgeAdapter
from mlflow.types.llm import ChatMessage
from mlflow.environment_variables... | 180 | 5,908 |
mlflow | mlflow/genai/judges/adapters/databricks_managed_judge_adapter.py | .py | from __future__ import annotations
import inspect
import json
import logging
from typing import TYPE_CHECKING, Any, Callable, TypeVar
if TYPE_CHECKING:
from mlflow.entities.trace import Trace
from mlflow.types.llm import ChatMessage, ToolDefinition
T = TypeVar("T") # Generic type for agentic loop return val... | 393 | 13,539 |
mlflow | mlflow/genai/judges/adapters/litellm_adapter.py | .py | from __future__ import annotations
import contextlib
import json
import logging
import re
import threading
from contextlib import ContextDecorator
from contextvars import ContextVar
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Iterator
import pydantic
if TYPE_CHECKING:
import litellm
... | 657 | 25,723 |
mlflow | mlflow/genai/judges/adapters/base_adapter.py | .py | from __future__ import annotations
import logging
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 imp... | 178 | 6,300 |
mlflow | mlflow/genai/judges/adapters/gateway_adapter.py | .py | """Gateway-based judge adapter with tool-calling loop support.
Uses the MLflow Gateway provider infrastructure for request/response
transformation and provider configuration, with retry logic, context
window management, and proactive pruning.
"""
from __future__ import annotations
import json
import logging
from dat... | 672 | 26,629 |
mlflow | mlflow/genai/scorers/aggregation.py | .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, ... | 116 | 4,049 |
mlflow | mlflow/genai/scorers/__init__.py | .py | from typing import TYPE_CHECKING
from mlflow.genai.scorers.base import Scorer, ScorerSamplingConfig, make_scorer_ensemble, scorer
from mlflow.genai.scorers.ensemble import agg_all, agg_any, majority_vote, maximum, mean, minimum
from mlflow.genai.scorers.registry import delete_scorer, get_scorer, list_scorers
# Metada... | 154 | 4,717 |
mlflow | mlflow/genai/scorers/registry.py | .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 base64 import urlsafe_b64encode
from collections.abc import Callable
... | 1,127 | 43,824 |
mlflow | mlflow/genai/scorers/llm_backend.py | .py | """Shared LLM client for scorer packages and simulator.
Provides a single routing layer so that DeepEval, RAGAS, Phoenix, TruLens
scorers and the conversation simulator all resolve model URIs and make
chat completion calls through the same code path.
Note: This is NOT intended for judge adapters, which need lower-lev... | 293 | 10,785 |
mlflow | mlflow/genai/scorers/builtin_scorers.py | .py | import copy
import inspect
import json
import logging
import math
import re
from abc import abstractmethod
from dataclasses import asdict, dataclass
from typing import TYPE_CHECKING, Any, Literal
import pydantic
if TYPE_CHECKING:
from mlflow.genai.utils.type import FunctionCall
from mlflow.types.llm import Ch... | 3,673 | 130,749 |
mlflow | mlflow/genai/scorers/ensemble.py | .py | """Built-in ensemble functions for ``make_scorer_ensemble``.
Each function receives the list of per-sub-scorer values and returns a single
``Feedback``. The parameter is named ``values`` on purpose: ``make_scorer_ensemble``
introspects the parameter name to decide whether to pass raw values or full
``Feedback`` object... | 168 | 6,925 |
mlflow | mlflow/genai/scorers/job.py | .py | """Huey job functions for async scorer invocation."""
import logging
import os
import random
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
from contextlib import nullcontext
from dataclasses import asdict, dataclass, field
from typing import Any
from mlflow.entiti... | 517 | 18,719 |
mlflow | mlflow/genai/scorers/scorer_utils.py | .py | # This file contains utility functions for scorer functionality.
