feat(stage5): implement claim extraction — atomic verifiable claims from sources
- Claim model with provenance, evidence_span, attribution, claim_type
- Stage5Extractor: LLM-based atomic claim extraction from source content
- Never summarizes — always extracts atomic, verifiable claims
- Claims require evidence span (exact quote from source)
- Attribution per claim (who says what)
- Claim types: fact, opinion, prediction, recommendation, claim
- Confidence score 0.0–1.0 per claim
- Bounded concurrency, SSRF-safe, max content truncation
- REST API: GET/POST /research/{run_id}/claims
- 36 tests: parsing, edge cases, integration, validation
This commit is contained in:
NSCT Agent
211
src/nsct/api/claims.py
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211
src/nsct/api/claims.py
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@@ -0,0 +1,211 @@
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"""Claim Extraction API — POST /research/claims extracts atomic verifiable claims.
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Nutzt Stage 5 (Claim Extraction) um aus den extrahierten Quelltexten
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atomare, provenance-gesicherte Claims zu generieren.
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Pipeline:
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1. Research-Run laden (nach Stage 4)
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2. Für jede Source → LLM-Claim-Extraction
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3. Claims in DB speichern
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4. ResearchRun Progress-Flag setzen (stage: EXTRACTED)
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"""
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from __future__ import annotations
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import logging
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import os
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from typing import Any
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from uuid import UUID
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel, Field
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from nsct.config import AppSettings
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from nsct.models.claim import Claim, ClaimExtractionResult, ClaimType
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from nsct.stages.stage5_extract_claims import Stage5Extractor
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logger = logging.getLogger(__name__)
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router = APIRouter()
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# ---------------------------------------------------------------------------
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# Request / Response Schemas
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# ---------------------------------------------------------------------------
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class ClaimExtractionRequest(BaseModel):
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"""Input für Claim Extraction."""
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research_run_id: str = Field(
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...,
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description="UUID des Research-Runs (Stage 4 abgeschlossen).",
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)
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source_ids: list[str] | None = Field(
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default=None,
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description=(
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"Optionale Liste von Source-IDs. "
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"Wenn None → alle Sources des Runs werden verarbeitet."
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),
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)
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class ClaimResponse(BaseModel):
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"""Ein einzelner extrahierter Claim."""
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id: str
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research_run_id: str
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source_id: str
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claim_text: str
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evidence_span: str
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claim_type: str
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source_url: str
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confidence: float
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metadata: dict[str, Any] = {}
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created_at: str
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class ClaimExtractionResponse(BaseModel):
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"""Ergebnis der Claim Extraction."""
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research_run_id: str
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total_claims: int = Field(..., description="Anzahl extrahierter Claims.")
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sources_processed: int = Field(..., description="Anzahl verarbeiteter Sources.")
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claims: list[ClaimResponse] = Field(
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..., description="Alle extrahierten Claims."
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)
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errors: list[str] = Field(
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default_factory=list,
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description="Fehlermeldungen bei fehlerhaften Sources.",
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)
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stage: str = Field(
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"EXTRACTED",
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description="Progress-Flag: EXTRACTED.",
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)
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# ---------------------------------------------------------------------------
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# Endpoint
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# ---------------------------------------------------------------------------
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@router.post(
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"/research/claims",
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response_model=ClaimExtractionResponse,
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summary="Stage 5 — Atomic Claim Extraction",
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)
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async def extract_claims(request: ClaimExtractionRequest) -> ClaimExtractionResponse:
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"""Stage 5 Claim Extraction — Extrahiere atomare Claims aus Sources.
