feat(H): H-EXT/H-ENT/H-AUTO/H-CONF — LLM knowledge extraction (entities, relationships, auto-create in GraphRAG, confidence scoring with review queue), 31 tests passing

app/ai/knowledge_extraction.py

@@ -0,0 +1,361 @@
+"""Knowledge extraction — LLM-based relationship and entity extraction (H-EXT, H-ENT, H-AUTO, H-CONF).
+
+Analyzes texts from knowledge sources (wiki, DMS, mail, communication)
+and extracts:
+- Named entities (persons, companies, projects) — H-ENT
+- Relationships between entities — H-EXT
+- Auto-creates relationships in GraphRAG — H-AUTO
+- Confidence scores with low-confidence → review queue — H-CONF
+"""
+
+from __future__ import annotations
+
+import logging
+import uuid
+from dataclasses import dataclass, field
+from typing import Any
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ExtractedEntity:
+    """A named entity extracted from text (H-ENT)."""
+
+    name: str
+    entity_type: str  # person, company, project, location, date, other
+    mentions: list[int] = field(default_factory=list)  # character positions
+    confidence: float = 1.0
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ExtractedRelationship:
+    """A relationship between two entities extracted from text (H-EXT)."""
+
+    source_entity: str
+    source_type: str
+    target_entity: str
+    target_type: str
+    relationship_type: str  # works_for, related_to, has_email, etc.
+    confidence: float = 0.0
+    evidence: str = ""  # Text snippet that supports this relationship
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class ExtractionResult:
+    """Result of a knowledge extraction run."""
+
+    entities: list[ExtractedEntity] = field(default_factory=list)
+    relationships: list[ExtractedRelationship] = field(default_factory=list)
+    source_type: str = ""
+    source_id: str = ""
+    tenant_id: str = ""
+    overall_confidence: float = 0.0
+
+
+# ─── Extraction prompt ───────────────────────────────────────────────────────
+
+_EXTRACTION_SYSTEM_PROMPT = """You are a knowledge extraction assistant. Analyze the given text and extract:
+
+1. Named entities (persons, companies, projects, locations, dates)
+2. Relationships between entities (e.g., "works_for", "related_to", "has_email", "part_of")
+
+Return JSON in this format:
+{
+  "entities": [
+    {"name": "John Doe", "type": "person", "confidence": 0.95}
+  ],
+  "relationships": [
+    {
+      "source": "John Doe",
+      "source_type": "person",
+      "target": "Acme Corp",
+      "target_type": "company",
+      "type": "works_for",
+      "confidence": 0.9,
+      "evidence": "John Doe works at Acme Corp"
+    }
+  ]
+}
+
+Only extract explicitly stated facts. Do not infer or hallucinate.
+If no entities or relationships are found, return empty arrays."""
+
+
+async def extract_knowledge(
+    text: str,
+    tenant_id: uuid.UUID,
+    source_type: str = "",
+    source_id: str = "",
+    llm_model: str | None = None,
+) -> ExtractionResult:
+    """Extract entities and relationships from text using LLM (H-EXT, H-ENT).
+
+    Args:
+        text: The text to analyze.
+        tenant_id: Tenant ID for multi-tenancy.
+        source_type: Source type (wiki, dms, mail, communication).
+        source_id: Source entity ID.
+        llm_model: Optional LLM model override.
+
+    Returns:
+        ExtractionResult with entities and relationships.
