o i.@sddlmZddlmZddlZddlmZddlmZddlZddlZddl Z ddl m Z ddl m Z ddl m Z dd l mZdd l mZdd lmZdd lmZdd lmZddlmZddlmZGdddeZGdddeZeGdddeZeGdddeZeGdddeZeeeeeeeffZddddddZd.d e eee fd!efd"d#Z d.d e eee fd!efd$d%Z!d.d e eee fd!efd&d'Z"d.d e eee fd!efd(d)Z#d.d e eee fd!efd*d+Z$Gd,d-d-eZ%dS)/)ABC)abstractmethodN)asdict) dataclass)Any)Literal)Optional)Protocol)Union) BaseEvaluator)EvaluatorContext)EvaluatorResult)_validate_evaluator_name)JSONTypec @sVeZdZdeedeeeefdeeeefdedeeeefdef ddZd S) LLMClientprovidermessages json_schemamodel model_paramsreturncCsdSN)selfrrrrrrrX/home/ubuntu/.local/lib/python3.10/site-packages/ddtrace/llmobs/_evaluators/llm_judge.py__call__szLLMClient.__call__N) __name__ __module__ __qualname__rstrlistdictrrrrrrrsrc@seZdZUdZeed<eed<eeed<ee defddZ e de ee ffdd Z d e ee fde ee ffd d Zd S)BaseStructuredOutputz5Abstract base class for LLM Judge structured outputs. description reasoningreasoning_descriptionrcCdS)z/Return the label key for the evaluation result.Nrrrrrlabel(zBaseStructuredOutput.labelcCr&)z-Return the JSON schema for structured output.Nrr'rrrto_json_schema-r)z#BaseStructuredOutput.to_json_schema label_schemacCsD|j|i}|jg}|jrd|jpdd|d<|dd||ddS)zABuild JSON schema with the label property and optional reasoning.stringz%Explanation for the evaluation resulttyper#r$objectF)r. propertiesrequiredadditionalProperties)r(r$r%append)rr+r0r1rrr _build_schema1s  z"BaseStructuredOutput._build_schemaN)rrr__doc__r__annotations__boolrpropertyrr(r!rr*r4rrrrr"!s  &r"c@sneZdZUdZeed<dZeed<dZe eed<dZ e eed<e defd d Z de eeffd d ZdS) BooleanStructuredOutputzBoolean structured output for true/false evaluations. 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LLMJudge enables automated evaluation of LLM outputs using another LLM as the judge. It supports multiple providers (OpenAI, Anthropic, Azure OpenAI, Vertex AI, Bedrock) and output formats for flexible evaluation criteria. Supported Output Types: - ``BooleanStructuredOutput``: Returns True/False with optional pass/fail assessment. - ``ScoreStructuredOutput``: Returns a numeric score within a defined range with optional thresholds. - ``CategoricalStructuredOutput``: Returns one of predefined categories with optional pass values. - ``dict[str, JSONType]``: Custom JSON schema for arbitrary structured responses. Template Variables: Prompts support ``{{field.path}}`` syntax to inject context from the evaluated span: - ``{{input_data}}``: The span's input data - ``{{output_data}}``: The span's output data - ``{{expected_output}}``: Expected output for comparison (if available) - ``{{metadata.key}}``: Access nested metadata fields Args: user_prompt: The prompt template sent to the judge LLM. Use ``{{field}}`` syntax to inject span context. system_prompt: Optional system prompt to set judge behavior/persona. Does not support template variables. structured_output: Output format specification (BooleanStructuredOutput, ScoreStructuredOutput, CategoricalStructuredOutput, or a custom JSON schema dict). provider: LLM provider to use. Supported values: ``"openai"``, ``"anthropic"``, ``"azure_openai"``, ``"vertexai"``, ``"bedrock"``. Required if ``client`` is not provided. model: Model identifier (e.g., ``"gpt-4o"``, ``"claude-sonnet-4-20250514"``). model_params: Additional parameters passed to the LLM API (e.g., temperature). client: Custom