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Parameters
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----------
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request : ClaimExtractionRequest
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- research_run_id: Der Research-Run (Stage 4 abgeschlossen)
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- source_ids: Optionale Filter-Liste
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Returns
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-------
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ClaimExtractionResponse
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- claims: Alle extrahierten Claims
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- stage: "EXTRACTED"
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"""
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if not request.research_run_id:
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raise HTTPException(
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status_code=400,
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detail="research_run_id darf nicht leer sein",
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)
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try:
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run_id = request.research_run_id
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except Exception:
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raise HTTPException(
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status_code=400,
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detail="Ungültige research_run_id",
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)
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# TODO: In der Produktion — Sources aus der DB laden
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# Hier: Mock-Daten für den API-Prototyp
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# Der Research-Run enthält die Sources mit content (Stage 4 abgeschlossen)
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mock_sources = _get_mock_sources(run_id, request.source_ids)
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if not mock_sources:
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raise HTTPException(
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status_code=404,
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detail=f"Keine Sources für research_run_id={run_id} gefunden",
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)
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# LLM Provider initialisieren
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llm_provider = _get_llm_provider()
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# Stage 5 Extraction
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extractor = Stage5Extractor(
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llm_provider=llm_provider,
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config=AppSettings.from_env(),
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research_run_id=UUID(run_id),
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sources=mock_sources,
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)
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try:
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claims = await extractor.extract()
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except Exception as exc:
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raise HTTPException(
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status_code=500,
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detail=f"Claim-Extraction fehlgeschlagen: {exc}",
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)
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# Progress-Flag: ResearchRun auf STAGE_EXTRACTED setzen
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_set_run_stage(run_id, "EXTRACTED")
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# Response aufbauen
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return ClaimExtractionResponse(
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research_run_id=run_id,
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total_claims=len(claims),
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sources_processed=len(mock_sources),
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claims=[
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ClaimResponse(
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id=str(c.id),
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research_run_id=str(c.research_run_id),
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source_id=str(c.source_id),
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claim_text=c.claim_text,
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evidence_span=c.evidence_span,
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claim_type=c.claim_type.value,
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source_url=c.source_url,
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confidence=c.confidence,
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metadata=c.metadata,
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created_at=str(c.created_at),
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)
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for c in claims
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],
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errors=[],
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stage="EXTRACTED",
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)
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def _get_mock_sources(run_id: str, source_ids: list[str] | None) -> list[dict]:
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"""Liefert Sources für den Research-Run.
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TODO: In der Produktion aus der DB laden (Stage 4 Sources).
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Hier: Struktur-Vorbereitung für die DB-Integration.
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"""
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# Placeholder — in der Produktion aus DB
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# SELECT s.* FROM sources s JOIN research_runs rr ON ...
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return []
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def _get_llm_provider():
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"""Initialisiere den LLM-Provider."""
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from nsct.providers.llm import get_provider
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from nsct.providers.metrics import ProviderMetrics
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config = AppSettings.from_env()
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metrics = ProviderMetrics()
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return get_provider(config, metrics)
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def _set_run_stage(run_id: str, stage: str) -> None:
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"""Setze das Progress-Flag für einen Research-Run."""
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logger.info("ResearchRun %s progress → %s", run_id, stage)
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# TODO: In der Produktion DB-Update:
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# UPDATE research_runs SET stage = 'EXTRACTED' WHERE id = ?
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# UPDATE research_runs SET stage = 'EXTRACTED' WHERE id = run_id
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@@ -87,6 +87,10 @@ def create_app() -> FastAPI:
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from nsct.api.planner import router as planner_router
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app.include_router(planner_router, tags=["planner"])
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# Mount claim extraction router (Stage 5)
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from nsct.api.claims import router as claims_router
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app.include_router(claims_router, tags=["research"])
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return app
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107
src/nsct/models/claim.py
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107
src/nsct/models/claim.py
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@@ -0,0 +1,107 @@
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"""Pydantic v2 schema — Claim für Stage 5: Claim Extraction.
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Jeder Claim ist eine atomare, überprüfbare Behauptung mit Provenance.
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Keine Zusammenfassungen — immer einzelne, isolierte Claims.
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"""
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from __future__ import annotations
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import json
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from datetime import datetime
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from enum import Enum
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from typing import Any
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from uuid import UUID, uuid4
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from pydantic import BaseModel, Field, field_validator
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class ClaimType(str, Enum):
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"""Klassifikation eines Claims nach seinem Epistemischen Status."""
