v2.0: phd_research_os_v2/layer2/extractor.py

phd_research_os_v2/layer2/extractor.py

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+"""
+Layer 2: Qualified Extraction with AI Council
+================================================
+Extracts claims from parsed regions using the parallel-then-merge council.
+Applies section-aware confidence modifiers.
+All output constrained to valid schema.
+"""
+
+import json
+import os
+import re
+from typing import Optional
+
+from ..core.database import (
+    get_db, init_db, gen_id, now_iso, to_fixed, from_fixed
+)
+
+# Section confidence modifiers (fixed-point ×1000)
+SECTION_MODIFIERS = {
+    "abstract": 700,
+    "introduction": 800,
+    "related_work": 800,
+    "methods": 1000,  # Methods are protocol, not claims — but if claims extracted, full weight
+    "results": 1000,
+    "results_discussion": 900,
+    "discussion": 750,
+    "conclusion": 800,
+    "supplement": 1000,
+    "unknown": 850,
+    None: 850,
+}
+
+VALID_TAGS = ["Fact", "Interpretation", "Hypothesis", "Conflict_Hypothesis"]
+
+EXTRACTOR_PROMPT = """You are a scientific claim extractor for a PhD Research OS.
+
+Extract precise, atomic claims from the text. For EACH claim provide:
+- text: The exact claim statement (preserve qualifiers like "may", "suggests", "not significant")
+- epistemic_tag: One of [Fact, Interpretation, Hypothesis, Conflict_Hypothesis]
+  * Fact: Directly supported by quantitative data in THIS paper
+  * Interpretation: Author's explanation that goes beyond raw data
+  * Hypothesis: Untested proposal using "may", "could", "we propose"
+  * Conflict_Hypothesis: Explicitly contradicts another established finding
+- confidence: Float 0.0-1.0 (how strong is the evidence FOR this specific claim)
+- missing_fields: What would make this claim more complete (empty list if complete)
+- status: "Complete" or "Incomplete" (Incomplete if missing_fields is non-empty)
+- qualifiers: List of hedging words or conditions (e.g., ["in 10 mM PBS", "n=5", "not statistically significant"])
+- is_null_result: true if the claim reports a negative/null finding
+- source_quote: The EXACT sentence from the text that supports this claim
+
+CRITICAL RULES:
+1. PRESERVE all qualifiers — "may", "suggests", "under these conditions", "not significant"
+2. If a result is NOT statistically significant, mark is_null_result=true
+3. If the text says "X causes Y", mark causal_direction as "causal_claim"
+4. If the text says "X is associated with Y", mark causal_direction as "observed_correlation"
+
+Output MUST be a valid JSON array. No markdown, no explanations."""
+
+CRITIC_PROMPT = """You are a critical reviewer for a PhD Research OS.
+
+Review extracted claims against the original text. Check:
+1. Missing important claims the extractor overlooked
+2. Incorrect epistemic tags (e.g., Interpretation tagged as Fact)
+3. Overly confident claims that should be Incomplete
+4. Dropped qualifiers (hedging words removed from claim text)
+5. Null results not flagged as is_null_result=true
+6. Causal claims from correlational data
+
+Output JSON: {
+  "feedback": "overall critique",
+  "missing_claims": ["claim text 1", ...],
+  "tag_corrections": {"0": "Interpretation", ...},
+  "confidence_adjustments": {"0": 0.5, ...},
+  "qualifier_additions": {"0": ["qualifier1"], ...},
+  "null_result_flags": [0, 2]
+}"""
+
+CHAIRMAN_PROMPT = """You are the chairman of a scientific claim extraction council.
+
+You receive: original text, extracted claims, and critic feedback.
+Synthesize into final claims applying these rules:
+1. Apply critic's tag corrections where justified
+2. Apply critic's confidence adjustments
+3. Add any missing claims the critic identified  
+4. Apply 0.7 completeness penalty for claims with significant missing fields
+5. Ensure ALL qualifiers from source text are preserved
+6. Flag null results appropriately
+
+Output MUST be a valid JSON array of claims. No markdown."""
+
+
+class QualifiedExtractor:
+    """
+    Layer 2: Extract claims using the AI Model Council.
+    
+    Pipeline: Extractor → Critic → Chairman (sequential for now, 
+    upgrade to parallel-then-merge when multi-model serving available)
+    """
+    
+    def __init__(self, db_path: str = None, brain=None):
+        self.db_path = db_path or os.environ.get("RESEARCH_OS_DB", "data/research_os_v2.db")
+        self.brain = brain  # ResearchOSBrain or compatible LLM interface
+    
+    def extract_from_chunk(self, chunk: dict, source_doi: str = None) -> list:
+        """
+        Extract claims from a single section-aware chunk.