LLM client implementing the ``LLMClient`` protocol. If provided, ``provider`` is not required. name: Optional evaluator name for identification in results. client_options: Provider-specific configuration options. Supported keys vary by provider: **OpenAI:** - ``api_key``: API key. Falls back to ``OPENAI_API_KEY`` env var. **Anthropic:** - ``api_key``: API key. Falls back to ``ANTHROPIC_API_KEY`` env var. **Azure OpenAI:** - ``api_key``: API key. Falls back to ``AZURE_OPENAI_API_KEY`` env var. - ``azure_endpoint``: Endpoint URL. Falls back to ``AZURE_OPENAI_ENDPOINT``. - ``api_version``: API version (default: "2024-10-21"). Falls back to ``AZURE_OPENAI_API_VERSION``. - ``azure_deployment``: Deployment name. Falls back to ``AZURE_OPENAI_DEPLOYMENT`` or uses ``model`` param. **Vertex AI:** - ``project``: Google Cloud project ID. Falls back to ``GOOGLE_CLOUD_PROJECT`` or ``GCLOUD_PROJECT`` env var, or the project inferred from default credentials. - ``location``: Region (default: "us-central1"). Falls back to ``GOOGLE_CLOUD_REGION`` or ``GOOGLE_CLOUD_LOCATION``. - ``credentials``: Optional service account credentials object. Falls back to Application Default Credentials (ADC), which respects the ``GOOGLE_APPLICATION_CREDENTIALS`` env var. **Bedrock:** - ``aws_access_key_id``: AWS access key. Falls back to ``AWS_ACCESS_KEY_ID`` env var. - ``aws_secret_access_key``: AWS secret key. Falls back to ``AWS_SECRET_ACCESS_KEY`` env var. - ``aws_session_token``: Session token. Falls back to ``AWS_SESSION_TOKEN`` env var. - ``region_name``: AWS region (default: "us-east-1"). Falls back to ``AWS_REGION`` or ``AWS_DEFAULT_REGION``. - ``profile_name``: AWS profile name. Falls back to ``AWS_PROFILE``. Raises: ValueError: If neither ``client`` nor ``provider`` is provided. Examples: Boolean evaluation with pass/fail assessment:: judge = LLMJudge( provider="openai", model="gpt-5-mini", user_prompt="Is this response factually accurate? Response: {{output_data}}", structured_output=BooleanStructuredOutput( description="Whether the response is factually accurate", reasoning=True, pass_when=True, ), ) Score-based evaluation with thresholds:: judge = LLMJudge( provider="anthropic", model="claude-haiku-4-5-20250514", user_prompt="Rate the helpfulness of this response (1-10): {{output_data}}", structured_output=ScoreStructuredOutput( description="Helpfulness score", min_score=1, max_score=10, min_threshold=7, # Scores >= 7 pass ), ) Categorical evaluation:: judge = LLMJudge( provider="openai", model="gpt-5-mini", user_prompt="Classify the sentiment: {{output_data}}", structured_output=CategoricalStructuredOutput( categories={ "positive": "The response has a positive sentiment", "neutral": "The response has a neutral sentiment", "negative": "The response has a negative sentiment", }, pass_values=["positive", "neutral"], ), ) )rerT)r\rUrVrZr[zVProvide either 'client' or 'provider' (openai/anthropic/azure_openai/vertexai/bedrock)N)super__init___system_prompt _user_prompt_structured_output _model_params _provider_model_clientrrrrrr}) rrrrrrrrxrer\ __class__rrrs* zLLMJudge.