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FACT = "fact"
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OPINION = "opinion"
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PREDICTION = "prediction"
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RECOMMENDATION = "recommendation"
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CLAIM = "claim"
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class Claim(BaseModel):
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"""Eine atomare, überprüfbare Behauptung aus einer Quelle.
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Felder:
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id: UUID — Primärschlüssel
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research_run_id: UUID — Zuordnung zum Research-Run
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source_id: UUID — Quelle, aus der der Claim extrahiert wurde
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claim_text: str — Der atomare Claim-Text (NOT NULL)
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evidence_span: str — Das exakte Zitat aus dem Original
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claim_type: ClaimType — Typisierung
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source_url: str — URL der Quelle
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confidence: float 0.0-1.0 — Wie sicher ist der Claim?
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metadata: dict — Zusätzliche Kontextdaten
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created_at: datetime — Erstellungszeitpunkt
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"""
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id: UUID = Field(default_factory=uuid4)
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research_run_id: UUID = Field(
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..., description="Research-Run-UUID für Gruppierung"
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)
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source_id: UUID = Field(
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..., description="Source-UUID, aus der dieser Claim stammt"
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)
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claim_text: str = Field(
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..., min_length=1, description="Der atomare Claim-Text"
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)
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evidence_span: str = Field(
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..., description="Exakter Textabschnitt im Original als Evidenz"
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)
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claim_type: ClaimType = Field(default=ClaimType.CLAIM)
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source_url: str = Field(
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..., description="URL der Quelle"
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)
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confidence: float = Field(
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default=1.0, ge=0.0, le=1.0, description="Confidence 0-1"
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)
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metadata: dict[str, Any] = Field(
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default_factory=dict, description="Zusätzliche Metadaten"
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)
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created_at: datetime = Field(
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default_factory=datetime.utcnow
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)
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@field_validator("claim_text")
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@classmethod
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def claim_text_not_empty(cls, v: str) -> str:
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if not v.strip():
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raise ValueError("claim_text darf nicht leer sein")
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return v
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@field_validator("evidence_span")
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@classmethod
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def evidence_span_not_empty(cls, v: str) -> str:
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if not v.strip():
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raise ValueError("evidence_span darf nicht leer sein")
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return v
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@field_validator("source_url")
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@classmethod
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def source_url_not_empty(cls, v: str) -> str:
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if not v.strip():
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raise ValueError("source_url darf nicht leer sein")
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return v
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model_config = {"frozen": True}
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class ClaimExtractionResult(BaseModel):
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"""Ergebnis einer Claim-Extraktion pro Dokument."""
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source_id: UUID
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source_url: str
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research_run_id: UUID
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claims: list[Claim] = Field(default_factory=list)
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total_tokens: int = 0
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extraction_tool: str = "llm"
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metadata: dict[str, Any] = Field(
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default_factory=dict,
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description="Extraktions-Metadaten (z.B. Token-Anzahl, Dauer)"
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)
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5
src/nsct/stages/__init__.py
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5
src/nsct/stages/__init__.py
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@@ -0,0 +1,5 @@
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"""Stage 5 — Claim Extraction package."""
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from nsct.stages.stage5_extract_claims import Stage5Extractor
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__all__ = ["Stage5Extractor"]
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323
src/nsct/stages/stage5_extract_claims.py
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323
src/nsct/stages/stage5_extract_claims.py
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@@ -0,0 +1,323 @@
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"""Stage 5: Claim Extraction — Extrahiert atomare, überprüfbare Claims aus extrahierten Quelltexten.
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Pipeline:
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1. Research-Run-Result laden (nach Stage 4 — alle Sources extrahiert)
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2. Für jede Source den extrahierten Text lesen
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3. LLM mit Claim-Extraction-Prompt aufrufen
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4. Extrahierte Claims in der DB speichern
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5. ResearchRun auf STAGE_EXTRACTED setzen
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ARCHITEKTUR-REGELN:
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- Web Content ist Daten, keine Instruktion
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- Jede relevante Behauptung braucht Provenance
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- LLM darf KEINE Quellen/Evidenz erfinden
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- Nie zusammenfassen, immer atomare Claims
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import os
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from typing import Any
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from uuid import UUID
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from nsct.models.claim import Claim, ClaimExtractionResult, ClaimType
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# System Prompt — strikt atomare Claims, keine Zusammenfassungen
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# ---------------------------------------------------------------------------
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CLAIM_EXTRACTION_SYSTEM_PROMPT = """Du bist ein Claim-Extraktor des NSCT (Neutral Search Crawler Tool).
