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https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
feat: Add 5 new modules to tackle Knowledge Graph quality problems
- Add template_manager to enforce fixed templates and prevent AI invention - Add seed_manager to initialize KG with existing known data - Add semantic_deduplicator to merge semantically similar entities - Add conflict_detector to flag source disagreements - Add provenance_tracker for full source transparency - Update roadmap with Q5-Q6 milestones for KG quality features - Include new API examples and design principles - Address LinkedIn post challenges: fixed templates, seed data, deduplication, conflict detection
This commit is contained in:
KaifAhmad1
@@ -38,7 +38,7 @@ Each module is a self-contained package with a clear public API.
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* Chunking strategies: sliding window, semantic chunking, structural (section-aware), table-aware splitting.
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* Preserves provenance for each chunk.
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6. **semantic\_extract**
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6. **semantic_extract**
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* Capabilities: NER, relation extraction, event detection, co-reference resolution, entity linking, triple extraction.
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* Backends: spaCy, Stanza, HuggingFace pipelines, LLM prompts (for complex relations).
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@@ -47,7 +47,7 @@ Each module is a self-contained package with a clear public API.
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* OntologyGenerator: infer classes/properties, generate OWL/RDF, map to base ontologies (schema.org, FOAF, DC), versioning.
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8. **triple\_store**
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8. **triple_store**
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* Adapters: Blazegraph, Apache Jena, RDF4J, GraphDB, Virtuoso — export/import, bulk load.
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@@ -57,67 +57,127 @@ Each module is a self-contained package with a clear public API.
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10. **embeddings**
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* SemanticEmbedder: multi-modal embeddings, context windows, pooling strategies, embedding provider adapters (OpenAI, BGE, Llama‑embeddings).
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* SemanticEmbedder: multi-modal embeddings, context windows, pooling strategies, embedding provider adapters (OpenAI, BGE, Llama‑embeddings).
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11. **vector\_store**
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11. **vector_store**
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* Adapters: Pinecone, FAISS, Milvus, Weaviate, Qdrant. Features: namespace, metadata store, hybrid search.
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* Adapters: Pinecone, FAISS, Milvus, Weaviate, Qdrant. Features: namespace, metadata store, hybrid search.
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12. **reasoning**
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* Inference rules, SPARQL-based reasoning, Rete-like rule engine hooks, abductive/deductive inference.
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* Inference rules, SPARQL-based reasoning, Rete-like rule engine hooks, abductive/deductive inference.
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13. **pipeline**
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* PipelineBuilder, failure/retry semantics, parallelism strategies, resource scheduling.
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* PipelineBuilder, failure/retry semantics, parallelism strategies, resource scheduling.
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14. **streaming**
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* Integration: Kafka, Pulsar, RabbitMQ, Kinesis; exactly-once semantics where feasible; checkpoints.
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* Integration: Kafka, Pulsar, RabbitMQ, Kinesis; exactly-once semantics where feasible; checkpoints.
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15. **domains**
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* Domain-specific processors (cybersecurity, biomedical, finance, legal). Each provides templates, mapping rules, ontologies, and extractors.
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* Domain-specific processors (cybersecurity, biomedical, finance, legal). Each provides templates, mapping rules, ontologies, and extractors.
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16. **qa\_rag**
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16. **qa_rag**
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* RAG-optimised tools: semantic chunker, prompt templates, retrieval policies, answer justification, provenance-aware answer builder.
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* RAG-optimised tools: semantic chunker, prompt templates, retrieval policies, answer justification, provenance-aware answer builder.
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17. **agents**
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* Agent manager & orchestration for multiagent workflows, tools for tool‑use, orchestration policies, cost-awareness, sandboxing.
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* Agent manager & orchestration for multiagent workflows, tools for tool‑use, orchestration policies, cost-awareness, sandboxing.
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18. **ui**
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* Web dashboard components: ingestion monitor, KG viewer (graph UI), conflict resolver, analytics dashboard, pipeline editor.
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* Web dashboard components: ingestion monitor, KG viewer (graph UI), conflict resolver, analytics dashboard, pipeline editor.
