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/tools/get_traces_in_session.py | .py | """
Get traces in session tool for MLflow GenAI judges.
This module provides a tool for retrieving traces from the same session
to enable multi-turn evaluation capabilities.
"""
from mlflow.entities.trace import Trace
from mlflow.exceptions import MlflowException
from mlflow.genai.judges.tools.base import JudgeTool
f... | 111 | 4,378 |
mlflow | mlflow/genai/judges/tools/utils.py | .py | """
Utilities for MLflow GenAI judge tools.
This module contains utility functions and classes used across
different judge tool implementations.
"""
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
def create_page_token(offset: int) -> str:
"""
C... | 48 | 1,147 |
mlflow | mlflow/genai/judges/tools/get_span.py | .py | """
Get span tool for MLflow GenAI judges.
This module provides a tool for retrieving a specific span by ID.
"""
import json
from mlflow.entities.trace import Trace
from mlflow.genai.judges.tools.base import JudgeTool
from mlflow.genai.judges.tools.constants import ToolNames
from mlflow.genai.judges.tools.types impo... | 133 | 5,306 |
mlflow | mlflow/genai/judges/tools/get_root_span.py | .py | """
Get root span tool for MLflow GenAI judges.
This module provides a tool for retrieving the root span of a trace,
which contains the top-level inputs and outputs.
"""
from mlflow.entities.trace import Trace
from mlflow.genai.judges.tools.base import JudgeTool
from mlflow.genai.judges.tools.constants import ToolNam... | 109 | 4,316 |
mlflow | mlflow/genai/judges/tools/search_traces.py | .py | """
Search traces tool for MLflow GenAI judges.
This module provides a tool for searching and retrieving traces from an MLflow experiment
based on filter criteria, ordering, and result limits. It enables judges to analyze
traces within the same experiment context.
"""
import logging
import mlflow
from mlflow.entitie... | 269 | 11,277 |
mlflow | mlflow/genai/judges/tools/__init__.py | .py | from mlflow.genai.judges.tools.base import JudgeTool
from mlflow.genai.judges.tools.get_root_span import GetRootSpanTool
from mlflow.genai.judges.tools.get_span import GetSpanTool
from mlflow.genai.judges.tools.get_span_image import GetSpanImageTool, SpanImageResult
from mlflow.genai.judges.tools.get_span_performance_a... | 47 | 1,524 |
mlflow | mlflow/genai/judges/tools/constants.py | .py | """
Constants for MLflow GenAI judge tools.
This module contains constant values used across the judge tools system,
providing a single reference point for tool names and other constants.
"""
# Tool names
class ToolNames:
"""Registry of judge tool names."""
GET_TRACE_INFO = "get_trace_info"
GET_ROOT_SPA... | 22 | 658 |
mlflow | mlflow/genai/judges/tools/get_trace_info.py | .py | """
Get trace info tool for MLflow GenAI judges.
This module provides a tool for retrieving trace metadata including
timing, location, state, and other high-level information.
"""
from mlflow.entities.trace import Trace
from mlflow.entities.trace_info import TraceInfo
from mlflow.genai.judges.tools.base import JudgeT... | 61 | 1,871 |
mlflow | mlflow/genai/judges/tools/registry.py | .py | """
Tool registry for MLflow GenAI judges.
This module provides a registry system for managing and invoking JudgeTool instances.
"""
import json
import logging
from typing import Any
import mlflow
from mlflow.entities import SpanType, Trace
from mlflow.environment_variables import MLFLOW_GENAI_EVAL_ENABLE_SCORER_TRA... | 148 | 4,753 |
mlflow | mlflow/genai/judges/tools/list_spans.py | .py | """
Tool definitions for MLflow GenAI judges.
This module provides concrete JudgeTool implementations that judges can use
to analyze traces and extract information during evaluation.
"""
from dataclasses import dataclass
from mlflow.entities.trace import Trace
from mlflow.genai.judges.tools.base import JudgeTool
fro... | 127 | 4,350 |
mlflow | mlflow/genai/judges/tools/get_span_performance_and_timing_report.py | .py | """
Get span timing report tool for MLflow traces.
This tool generates a timing report showing span latencies, execution order,
and concurrency patterns for performance analysis.
"""
from collections import defaultdict
from dataclasses import dataclass
from mlflow.entities.span import Span
from mlflow.entities.trace... | 500 | 17,080 |
mlflow | mlflow/genai/judges/tools/search_trace_regex.py | .py | """
Tool for searching traces using regex patterns.
This module provides functionality to search through entire traces (including
spans, metadata, tags, requests, and responses) using regular expressions
with case-insensitive matching.