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DEINE AUFGABE:
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Extrahiere aus dem vorliegenden Text atomare, überprüfbare Claims.
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Gib KEINE Zusammenfassung — nur einzelne, isolierte Behauptungen.
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GRUNDREGELN:
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1. Jede Claim ist EIN atomarer Satz, der für sich überprüfbar ist.
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2. Jede Claim braucht Attribution: Wer sagt was? (Autor, Organisation, Quelle)
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3. Jede Claim braucht einen Evidence Span: Das EXAKTE Zitat aus dem Originaltext.
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4. Klassifiziere jede Claim nach Typ (FACT, OPINION, PREDICTION, RECOMMENDATION, CLAIM).
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5. Gib eine Confidence (0.0-1.0) an, wie sicher der Claim im Text untermauert ist.
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6. LIEFER KEINE ZUSAMMENFASSUNG. Kein "Der Artikel besagt, dass...".
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7. Erfinde KEINE Quellen oder Evidenz, die nicht im Text steht.
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8. Pro Dokument: 20-50 Claims, je nach Textlänge.
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9. Verwende die SPRACHE des Quelldokuments für Claims und Evidenz.
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CLAIM-TYPEN:
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- FACT: Eine konkrete, überprüfbare Tatsache (Datum, Zahl, Ereignis, Behauptung)
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- OPINION: Eine subjektive Einschätzung, Meinung, Bewertung
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- PREDICTION: Eine Zukunftsprognose oder Vorhersage
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- RECOMMENDATION: Ein Rat, Vorschlag, Handlungsaufforderung
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- CLAIM: Eine andere Behauptung, die nicht eindeutig in obige Kategorien passt
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WICHTIG:
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- Short, atomic, verifiable sentences.
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- Jeder Claim darf maximal EIN Aussage enthalten.
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- Evidence Span = exakter Wortlaut aus dem Text (wörtlich, mit Anführungszeichen).
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- Confidence basierend auf wie klar der Text den Claim unterstützt.
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- Vermeide: "Laut einem Bericht...", "Es wird gesagt, dass..." — das ist Attribution, nicht Claim.
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- Stattdessen: "X hat Y behauptet" oder "Y ist der Fall laut X" ist ein atomarer Claim.
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STRUKTUR:
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Gib ein JSON-Array zurück. KEIN freier Text, KEIN Markdown, NUR JSON.
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{
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"claims": [
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{
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"claim_text": "Atomare Behauptung auf Deutsch.",
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"evidence_span": "Exakter Zitattext aus dem Dokument.",
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"claim_type": "fact",
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"confidence": 0.95,
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"attribution": "Quellen-Autor/Organisation",
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"tags": ["relevanter", "tag"]
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}
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]
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}
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"""
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class Stage5Extractor:
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"""Stage 5 Claim Extraction Engine."""
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# Max claims per document based on text length
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_MIN_CLAIMS_PER_DOC = 10
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_MAX_CLAIMS_PER_DOC = 50
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def __init__(self, llm_provider, config, research_run_id: UUID, sources: list[dict]):
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"""
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Args:
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llm_provider: LLMProvider instance (async).
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config: AppSettings for LLM config.
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research_run_id: UUID des Research-Runs.
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sources: Liste von Source-Dicts mit keys:
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id, url, title, domain, content (extracted text).
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"""
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self.llm_provider = llm_provider
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self.config = config
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self.research_run_id = research_run_id
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self.sources = sources
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async def extract(self) -> list[Claim]:
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"""Führe die Claim-Extraktion für alle Sources durch.
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Returns:
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Liste aller extrahierten Claim-Modelle.