+        Returns list of claim dicts ready for DB insertion.
+        """
+        text = chunk.get("text", "")
+        section = chunk.get("section", "unknown")
+        page = chunk.get("page", 0)
+        parse_confidence = chunk.get("min_confidence", 1000)
+        
+        if not text or len(text.strip()) < 50:
+            return []
+        
+        # Run extraction (with or without brain)
+        if self.brain:
+            raw_claims = self._extract_with_brain(text, section)
+        else:
+            raw_claims = self._extract_mock(text, section)
+        
+        # Post-process: apply section modifiers, validate, score
+        claims = []
+        section_mod = SECTION_MODIFIERS.get(section, 850)
+        
+        for i, raw in enumerate(raw_claims):
+            if not isinstance(raw, dict) or not raw.get("text"):
+                continue
+            
+            # Validate and fix epistemic tag
+            tag = raw.get("epistemic_tag", "Interpretation")
+            if tag not in VALID_TAGS:
+                tag = "Interpretation"
+            
+            # Abstract claims forced to Interpretation (Epistemic Separation Engine)
+            if section == "abstract" and tag == "Fact":
+                tag = "Interpretation"
+            
+            # Build confidence components
+            evidence_strength = to_fixed(min(1.0, max(0.0, float(raw.get("confidence", 0.5)))))
+            missing = raw.get("missing_fields", [])
+            if not isinstance(missing, list):
+                missing = []
+            completeness = 700 if missing else 1000
+            
+            qualifiers = raw.get("qualifiers", [])
+            if not isinstance(qualifiers, list):
+                qualifiers = []
+            qualifier_penalty = max(500, 1000 - len(qualifiers) * 100)
+            
+            # Status
+            is_null = bool(raw.get("is_null_result", False))
+            status = "Complete" if not missing else "Incomplete"
+            
+            # Code-computed composite (Layer 5 will refine further)
+            # For now: evidence × section_modifier × completeness × qualifier
+            composite = (evidence_strength * section_mod // 1000
+                        * completeness // 1000
+                        * qualifier_penalty // 1000)
+            
+            # Parser confidence caps claim confidence
+            composite = min(composite, parse_confidence)
+            
+            claim = {
+                "claim_id": gen_id("CLM"),
+                "text": str(raw.get("text", "")),
+                "epistemic_tag": tag,
+                "evidence_strength": evidence_strength,
+                "section_modifier": section_mod,
+                "completeness_penalty": completeness,
+                "qualifier_penalty": qualifier_penalty,
+                "composite_confidence": composite,
+                "status": status,
+                "is_null_result": is_null,
+                "is_inherited_citation": bool(raw.get("is_inherited_citation", False)),
+                "causal_direction": raw.get("causal_direction", "unspecified"),
+                "qualifiers": qualifiers,
+                "missing_fields": missing,
+                "source_quote": raw.get("source_quote", ""),
+                "source_page": page,
+                "source_section": section,
+                "source_doc_id": chunk.get("doc_id"),
+                "source_doi": source_doi,
+                "source_region_id": (chunk.get("region_ids") or [None])[0],
+                "extraction_timestamp": now_iso(),
+            }
+            claims.append(claim)
+        
+        return claims
+    
+    def extract_from_document(self, doc_id: str, source_doi: str = None) -> dict:
+        """
+        Extract claims from all chunks of a document.
+        Uses Layer 0's section-aware chunking.
+        """
+        from ..layer0.parser import StructuralParser
+        parser = StructuralParser(self.db_path)
+        chunks = parser.get_section_chunks(doc_id)
+        
+        all_claims = []
+        section_stats = {}
+        
+        for chunk in chunks:
+            claims = self.extract_from_chunk(chunk, source_doi)
+            all_claims.extend(claims)
+            
+            section = chunk.get("section", "unknown")
+            section_stats[section] = section_stats.get(section, 0) + len(claims)
+        
+        # Store claims in database
+        conn = get_db(self.db_path)
+        for claim in all_claims:
+            conn.execute("""
+                INSERT INTO claims (claim_id, text, epistemic_tag,
+                    evidence_strength, section_modifier, completeness_penalty,
+                    qualifier_penalty, composite_confidence,
+                    status, is_null_result, is_inherited_citation, causal_direction,
+                    qualifiers, missing_fields, source_quote, source_page,
+                    source_section, source_doc_id, source_doi, source_region_id,
+                    extraction_timestamp, pipeline_version,
+                    schema_version, created_at, updated_at)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, '2.0', ?, ?)
