poimen-workflows/activity/synthesis.go

package activity

import (
	"context"
	"crypto/sha256"
	"encoding/hex"
	"fmt"
	"log/slog"
	"regexp"
	"strings"

	"github.com/rockliang/poimen/workflows/internal/memory"
)

// SynthesisActivities holds dependencies for synthesis pipeline activities.
type SynthesisActivities struct {
	memClient *memory.Client
}

// NewSynthesisActivities creates synthesis activities with a memory service client.
func NewSynthesisActivities(memClient *memory.Client) *SynthesisActivities {
	return &SynthesisActivities{memClient: memClient}
}

// SynthesisInput mirrors workflow.SynthesisInput for activity deserialization.
type SynthesisInput struct {
	Project string   `json:"project"`
	Source  string   `json:"source"`
	Text    string   `json:"text"`
	Kind    string   `json:"kind"`
	Tags    []string `json:"tags,omitempty"`
}

// ExtractedEntity represents an entity found during synthesis.
type ExtractedEntity struct {
	Name       string  `json:"name"`
	EntityType string  `json:"entity_type"`
	Confidence float64 `json:"confidence"`
}

// ExtractedFact represents a fact extracted during synthesis.
type ExtractedFact struct {
	Subject    string  `json:"subject"`
	Predicate  string  `json:"predicate"`
	Object     string  `json:"object"`
	Confidence float64 `json:"confidence"`
}

// ContradictionResult represents a contradiction detection result.
type ContradictionResult struct {
	FactA        ExtractedFact `json:"fact_a"`
	FactB        ExtractedFact `json:"fact_b"`
	Severity     string        `json:"severity"`
	AutoResolved bool          `json:"auto_resolved"`
	QueuedReview bool          `json:"queued_review"`
}

// PersistInput groups all synthesis results for persistence.
type PersistInput struct {
	ChunkID        string                `json:"chunk_id"`
	Project        string                `json:"project"`
	Source         string                `json:"source"`
	Kind           string                `json:"kind"`
	Entities       []ExtractedEntity     `json:"entities"`
	Facts          []ExtractedFact       `json:"facts"`
	Contradictions []ContradictionResult `json:"contradictions"`
}

// ChunkAndEmbedActivity chunks text and generates a chunk ID.
// Stage 1: Creates a deterministic chunk ID from content hash,
// then ingests via memory service for embedding generation.
func (s *SynthesisActivities) ChunkAndEmbedActivity(ctx context.Context, input SynthesisInput) (string, error) {
	logger := slog.Default()

	// Generate deterministic chunk ID from content
	hash := sha256.Sum256([]byte(input.Text))
	chunkID := "chunk-" + hex.EncodeToString(hash[:8])

	logger.Info("chunking text", "chunk_id", chunkID, "text_len", len(input.Text))

	// Ingest via memory service (generates embedding)
	_, err := s.memClient.Ingest(ctx, &memory.IngestRequest{
		Project: input.Project,
		Source:  input.Source,
		Kind:    input.Kind,
		Text:    input.Text,
		Metadata: map[string]interface{}{
			"chunk_id": chunkID,
			"tags":     input.Tags,
		},
	})
	if err != nil {
		return "", fmt.Errorf("ingest chunk: %w", err)
	}

	return chunkID, nil
}

// ExtractEntitiesActivity extracts entities from text using pattern matching
// and wiki-link detection. LLM extraction is a future enhancement.
// Stage 2: Returns entities with confidence scores.
func (s *SynthesisActivities) ExtractEntitiesActivity(ctx context.Context, chunkID string, text string) ([]ExtractedEntity, error) {
	logger := slog.Default()
	logger.Info("extracting entities", "chunk_id", chunkID)

	entities := make([]ExtractedEntity, 0)
	seen := make(map[string]bool)

	// Pattern 1: Wiki-link extraction [[EntityName]]
	wikiPattern := regexp.MustCompile(`\[\[([^\]]+)\]\]`)
	for _, match := range wikiPattern.FindAllStringSubmatch(text, -1) {
		name := strings.TrimSpace(match[1])
		if !seen[name] {
			entities = append(entities, ExtractedEntity{
				Name:       name,
				EntityType: "reference",
				Confidence: 0.95,
			})
			seen[name] = true
		}
	}