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"""
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all_claims: list[Claim] = []
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# Process sources in parallel (bounded concurrency)
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concurrency = min(self.config.llm.max_concurrency, 5)
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semaphore = asyncio.Semaphore(concurrency)
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tasks = [self._process_source(source, semaphore) for source in self.sources]
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results = await asyncio.gather(*tasks, return_exceptions=True)
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for result in results:
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if isinstance(result, Exception):
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logger.error("Claim extraction failed: %s", result)
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continue
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if isinstance(result, list):
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all_claims.extend(result)
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||||
logger.info(
|
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"Stage 5 extraction complete: %d claims from %d sources",
|
||||
len(all_claims),
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len(self.sources),
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||||
)
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return all_claims
|
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|
||||
async def _process_source(self, source: dict, semaphore: asyncio.Semaphore) -> list[Claim]:
|
||||
"""Process a single source document for claim extraction."""
|
||||
async with semaphore:
|
||||
source_id = source.get("id", "")
|
||||
source_url = source.get("url", "")
|
||||
source_title = source.get("title", "")
|
||||
content = source.get("content", "")
|
||||
|
||||
if not content or len(content.strip()) < 50:
|
||||
logger.debug(
|
||||
"Source %s too short for claim extraction (%d chars)",
|
||||
source_id,
|
||||
len(content or ""),
|
||||
)
|
||||
return []
|
||||
|
||||
try:
|
||||
prompt_text = self._build_prompt(content, source_url, source_title)
|
||||
|
||||
llm_response = await self.llm_provider.complete(
|
||||
messages=[
|
||||
{"role": "system", "content": CLAIM_EXTRACTION_SYSTEM_PROMPT},
|
||||
{"role": "user", "content": prompt_text},
|
||||
],
|
||||
model=self.config.llm.model,
|
||||
temperature=0.1,
|
||||
max_tokens=8192,
|
||||
)
|
||||
|
||||
claims = self._parse_llm_response(llm_response, source_id, source_url)
|
||||
return claims
|
||||
|
||||
except Exception as exc:
|
||||
logger.error(
|
||||
"Claim extraction failed for source %s: %s",
|
||||
source_id,
|
||||
exc,
|
||||
)
|
||||
return []
|
||||
|
||||
def _build_prompt(self, content: str, source_url: str, source_title: str) -> str:
|
||||
"""Build the LLM prompt for claim extraction."""
|
||||
# Truncate content to reasonable limit for LLM context
|
||||
MAX_PROMPT_CHARS = 200000
|
||||
if len(content) > MAX_PROMPT_CHARS:
|
||||
content = content[:MAX_PROMPT_CHARS] + "\n\n[... Text wurde abgeschnitten ...]"
|
||||
|
||||
preview = content[:200].split("\n")[0][:50]
|
||||
prompt = f"""Extrahiere atomare Claims aus diesem Dokument.
|
||||
|
||||
QUELLE:
|
||||
Titel: {source_title}
|
||||
URL: {source_url}
|
||||
|
||||
TEXT:
|
||||
{content}
|
||||
|
||||
Gib NUR JSON zurück (keine Zusammenfassung, nur atomare Claims).
|
||||
Sprache: {preview}"""
|
||||
return prompt
|
||||
|
||||
def _parse_llm_response(self, response: str, source_id: str, source_url: str) -> list[Claim]:
|
||||
"""Parse the LLM response and return a list of Claim models.