import ast
import inspect
import json
import logging
import re
from textwrap import dedent
from typing import TYPE_CHECKING, Any, Callable
from mlflow.exceptions import INVALID_PARAMETER_VALUE, MlflowException
if TYPE_CHECKING:
from mlflow.genai.ut... | 333 | 11,981 |
mlflow | mlflow/genai/scorers/base.py | .py | import functools
import importlib
import inspect
import json
import logging
from contextvars import ContextVar
from dataclasses import asdict, dataclass, fields
from enum import Enum
from typing import Any, Callable, ClassVar, Literal, TypeAlias, TypeVar, overload
from pydantic import BaseModel, PrivateAttr
import ml... | 1,747 | 74,547 |
mlflow | mlflow/genai/scorers/validation.py | .py | import importlib
import logging
from collections import defaultdict
from typing import Any, Callable
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.base import AggregationFunc, Scorer
from mlflow.genai.scorers.builtin_scorers import (
BuiltInScorer,
MissingColumnsException,
get_all... | 204 | 7,694 |
mlflow | mlflow/genai/scorers/phoenix/utils.py | .py | from __future__ import annotations
from typing import Any
from mlflow.entities.trace import Trace
from mlflow.exceptions import MlflowException
from mlflow.genai.utils.trace_utils import (
extract_retrieval_context_from_trace,
parse_inputs_to_str,
parse_outputs_to_str,
resolve_expectations_from_trace,... | 92 | 2,823 |
mlflow | mlflow/genai/scorers/phoenix/models.py | .py | from __future__ import annotations
from mlflow.genai.scorers.llm_backend import ScorerLLMClient
from mlflow.genai.scorers.phoenix.utils import _NoOpRateLimiter, check_phoenix_installed
class MlflowPhoenixModel:
"""Phoenix model adapter backed by the shared scorer LLM client.
Routes through native providers ... | 48 | 1,459 |
mlflow | mlflow/genai/scorers/phoenix/__init__.py | .py | """
Phoenix (Arize) integration for MLflow.
This module provides integration with Phoenix evaluators, allowing them to be used
with MLflow's scorer interface.
Example usage:
.. code-block:: python
from mlflow.genai.scorers.phoenix import get_scorer
scorer = get_scorer("Hallucination", model="openai:/gpt-4"... | 280 | 8,245 |
mlflow | mlflow/genai/scorers/phoenix/registry.py | .py | from __future__ import annotations
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.phoenix.utils import check_phoenix_installed
_METRIC_REGISTRY = {
"Hallucination": "HallucinationEvaluator",
"Relevance": "RelevanceEvaluator",
"Toxicity": "ToxicityEvaluator",
"QA": "QAEvaluator... | 51 | 1,740 |
mlflow | mlflow/genai/scorers/online/trace_checkpointer.py | .py | """Checkpoint management for trace-level online scoring."""
import json
import logging
import time
from dataclasses import asdict, dataclass
from mlflow.entities.experiment_tag import ExperimentTag
from mlflow.environment_variables import (
MLFLOW_ONLINE_SCORING_DEFAULT_TRACE_COMPLETION_BUFFER_SECONDS,
)
from mlf... | 111 | 4,550 |
mlflow | mlflow/genai/scorers/online/trace_processor.py | .py | """Online scoring processor for executing scorers on traces."""
import logging
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass
from mlflow.entities import Trace
from mlflow.environment_variables import MLFLOW_ONLINE_SCORING_MAX_WORKER_THREADS
from mlflow.g... | 297 | 11,956 |
mlflow | mlflow/genai/scorers/online/trace_loader.py | .py | """Trace loading utilities for online scoring."""
import logging
from mlflow.entities import Trace, TraceData, TraceInfo
from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository
from mlflow.store.tracking.abstract_store import AbstractStore
from mlflow.tracing.constant import SpansLocati... | 159 | 6,071 |
mlflow | mlflow/genai/scorers/online/__init__.py | .py | """Online scoring subpackage for scheduled scorer execution."""
