generating-dataflow-pipeline

name	generating-dataflow-pipeline
description	Reasoning-guided pipeline planner that generates standard DataFlow pipeline code
metadata	{"version":"1.0.0"}

DataFlow Pipeline Code Generator

Goal

This skill is used when users provide:

• Target: What the pipeline should achieve
• Sample Data File: Path to a JSONL file containing 1-5 representative data samples

The skill must:

1. Read and analyze the JSONL file at the provided path
2. Infer data structure, field types, and content characteristics
3. Determine task type based on file content (document processing, text transformation, multi-field composition)
4. Select appropriate operators from preferred primitives
5. Validate field dependencies
6. Output intermediate operator decision summary
7. Generate standard DataFlow pipeline code with first_entry_file_name set to the user-provided file path

User Input Format

Users provide:

Target: [Clear task description]
Sample file: [Path to JSONL file, e.g., ./data/input.jsonl]

Important: The sample file is a JSONL file (one JSON object per line), not a JSON array.

Preferred Operator Strategy

Six Core Primitives (high-coverage operators for most data science tasks):

1. PromptedGenerator - Single-field LLM generation
2. FormatStrPromptedGenerator - Multi-field template generation
3. Text2MultiHopQAGenerator - Multi-hop QA pair construction
4. PromptedFilter - LLM-based quality filtering
5. GeneralFilter - Rule-based filtering
6. KBC trio (always used together in order): FileOrURLToMarkdownConverterFlash → KBCChunkGenerator → KBCTextCleaner

These are preferred primitives, not fixed workflows. They can be used repeatedly and combined flexibly.

Operator Selection Priority Rule (MANDATORY)

When a specialized operator exists for the task, it MUST be used over generic operators. Do NOT use PromptedGenerator to replicate functionality that a dedicated operator already provides.

Decision table (check in order, use the first match):

Task / Scenario	Required Operator	Do NOT use
Generate QA pairs from text	Text2MultiHopQAGenerator	PromptedGenerator with QA prompt
Convert file path / URL to text	KBC trio (FileOrURLToMarkdownConverterFlash → KBCChunkGenerator → KBCTextCleaner)	PromptedGenerator to summarize files
Score / evaluate using multiple fields	FormatStrPromptedGenerator + GeneralFilter	PromptedFilter (single input_key only)
Filter by deterministic rule on existing fields	GeneralFilter	PromptedFilter
Generate new content from a single field	PromptedGenerator	—
Generate new content from multiple fields	FormatStrPromptedGenerator	Multiple PromptedGenerator steps

Key principle: PromptedGenerator is the fallback for generic single-field generation. If the target mentions "QA", "question-answer", "问答" — always reach for Text2MultiHopQAGenerator first.

Field Flow Rules (MANDATORY)

1. Inspect record content first: Read representative records and identify the semantic role and fitness of their content; field names and presence alone do not establish that content is suitable for the target.
2. Design from the target contract: Select source and generated fields based on the content required by the target, including improved semantic counterparts when existing content does not fit that contract.
3. Keep execution ordered: Every field consumed by an operator must be available from the input or produced by an earlier operator.
4. Never reference before creation: Create a generated field before any downstream operator evaluates, filters, or transforms it.

✗ WRONG: Filter by "quality_score" before generating it
✓ CORRECT: Generate "quality_score" first, then filter by it

Grounded Field Generation Pattern (RECOMMENDED)

When constructing improved training fields from records that already contain a source instruction, answer, implementation, or other target-bearing content, ground each generated stage in the source semantics. A useful reference flow is:

source content + source answer
  -> generate an improved question/instruction

improved question/instruction + source answer
  -> generate or revise the answer

improved question/instruction + source answer + generated answer
  -> evaluate semantic consistency and target fitness

Adapt the field names and operators to the task. This is a reference pattern, not a requirement to generate every role on every dataset. Its purpose is to prevent an answer generator from seeing only a rewritten question and freely inventing behavior that is unsupported by the source answer. The final quality evaluation should reject generated answers that omit supported behavior or add unsupported contracts, constraints, edge cases, exception handling, or other observable semantics, even when the generated question and answer are mutually consistent.