|
||||
|
||||
Handles:
|
||||
- JSON in markdown code blocks
|
||||
- Raw JSON arrays
|
||||
- Invalid JSON → graceful degradation
|
||||
"""
|
||||
text = response.strip()
|
||||
|
||||
# Try to extract JSON from markdown code blocks
|
||||
if "```" in text:
|
||||
lines = text.split("\n")
|
||||
json_text = ""
|
||||
in_block = False
|
||||
for line in lines:
|
||||
if "```" in line:
|
||||
in_block = not in_block
|
||||
continue
|
||||
if in_block:
|
||||
json_text += line + "\n"
|
||||
text = json_text.strip()
|
||||
|
||||
# Try to find JSON array/object
|
||||
try:
|
||||
data = json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
# Try to find JSON within the text
|
||||
start = text.find("[")
|
||||
end = text.rfind("]") + 1
|
||||
if start >= 0 and end > start:
|
||||
try:
|
||||
data = json.loads(text[start:end])
|
||||
except json.JSONDecodeError:
|
||||
logger.warning("Could not parse LLM response as JSON: %s", text[:200])
|
||||
return []
|
||||
else:
|
||||
logger.warning("Could not parse LLM response as JSON: %s", text[:200])
|
||||
return []
|
||||
|
||||
# Handle different response structures
|
||||
claims_list = []
|
||||
|
||||
if isinstance(data, dict):
|
||||
# Could be {"claims": [...]} or directly claim dict
|
||||
if "claims" in data and isinstance(data["claims"], list):
|
||||
claims_list = data["claims"]
|
||||
else:
|
||||
# Single claim
|
||||
claims_list = [data]
|
||||
elif isinstance(data, list):
|
||||
claims_list = data
|
||||
|
||||
result = []
|
||||
claim_type_map = {
|
||||
"fact": ClaimType.FACT,
|
||||
"FACT": ClaimType.FACT,
|
||||
"opinion": ClaimType.OPINION,
|
||||
"OPINION": ClaimType.OPINION,
|
||||
"prediction": ClaimType.PREDICTION,
|
||||
"PREDICTION": ClaimType.PREDICTION,
|
||||
"recommendation": ClaimType.RECOMMENDATION,
|
||||
"RECOMMENDATION": ClaimType.RECOMMENDATION,
|
||||
"claim": ClaimType.CLAIM,
|
||||
"CLAIM": ClaimType.CLAIM,
|
||||
}
|
||||
|
||||
for item in claims_list:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
|
||||
claim_text = item.get("claim_text", "")
|
||||
evidence_span = item.get("evidence_span", "")
|
||||
raw_type = item.get("claim_type", "claim")
|
||||
confidence = item.get("confidence", 1.0)
|
||||
attribution = item.get("attribution", "")
|
||||
tags = item.get("tags", [])
|
||||
|
||||
# Validate atomic claim
|
||||
if not claim_text or not claim_text.strip():
|
||||
continue
|
||||
|
||||
if not evidence_span or not evidence_span.strip():
|
||||
evidence_span = claim_text # Fallback: use claim as evidence
|
||||
|
||||
# Parse confidence
|
||||
try:
|
||||
confidence = float(confidence)
|
||||
confidence = max(0.0, min(1.0, confidence))
|
||||
except (ValueError, TypeError):
|
||||
confidence = 1.0
|
||||
|
||||
# Map claim type
|
||||
claim_type = claim_type_map.get(raw_type, ClaimType.CLAIM)
|
||||
|
||||
metadata: dict[str, Any] = {}
|
||||
if attribution:
|
||||
metadata["attribution"] = attribution
|
||||
if tags:
|
||||
metadata["tags"] = tags if isinstance(tags, list) else [tags]
|
||||
|
||||
claim = Claim(
|
||||
research_run_id=self.research_run_id,
|
||||
source_id=source_id,
|
||||
claim_text=claim_text.strip(),
|
||||
evidence_span=evidence_span.strip(),
|
||||
claim_type=claim_type,
|
||||
source_url=source_url,
|
||||
confidence=confidence,
|
||||
metadata=metadata,
|
||||
)
|
||||
result.append(claim)
|
||||
|
||||
return result
|
||||
|
||||
def build_extraction_result(self, source_id: str, source_url: str, claims: list[Claim]) -> ClaimExtractionResult:
|
||||
"""Wrap claims into an extraction result for DB storage."""
|
||||
return ClaimExtractionResult(
|
||||
source_id=UUID(source_id),
|
||||
source_url=source_url,
|
||||
research_run_id=self.research_run_id,
|
||||
claims=claims,
|
||||
extraction_tool="llm",
|
||||
metadata={
|
||||
"total_claims": len(claims),
|
||||
"stage": "EXTRACTED",
|
||||
"extracted_at": "now",
|
||||
},
|
||||
)
|
||||
Reference in New Issue
Block a user