	// Pattern 2: Capitalized proper nouns (simple NER)
	properNounPattern := regexp.MustCompile(`\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\b`)
	for _, match := range properNounPattern.FindAllStringSubmatch(text, -1) {
		name := match[1]
		if !seen[name] && !isCommonWord(name) && len(name) > 2 {
			entities = append(entities, ExtractedEntity{
				Name:       name,
				EntityType: classifyEntity(name),
				Confidence: 0.70,
			})
			seen[name] = true
		}
	}

	// Pattern 3: Technical terms (ALL_CAPS or camelCase)
	techPattern := regexp.MustCompile(`\b([A-Z][A-Z_]{2,}|[a-z]+[A-Z][a-zA-Z]+)\b`)
	for _, match := range techPattern.FindAllStringSubmatch(text, -1) {
		name := match[1]
		if !seen[name] {
			entities = append(entities, ExtractedEntity{
				Name:       name,
				EntityType: "technical",
				Confidence: 0.65,
			})
			seen[name] = true
		}
	}

	logger.Info("entities extracted", "count", len(entities))
	return entities, nil
}

// ExtractFactsActivity extracts subject-predicate-object facts from text.
// Stage 3: Pattern-based extraction with entity context.
func (s *SynthesisActivities) ExtractFactsActivity(ctx context.Context, chunkID string, text string, entities []ExtractedEntity) ([]ExtractedFact, error) {
	logger := slog.Default()
	logger.Info("extracting facts", "chunk_id", chunkID, "entity_count", len(entities))

	facts := make([]ExtractedFact, 0)

	// Build entity name set for matching
	entityNames := make(map[string]bool)
	for _, e := range entities {
		entityNames[strings.ToLower(e.Name)] = true
	}

	// Pattern: "X uses/runs/has Y"
	verbPatterns := []struct {
		pattern   *regexp.Regexp
		predicate string
	}{
		{regexp.MustCompile(`(?i)(\w+(?:\s+\w+)?)\s+uses?\s+(.+?)(?:\.|,|$)`), "uses"},
		{regexp.MustCompile(`(?i)(\w+(?:\s+\w+)?)\s+runs?\s+(?:on\s+)?(.+?)(?:\.|,|$)`), "runs_on"},
		{regexp.MustCompile(`(?i)(\w+(?:\s+\w+)?)\s+(?:has|have)\s+(.+?)(?:\.|,|$)`), "has"},
		{regexp.MustCompile(`(?i)(\w+(?:\s+\w+)?)\s+(?:is|are)\s+(.+?)(?:\.|,|$)`), "is"},
		{regexp.MustCompile(`(?i)(\w+(?:\s+\w+)?)\s+(?:depends?\s+on|requires?)\s+(.+?)(?:\.|,|$)`), "depends_on"},
		{regexp.MustCompile(`(?i)(\w+(?:\s+\w+)?)\s+(?:connects?\s+to|talks?\s+to)\s+(.+?)(?:\.|,|$)`), "connects_to"},
	}

	for _, vp := range verbPatterns {
		for _, match := range vp.pattern.FindAllStringSubmatch(text, -1) {
			subject := strings.TrimSpace(match[1])
			object := strings.TrimSpace(match[2])

			// Boost confidence if subject/object are known entities
			confidence := 0.60
			if entityNames[strings.ToLower(subject)] {
				confidence += 0.15
			}
			if entityNames[strings.ToLower(object)] {
				confidence += 0.15
			}

			facts = append(facts, ExtractedFact{
				Subject:    subject,
				Predicate:  vp.predicate,
				Object:     object,
				Confidence: confidence,
			})
		}
	}

	logger.Info("facts extracted", "count", len(facts))
	return facts, nil
}

// DetectContradictionsActivity detects contradictions between new facts
// and existing knowledge. Uses pre-filter to avoid unnecessary comparisons.
// Stage 4: Returns contradictions with severity and review status.
func (s *SynthesisActivities) DetectContradictionsActivity(ctx context.Context, project string, facts []ExtractedFact) ([]ContradictionResult, error) {
	logger := slog.Default()
	logger.Info("detecting contradictions", "project", project, "fact_count", len(facts))

	contradictions := make([]ContradictionResult, 0)

	for _, fact := range facts {
		// Query existing facts about the same subject
		query := fmt.Sprintf("%s %s", fact.Subject, fact.Predicate)
		results, err := s.memClient.Query(ctx, &memory.QueryRequest{
			Project:     project,
			Query:       query,
			LevelFilter: []string{"L1", "L2"},
			Floor:       0.7,
			Limit:       5,
		})
		if err != nil {
			logger.Warn("query for contradictions failed", "error", err, "subject", fact.Subject)
			continue // Non-fatal: skip this fact
		}