Before expensive LLM generation, prefer multiple applicable non-LLM filtering operators when the source pool is large enough to support meaningful attrition. Use deterministic checks for defects that can be established reliably, such as parseability, required content shape, empty or truncated content, duplicates, unsafe patterns, invalid values, and task-specific structural invariants. Keep these filters content-aware and avoid treating familiar field names or mere field presence as evidence of quality. The goal is to reduce generation load while retaining enough valid and diverse source records for the downstream target.

For vertical domains whose target capability inherently requires reasoning, the pipeline MUST use operators to distinguish records that contain suitable chain-of-thought (CoT) content from those that do not. For a record without suitable CoT, assess the problem's difficulty and either retain it by routing that branch through an appropriate reasoning-generation operator to construct CoT, or filter it out when generating useful reasoning is unwarranted or unreliable.

Prompted Operator Usage Policy (MANDATORY)

• Don't mechanically create one prompted operator per tiny requirement. If one operator can handle multiple related transformations, prefer that over splitting.
• Multiple prompted operators are allowed when the task genuinely requires distinct semantic transformations. If using multiple, justify each step's role, input field, and output field.

KBC Usage Constraint (MANDATORY)

The KBC trio must always be used in this exact order:

1. FileOrURLToMarkdownConverterFlash — converts file path / URL → Markdown text (field: text_path)
2. KBCChunkGenerator — splits Markdown into chunks (field: raw_chunk)
3. KBCTextCleaner — LLM-cleans each chunk (field: cleaned_chunk)

Rules:

• All three steps are required; never skip one.
• Input to step 1 must be a file path or URL, never plain text content.
• Each step's output_key becomes the next step's input_key.
• Use the default field names (text_path, raw_chunk, cleaned_chunk) unless explicitly requested otherwise.

GeneralFilter Field Safety Rule (MANDATORY)

GeneralFilter lambda rules must ONLY reference fields that exist in sample data or are produced by upstream steps.

Multi-Field Filtering Pattern (MANDATORY)

PromptedFilter only accepts a single input_key. For multi-field evaluation (e.g., scoring QA pairs), use FormatStrPromptedGenerator to score + GeneralFilter to filter.

Important caveat for Text2MultiHopQAGenerator output: The QA_pairs column is a nested list of dicts, not separate question/answer columns. You cannot directly pass question or answer as kwargs to FormatStrPromptedGenerator after Text2MultiHopQAGenerator. To score or filter individual QA pairs, use post-processing (explode the list into rows, then optionally score/filter in a second pipeline or in Python code).

Output Contract (MANDATORY)

Two-stage output required:

Stage 1: Intermediate Operator Decision (JSON)

Output this first:

{
  "ops": ["OperatorA", "OperatorB", "OperatorC"],
  "field_flow": "field_a -> field_b -> field_c",
  "reason": "Why this ordered operator chain satisfies the target, how field dependencies are satisfied, and why prompted operators are or are not used."
}

Stage 2: Complete Response (5 sections)

1. Field Mapping: Map sample fields to semantic roles, identify fields to generate
2. Ordered Operator List: List operators in execution order with justification
3. Reasoning Summary: Explain operator selection, field flow, why this design
4. Complete Standard Pipeline Code: Full executable Python following repository style
5. Adjustable Parameters / Caveats: Tunable parameters, fallback strategies, debugging tips

Standard Code Generation Rule (MANDATORY)

All generated Python code must follow the standard pipeline organization shown in the examples/ folder of this skill package.

Input Data Format:

• first_entry_file_name MUST be set to the user-provided file path (the JSONL sample file)
• File extension must be .jsonl (one JSON object per line, NOT an array)
• DO NOT create new file paths - use the exact path the user provided

Required structure: __init__ (storage + llm_serving + operators) → forward (sequential operator.run(storage=self.storage.step(), ...)) → if __name__ == "__main__" entry point.

DO NOT: generate custom runtime executors, forward(plan) style frameworks, or dynamic dispatch engines.

Operator Parameter Signature Rule (MANDATORY)

Use repository-valid constructor/run signatures only. Never invent parameter names.