from mlflow.genai.scorers.online.entities import (
CompletedSession,
OnlineScorer,
OnlineScoringConfig,
)
from mlflow.genai.scorers.online.sampler import OnlineScorerSampler
from mlflow.genai.scorers.online.session_checkpointer import OnlineSe... | 26 | 958 |
mlflow | mlflow/genai/scorers/online/constants.py | .py | """Constants for online scoring."""
from mlflow.tracing.constant import TraceMetadataKey
# Maximum lookback period to prevent getting stuck on old failing traces (1 hour)
MAX_LOOKBACK_MS = 60 * 60 * 1000
# Maximum traces to include in a single scoring job
MAX_TRACES_PER_JOB = 500
# Maximum sessions to include in a ... | 16 | 522 |
mlflow | mlflow/genai/scorers/online/session_processor.py | .py | """Session-level online scoring processor for executing scorers on completed sessions."""
import logging
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass, field
from mlflow.entities.assessment import Assessment
from mlflow.environment_variables import MLFLOW_ONLINE_SCO... | 343 | 14,714 |
mlflow | mlflow/genai/scorers/online/entities.py | .py | """
Online scorer entities and configuration.
This module contains entities for online scorer configuration used by the store layer
and online scoring infrastructure.
"""
from dataclasses import dataclass
@dataclass
class OnlineScoringConfig:
"""
Internal entity representing the online configuration for a s... | 65 | 1,864 |
mlflow | mlflow/genai/scorers/online/session_checkpointer.py | .py | """Checkpoint management for session-level online scoring."""
import json
import logging
import time
from dataclasses import asdict, dataclass
from mlflow.entities.experiment_tag import ExperimentTag
from mlflow.environment_variables import (
MLFLOW_ONLINE_SCORING_DEFAULT_SESSION_COMPLETION_BUFFER_SECONDS,
)
from... | 106 | 4,079 |
mlflow | mlflow/genai/scorers/online/sampler.py | .py | """Dense sampling strategy for online scoring."""
import hashlib
import logging
from collections import defaultdict
from typing import TYPE_CHECKING
from mlflow.genai.scorers.base import Scorer
if TYPE_CHECKING:
from mlflow.genai.scorers.online.entities import OnlineScorer
_logger = logging.getLogger(__name__)
... | 107 | 3,896 |
mlflow | mlflow/genai/scorers/guardrails/utils.py | .py | from __future__ import annotations
from typing import Any
from mlflow.entities.trace import Trace
from mlflow.exceptions import MlflowException
from mlflow.genai.utils.trace_utils import (
parse_inputs_to_str,
parse_outputs_to_str,
resolve_inputs_from_trace,
resolve_outputs_from_trace,
)
def check_g... | 58 | 1,741 |
mlflow | mlflow/genai/scorers/guardrails/__init__.py | .py | """
Guardrails AI integration for MLflow.
This module provides integration with Guardrails AI validators, allowing them to be used
with MLflow's scorer interface for LLM safety, PII detection, and content quality evaluation.
Example usage:
.. code-block:: python
from mlflow.genai.scorers.guardrails import Toxic... | 335 | 9,938 |
mlflow | mlflow/genai/scorers/guardrails/registry.py | .py | from __future__ import annotations
from mlflow.exceptions import MlflowException
_SUPPORTED_VALIDATORS = [
"ToxicLanguage",
"NSFWText",
"DetectJailbreak",
"DetectPII",
"SecretsPresent",
"GibberishText",
]
def get_validator_class(validator_name: str):
"""
Get Guardrails AI validator c... | 44 | 1,343 |
mlflow | mlflow/genai/scorers/trulens/utils.py | .py | from __future__ import annotations
import logging
from typing import Any
from mlflow.entities.trace import Trace
from mlflow.genai.scorers.trulens.registry import build_trulens_args
from mlflow.genai.utils.trace_utils import (
extract_retrieval_context_from_trace,
parse_inputs_to_str,
parse_outputs_to_str... | 106 | 3,380 |
mlflow | mlflow/genai/scorers/trulens/models.py | .py | from __future__ import annotations
from typing import TYPE_CHECKING, Any
import pydantic
from mlflow.exceptions import MlflowException
from mlflow.genai.scorers.llm_backend import ScorerLLMClient
from mlflow.genai.utils.message_utils import serialize_chat_messages_to_prompts
if TYPE_CHECKING:
from typing import... | 123 | 4,241 |
mlflow | mlflow/genai/scorers/trulens/__init__.py | .py | """
TruLens evaluation framework integration for MLflow.