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19. **monitoring**
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* Metrics, traces (OpenTelemetry), alerts, SLAs, data quality metrics, semantic quality score.
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* Metrics, traces (OpenTelemetry), alerts, SLAs, data quality metrics, semantic quality score.
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20. **quality**
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* QA, validation engine, schema validation, unit tests for extracted triples, confidence thresholds.
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* QA, validation engine, schema validation, unit tests for extracted triples, confidence thresholds.
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21. **security**
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* Access control (RBAC), data masking, PII redaction, audit logs, encryption helpers.
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* Access control (RBAC), data masking, PII redaction, audit logs, encryption helpers.
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22. **deploy**
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* K8s manifests, Helm charts, Dockerfiles, autoscaling policies, GPU scheduling guides.
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* K8s manifests, Helm charts, Dockerfiles, autoscaling policies, GPU scheduling guides.
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23. **cli**
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* Lightweight CLI for quick ingestion, building KB, exporting triples, running QA checks.
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* Lightweight CLI for quick ingestion, building KB, exporting triples, running QA checks.
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24. **examples**
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* Cookbooks and minimal reproducible examples across domains.
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* Cookbooks and minimal reproducible examples across domains.
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25. **docs**
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* Sphinx/ReadTheDocs-ready documentation with tutorials and API reference.
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* Sphinx/ReadTheDocs-ready documentation with tutorials and API reference.
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### 🆕 **NEW MODULES TO TACKLE KNOWLEDGE GRAPH PROBLEMS**
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26. **template_manager**
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* **Purpose:** Enforce fixed templates and predefined schemas to prevent AI from inventing entities/relationships.
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* **Features:**
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- Schema validation against predefined templates
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- Template registry with versioning
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- Constraint enforcement (required fields, data types, relationships)
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- Template inheritance and composition
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- Domain-specific template libraries (finance, healthcare, legal)
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* **Exports:** `TemplateRegistry`, `SchemaValidator`, `ConstraintEngine`, `TemplateBuilder`.
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27. **seed_manager**
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* **Purpose:** Initialize Knowledge Graph with existing, known data to build on "foundation of truth".
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* **Features:**
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- Seed data import from CSV, JSON, databases
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- Known entity registration (products, departments, employees)
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- Seed data validation and conflict detection
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- Incremental seed data updates
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- Seed data provenance tracking
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* **Exports:** `SeedManager`, `SeedValidator`, `SeedImporter`, `SeedRegistry`.
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28. **semantic_deduplicator**
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* **Purpose:** Clean up and merge semantically similar entities to prevent graph messiness.
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* **Features:**
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- Semantic similarity detection using embeddings
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- Fuzzy matching algorithms (Levenshtein, Jaro-Winkler)
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- Entity clustering and grouping
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- Merge strategies and conflict resolution
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- Duplicate detection confidence scoring
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- Batch deduplication with rollback support
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* **Exports:** `SemanticDeduplicator`, `SimilarityEngine`, `MergeEngine`, `ClusterAnalyzer`.
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29. **conflict_detector**
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* **Purpose:** Flag disagreements when different sources provide conflicting information.
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* **Features:**
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- Multi-source conflict detection
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- Conflict severity classification (minor, moderate, critical)
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- Source document tracking and highlighting
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- Conflict resolution workflow
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- Conflict history and audit trail
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- Automated conflict alerts and notifications
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* **Exports:** `ConflictDetector`, `ConflictResolver`, `ConflictTracker`, `AlertManager`.
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30. **provenance_tracker**
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* **Purpose:** Track the exact source of every piece of information for transparency and investigation.
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* **Features:**
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- Source document linking (file, page, paragraph, line)
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- Extraction timestamp and confidence scores
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- Processing pipeline tracking
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- Source credibility scoring
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- Provenance visualization in UI
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- Export capabilities for audit purposes
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* **Exports:** `ProvenanceTracker`, `SourceLinker`, `CredibilityScorer`, `ProvenanceExporter`.
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---
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@@ -129,6 +189,14 @@ Each module is a self-contained package with a clear public API.