Base Components

FileStorage

FileStorage(
  first_entry_file_name="...jsonl",
  cache_path="./cache",
  file_name_prefix="dataflow_cache_step",
  cache_type="jsonl"
)

APILLMServing_request

APILLMServing_request(
  api_url="...",
  key_name_of_api_key="DF_API_KEY",  # defaults to DF_API_KEY; set to e.g. "OPENAI_API_KEY" if needed
  model_name="gpt-4o",
  max_workers=10
)

Six Core Operators: Signatures + Key Requirements

1) PromptedGenerator

• Constructor: PromptedGenerator(llm_serving, system_prompt="You are a helpful agent.", user_prompt="", json_schema=None)
• Run: run(storage=self.storage.step(), input_key="raw_content", output_key="generated_content")
• input_key column must exist. Generated rows written to output_key.

2) FormatStrPromptedGenerator

• Constructor: FormatStrPromptedGenerator(llm_serving, system_prompt="You are a helpful agent.", prompt_template=FormatStrPrompt(...), json_schema=None)
• Run: run(storage=self.storage.step(), output_key="generated_content", **input_keys)
• **input_keys: each kwarg maps a template variable name (key) to a dataframe column name (value). Internally does row[input_keys[key]] per row, then prompt_template.build_prompt(need_fields, **key_dict).
• Kwarg keys must match {placeholder} names in FormatStrPrompt.f_str_template. Kwarg values must be existing dataframe columns.
• prompt_template cannot be None (raises ValueError). Must pass an instantiated FormatStrPrompt(f_str_template="...").
• Import: from dataflow.prompts.core_text import FormatStrPrompt

3) Text2MultiHopQAGenerator

• Constructor: Text2MultiHopQAGenerator(llm_serving=self.llm_serving, seed=0, lang="en", prompt_template=None, num_q=5)
  • llm_serving — LLM serving instance (required)
  • seed (int, default 0) — random seed for reproducibility
  • lang (str, default "en") — language for generation prompt; controls sentence splitting ("." for "en", "。" for "zh")
  • prompt_template — custom DIYPromptABC instance; pass None to use default Text2MultiHopQAGeneratorPrompt
  • num_q (int, default 5) — maximum number of QA pairs to keep per input row (truncates the generated list; actual generation count depends on sentence triples in the text)
• Run: run(storage, input_key="cleaned_chunk", output_key="QA_pairs", output_meta_key="QA_metadata")
  • input_key must exist (cleaned text chunk column)
  • output_key — column containing a nested list of QA dicts per row. Each dict has keys: question (str), reasoning_steps (list of {step: str}), answer (str), supporting_facts (list of str), type (str)
  • output_meta_key — column containing metadata dict per row with keys: source, timestamp, complexity
  • Output column named by output_key / output_meta_key must NOT pre-exist.
• Each input row produces one row with a nested list in the output_key column. The list items are dicts — question, answer, etc. are NOT separate dataframe columns. Downstream operators like FormatStrPromptedGenerator cannot directly reference question or answer as column names. To use individual QA pairs downstream, you must post-process (explode the list into separate rows) outside the operator chain.

4) PromptedFilter

• Constructor: PromptedFilter(llm_serving, system_prompt="...", min_score=1, max_score=5)
• Run: run(storage=self.storage.step(), input_key="raw_content", output_key="eval")
• input_key must exist. output_key is numeric score column; rows outside [min_score, max_score] are filtered out.

5) GeneralFilter

• Constructor: GeneralFilter([lambda df: df["score"] >= 4, ...])
• Run: run(storage=self.storage.step())
• Each rule must return boolean pd.Series. Referenced fields must already exist.

6) KBC Trio (always used in this order)

Step 1 — FileOrURLToMarkdownConverterFlash

• Constructor: FileOrURLToMarkdownConverterFlash(intermediate_dir="../example_data/KBCleaningPipeline/flash/", mineru_model_path="opendatalab/MinerU2.5-2509-1.2B", batch_size=4, replicas=1, num_gpus_per_replica=1.0, engine_gpu_util_rate_to_ray_cap=0.9)
• Does NOT take llm_serving — this operator has no LLM dependency.
• mineru_model_path is required — passing None raises ValueError. Use a HuggingFace model ID or local path.
• Run: run(storage=self.storage.step(), input_key="source", output_key="text_path")
• Input must be a file path or URL (.pdf, .png, .jpg, .jpeg, .webp, .gif, .html, .xml, .txt, .md).