This module provides integration with TruLens feedback functions, allowing them to be used
with MLflow's scorer interface.
Example usage:
.. code-block:: python
from mlflow.genai.scorers.trulens import get_scorer
scorer = get_scorer("Groundedness", m... | 301 | 8,531 |
mlflow | mlflow/genai/scorers/trulens/registry.py | .py | from __future__ import annotations
import re
from typing import Any
# Mapping: metric name -> (feedback method name, argument mapping)
# Argument mapping: generic key -> TruLens-specific argument name
_METRIC_REGISTRY: dict[str, tuple[str, dict[str, str]]] = {
# RAG metrics
"Groundedness": (
"grounded... | 65 | 2,224 |
mlflow | mlflow/genai/scorers/trulens/scorers/agent_trace.py | .py | """
Agent trace scorers for goal-plan-action alignment evaluation.
These scorers analyze agent execution traces to detect internal errors and
evaluate the quality of agent reasoning, planning, and tool usage.
Based on TruLens' benchmarked goal-plan-action alignment evaluations which achieve
95% error coverage against... | 277 | 7,729 |
mlflow | mlflow/genai/scorers/trulens/scorers/__init__.py | .py | from mlflow.genai.scorers.trulens.scorers.agent_trace import (
ExecutionEfficiency,
LogicalConsistency,
PlanAdherence,
PlanQuality,
ToolCalling,
ToolSelection,
TruLensAgentScorer,
)
__all__ = [
"TruLensAgentScorer",
"LogicalConsistency",
"ExecutionEfficiency",
"PlanAdherence... | 20 | 384 |
mlflow | mlflow/genai/scorers/google_adk/utils.py | .py | """Utility functions for Google ADK integration."""
from __future__ import annotations
import asyncio
import concurrent.futures
import json
from typing import Any
from mlflow.entities.trace import Trace
from mlflow.exceptions import MlflowException
GOOGLE_ADK_NOT_INSTALLED_ERROR_MESSAGE = (
"Google ADK scorers ... | 164 | 5,479 |
mlflow | mlflow/genai/scorers/google_adk/__init__.py | .py | """
Google ADK integration for MLflow.
This module provides integration with Google Agent Development Kit (ADK) evaluators,
allowing them to be used with MLflow's scorer interface for agent evaluation.
Example usage:
.. code-block:: python
from mlflow.genai.scorers.google_adk import ToolTrajectory, ResponseMatc... | 722 | 24,388 |
mlflow | mlflow/genai/scorers/google_adk/registry.py | .py | """Registry of Google ADK scorers exposed through ``get_scorer``."""
from __future__ import annotations
from mlflow.exceptions import MlflowException
def get_scorer_class(metric_name: str):
"""Return the Google ADK scorer class registered under ``metric_name``."""
from mlflow.genai.scorers.google_adk import... | 33 | 920 |
mlflow | mlflow/genai/scorers/ragas/utils.py | .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_... | 307 | 10,230 |
mlflow | mlflow/genai/scorers/ragas/models.py | .py | from __future__ import annotations
import json
import typing as t
from openai import AsyncOpenAI
from pydantic import BaseModel
from ragas.embeddings import OpenAIEmbeddings
from ragas.llms import InstructorBaseRagasLLM
from mlflow.genai.judges.utils.parsing_utils import _strip_markdown_code_blocks
from mlflow.genai... | 82 | 2,594 |
mlflow | mlflow/genai/scorers/ragas/__init__.py | .py | """
RAGAS integration for MLflow.
This module provides integration with RAGAS metrics, allowing them to be used
with MLflow's judge interface.
