Generate Ragas QA directly from WeKnora chunks

沈秋雨
Commit abad6fce ... abad6fcea91dbc01b7be3bdeaa11857b7635d1eb authored 2026-04-22 12:12:04 +0800 by 沈秋雨
Showing 5 changed files with 163 additions and 20 deletions
.env.example
README.md
TESTING_GUIDE.md
configs/eval.yaml
src/weknora_eval/testset.py
--- a/.env.example
View file @abad6fc
+++ b/.env.example
View file @abad6fc
@@ -22,6 +22,7 @@ RAGAS_JUDGE_MODEL=gpt-4o-mini
 RAGAS_EMBEDDING_MODEL=text-embedding-3-small
 TESTSET_SIZE=50
+TESTSET_RAGAS_MODE=direct
 TESTSET_MAX_DOCUMENT_CHARS=2000
 TESTSET_SOURCE_MULTIPLIER=3
 TESTSET_GENERATOR_MAX_TOKENS=4096
--- a/README.md
View file @abad6fc
+++ b/README.md
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@@ -68,7 +68,7 @@ python scripts/10_report.py
 首轮建议只使用 2 个 PDF、1 个 XLSX 和 10 条审核通过 QA，确认 `retrieved_contexts`、`response`、Ragas 输入字段都正常后再扩展样本量。
-默认 `04_parse_docs.py` 从 WeKnora 导出的 `data/exported/chunks.jsonl` 构造测试集来源，不再重复调用外部 PDF 解析器。`05_generate_testset.py` 默认使用 Ragas 结合评估侧 LLM 自动生成 QA；生成阶段会用 `TESTSET_MAX_DOCUMENT_CHARS` 限制单条来源上下文长度，并用 `TESTSET_GENERATOR_MAX_TOKENS` 控制生成输出预算，避免和后续评测用的 `ragas.max_tokens` 混在一起。`local`、`mineru` 和 `rule_based` 只作为可选实验/兜底配置保留。
+默认 `04_parse_docs.py` 从 WeKnora 导出的 `data/exported/chunks.jsonl` 构造测试集来源，不再重复调用外部 PDF 解析器。`05_generate_testset.py` 默认使用 Ragas 结合评估侧 LLM 自动生成 QA；生成阶段使用 `TESTSET_RAGAS_MODE=direct`，直接把 WeKnora chunks 组装成 Ragas KnowledgeGraph 并生成单跳 QA，避免 Ragas 默认文档预处理链路重新抽标题、摘要和实体。生成阶段还会用 `TESTSET_MAX_DOCUMENT_CHARS` 限制单条来源上下文长度，并用 `TESTSET_GENERATOR_MAX_TOKENS` 控制生成输出预算，避免和后续评测用的 `ragas.max_tokens` 混在一起。`local`、`mineru` 和 `rule_based` 只作为可选实验/兜底配置保留。
 ## 主要产物
--- a/TESTING_GUIDE.md
View file @abad6fc
+++ b/TESTING_GUIDE.md
View file @abad6fc
@@ -293,10 +293,13 @@ max_tokens: 4096
 如果 `05_generate_testset.py` 在生成 QA 时出现 `LLMDidNotFinishException`，优先不要继续盲目调大 `ragas.max_tokens`。`05` 有独立的生成预算和输入长度：
 ```bash
+TESTSET_RAGAS_MODE=direct
 TESTSET_GENERATOR_MAX_TOKENS=4096
 TESTSET_MAX_DOCUMENT_CHARS=2000
 ```
+`direct` 模式会跳过 Ragas 默认的 `HeadlinesExtractor`、`SummaryExtractor`、`NERExtractor` 文档预处理链路，直接把 WeKnora chunks 组装成 Ragas KnowledgeGraph 并生成单跳 QA。`prechunked` 和 `langchain_docs` 仅用于对比实验，遇到本地 vLLM 结构化输出不稳定时不建议使用。
 如果 vLLM 仍然报生成未完成，先把 `TESTSET_SIZE` 降到 3，再把 `TESTSET_MAX_DOCUMENT_CHARS` 调到 1000-1500 验证链路；`ragas.max_tokens` 主要用于后续评测阶段，不应该拿来无限放大测试集生成阶段的输出长度。
 ### WeKnora 问答没有 retrieved_contexts
--- a/configs/eval.yaml
View file @abad6fc
+++ b/configs/eval.yaml
View file @abad6fc
@@ -10,6 +10,7 @@ weknora:
 testset:
  size: "${TESTSET_SIZE:-50}"
  generator: "ragas"  # ragas or rule_based
+  ragas_mode: "${TESTSET_RAGAS_MODE:-direct}"  # direct, prechunked, or langchain_docs
  include_pdf: true
  include_xlsx: true
  min_context_chars: 80
--- a/src/weknora_eval/testset.py
View file @abad6fc
+++ b/src/weknora_eval/testset.py
View file @abad6fc
@@ -8,7 +8,10 @@ from typing import Any
 from langchain_core.documents import Document
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from ragas.run_config import RunConfig
+from ragas.testset.graph import KnowledgeGraph, Node, NodeType
+from ragas.testset.persona import Persona
 from ragas.testset import TestsetGenerator
+from ragas.testset.synthesizers.single_hop.specific import SingleHopSpecificQuerySynthesizer
 from weknora_eval.config import require_config
 from weknora_eval.loaders import read_jsonl, write_jsonl
@@ -46,6 +49,7 @@ def generate_ragas_testset(
    generator_max_tokens = int(
        testset_config.get("generator_max_tokens", ragas_config.get("max_tokens", 4096))