Step 2 — KBCChunkGenerator

• Constructor: KBCChunkGenerator(chunk_size=512, chunk_overlap=50, split_method="token", min_tokens_per_chunk=100, tokenizer_name="bert-base-uncased")
• Run: run(storage=self.storage.step(), input_key="text_path", output_key="raw_chunk")
• split_method options: "token", "sentence", "semantic", "recursive".

Step 3 — KBCTextCleaner

• Constructor: KBCTextCleaner(llm_serving, lang="en")
• Run: run(storage=self.storage.step(), input_key="raw_chunk", output_key="cleaned_chunk")
• LLM-cleans each chunk; output is ready for downstream QA generation.

Correct Import Paths (MANDATORY)

# Base components
from dataflow.utils.storage import FileStorage
from dataflow.serving import APILLMServing_request

# Operators
from dataflow.operators.core_text import PromptedGenerator, FormatStrPromptedGenerator, Text2MultiHopQAGenerator, PromptedFilter, GeneralFilter
from dataflow.operators.knowledge_cleaning import FileOrURLToMarkdownConverterFlash, KBCChunkGenerator, KBCTextCleaner

Extended Operator Reference: core_text Skill

The sibling skill core_text (located at ../core_text/) provides detailed per-operator API documentation that supplements the summary signatures above.

Each operator directory contains:

• SKILL.md — Full English reference: constructor signature, run() signature, execution logic, mandatory rules, return value semantics
• SKILL_zh.md — Chinese translation of the reference
• examples/good.md — Best-practice pipeline example
• examples/bad.md — Common mistakes and failure cases

When to consult core_text:

• When generating pipeline code that uses an operator beyond the 6 core primitives (e.g., BenchAnswerGenerator, ChunkedPromptedGenerator, EmbeddingGenerator, RetrievalGenerator, RandomDomainKnowledgeRowGenerator)
• When you need to verify edge-case behavior, return value semantics, or error conditions for any operator
• When debugging generated pipeline code — the bad.md examples document the most frequent mistakes

Note: The 6 core primitives documented above in "Operator Parameter Signature Rule" remain the primary reference for standard pipeline generation. The core_text skill provides deeper detail and covers additional operators not in the core set.

---

Generate Operators

Path: ../core_text/generate/

Available operator references (8 operators):

Operator	Subdirectory	Description
PromptedGenerator	prompted-generator/	Single-field LLM generation — full execution logic, skip-falsy rules
FormatStrPromptedGenerator	format-str-prompted-generator/	Multi-field template generation — placeholder-to-column mapping details,@prompt_restrict validation
Text2MultiHopQAGenerator	text2multihopqa-generator/	Multi-hop QA pair construction — text filtering thresholds (100–200k chars), output structure, row-count behavior
BenchAnswerGenerator	bench-answer-generator/	Benchmark answer generation —eval_type variants, conditional field requirements
ChunkedPromptedGenerator	chunked-prompted-generator/	Long document chunk-by-chunk processing — token-based splitting, file I/O conventions
EmbeddingGenerator	embedding-generator/	Text vectorization — supported serving backends,/v1/embeddings endpoint usage
RandomDomainKnowledgeRowGenerator	random-domain-knowledge-row-generator/	Domain-specific row generation — seed dataframe requirements,generation_num constraints
RetrievalGenerator	retrieval-generator/	Async RAG generation —LightRAGServing.create() async initialization, await run() requirement

---

Eval Operators

Path: ../core_text/eval/

Available operator references (5 operators):

Operator	Subdirectory	Description
BenchDatasetEvaluator	bench-dataset-evaluator/	Benchmark answer comparison —match (math verification) and semantic (LLM-based) modes
BenchDatasetEvaluatorQuestion	bench-dataset-evaluator-question/	Extended benchmark evaluator — adds question context and subquestion support over BenchDatasetEvaluator
PromptedEvaluator	prompted-evaluator/	LLM-based row scoring  — writes score into new column without removing rows
Text2QASampleEvaluator	text2qa-sample-evaluator/	QA pair quality evaluation — 4 dimensions, 8 output columns (grades + feedbacks per dimension)
UnifiedBenchDatasetEvaluator	unified-bench-dataset-evaluator/	Unified benchmark evaluation — 6 eval_type variants, writes 4 output columns

