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Documentation Index

Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt

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PyPI

0.3

0.3.0
Fiddler Evals SDK
February 5, 2026
  • New Evaluators
    • Context Relevance (New): Measures whether retrieved documents are relevant to the user query. Ordinal scoring — High (1.0), Medium (0.5), Low (0.0) with detailed reasoning.
    • RAG Faithfulness (New): LLM-as-a-Judge evaluator that assesses whether the response is grounded in the retrieved documents. Binary scoring — Yes (1.0) / No (0.0) with detailed reasoning.
    • CustomJudge (New): Build custom LLM-as-a-Judge evaluators using prompt_template with Jinja {{ placeholder }} syntax and output_fields for structured evaluation results.
  • Enhancements
    • Answer Relevance 2.0: Upgraded from binary to ordinal scoring — High (1.0), Medium (0.5), Low (0.0) with detailed reasoning.
    • Ordinal Score Bounding: Ordinal scores from the scoring API are now bounded to [0, 1].
0.2.0
Fiddler Evals SDK
November 19, 2025
  • Enhancements
    • Model and Credential Parameters: model and credential are now parameters on LLM-as-a-Judge evaluators, enabling configuration of the LLM used for evaluation.
    • Evaluator-Level Score Function Mapping: Evaluators now support score_fn_kwargs_mapping at the evaluator level for more flexible parameter binding.
    • Score Name Prefix: Added support for custom score name prefixes on evaluators.
    • Evals API Error Handling: Improved error handling and messaging for Evals API responses.
    • Coherence Prompt Input Required: The prompt input for the Coherence evaluator is now required.
    • Removed Pandas Core Dependency: Pandas moved from core to optional dependency, reducing install footprint.
    • Docstring Standardization: Fixed docstring errors and standardized documentation format across all evaluators.
  • Removals
    • Toxicity Evaluator Removed: The Toxicity evaluator has been removed from the SDK.
0.1.1
Fiddler Evals SDK
October 8, 2025
  • Initial Release
    • Core SDK with HTTP client, entity management (Project, Application, Dataset, Experiment), and the evaluate() function for running experiments.
    • Evaluators: AnswerRelevance, Coherence, Conciseness, Sentiment, TopicClassification, FTLPromptSafety, FTLResponseFaithfulness, RegexSearch, and support for user-defined function evaluators.
    • Data Input: Load test cases from pandas DataFrames, CSV files, or JSONL files.
    • Concurrent Processing: Parallel evaluation with ThreadPoolExecutor and tqdm progress tracking.
    • PyPI Publishing: Available as pip install fiddler-evals.