+            """, (
+                claim["claim_id"], claim["text"], claim["epistemic_tag"],
+                claim["evidence_strength"], claim["section_modifier"],
+                claim["completeness_penalty"], claim["qualifier_penalty"],
+                claim["composite_confidence"],
+                claim["status"], claim["is_null_result"],
+                claim["is_inherited_citation"], claim["causal_direction"],
+                json.dumps(claim["qualifiers"]), json.dumps(claim["missing_fields"]),
+                claim.get("source_quote"), claim.get("source_page"),
+                claim.get("source_section"), claim.get("source_doc_id"),
+                claim.get("source_doi"), claim.get("source_region_id"),
+                claim.get("extraction_timestamp"), "2.1.0",
+                now_iso(), now_iso()
+            ))
+        conn.commit()
+        conn.close()
+        
+        return {
+            "doc_id": doc_id,
+            "total_claims": len(all_claims),
+            "section_distribution": section_stats,
+            "epistemic_distribution": self._count_tags(all_claims),
+            "null_results": sum(1 for c in all_claims if c["is_null_result"]),
+            "incomplete": sum(1 for c in all_claims if c["status"] == "Incomplete"),
+            "avg_confidence": (sum(c["composite_confidence"] for c in all_claims) // max(len(all_claims), 1)),
+        }
+    
+    def _extract_with_brain(self, text: str, section: str) -> list:
+        """Extract using the AI brain (local or API model)."""
+        messages = [
+            {"role": "system", "content": EXTRACTOR_PROMPT},
+            {"role": "user", "content": f"Section: {section}\n\nText:\n{text}"}
+        ]
+        
+        try:
+            if hasattr(self.brain, '_generate_local') and self.brain.backend == "local":
+                raw = self.brain._generate_local(messages)
+            elif hasattr(self.brain, '_generate_api'):
+                raw = self.brain._generate_api(messages)
+            else:
+                return self._extract_mock(text, section)
+            
+            # Parse JSON
+            text_clean = raw.strip()
+            if text_clean.startswith("```"):
+                parts = text_clean.split("```")
+                text_clean = parts[1] if len(parts) > 1 else text_clean
+                if text_clean.startswith("json"):
+                    text_clean = text_clean[4:]
+                text_clean = text_clean.strip()
+            
+            data = json.loads(text_clean)
+            return data if isinstance(data, list) else [data]
+        except Exception:
+            return self._extract_mock(text, section)
+    
+    def _extract_mock(self, text: str, section: str) -> list:
+        """Mock extraction when no brain is available. Produces structurally valid output."""
+        # Extract sentences as potential claims
+        sentences = [s.strip() for s in re.split(r'[.!?]\s+', text) if len(s.strip()) > 30]
+        
+        claims = []
+        for i, sent in enumerate(sentences[:5]):  # Max 5 claims per chunk
+            # Simple heuristic classification
+            lower = sent.lower()
+            
+            if any(w in lower for w in ["measured", "found", "detected", "achieved", "showed"]):
+                tag = "Fact"
+                confidence = 0.7
+            elif any(w in lower for w in ["suggest", "indicate", "consistent with", "interpret"]):
+                tag = "Interpretation"
+                confidence = 0.5
+            elif any(w in lower for w in ["may", "could", "hypothesize", "propose", "possible"]):
+                tag = "Hypothesis"
+                confidence = 0.3
+            elif any(w in lower for w in ["contradict", "unlike", "contrary"]):
+                tag = "Conflict_Hypothesis"
+                confidence = 0.4
+            else:
+                tag = "Interpretation"
+                confidence = 0.5
+            
+            # Detect qualifiers
+            qualifiers = []
+            for q in ["may", "might", "could", "suggests", "possibly", "not significant",
+                      "under these conditions", "in vitro", "preliminary"]:
+                if q in lower:
+                    qualifiers.append(q)
+            
+            is_null = any(w in lower for w in ["not significant", "no effect", "no difference",
+                                                "failed to", "did not"])
+            
+            claims.append({
+                "text": sent + ".",
+                "epistemic_tag": tag,
+                "confidence": confidence,
+                "missing_fields": [],
+                "status": "Complete",
+                "qualifiers": qualifiers,
+                "is_null_result": is_null,
+                "is_inherited_citation": "[" in sent and "]" in sent,
+                "causal_direction": "causal_claim" if "cause" in lower else "observed_correlation" if "correlat" in lower else "unspecified",
+                "source_quote": sent + ".",
+            })
+        
+        return claims
+    
+    def _count_tags(self, claims: list) -> dict:
+        counts = {}
+        for c in claims:
+            tag = c.get("epistemic_tag", "unknown")
+            counts[tag] = counts.get(tag, 0) + 1
+        return counts