    )
+    ragas_mode = str(testset_config.get("ragas_mode", "direct"))
    source_rows = [
        row
@@ -71,11 +75,12 @@ def generate_ragas_testset(
        for row in selected_source_rows
    ]
    logger.info(
-        "Generating Ragas testset: target_size=%s source_documents=%s max_document_chars=%s generator_max_tokens=%s",
+        "Generating Ragas testset: target_size=%s source_documents=%s max_document_chars=%s generator_max_tokens=%s ragas_mode=%s",
        size,
        len(documents),
        max_document_chars,
        generator_max_tokens,
+        ragas_mode,
    )
    llm = ChatOpenAI(
@@ -86,32 +91,165 @@ def generate_ragas_testset(
        max_tokens=generator_max_tokens,
        timeout=int(ragas_config.get("timeout_seconds", 600)),
    )
-    embeddings = OpenAIEmbeddings(
+    run_config = RunConfig(
-        model=str(require_config(config, "ragas.embedding_model")),
+        timeout=int(ragas_config.get("timeout_seconds", 600)),
-        api_key=_required_ragas_value(ragas_config, "embedding_api_key"),
+        max_workers=int(ragas_config.get("max_workers", 1)),
-        base_url=_required_ragas_value(ragas_config, "embedding_base_url"),
+    )
-        tiktoken_enabled=False,
+    if ragas_mode == "direct":
-        check_embedding_ctx_length=False,
+        result = _generate_ragas_direct(llm, documents, size, run_config)
-        request_timeout=int(ragas_config.get("timeout_seconds", 600)),
+    elif ragas_mode == "prechunked":
+        result = _generate_ragas_prechunked(
+            config, ragas_config, llm, documents, size, run_config
+        )
+    elif ragas_mode == "langchain_docs":
+        result = _generate_ragas_langchain_docs(
+            config, ragas_config, llm, documents, size, run_config
+        )
+    else:
+        raise ValueError(f"Unsupported testset.ragas_mode: {ragas_mode}")
+    ragas_rows = result.to_list()
+    rows = _normalize_ragas_rows(ragas_rows, selected_source_rows)
+    write_jsonl(output_path, rows)
+    return rows
+def _generate_ragas_direct(
+    llm: ChatOpenAI,
+    documents: list[Document],
+    size: int,
+    run_config: RunConfig,
+) -> Any:
+    ragas_llm = _wrap_langchain_llm(llm)
+    kg = KnowledgeGraph(
+        nodes=[
+            Node(
+                type=NodeType.CHUNK,
+                properties={
+                    "page_content": document.page_content,
+                    "document_metadata": document.metadata,
+                    "entities": _generation_terms(document),
+                    "themes": _generation_terms(document),
+                },
+            )
+            for document in documents
+            if document.page_content.strip()
+        ]
+    )
+    generator = TestsetGenerator(
+        llm=ragas_llm,
+        embedding_model=None,
+        knowledge_graph=kg,
+        persona_list=[
+            Persona(
+                name="合同审核人员",
+                role_description="关注合同条款、权利归属、授权范围和履约义务。",
+            ),
+            Persona(
+                name="业务运营人员",
+                role_description="关注文档中可用于业务执行和信息核验的事实。",
+            ),
+            Persona(
+                name="法务合规人员",
+                role_description="关注协议、版权、授权、责任和风险表述。",
+            ),
+        ],
+    )
+    generate_kwargs: dict[str, Any] = {
+        "testset_size": size,
+        "query_distribution": [(SingleHopSpecificQuerySynthesizer(llm=ragas_llm), 1.0)],
