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from typing import List, Dict, Literal
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# Type definition
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ConversationDict = Dict[Literal["role", "content"], str]
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Conversation = List[ConversationDict]
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# Example conversation
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conversation = [
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    {"role": "user", "content": "Hello! Can you help me?"},
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    {"role": "assistant", "content": "Hi there! I'd be happy to help. What do you need?"},
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    {"role": "user", "content": "I need information about Python"},
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    {"role": "assistant", "content": "Python is a versatile programming language..."},
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]

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from typing import Any
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from opik.evaluation.metrics import score_result
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from opik.evaluation.metrics.conversation import (
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    ConversationThreadMetric,
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    types as conversation_types,
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)
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class ConversationLengthMetric(ConversationThreadMetric):
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    """
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    A simple metric that counts the number of conversation turns.
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    """
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    def __init__(self, name: str = "conversation_length_score"):
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        super().__init__(name)
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    def score(
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        self, conversation: conversation_types.Conversation, **kwargs: Any
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    ) -> score_result.ScoreResult:
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        """
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        Score based on conversation length.
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        Args:
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            conversation: List of conversation messages with 'role' and 'content'.
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            **kwargs: Additional arguments (ignored).
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        """
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        # Count assistant responses (each represents one conversation turn)
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        num_turns = sum(1 for msg in conversation if msg["role"] == "assistant")
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        return score_result.ScoreResult(
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            name=self.name,
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            value=num_turns,
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            reason=f"Conversation has {num_turns} turns"
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        )

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import pydantic
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class ConversationQualityScore(pydantic.BaseModel):
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    """Schema for LLM judge output."""
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    score_value: float  # Score between 0.0 and 1.0
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    reason: str  # Explanation for the score
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    __hash__ = object.__hash__

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def create_evaluation_prompt(conversation: list) -> str:
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    """
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    Create a prompt that asks the LLM to evaluate conversation quality.
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    """
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    return f"""Evaluate the quality of the assistant's responses in this conversation.
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Consider the following criteria:
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1. Helpfulness: Does the assistant provide useful, relevant information?
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2. Clarity: Are the responses clear and easy to understand?
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3. Consistency: Does the assistant maintain context across turns?
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4. Professionalism: Is the tone appropriate and respectful?
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Return a JSON object with:
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- score_value: A number between 0.0 (poor) and 1.0 (excellent)
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- reason: A brief explanation of your assessment
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Conversation:
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{conversation}
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Your evaluation (JSON only):
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"""

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import logging
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from typing import Optional, Union, Any
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import pydantic
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from opik import exceptions
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from opik.evaluation.metrics import score_result
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from opik.evaluation.metrics.conversation import (
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    ConversationThreadMetric,
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    types as conversation_types,
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)
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from opik.evaluation.metrics.llm_judges import parsing_helpers
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from opik.evaluation.models import base_model, models_factory
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LOGGER = logging.getLogger(__name__)
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class ConversationQualityMetric(ConversationThreadMetric):
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    """
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    An LLM-as-judge metric that evaluates conversation quality.
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    Args:
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        model: The LLM to use as a judge (e.g., "gpt-4", "claude-3-5-sonnet-20241022").
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               If None, uses the default model.
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        name: The name of this metric.
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        track: Whether to track the metric in Opik.
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        project_name: Optional project name for tracking.
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    """
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    def __init__(
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        self,
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        model: Optional[Union[str, base_model.OpikBaseModel]] = None,
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        name: str = "conversation_quality_score",
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        track: bool = True,
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        project_name: Optional[str] = None,
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    ):
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        super().__init__(name=name, track=track, project_name=project_name)
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        self._init_model(model)
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    def _init_model(
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        self, model: Optional[Union[str, base_model.OpikBaseModel]]
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    ) -> None:
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        """Initialize the LLM model for judging."""
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        if isinstance(model, base_model.OpikBaseModel):
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            self._model = model
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        else:
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            # Get model from factory (supports various providers via LiteLLM)
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            self._model = models_factory.get(model_name=model)
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    def score(
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        self,
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        conversation: conversation_types.Conversation,
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        **kwargs: Any,
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    ) -> score_result.ScoreResult:
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        """
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        Evaluate the conversation quality using an LLM judge.
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        Args:
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            conversation: List of conversation messages.
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            **kwargs: Additional arguments (ignored).
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        Returns:
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            ScoreResult with value between 0.0 and 1.0.
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        """
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        try:
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            # Create the evaluation prompt
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            llm_query = create_evaluation_prompt(conversation)
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            # Call the LLM with structured output
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            model_output = self._model.generate_string(
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                input=llm_query,
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                response_format=ConversationQualityScore,
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            )
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            # Parse the LLM response
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            score_data = self._parse_llm_output(model_output)
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            # Ensure score is within valid range [0.0, 1.0]
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            validated_score = max(0.0, min(1.0, score_data.score_value))
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            return score_result.ScoreResult(
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                name=self.name,
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                value=validated_score,
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                reason=score_data.reason,
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            )
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        except Exception as e:
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            LOGGER.error(f"Failed to calculate conversation quality: {e}")
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            raise exceptions.MetricComputationError(
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                f"Failed to calculate conversation quality: {e}"
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            ) from e
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    def _parse_llm_output(self, model_output: str) -> ConversationQualityScore:
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        """Parse and validate the LLM's output."""
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        try:
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            # Extract JSON from the model output
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            dict_content = parsing_helpers.extract_json_content_or_raise(
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                model_output
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            )
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            # Validate against schema
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            return ConversationQualityScore.model_validate(dict_content)
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        except pydantic.ValidationError as e:
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            LOGGER.warning(
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                f"Failed to parse LLM output: {model_output}, error: {e}",
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                exc_info=True,
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            )
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            raise

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from opik.evaluation import evaluate_threads
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# Initialize the metric with your preferred judge model
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quality_metric = ConversationQualityMetric(
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    model="gpt-4o",  # or "claude-3-5-sonnet-20241022", etc.
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    name="conversation_quality"
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)
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# Evaluate threads in your project
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results = evaluate_threads(
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    project_name="my_chatbot_project",
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    eval_project_name="quality_evaluation",
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    metrics=[quality_metric],
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)

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from opik.evaluation import evaluate_threads
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# Initialize your metrics
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conversation_length_metric = ConversationLengthMetric()
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quality_metric = ConversationQualityMetric(model="gpt-4o")
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# Evaluate threads in your project
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results = evaluate_threads(
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    project_name="my_chatbot_project",
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    filter_string='status = "inactive"',
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    eval_project_name="chatbot_evaluation",
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    metrics=[conversation_length_metric, quality_metric],
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    trace_input_transform=lambda x: x["input"],
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    trace_output_transform=lambda x: x["output"],
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)