Example usage:
.. code-block:: python
from mlflow.genai.scorers.ragas import get_scorer
judge = get_scorer("Faithfulness", model="openai:/gpt-4")
feedback = jud... | 394 | 12,777 |
mlflow | mlflow/genai/scorers/ragas/registry.py | .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_... | 160 | 6,163 |
mlflow | mlflow/genai/scorers/ragas/scorers/__init__.py | .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.genai.scorers.ragas.scorers.agentic_metrics import (
AgentGoalAccuracyWithoutReference,
AgentGoalAccuracyWithReference,
ToolC... | 322 | 9,441 |
mlflow | mlflow/genai/scorers/ragas/scorers/agentic_metrics.py | .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.docstring_utils import format_docstring
@format_docstring(_MODEL_API_DOC)
class TopicAdherence(RagasScorer):
"""
Evaluate... | 196 | 6,168 |
mlflow | mlflow/genai/scorers/ragas/scorers/rag_metrics.py | .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... | 250 | 6,776 |
mlflow | mlflow/genai/scorers/ragas/scorers/comparison_metrics.py | .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.docstring_utils import format_docstring
@format_docstring(_MODEL_API_DOC)
class FactualCorrectness(RagasScorer):
"""
Eval... | 170 | 4,406 |
mlflow | mlflow/genai/scorers/deepeval/utils.py | .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 (
... | 227 | 7,541 |
mlflow | mlflow/genai/scorers/deepeval/models.py | .py | from __future__ import annotations
import json
from typing import Any
from deepeval.models.base_model import DeepEvalBaseLLM
from pydantic import ValidationError
from mlflow.genai.scorers.llm_backend import ScorerLLMClient
def _build_json_prompt_with_schema(prompt: str, schema) -> str:
return (
f"{prom... | 91 | 3,181 |
mlflow | mlflow/genai/scorers/deepeval/__init__.py | .py | """
DeepEval integration for MLflow.
This module provides integration with DeepEval metrics, allowing them to be used
with MLflow's scorer interface.
Example usage:
.. code-block:: python
from mlflow.genai.scorers.deepeval import get_scorer
scorer = get_scorer("AnswerRelevancy", threshold=0.7, model="opena... | 322 | 9,783 |
mlflow | mlflow/genai/scorers/deepeval/registry.py | .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... | 91 | 4,263 |
mlflow | mlflow/genai/scorers/deepeval/scorers/safety_metrics.py | .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.docstring_utils import format_docstring
@format_docstring(... | 185 | 5,776 |
mlflow | mlflow/genai/scorers/deepeval/scorers/__init__.py | .py | """DeepEval metric scorers organized by category."""
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.genai.scorers.deepeval.scorers.agentic_metrics import (
ArgumentCorrectne... | 236 | 5,896 |
mlflow | mlflow/genai/scorers/deepeval/scorers/agentic_metrics.py | .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.docstring_utils import format_docstring
@format_docstring(_MODE... | 174 | 5,560 |
mlflow | mlflow/genai/scorers/deepeval/scorers/rag_metrics.py | .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.docstring_utils import format_docstring
@form... | 147 | 5,124 |
mlflow | mlflow/genai/scorers/deepeval/scorers/conversational_metrics.py | .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.docstring_utils import format_docstring
@form... | 218 | 6,896 |
mlflow | mlflow/genai/git_versioning/__init__.py | .py | import logging
from typing_extensions import Self
import mlflow
from mlflow.genai.git_versioning.git_info import GitInfo, GitOperationError
from mlflow.telemetry.events import GitModelVersioningEvent
from mlflow.telemetry.track import record_usage_event
from mlflow.tracking.fluent import _set_active_model
from mlflow... | 162 | 5,382 |
mlflow | mlflow/genai/git_versioning/git_info.py | .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):
... | 101 | 3,276 |
mlflow | mlflow/genai/prompts/utils.py | .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.... | 13 | 508 |
mlflow | mlflow/genai/prompts/__init__.py | .py | import json
import warnings
from contextlib import contextmanager
from typing import Any
from pydantic import BaseModel
import mlflow.tracking._model_registry.fluent as registry_api
from mlflow.entities.model_registry.prompt import Prompt
from mlflow.entities.model_registry.prompt_version import (
PromptModelConf... | 415 | 14,905 |
mlflow | mlflow/genai/datasets/databricks_evaluation_dataset_source.py | .py | from typing import Any
from mlflow.data.dataset_source import DatasetSource
class DatabricksEvaluationDatasetSource(DatasetSource):
"""
Represents a Databricks Evaluation Dataset source.