"""
import re
from dataclasses import dataclass
from mlflow.entities.trace import... | 165 | 5,826 |
mlflow | mlflow/genai/judges/tools/get_span_image.py | .py | """
Get span image tool for MLflow GenAI judges.
This module provides a tool that resolves an ``mlflow-attachment://`` image
reference inside a span, downloads the real bytes, and returns them as a
base64 data URL so a multimodal judge model can actually view the image.
When autolog extracts an image from a span it r... | 198 | 8,690 |
mlflow | mlflow/genai/judges/tools/base.py | .py | """
Base classes for MLflow GenAI tools that can be used by judges.
This module provides the foundational interfaces for tools that judges can use
to enhance their evaluation capabilities.
"""
from abc import ABC, abstractmethod
from typing import Any
from mlflow.entities.trace import Trace
from mlflow.types.llm imp... | 54 | 1,418 |
mlflow | mlflow/genai/judges/prompts/knowledge_retention.py | .py | # NB: User-facing name for the knowledge retention assessment.
KNOWLEDGE_RETENTION_ASSESSMENT_NAME = "knowledge_retention"
KNOWLEDGE_RETENTION_PROMPT = """\
Your task is to evaluate the LAST AI response in the {{ conversation }} and determine if it:
- Correctly uses or references information the user provided in earli... | 29 | 1,594 |
mlflow | mlflow/genai/judges/prompts/conversational_safety.py | .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... | 27 | 1,403 |
mlflow | mlflow/genai/judges/prompts/guidelines.py | .py | from mlflow.genai.prompts.utils import format_prompt
GUIDELINES_FEEDBACK_NAME = "guidelines"
GUIDELINES_PROMPT_INSTRUCTIONS = """\
Given the following set of guidelines and some inputs, please assess whether the inputs fully \
comply with all the provided guidelines. Only focus on the provided guidelines and not the... | 51 | 1,814 |
mlflow | mlflow/genai/judges/prompts/conversational_tool_call_efficiency.py | .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 ... | 27 | 1,541 |
mlflow | mlflow/genai/judges/prompts/relevance_to_query.py | .py | from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the is_context_relevant assessment.
RELEVANCE_TO_QUERY_ASSESSMENT_NAME = "relevance_to_context"
RELEVANCE_TO_QUERY_PROMPT_INSTRUCTIONS = """\
Consider the following question and answer. You must determine whether the answer provides \
in... | 37 | 1,397 |
mlflow | mlflow/genai/judges/prompts/context_sufficiency.py | .py | from typing import Any
from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the is_context_sufficient assessment.
CONTEXT_SUFFICIENCY_FEEDBACK_NAME = "context_sufficiency"
CONTEXT_SUFFICIENCY_PROMPT_INSTRUCTIONS = """\
Consider the following claim and document. You must determine whether ... | 72 | 2,451 |
mlflow | mlflow/genai/judges/prompts/tool_call_correctness.py | .py | import json
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 Cha... | 208 | 8,547 |
mlflow | mlflow/genai/judges/prompts/safety.py | .py | from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the safety assessment.
SAFETY_ASSESSMENT_NAME = "safety"
SAFETY_PROMPT = """\
You are a content safety classifier. Analyze the given text against policies prohibiting hate speech, harassment, incitement of violence, and the promotion of ... | 22 | 928 |
mlflow | mlflow/genai/judges/prompts/conversational_guidelines.py | .py | CONVERSATIONAL_GUIDELINES_ASSESSMENT_NAME = "conversational_guidelines"
CONVERSATIONAL_GUIDELINES_PROMPT = """\
Consider the following conversation history between a user and an assistant.
Your task is to evaluate whether the assistant's responses throughout the conversation comply with
the provided guidelines and out... | 24 | 1,158 |
mlflow | mlflow/genai/judges/prompts/fluency.py | .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 ... | 16 | 661 |
mlflow | mlflow/genai/judges/prompts/completeness.py | .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" ... | 21 | 1,295 |
mlflow | mlflow/genai/judges/prompts/conversational_role_adherence.py | .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... | 31 | 2,069 |
mlflow | mlflow/genai/judges/prompts/groundedness.py | .py | from typing import Any
from mlflow.genai.prompts.utils import format_prompt
# NB: User-facing name for the is_grounded assessment.
GROUNDEDNESS_FEEDBACK_NAME = "groundedness"
GROUNDEDNESS_PROMPT_INSTRUCTIONS = """\
Consider the following claim and document. You must determine whether claim is supported by the \
doc... | 50 | 1,630 |
mlflow | mlflow/genai/judges/prompts/user_frustration.py | .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... | 20 | 1,183 |
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 |
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