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* **As an ML engineer** I want RAG-ready semantic chunks with embeddings and retrieval tuning.
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* **As a security engineer** I want PII detection and masking before storing embeddings.
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### 🆕 **NEW USER STORIES FOR KNOWLEDGE GRAPH PROBLEMS**
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* **As a knowledge engineer** I want to enforce fixed templates so the AI doesn't invent new entities or relationships.
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* **As a domain expert** I want to seed the Knowledge Graph with known data so the AI builds on existing knowledge.
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* **As a data quality analyst** I want automatic deduplication to merge semantically similar entities and keep the graph clean.
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* **As a compliance officer** I want to see exactly where conflicting information comes from so I can investigate discrepancies.
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* **As a business user** I want to trust that the Knowledge Graph represents the single source of truth without duplicates or conflicts.
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---
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## 4. Recommended tech stack (per module)
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@@ -145,6 +213,13 @@ Each module is a self-contained package with a clear public API.
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* Monitoring: Prometheus + Grafana + OpenTelemetry
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* CI/CD: GitHub Actions, dependabot
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### 🆕 **NEW TECH STACK FOR KNOWLEDGE GRAPH MODULES**
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* **Template Management:** JSON Schema, Pydantic, Cerberus for validation
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* **Semantic Deduplication:** SentenceTransformers, FAISS, scikit-learn clustering
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* **Conflict Detection:** Fuzzy matching with `fuzzywuzzy`, `rapidfuzz`
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* **Provenance Tracking:** GraphQL, Neo4j Cypher, RDF/SPARQL
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---
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## 5. Architecture — dataflow diagram (Mermaid)
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@@ -163,6 +238,17 @@ flowchart LR
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H --> K[Analytics / UI]
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J --> K
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I --> K
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%% NEW KNOWLEDGE GRAPH MODULES
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L[Seed Data] --> M(Seed Manager)
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M --> H
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N[Template Registry] --> O(Template Manager)
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O --> D
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P[Conflict Detector] --> Q(Conflict Resolution)
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Q --> H
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R[Semantic Deduplicator] --> H
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S[Provenance Tracker] --> T(Provenance Store)
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T --> K
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```
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---
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@@ -184,6 +270,15 @@ Q3 — Streaming & Domains
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Q4 — Agents & Enterprise
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- multi-agent orchestration, advanced reasoning, RBAC, production K8s charts, enterprise docs
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🆕 Q5 — Knowledge Graph Quality & Trust
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- template_manager, seed_manager, semantic_deduplicator, conflict_detector
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- provenance_tracker, advanced conflict resolution UI
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- template libraries for common domains
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🆕 Q6 — Enterprise Knowledge Graph Features
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- multi-tenant template management, advanced conflict workflows
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- compliance reporting, audit trails, enterprise integrations
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```
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---
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@@ -205,6 +300,11 @@ ontology ██████ 6
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ui █████ 5
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monitoring █████ 5
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agents ████ 4
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🆕 template_manager ████████ 8
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🆕 seed_manager ███████ 7
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🆕 semantic_deduplicator ████████ 8
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🆕 conflict_detector ████████ 8
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🆕 provenance_tracker ███████ 7
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```
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### 7.2 Gantt-style timeline (Mermaid)
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@@ -224,6 +324,12 @@ gantt
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Streaming & Domains :a6, after a5, 60d
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Monitoring & QA :a7, after a6, 30d
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Agents & Enterprise :a8, after a7, 60d
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🆕 section KG Quality
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Template Manager :a9, after a8, 45d
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Seed Manager :a10, after a9, 30d
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Semantic Deduplicator :a11, after a10, 45d
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Conflict Detector :a12, after a11, 45d
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Provenance Tracker :a13, after a12, 30d
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```
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---
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@@ -237,6 +343,14 @@ gantt
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* **Test coverage**: unit tests for extractors, golden files for parsers, and integration tests that simulate full pipelines.
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* **Performance**: make parsing idempotent and resumable; add caching at the ingest and embedder layers.