This source is used for datasets managed by the Databricks agents SDK.
"""
def __init__(
self,
... | 103 | 3,057 |
mlflow | mlflow/genai/datasets/evaluation_dataset.py | .py | from datetime import datetime
from typing import TYPE_CHECKING, Any
from mlflow.data import Dataset
from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin
from mlflow.entities.evaluation_dataset import (
EvaluationDataset as _EntityEvaluationDataset,
)
from mlflow.genai.datasets.databricks_eval... | 360 | 13,412 |
mlflow | mlflow/genai/datasets/__init__.py | .py | """
Databricks Agent Datasets Python SDK. For more details see Databricks Agent Evaluation:
<https://docs.databricks.com/en/generative-ai/agent-evaluation/index.html>
The API docs can be found here:
<https://api-docs.databricks.com/python/databricks-agents/latest/databricks_agent_eval.html#datasets>
"""
import loggi... | 798 | 28,145 |
mlflow | mlflow/genai/datasets/entities.py | .py | from dataclasses import dataclass
from datetime import datetime, timedelta
def _format_datetime_for_repr(value: datetime) -> str:
formatted = value.isoformat(sep=" ", timespec="seconds")
if value.utcoffset() == timedelta(0):
return formatted.removesuffix("+00:00") + " UTC"
return formatted
@data... | 33 | 941 |
mlflow | mlflow/genai/evaluation/rate_limiter.py | .py | """Thread-safe rate limiters for evaluation harness."""
from __future__ import annotations
import abc
import contextlib
import logging
import threading
import time
from typing import Callable
_logger = logging.getLogger(__name__)
@contextlib.contextmanager
def eval_retry_context():
"""Disable downstream 429 re... | 194 | 6,570 |
mlflow | mlflow/genai/evaluation/telemetry.py | .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... | 142 | 4,375 |
mlflow | mlflow/genai/evaluation/utils.py | .py | import json
import logging
import math
from typing import TYPE_CHECKING, Any, Collection
from mlflow.entities import Assessment, Trace, TraceData
from mlflow.entities.assessment import DEFAULT_FEEDBACK_NAME, Feedback
from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType
from mlflow.entit... | 467 | 16,696 |
mlflow | mlflow/genai/evaluation/__init__.py | .py | from mlflow.genai.evaluation.base import evaluate, to_predict_fn
__all__ = ["evaluate", "to_predict_fn"]
| 4 | 106 |
mlflow | mlflow/genai/evaluation/entities.py | .py | """Entities for evaluation."""
import hashlib
import json
from dataclasses import dataclass, field
from typing import Any, Callable
import pandas as pd
from mlflow.entities.assessment import Expectation, Feedback
from mlflow.entities.assessment_source import AssessmentSource, AssessmentSourceType
from mlflow.entitie... | 338 | 12,121 |
mlflow | mlflow/genai/evaluation/session_utils.py | .py | """Utilities for session-level (multi-turn) evaluation."""
from __future__ import annotations
import traceback
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor
from typing import TYPE_CHECKING, Any
from mlflow.entities.assessment import Feedback
from mlflow.entities.assessment_er... | 211 | 7,657 |
mlflow | mlflow/genai/evaluation/context.py | .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... | 146 | 4,020 |
mlflow | mlflow/genai/evaluation/job.py | .py | """Huey job function for the UI-triggered `mlflow.genai.evaluate` flow.