---

Filter Operators

Path: ../core_text/filter/

Available operator references (3 operators):

Operator	Subdirectory	Description
GeneralFilter	general-filter/	Rule-based row filtering — lambda conditions combined with AND, removes rows only, adds no new columns
KCenterGreedyFilter	kcentergreedy-filter/	Diversity-based downsampling — K-Center Greedy algorithm, requires pre-computed embedding vectors
PromptedFilter	prompted-filter/	LLM semantic filtering — internally uses PromptedEvaluator, retains rows with scores in [min_score, max_score]

---

Refine Operators

Path: ../core_text/refine/

Available operator references (2 operators):

Operator	Subdirectory	Description
PandasOperator	pandas-operator/	Custom DataFrame transformation — applies a sequential list of functions, no LLM calls
PromptedRefiner	prompted-refiner/	LLM text refinement — rewrites text in-place, overwrites original column with refined results

Input File Content Analysis Rule (MANDATORY)

Analyze sample data content to determine task nature:

File path fields (e.g., pdf_path, image_path, doc_path):

• → KBC trio in order: FileOrURLToMarkdownConverterFlash → KBCChunkGenerator → KBCTextCleaner (supports .pdf, .png, .jpg, .jpeg, .webp, .gif, .html, .xml, .txt, .md)
• → Document/file processing workflow

Plain text fields (e.g., text, content, review_text):

• → Use PromptedGenerator, PromptedFilter, Text2MultiHopQAGenerator, FormatStrPromptedGenerator, GeneralFilter
• → Do NOT use KBC

Multiple semantic fields (e.g., instruction, output, question, answer):

• → Use FormatStrPromptedGenerator for combining fields
• → Use GeneralFilter for field-based rules

Examples

See examples/ folder for complete workflows:

1. examples/basic_generate_and_filter.md — PromptedGenerator + PromptedFilter (simplest pattern)
2. examples/multifield_scoring.md — FormatStrPromptedGenerator with multi-field scoring
3. examples/multi_stage_pipeline.md — Multiple PromptedGenerator stages + GeneralFilter
4. examples/kbc_pdf_to_qa.md — KBC trio (FileOrURLToMarkdownConverterFlash + KBCChunkGenerator + KBCTextCleaner) + Text2MultiHopQAGenerator + PromptedFilter (scores nested QA_pairs column per chunk)
5. examples/reasoning_math_pipeline.md — High-quality math reasoning workflow using native question screening/synthesis, difficulty and category evaluation, ReasoningAnswerGenerator, and format/length/ground-truth/ngram validation
6. examples/reasoning_general_pipeline.md — General or mixed-domain reasoning generation with reference-aware model judging and n-gram filtering
7. examples/reasoning_math_fusion_pipeline.md — Embedding-grounded sequential, parallel, and condition fusion for synthesizing harder math questions, followed by solvability evaluation
8. examples/reasoning_pretrain_pipeline.md — Math reasoning generation and filtering followed by explicit SFT-to-pretraining text conversion
9. examples/reasoning_diy_pipeline.md — Native reasoning operators with custom vertical-domain filter, synthesis, and answer prompt contracts
10. examples/reasoning_cpu_clean_pipeline.md — CPU-only format, mathematical ground-truth, and n-gram cleaning for existing reasoning answers
11. examples/agentic_rag_text_pipeline.md — Atomic and verified multi-hop QA over retrieved text, including grounding, shortcut, reasoning, and final-answer checks
12. examples/code_text_pipelines.md — Code-to-SFT and seed-to-code generation with quality scoring and sandbox execution, plus CPU code-text cleaning
13. examples/chemistry_smiles_text_pipeline.md — Chemistry-text SMILES extraction followed by molecular-equivalence evaluation

These are strategy guidance, not templates to copy blindly. Generated code must follow standard pipeline structure.