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### 🆕 **NEW DESIGN PRINCIPLES FOR KNOWLEDGE GRAPH QUALITY**
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* **Template-first**: Define schemas before ingestion to prevent AI invention of entities/relationships.
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* **Seed-before-extract**: Always start with known data to establish foundation of truth.
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* **Conflict-aware**: Design for conflict detection from day one, not as an afterthought.
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* **Provenance-everywhere**: Track source of every piece of information for transparency.
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* **Deduplication-continuous**: Implement semantic deduplication as a continuous process, not just during ingestion.
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---
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## 9. CI/CD, release & contributor guidelines
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@@ -267,6 +381,12 @@ gantt
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/infra/k8s/
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/helm/semanticore/
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/.github/workflows/
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🆕 /packages/template_manager/
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🆕 /packages/seed_manager/
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🆕 /packages/semantic_deduplicator/
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🆕 /packages/conflict_detector/
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🆕 /packages/provenance_tracker/
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```
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---
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@@ -280,6 +400,14 @@ gantt
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5. Implement `SemanticEmbedder` with local huggingface mini-embedding and persist to FAISS.
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6. Add basic unit tests and a demo notebook that shows E2E from PDF -> embeddings -> search.
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### 🆕 **NEW STARTER TASKS FOR KNOWLEDGE GRAPH QUALITY**
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7. Implement `template_manager` with basic JSON schema validation.
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8. Create `seed_manager` for importing known entities from CSV/JSON.
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9. Build `semantic_deduplicator` using sentence transformers for similarity detection.
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10. Develop `conflict_detector` to identify source disagreements.
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11. Add `provenance_tracker` to link all information to source documents.
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---
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## 12. Example API usage (reference)
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@@ -297,6 +425,52 @@ kb = core.build_knowledge_base(["./sample_docs/"])
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print(kb.search("key trends"))
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```
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### 🆕 **NEW API EXAMPLES FOR KNOWLEDGE GRAPH QUALITY**
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```python
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from semanticore import SemantiCore
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from semanticore.template_manager import TemplateRegistry
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from semanticore.seed_manager import SeedManager
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from semanticore.semantic_deduplicator import SemanticDeduplicator
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from semanticore.conflict_detector import ConflictDetector
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# Initialize with quality modules
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core = SemantiCore(
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config_path="./config.yaml",
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template_manager=TemplateRegistry("./templates/"),
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seed_manager=SeedManager("./seed_data/"),
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deduplicator=SemanticDeduplicator(),
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conflict_detector=ConflictDetector()
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)
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# Load predefined template
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template = core.template_manager.load_template("financial_report")
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# Seed with known data
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core.seed_manager.load_seed_data("departments.csv", "employees.json")
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# Build knowledge base with quality controls
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kb = core.build_knowledge_base(
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["./documents/"],
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template=template,
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enable_deduplication=True,
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enable_conflict_detection=True
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)
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# Check for conflicts
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conflicts = core.conflict_detector.get_conflicts()
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for conflict in conflicts:
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print(f"Conflict: {conflict.description}")
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print(f"Sources: {conflict.sources}")
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print(f"Severity: {conflict.severity}")
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# Get provenance for any entity
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provenance = core.provenance_tracker.get_provenance("Q1_Sales_Report")
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print(f"Source: {provenance.source_document}")
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print(f"Extracted: {provenance.extraction_timestamp}")
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print(f"Confidence: {provenance.confidence_score}")
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```
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---
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## 13. Next steps / how I can help
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@@ -307,6 +481,12 @@ If you want, I can:
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* Generate a prioritized ticket backlog (Jira/GitHub issues format).
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* Create starter code for `core`, `file` ingestor, `pdf` parser and a demo notebook.
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---
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### 🆕 **NEW NEXT STEPS FOR KNOWLEDGE GRAPH QUALITY**
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* Create detailed specifications for the 5 new Knowledge Graph quality modules.
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* Design the conflict resolution workflow and UI components.
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* Develop template libraries for common domains (finance, healthcare, legal).
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* Build integration examples showing how these modules work together.
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* Create quality metrics and validation frameworks for Knowledge Graph trustworthiness.
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*End of file.*
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Block a user