+    """
+    if not text or len(text.strip()) < 10:
+        return ExtractionResult(
+            source_type=source_type,
+            source_id=source_id,
+            tenant_id=str(tenant_id),
+        )
+
+    try:
+        from app.ai.llm_client import llm_complete
+
+        messages = [
+            {"role": "system", "content": _EXTRACTION_SYSTEM_PROMPT},
+            {"role": "user", "content": f"Analyze this text:\n\n{text[:8000]}"},
+        ]
+
+        response = await llm_complete(
+            model=llm_model or "ollama/deepseek-v4-flash",
+            messages=messages,
+            temperature=0.1,
+            max_tokens=2000,
+        )
+
+        # Parse LLM response
+        import json
+
+        raw = response.get("content", "")
+        # Try to extract JSON from response
+        try:
+            data = json.loads(raw)
+        except json.JSONDecodeError:
+            # Try to find JSON in the response
+            start = raw.find("{")
+            end = raw.rfind("}") + 1
+            if start >= 0 and end > start:
+                data = json.loads(raw[start:end])
+            else:
+                logger.warning("Failed to parse extraction response as JSON")
+                return ExtractionResult(
+                    source_type=source_type,
+                    source_id=source_id,
+                    tenant_id=str(tenant_id),
+                )
+
+        # Build ExtractionResult
+        entities: list[ExtractedEntity] = []
+        for ent in data.get("entities", []):
+            entities.append(ExtractedEntity(
+                name=ent.get("name", ""),
+                entity_type=ent.get("type", "other"),
+                confidence=ent.get("confidence", 0.5),
+                metadata=ent.get("metadata", {}),
+            ))
+
+        relationships: list[ExtractedRelationship] = []
+        for rel in data.get("relationships", []):
+            relationships.append(ExtractedRelationship(
+                source_entity=rel.get("source", ""),
+                source_type=rel.get("source_type", "other"),
+                target_entity=rel.get("target", ""),
+                target_type=rel.get("target_type", "other"),
+                relationship_type=rel.get("type", "related_to"),
+                confidence=rel.get("confidence", 0.5),
+                evidence=rel.get("evidence", ""),
+                metadata=rel.get("metadata", {}),
+            ))
+
+        # Calculate overall confidence
+        all_confidences = [e.confidence for e in entities] + [r.confidence for r in relationships]
+        overall = sum(all_confidences) / len(all_confidences) if all_confidences else 0.0
+
+        return ExtractionResult(
+            entities=entities,
+            relationships=relationships,
+            source_type=source_type,
+            source_id=source_id,
+            tenant_id=str(tenant_id),
+            overall_confidence=overall,
+        )
+
+    except Exception as e:
+        logger.warning("Knowledge extraction failed: %s", e)
+        return ExtractionResult(
+            source_type=source_type,
+            source_id=source_id,
+            tenant_id=str(tenant_id),
+        )
+
+
+# ─── Confidence scoring (H-CONF) ─────────────────────────────────────────────
+
+LOW_CONFIDENCE_THRESHOLD = 0.6
+
+
+def is_low_confidence(confidence: float) -> bool:
+    """Check if a confidence score is below the review threshold (H-CONF)."""
+    return confidence < LOW_CONFIDENCE_THRESHOLD
+
+
+def filter_high_confidence(
+    relationships: list[ExtractedRelationship],
+    threshold: float = LOW_CONFIDENCE_THRESHOLD,
+) -> tuple[list[ExtractedRelationship], list[ExtractedRelationship]]:
+    """Split relationships into high-confidence and low-confidence (review queue).
+
+    Returns:
+        Tuple of (high_confidence, low_confidence) lists.
+    """
+    high = [r for r in relationships if r.confidence >= threshold]
+    low = [r for r in relationships if r.confidence < threshold]
+    return high, low
+
+
+# ─── Auto-relationship creation in GraphRAG (H-AUTO) ─────────────────────────
+
+
+async def auto_create_relationships(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    extraction: ExtractionResult,
+    min_confidence: float = LOW_CONFIDENCE_THRESHOLD,
+) -> dict[str, Any]:
+    """Auto-create extracted relationships in GraphRAG (H-AUTO).
+
+    Only creates relationships with confidence >= min_confidence.
+    Low-confidence relationships are returned for the review queue.
+
+    Returns:
+        Dict with ``created``, ``skipped_low_confidence``, ``errors`` counts.
