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Reference documentation for DataFlow's coretext operators — 8 generators, 3 filters, 2 refiners and 5 evaluators. Read by generating-dataflow-pipeline when a task needs an operator beyond the six core primitives. This is a reference package, not a workflow: it is consulted, never invoked directly.
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Reference documentation for the BenchDatasetEvaluatorQuestion operator. Extended version of BenchDatasetEvaluator with question and subquestion support. Use when: evaluating answers with question context or multiple subquestions.
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Reference documentation for the BenchDatasetEvaluator operator. Covers the constructor, two comparison modes (match/semantic), and pipeline usage. Use when: comparing predicted answers against ground truth answers in benchmark evaluation.
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Reference documentation for the PromptedEvaluator operator. Use when: scoring text quality with LLM without filtering rows.
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Reference documentation for the Text2QASampleEvaluator operator. Use when: evaluating QA pair quality across multiple dimensions.
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Reference documentation for the UnifiedBenchDatasetEvaluator operator. Use when: evaluating model answers on benchmark datasets.
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Reference documentation for the GeneralFilter operator. Covers the constructor, rule-based filtering logic, and pipeline usage notes. Use when: filtering rows based on column value conditions that can be expressed as lambda functions without LLM calls.
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Reference documentation for the KCenterGreedyFilter operator. Covers the constructor, K-Center Greedy algorithm behavior, embedding serving requirements, and pipeline usage notes. Use when: downsampling a large dataset by semantic diversity using embedding vectors.
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Reference documentation for the PromptedFilter operator. Covers the constructor, actual scoring and filtering behavior, and pipeline usage notes. Use when: filtering rows based on LLM semantic quality judgment rather than simple rule-based conditions.
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Reference documentation for the BenchAnswerGenerator operator. Covers the constructor, full run() signature, actual generation behavior, and integration notes for unified bench evaluation pipelines. Use when: generating model answers from benchmark question rows before passing the dataframe into…
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Reference documentation for the ChunkedPromptedGenerator operator. Covers the constructor, file-path based chunking flow, actual prompt construction, and output file writing behavior. Use when: the dataframe stores file paths, the file content may exceed a single LLM context window, and you want the generated results…
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Reference documentation for the EmbeddingGenerator operator. Covers the constructor, embedding serving requirements, actual dataframe flow, and runnable pipeline usage. Use when: converting one text column in a dataframe into embedding vectors for retrieval, clustering, similarity search, or downstream vector-based…
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Reference documentation for the FormatStrPromptedGenerator operator. Covers the constructor, prompt template restrictions, placeholder-to-column mapping, actual prompt-building logic, and runnable example usage. Use when: one generation task needs multiple dataframe columns combined into a single prompt through a…
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Reference documentation for the PromptedGenerator operator. Covers constructor parameters, run() signature, actual row-processing behavior, and pipeline usage notes. Use when: integrating PromptedGenerator into a DataFlow pipeline for single-field LLM generation.
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Reference documentation for the RandomDomainKnowledgeRowGenerator operator. [Purpose] Calls an LLM repeatedly with the same domain-generation prompt and writes the generated results into one column of an existing DataFrame. [When to use] Use it when you already have a seed DataFrame with the exact target row count and…
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Reference documentation for the RetrievalGenerator operator. [Purpose] Reads one text column from storage, forwards every non-empty row to llmserving.generatefrominput(...), and writes the returned list into a new output column. [Default backend] Use LightRAGServing by default. [Important] run() is async. The operator…
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Reference documentation for the Text2MultiHopQAGenerator operator. [Purpose] Generates multi-hop QA pairs from one text column and writes two output columns: one for qapairs and one for metadata. [When to use] Use it when you want reasoning-style QA pairs derived from longer text chunks. If only simple single-hop QA…
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Reference documentation for the PandasOperator operator. Use when: applying custom DataFrame transformations without LLM.
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Reference documentation for the PromptedRefiner operator. Use when: refining text with LLM, overwriting original column.
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A DataFlow development context guide for coding agents. DataFlow is a framework for building data-processing operators and pipelines.
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Build production-grade DataFlow operator scaffolds (generate/filter/refine/eval) for Codex and coding agents. Trigger when users ask to create/new/scaffold operators, add OPERATORREGISTRY registration, generate DataFlowStorage-based CLI wrappers, or generate operator unit/registry/smoke tests.
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Plan and write a standard DataFlow pipeline from a target and representative JSONL data. Use when a user asks to select DataFlow operators, trace field dependencies, generate runnable pipeline code, or repair a pipeline with schema or field-flow errors.
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Build reusable DataFlow prompttemplate classes for existing operators with two-round structured intake, two-stage auditable outputs, and static acceptance checks. Trigger when users ask to generate/rewrite/optimize prompttemplate or reuse operator logic with new prompt requirements.