		for _, r := range results.Results {
			// Pre-filter: check if result mentions same subject + different object
			if containsSubject(r.Text, fact.Subject) && contradicts(r.Text, fact) {
				severity := "low"
				if r.Score > 0.9 {
					severity = "high"
				} else if r.Score > 0.8 {
					severity = "medium"
				}

				autoResolved := severity == "low"
				contradictions = append(contradictions, ContradictionResult{
					FactA: ExtractedFact{
						Subject:   fact.Subject,
						Predicate: fact.Predicate,
						Object:    r.Text,
					},
					FactB:        fact,
					Severity:     severity,
					AutoResolved: autoResolved,
					QueuedReview: !autoResolved,
				})
			}
		}
	}

	logger.Info("contradictions detected", "count", len(contradictions))
	return contradictions, nil
}

// PersistSynthesisActivity saves all synthesis results to the memory service.
// Stage 5: Persists entities, facts, and queues contradictions for review.
func (s *SynthesisActivities) PersistSynthesisActivity(ctx context.Context, input PersistInput) error {
	logger := slog.Default()
	logger.Info("persisting synthesis results",
		"chunk_id", input.ChunkID,
		"entities", len(input.Entities),
		"facts", len(input.Facts),
		"contradictions", len(input.Contradictions),
	)

	// Persist entities as knowledge records
	for _, entity := range input.Entities {
		_, err := s.memClient.Ingest(ctx, &memory.IngestRequest{
			Project: input.Project,
			Source:  input.Source,
			Kind:    "L1",
			Text:    fmt.Sprintf("Entity: %s (type: %s, confidence: %.2f)", entity.Name, entity.EntityType, entity.Confidence),
			Metadata: map[string]interface{}{
				"chunk_id":    input.ChunkID,
				"entity_type": entity.EntityType,
				"entity_name": entity.Name,
			},
		})
		if err != nil {
			logger.Warn("failed to persist entity", "entity", entity.Name, "error", err)
		}
	}

	// Persist facts
	for _, fact := range input.Facts {
		_, err := s.memClient.Ingest(ctx, &memory.IngestRequest{
			Project: input.Project,
			Source:  input.Source,
			Kind:    "L1",
			Text:    fmt.Sprintf("%s %s %s", fact.Subject, fact.Predicate, fact.Object),
			Metadata: map[string]interface{}{
				"chunk_id":  input.ChunkID,
				"subject":   fact.Subject,
				"predicate": fact.Predicate,
				"object":    fact.Object,
			},
		})
		if err != nil {
			logger.Warn("failed to persist fact", "subject", fact.Subject, "error", err)
		}
	}

	logger.Info("synthesis persisted", "chunk_id", input.ChunkID)
	return nil
}

// --- helpers ---

func isCommonWord(word string) bool {
	common := map[string]bool{
		"The": true, "This": true, "That": true, "These": true,
		"There": true, "When": true, "Where": true, "What": true,
		"Which": true, "How": true, "But": true, "And": true,
		"For": true, "Not": true, "You": true, "All": true,
		"Can": true, "Her": true, "Was": true, "One": true,
		"Our": true, "Out": true, "Are": true, "Has": true,
		"Its": true, "May": true, "New": true, "Now": true,
		"Old": true, "See": true, "Way": true, "Who": true,
	}
	return common[word]
}

func classifyEntity(name string) string {
	toolPatterns := []string{"Kubernetes", "Docker", "Nginx", "Redis", "Postgres", "ArgoCD", "Terraform", "Helm"}
	for _, t := range toolPatterns {
		if strings.EqualFold(name, t) {
			return "tool"
		}
	}
	return "concept"
}

func containsSubject(text, subject string) bool {
	return strings.Contains(strings.ToLower(text), strings.ToLower(subject))
}

func contradicts(existingText string, newFact ExtractedFact) bool {
	// Simple heuristic: if existing text mentions subject with a different value
	// for the same predicate pattern, it might contradict
	lower := strings.ToLower(existingText)
	subjectLower := strings.ToLower(newFact.Subject)
	objectLower := strings.ToLower(newFact.Object)

	// If text mentions subject but NOT the same object, potential contradiction
	return strings.Contains(lower, subjectLower) && !strings.Contains(lower, objectLower)
}