+    """
+    from app.plugins.builtins.graph_rag.models import EntityRelationship
+
+    created = 0
+    skipped = 0
+    errors = 0
+
+    high_conf, low_conf = filter_high_confidence(extraction.relationships, min_confidence)
+
+    for rel in high_conf:
+        try:
+            # Try to resolve entity names to actual entity IDs
+            # For now, store as typed relationships with name-based references
+            source_id = await _resolve_entity_id(db, tenant_id, rel.source_entity, rel.source_type)
+            target_id = await _resolve_entity_id(db, tenant_id, rel.target_entity, rel.target_type)
+
+            if source_id is None or target_id is None:
+                skipped += 1
+                continue
+
+            # Check if relationship already exists
+            existing = await db.execute(
+                select(EntityRelationship).where(
+                    EntityRelationship.tenant_id == tenant_id,
+                    EntityRelationship.source_type == rel.source_type,
+                    EntityRelationship.source_id == source_id,
+                    EntityRelationship.target_type == rel.target_type,
+                    EntityRelationship.target_id == target_id,
+                    EntityRelationship.relationship_type == rel.relationship_type,
+                )
+            )
+            if existing.scalar_one_or_none() is not None:
+                skipped += 1
+                continue
+
+            # Create new relationship
+            er = EntityRelationship(
+                tenant_id=tenant_id,
+                source_type=rel.source_type,
+                source_id=source_id,
+                target_type=rel.target_type,
+                target_id=target_id,
+                relationship_type=rel.relationship_type,
+                confidence=rel.confidence,
+                metadata={
+                    "evidence": rel.evidence,
+                    "source_type": extraction.source_type,
+                    "source_id": extraction.source_id,
+                    "auto_extracted": True,
+                },
+            )
+            db.add(er)
+            created += 1
+        except Exception as e:
+            logger.warning("Failed to auto-create relationship: %s", e)
+            errors += 1
+
+    await db.flush()
+
+    return {
+        "created": created,
+        "skipped_low_confidence": len(low_conf),
+        "skipped_existing": skipped,
+        "errors": errors,
+        "review_queue": [
+            {
+                "source": r.source_entity,
+                "target": r.target_entity,
+                "type": r.relationship_type,
+                "confidence": r.confidence,
+                "evidence": r.evidence,
+            }
+            for r in low_conf
+        ],
+    }
+
+
+async def _resolve_entity_id(
+    db: AsyncSession,
+    tenant_id: uuid.UUID,
+    name: str,
+    entity_type: str,
+) -> uuid.UUID | None:
+    """Try to resolve an entity name to an actual entity ID.
+
+    Searches contacts, companies, etc. by name.
+    Returns None if no match found.
+    """
+    try:
+        if entity_type == "person":
+            from app.models.contact import Contact
+            result = await db.execute(
+                select(Contact.id).where(
+                    Contact.tenant_id == tenant_id,
+                    Contact.deleted_at.is_(None),
+                    Contact.name.ilike(f"%{name}%"),
+                ).limit(1)
+            )
+            return result.scalar_one_or_none()
+        elif entity_type == "company":
+            from app.models.contact import Contact
+            result = await db.execute(
+                select(Contact.id).where(
+                    Contact.tenant_id == tenant_id,
+                    Contact.deleted_at.is_(None),
+                    Contact.is_company.is_(True),
+                    Contact.name.ilike(f"%{name}%"),
+                ).limit(1)
+            )
+            return result.scalar_one_or_none()
+    except Exception:
+        pass
+    return None
+
+
+__all__ = [
+    "ExtractedEntity",
+    "ExtractedRelationship",
+    "ExtractionResult",
+    "extract_knowledge",
+    "is_low_confidence",
+    "filter_high_confidence",
+    "auto_create_relationships",
+    "LOW_CONFIDENCE_THRESHOLD",
+]

tests/test_phase_h_wiki.py

@@ -275,3 +275,103 @@ class TestEvidenceReferences:
 
         refs = build_evidence_references([])
         assert refs == []
+
+
+# ─── H-EXT/H-ENT/H-AUTO/H-CONF: Knowledge Extraction ─────────────────────────
+
+
+class TestKnowledgeExtraction:
+    """Test the knowledge extraction module (H-EXT, H-ENT, H-AUTO, H-CONF)."""
+
+    def test_extracted_entity_dataclass(self):
+        """ExtractedEntity dataclass works correctly."""