__init__contextrcCs|jdur td||j|}|j}g}|r|d|d|d|dd}|jr;t|jtr6|j}n|j }| |j |||j|j }|jrO| |S|S)Nzmodel must be specifiedrrr)rr}_renderrrr3rrsr!r*rrr_parse_response)rrrrrrrvrrrevaluates"    zLLMJudge.evaluateml_app eval_namevariable_mappingcCsDt|tr |s td|}|dur|n|j}t|tr#|s'td|}t||jdur8tdt|j}|durPtd |jd t t| |}| |\}}} d|jpbddd ||j|dg} |jpsi| ||d } | dur| | d <|d ||| d } t|jtr|jnd} | r| | d<|| gdS)Nz!ml_app must be a non-empty stringz?eval_name must be provided either as argument or evaluator namez.provider must be specified to publish LLMJudgez sz3LLMJudge._apply_variable_mapping..z)\s*\}\}matchrcsd|ddS)Nz{{z}})group)r"rrrreplacesz1LLMJudge._apply_variable_mapping..replace)rcompilerMatchrsub)rrpatternr&rr%rrs " z LLMJudge._apply_variable_mappingr cCs\|j}|dur tdt|tr|||dddfS|j}|}||||d||fS)Nz:structured_output must be provided to publish an evaluatorrcrr)rr}rsr!_format_schema_for_providerr(r*_build_assessment_criteria)rr r schema_namergrrrrs   z+LLMJudge._build_publish_schema_and_criteriargr-cCs|dvr |d|dS|S)N>rTrVTrdr)rgr r-rrrr+)s z$LLMJudge._format_schema_for_providercCs~i}t|tr|jdur|j|d<n)t|tr,|jdur!|j|d<|jdur+|j|d<nt|tr;|jdur;|j|d<|p>dS)Nr:rArBrJ)rsr9r:r>rArBrHrJ)rcriteriarrrr,1s       z#LLMJudge._build_assessment_criteriacsDt|dtdtffdd dtjdtffdd }td||S) aRender a prompt template by substituting {{field.path}} placeholders with context values. Args: template: Prompt template string with {{field.path}} placeholders. context: EvaluatorContext containing input_data, output_data, expected_output, and metadata. Returns: Rendered prompt string with placeholders replaced by actual values. pathrcsH|d}|d}|ddD]}t|tr||}qdS|S)N.rr#)splitrzrsr!)r/rrNpart)ctxrrresolveMs   z!LLMJudge._render..resolver"csB|d}|durdSt|ttfrtj|ddSt|S)Nr#rl)indent)r$rrsr!r rrr)r"rN)r4rrr&Ws z!LLMJudge._render..replacez \{\{(.+?)\}\})rrrrr(r))rrrr&r)r3r4rrAs  zLLMJudge._renderrvc Cs~|rt|ts tdzt|}Wntjtfy)}ztd||d}~wwt|ts9tdt|j |j }t|trJt || ddS|durWt || ddSt |dd}|rd| |n|}t|tr{t|tsztdt|j n+t|trt|ttfstd t|j nt|trt|tstd t|j ||}|jr| dnd}t |||d |id S) abParse the LLM response and extract structured evaluation results. Args: response: Raw JSON string response from the LLM. Returns: EvaluatorResult with extracted value, reasoning, and assessment. Raises: ValueError: If the response is not valid JSON or doesn't match expected schema. z+Invalid response: expected non-empty stringzInvalid JSON response: Nz,Invalid JSON response: expected object, got r$)rNr$r(zExpected boolean, got zExpected number, got zExpected string, got raw_response)rNr$ assessmentmetadata)rsrr}rloadsJSONDecodeError TypeErrorr!r.rrr rzrrr9r7r>intrGrH_compute_assessmentr$) rrvdataerr(resultr8r$rrrrasH          zLLMJudge._parse_responserAcCs|j}t|tr|jdur||jkrdSdSt|tr)|jdur)||jvr'dSdSt|trs|j|j}}|dur[|dur[||krO||krL|krMdSdS||ksW||krYdSdS|durg||kredSdS|durs||krqdSdSdS)aCompute pass/fail assessment based on structured output thresholds. Args: result: The evaluation result value to assess. Returns: "pass" or "fail" if thresholds are configured, None otherwise. Note: For ScoreStructuredOutput with both min_threshold and max_threshold: - If max >= min: inclusive range, pass if min <= result <= max - If max < min: exclusive range, pass if result < max OR result > min Npassfail) rrsr9r:rHrJr>rArB)rrArmin_tmax_trrrr>s  zLLMJudge._compute_assessment)NNNNNNNN)NN)rrrr5rrStructuredOutputrr!rrrr r r rr staticmethodrrtuplerrr+r"r,rrr> __classcell__rrrrrs     B(         3rr)&abcrrr dataclassesrrrr{rtypingrrrr r ddtrace.llmobs._experimentr r r rddtrace.llmobs.typesrrr"r9r>rHr!rrFrrrrrrrrrrrsR                ' 2 G \l