This module backs the `POST /ajax-api/3.0/mlflow/genai/evaluate/invoke` endpoint
used by the "Run evaluation" modal's "Run judges" button.
"""
import logging
import os
import mlflow
from mlflow.client import MlflowClient
from mlflow.entities.run... | 61 | 2,118 |
mlflow | mlflow/genai/evaluation/constant.py | .py | class AgentEvaluationReserverKey:
"""
Expectation column names that are used by Agent Evaluation.
Ref: https://docs.databricks.com/aws/en/generative-ai/agent-evaluation/evaluation-schema
"""
EXPECTED_RESPONSE = "expected_response"
EXPECTED_RETRIEVED_CONTEXT = "expected_retrieved_context"
EX... | 48 | 1,260 |
mlflow | mlflow/genai/evaluation/base.py | .py | import inspect
import logging
import os
import time
from contextlib import nullcontext
from typing import TYPE_CHECKING, Any, Callable, NamedTuple
import mlflow
from mlflow.data.dataset import Dataset
from mlflow.entities.dataset_input import DatasetInput
from mlflow.entities.evaluation_dataset import EvaluationDatase... | 715 | 28,358 |
mlflow | mlflow/genai/evaluation/harness.py | .py | """Entry point to the evaluation harness"""
from __future__ import annotations
import logging
import queue
import threading
import time
import traceback
import uuid
from concurrent.futures import FIRST_COMPLETED, Future, ThreadPoolExecutor, as_completed, wait
from typing import Any, Callable
import pandas as pd
try... | 1,120 | 41,605 |
mlflow | mlflow/genai/agent_server/server.py | .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
... | 439 | 17,247 |
mlflow | mlflow/genai/agent_server/utils.py | .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]](
... | 48 | 1,656 |
mlflow | mlflow/genai/agent_server/__init__.py | .py | from mlflow.genai.agent_server.server import (
AgentServer,
get_invoke_function,
get_stream_function,
invoke,
stream,
)
from mlflow.genai.agent_server.utils import (
get_request_headers,
set_request_headers,
setup_mlflow_git_based_version_tracking,
)
__all__ = [
"set_request_headers... | 24 | 500 |
mlflow | mlflow/genai/agent_server/validator.py | .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"""
... | 67 | 2,527 |
mlflow | mlflow/genai/label_schemas/label_schemas.py | .py | import warnings
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, TypeVar
from mlflow.exceptions import MlflowException
from mlflow.genai.utils.enum_utils import StrEnum
from mlflow.protos import label_schemas_pb2 as _ls_pb
from mlflow.protos.databricks_pb2 import ... | 465 | 17,937 |
mlflow | mlflow/genai/label_schemas/__init__.py | .py | """
Label schemas define how reviewers annotate traces in the review UI.
By default a schema is managed in the MLflow tracking store and scoped to an
experiment (identity ``(experiment_id, name)``, with a server-generated
``schema_id``). On a Databricks tracking URI the same functions route to the
workspace's ReviewAp... | 318 | 11,349 |
mlflow | mlflow/genai/label_schemas/validation.py | .py | """
Server-side validation for label schemas.
Type immutability post-create is enforced server-side (the field is
documented as immutable but the entity does not enforce it on its own).
The validation surface is intentionally split:
- :py:func:`validate_schema_for_create` is called from the store layer's
create pa... | 320 | 12,453 |
mlflow | mlflow/genai/utils/type.py | .py | from __future__ import annotations
from typing import Any
from mlflow.types.chat import Function
class FunctionCall(Function):
arguments: str | dict[str, Any] | None = None
outputs: Any | None = None
exception: str | None = None
| 12 | 245 |
mlflow | mlflow/genai/utils/llm_utils.py | .py | from __future__ import annotations
import functools
import logging
import threading
import time
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any
import pydantic
import requests
import mlflow
from mlflow.gateway.config import EndpointType
from mlflow.genai.judges.adapters.litellm_adapter import... | 278 | 9,894 |
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