+        from app.ai.knowledge_extraction import ExtractedEntity
+
+        entity = ExtractedEntity(name="John Doe", entity_type="person", confidence=0.9)
+        assert entity.name == "John Doe"
+        assert entity.entity_type == "person"
+        assert entity.confidence == 0.9
+        assert entity.mentions == []
+        assert entity.metadata == {}
+
+    def test_extracted_relationship_dataclass(self):
+        """ExtractedRelationship dataclass works correctly."""
+        from app.ai.knowledge_extraction import ExtractedRelationship
+
+        rel = ExtractedRelationship(
+            source_entity="John Doe", source_type="person",
+            target_entity="Acme Corp", target_type="company",
+            relationship_type="works_for", confidence=0.85,
+            evidence="John works at Acme",
+        )
+        assert rel.source_entity == "John Doe"
+        assert rel.relationship_type == "works_for"
+        assert rel.confidence == 0.85
+        assert rel.evidence == "John works at Acme"
+
+    def test_extraction_result_dataclass(self):
+        """ExtractionResult dataclass works correctly."""
+        from app.ai.knowledge_extraction import ExtractionResult
+
+        result = ExtractionResult(source_type="wiki", source_id="abc", tenant_id="123")
+        assert result.entities == []
+        assert result.relationships == []
+        assert result.overall_confidence == 0.0
+
+    def test_is_low_confidence(self):
+        """is_low_confidence correctly identifies low confidence scores."""
+        from app.ai.knowledge_extraction import is_low_confidence, LOW_CONFIDENCE_THRESHOLD
+
+        assert is_low_confidence(0.3) is True
+        assert is_low_confidence(0.5) is True
+        assert is_low_confidence(0.59) is True
+        assert is_low_confidence(0.6) is False
+        assert is_low_confidence(0.9) is False
+        assert LOW_CONFIDENCE_THRESHOLD == 0.6
+
+    def test_filter_high_confidence(self):
+        """filter_high_confidence splits relationships correctly."""
+        from app.ai.knowledge_extraction import filter_high_confidence, ExtractedRelationship
+
+        rels = [
+            ExtractedRelationship("A", "person", "B", "company", "works_for", 0.9),
+            ExtractedRelationship("C", "person", "D", "company", "related_to", 0.3),
+            ExtractedRelationship("E", "person", "F", "company", "knows", 0.7),
+            ExtractedRelationship("G", "person", "H", "company", "met", 0.5),
+        ]
+        high, low = filter_high_confidence(rels)
+        assert len(high) == 2  # 0.9 and 0.7
+        assert len(low) == 2   # 0.3 and 0.5
+        assert high[0].confidence == 0.9
+        assert high[1].confidence == 0.7
+
+    def test_filter_high_confidence_custom_threshold(self):
+        """filter_high_confidence respects custom threshold."""
+        from app.ai.knowledge_extraction import filter_high_confidence, ExtractedRelationship
+
+        rels = [
+            ExtractedRelationship("A", "person", "B", "company", "works_for", 0.8),
+            ExtractedRelationship("C", "person", "D", "company", "related_to", 0.7),
+        ]
+        high, low = filter_high_confidence(rels, threshold=0.75)
+        assert len(high) == 1  # 0.8
+        assert len(low) == 1   # 0.7
+
+    @pytest.mark.asyncio
+    async def test_extract_knowledge_empty_text(self):
+        """extract_knowledge returns empty result for empty text."""
+        from app.ai.knowledge_extraction import extract_knowledge
+
+        result = await extract_knowledge("", uuid.uuid4())
+        assert result.entities == []
+        assert result.relationships == []
+        assert result.overall_confidence == 0.0
+
+    @pytest.mark.asyncio
+    async def test_extract_knowledge_short_text(self):
+        """extract_knowledge returns empty result for very short text."""
+        from app.ai.knowledge_extraction import extract_knowledge
+
+        result = await extract_knowledge("Hi", uuid.uuid4())
+        assert result.entities == []
+        assert result.relationships == []