+        "num_personas": 3,
+        "run_config": run_config,
+        "raise_exceptions": False,
+    }
+    if "batch_size" in inspect.signature(generator.generate).parameters:
+        generate_kwargs["batch_size"] = 1
+    return generator.generate(**generate_kwargs)
+def _generate_ragas_prechunked(
+    config: dict[str, Any],
+    ragas_config: dict[str, Any],
+    llm: ChatOpenAI,
+    documents: list[Document],
+    size: int,
+    run_config: RunConfig,
+) -> Any:
+    embeddings = _build_embeddings(config, ragas_config)
+    ragas_llm, ragas_embeddings = _wrap_langchain_models(llm, embeddings)
+    generator = TestsetGenerator(llm=ragas_llm, embedding_model=ragas_embeddings)
+    return generator.generate_with_chunks(
+        documents,
+        testset_size=size,
+        run_config=run_config,
+        raise_exceptions=False,
    )
+def _generate_ragas_langchain_docs(
+    config: dict[str, Any],
+    ragas_config: dict[str, Any],
+    llm: ChatOpenAI,
+    documents: list[Document],
+    size: int,
+    run_config: RunConfig,
+) -> Any:
+    embeddings = _build_embeddings(config, ragas_config)
    ragas_llm, ragas_embeddings = _wrap_langchain_models(llm, embeddings)
    generator = TestsetGenerator(llm=ragas_llm, embedding_model=ragas_embeddings)
    generate_kwargs: dict[str, Any] = {
        "testset_size": size,
-        "run_config": RunConfig(
+        "run_config": run_config,
-            timeout=int(ragas_config.get("timeout_seconds", 600)),
-            max_workers=int(ragas_config.get("max_workers", 1)),
-        ),
        "raise_exceptions": False,
    }
-    if "batch_size" in inspect.signature(generator.generate_with_langchain_docs).parameters:
+    return generator.generate_with_langchain_docs(documents, **generate_kwargs)
-        generate_kwargs["batch_size"] = 1
-    result = generator.generate_with_langchain_docs(documents, **generate_kwargs)
-    ragas_rows = result.to_list()
-    rows = _normalize_ragas_rows(ragas_rows, selected_source_rows)
+def _build_embeddings(
-    write_jsonl(output_path, rows)
+    config: dict[str, Any], ragas_config: dict[str, Any]
-    return rows
+) -> OpenAIEmbeddings:
+    return OpenAIEmbeddings(
+        model=str(require_config(config, "ragas.embedding_model")),
+        api_key=_required_ragas_value(ragas_config, "embedding_api_key"),
+        base_url=_required_ragas_value(ragas_config, "embedding_base_url"),
+        tiktoken_enabled=False,
+        check_embedding_ctx_length=False,
+        request_timeout=int(ragas_config.get("timeout_seconds", 600)),
+    )
+def _wrap_langchain_llm(llm: Any) -> Any:
+    try:
+        from ragas.llms import LangchainLLMWrapper
+    except ImportError:
+        return llm
+    return LangchainLLMWrapper(llm)
+def _generation_terms(document: Document) -> list[str]:
+    text = f"{document.metadata.get('source_file') or ''} {document.page_content}"
+    candidates = [
+        "合同条款",
+        "权利归属",
+        "著作权",
+        "邻接权",
+        "录音权利",
+        "词权利",
+        "曲权利",
+        "授权范围",
+        "作品信息",
+        "甲方",
+        "乙方",
+        "协议",
+        "付款",
+        "违约责任",
+        "期限",
+    ]
+    terms = [term for term in candidates if term in text]
+    source_file = str(document.metadata.get("source_file") or "").strip()
+    if source_file:
+        terms.append(source_file.rsplit(".", 1)[0][:40])
+    return terms[:6] or ["文档内容"]
 def _truncate_for_generation(content: str, max_chars: int) -> str: