semantica/SemantiCore_Development_Roadmap.md

SemantiCore — Modules, Features, Roadmap & Visuals

Purpose: A concise engineering guide listing every module, feature, responsibilities, recommended tech, and visual diagrams to help you design, implement, and ship the SemantiCore open‑source semantic toolkit.

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1. High-level overview

SemantiCore transforms raw, multi-format input into semantic knowledge (triples, ontologies, graphs, embeddings) suitable for RAG, multi-agent systems, GraphRAG, and domain-specific AI pipelines. The architecture is modular, pluggable, and cloud-native.

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2. Modules (catalog)

Each module is a self-contained package with a clear public API.

1. core

   • Responsibilities: orchestration, pipeline runner, configuration, plugin registry, lifecycle management.
   • Exports: SemantiCore, PipelineBuilder, Config, PluginManager.

2. ingest

   • Submodules: file, web, feed, stream, repo, email, db_export.
   • Features: format detection, connector registry, backpressure support, incremental ingestion, resume tokens.

3. parse

   • Submodules: pdf, docx, pptx, excel, html, jsonl, csv, latex, images, tables.
   • Features: layout-aware extraction, OCR integration (Tesseract/Google Vision), table extraction (Camelot, Tabula), figure extraction.

4. normalize

   • Responsibilities: text cleaning, language detection, encoding normalization, named entity canonicalization, date normalization.

5. split

   • Chunking strategies: sliding window, semantic chunking, structural (section-aware), table-aware splitting.
   • Preserves provenance for each chunk.

6. semantic_extract

   • Capabilities: NER, relation extraction, event detection, co-reference resolution, entity linking, triple extraction.
   • Backends: spaCy, Stanza, HuggingFace pipelines, LLM prompts (for complex relations).

7. ontology

   • OntologyGenerator: infer classes/properties, generate OWL/RDF, map to base ontologies (schema.org, FOAF, DC), versioning.

8. triple_store

   • Adapters: Blazegraph, Apache Jena, RDF4J, GraphDB, Virtuoso — export/import, bulk load.

9. kg (knowledge graph)

   • Graph builder, entity resolution, deduplication, seed manager, provenance, conflict detector, conflict resolution UI hooks.

10. embeddings

    • SemanticEmbedder: multi-modal embeddings, context windows, pooling strategies, embedding provider adapters (OpenAI, BGE, Llama‑embeddings).

11. vector_store

    • Adapters: Pinecone, FAISS, Milvus, Weaviate, Qdrant. Features: namespace, metadata store, hybrid search.

12. reasoning

    • Inference rules, SPARQL-based reasoning, Rete-like rule engine hooks, abductive/deductive inference.

13. pipeline

    • PipelineBuilder, failure/retry semantics, parallelism strategies, resource scheduling.

14. streaming

    • Integration: Kafka, Pulsar, RabbitMQ, Kinesis; exactly-once semantics where feasible; checkpoints.

15. domains

    • Domain-specific processors (cybersecurity, biomedical, finance, legal). Each provides templates, mapping rules, ontologies, and extractors.

16. qa_rag

    • RAG-optimised tools: semantic chunker, prompt templates, retrieval policies, answer justification, provenance-aware answer builder.

17. agents

    • Agent manager & orchestration for multiagent workflows, tools for tool‑use, orchestration policies, cost-awareness, sandboxing.

18. ui

    • Web dashboard components: ingestion monitor, KG viewer (graph UI), conflict resolver, analytics dashboard, pipeline editor.

19. monitoring

    • Metrics, traces (OpenTelemetry), alerts, SLAs, data quality metrics, semantic quality score.

20. quality

    • QA, validation engine, schema validation, unit tests for extracted triples, confidence thresholds.

21. security

    • Access control (RBAC), data masking, PII redaction, audit logs, encryption helpers.

22. deploy

    • K8s manifests, Helm charts, Dockerfiles, autoscaling policies, GPU scheduling guides.

23. cli

    • Lightweight CLI for quick ingestion, building KB, exporting triples, running QA checks.

24. examples

    • Cookbooks and minimal reproducible examples across domains.

25. docs

    • Sphinx/ReadTheDocs-ready documentation with tutorials and API reference.

🆕 NEW MODULES TO TACKLE KNOWLEDGE GRAPH PROBLEMS

26. template_manager

    • Purpose: Enforce fixed templates and predefined schemas to prevent AI from inventing entities/relationships.
    • Features:
      • Schema validation against predefined templates
      • Template registry with versioning
      • Constraint enforcement (required fields, data types, relationships)
      • Template inheritance and composition
      • Domain-specific template libraries (finance, healthcare, legal)
    • Exports: TemplateRegistry, SchemaValidator, ConstraintEngine, TemplateBuilder.

27. seed_manager

    • Purpose: Initialize Knowledge Graph with existing, known data to build on "foundation of truth".
    • Features:
      • Seed data import from CSV, JSON, databases
      • Known entity registration (products, departments, employees)
      • Seed data validation and conflict detection
      • Incremental seed data updates
      • Seed data provenance tracking
    • Exports: SeedManager, SeedValidator, SeedImporter, SeedRegistry.

28. semantic_deduplicator

    • Purpose: Clean up and merge semantically similar entities to prevent graph messiness.
    • Features:
      • Semantic similarity detection using embeddings
      • Fuzzy matching algorithms (Levenshtein, Jaro-Winkler)
      • Entity clustering and grouping
      • Merge strategies and conflict resolution
      • Duplicate detection confidence scoring
      • Batch deduplication with rollback support
    • Exports: SemanticDeduplicator, SimilarityEngine, MergeEngine, ClusterAnalyzer.

29. conflict_detector

    • Purpose: Flag disagreements when different sources provide conflicting information.
    • Features:
      • Multi-source conflict detection
      • Conflict severity classification (minor, moderate, critical)
      • Source document tracking and highlighting
      • Conflict resolution workflow
      • Conflict history and audit trail
      • Automated conflict alerts and notifications
    • Exports: ConflictDetector, ConflictResolver, ConflictTracker, AlertManager.

30. provenance_tracker

    • Purpose: Track the exact source of every piece of information for transparency and investigation.
    • Features:
      • Source document linking (file, page, paragraph, line)
      • Extraction timestamp and confidence scores
      • Processing pipeline tracking
      • Source credibility scoring
      • Provenance visualization in UI
      • Export capabilities for audit purposes
    • Exports: ProvenanceTracker, SourceLinker, CredibilityScorer, ProvenanceExporter.

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3. Features (by user story)

• As a data engineer I want pluggable ingestors so I can add new connectors quickly.
• As an ontology engineer I want automatic ontology suggestions and human-in-the-loop refinement.
• As an analyst I want a KG browser that shows provenance and conflict highlights.
• As an ML engineer I want RAG-ready semantic chunks with embeddings and retrieval tuning.
• As a security engineer I want PII detection and masking before storing embeddings.

🆕 NEW USER STORIES FOR KNOWLEDGE GRAPH PROBLEMS

• As a knowledge engineer I want to enforce fixed templates so the AI doesn't invent new entities or relationships.
• As a domain expert I want to seed the Knowledge Graph with known data so the AI builds on existing knowledge.
• As a data quality analyst I want automatic deduplication to merge semantically similar entities and keep the graph clean.
• As a compliance officer I want to see exactly where conflicting information comes from so I can investigate discrepancies.
• 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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4. Recommended tech stack (per module)

• Language: Python 3.10+ (type hints, pydantic models)
• Async: asyncio + anyio for I/O bound ingestion
• Parsing: pdfplumber, PyMuPDF, python-docx, openpyxl, beautifulsoup4
• OCR: tesserocr / Google Vision or AWS Textract adapters
• NLP: spaCy, transformers, flair, LLM adapters
• Vector stores: FAISS (local), Pinecone / Qdrant / Milvus
• KG: Neo4j for property graph + RDF store for triples
• Streaming: Kafka / Pulsar
• Infra: Docker + Kubernetes + Helm
• Monitoring: Prometheus + Grafana + OpenTelemetry
• CI/CD: GitHub Actions, dependabot

🆕 NEW TECH STACK FOR KNOWLEDGE GRAPH MODULES

• Template Management: JSON Schema, Pydantic, Cerberus for validation
• Semantic Deduplication: SentenceTransformers, FAISS, scikit-learn clustering
• Conflict Detection: Fuzzy matching with fuzzywuzzy, rapidfuzz
• Provenance Tracking: GraphQL, Neo4j Cypher, RDF/SPARQL

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5. Architecture — dataflow diagram (Mermaid)

flowchart LR
  A[Sources] -->|file/web/db/feeds| B(Ingest Layer)
  B --> C(Parse & Normalize)
  C --> D(Semantic Extract)
  D --> E{Branch}
  E -->|triples| F(Triple Store / RDF)
  E -->|embeddings| G(Vector Store)
  E -->|kg nodes| H(KG / Graph DB)
  F --> I[Reasoning & Ontology]
  G --> J[RAG & Agents]
  H --> K[Analytics / UI]
  J --> K
  I --> K
  
  %% NEW KNOWLEDGE GRAPH MODULES
  L[Seed Data] --> M(Seed Manager)
  M --> H
  N[Template Registry] --> O(Template Manager)
  O --> D
  P[Conflict Detector] --> Q(Conflict Resolution)
  Q --> H
  R[Semantic Deduplicator] --> H
  S[Provenance Tracker] --> T(Provenance Store)
  T --> K

---

6. Roadmap (quarterly milestones)

Q1 — MVP
 - core, ingest(file/web), parse(pdf/docx/html), split, embeddings(local FAISS), basic vector_store adapter
 - simple CLI

Q2 — KG + Triples
 - triple_extractor, ontology generator, triple store adapter, deduplication
 - basic KG UI

Q3 — Streaming & Domains
 - streaming connectors, feed processor, domain processors (cyber, biomedical)
 - monitoring & QA

Q4 — Agents & Enterprise
 - multi-agent orchestration, advanced reasoning, RBAC, production K8s charts, enterprise docs

🆕 Q5 — Knowledge Graph Quality & Trust
 - template_manager, seed_manager, semantic_deduplicator, conflict_detector
 - provenance_tracker, advanced conflict resolution UI
 - template libraries for common domains

🆕 Q6 — Enterprise Knowledge Graph Features
 - multi-tenant template management, advanced conflict workflows
 - compliance reporting, audit trails, enterprise integrations

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7. Visual charts

7.1 Module size and priority (simple bar chart)

Modules Priority
core           ██████████ 10
ingest         █████████  9
parse          ████████   8
semantic_extract ████████ 8
embeddings     ███████    7
vector_store   ███████    7
kg             ████████   8
ontology       ██████     6
ui             █████      5
monitoring     █████      5
agents         ████       4
🆕 template_manager ████████ 8
🆕 seed_manager     ███████  7
🆕 semantic_deduplicator ████████ 8
🆕 conflict_detector     ████████ 8
🆕 provenance_tracker    ███████  7

7.2 Gantt-style timeline (Mermaid)

gantt
    dateFormat  YYYY-MM-DD
    title SemantiCore high-level roadmap
    section MVP
    Core & Ingest       :done, a1, 2025-01-01, 45d
    Parsing & Chunking  :done, a2, after a1, 30d
    Embeddings & FAISS  :done, a3, after a2, 20d
    section KG
    Triple extractor    :a4, after a3, 30d
    Ontology generator  :a5, after a4, 20d
    section Scale
    Streaming & Domains :a6, after a5, 60d
    Monitoring & QA     :a7, after a6, 30d
    Agents & Enterprise :a8, after a7, 60d
    🆕 section KG Quality
    Template Manager    :a9, after a8, 45d
    Seed Manager        :a10, after a9, 30d
    Semantic Deduplicator :a11, after a10, 45d
    Conflict Detector   :a12, after a11, 45d
    Provenance Tracker  :a13, after a12, 30d

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8. Design notes & implementation guidance

• Start small: implement core, ingest.file, parse.pdf, split and embeddings with local FAISS. Aim for end-to-end pipeline that takes a PDF and returns embeddings + triples.
• Provenance-first: every chunk, triple, and entity MUST carry source, offset, and confidence.
• Schema-first approach: let teams register Pydantic models (business entities) early — this simplifies downstream validation and UX.
• Human-in-loop: provide UIs for ontology review, conflict resolution, and triple acceptance.
• Test coverage: unit tests for extractors, golden files for parsers, and integration tests that simulate full pipelines.
• Performance: make parsing idempotent and resumable; add caching at the ingest and embedder layers.

🆕 NEW DESIGN PRINCIPLES FOR KNOWLEDGE GRAPH QUALITY

• Template-first: Define schemas before ingestion to prevent AI invention of entities/relationships.
• Seed-before-extract: Always start with known data to establish foundation of truth.
• Conflict-aware: Design for conflict detection from day one, not as an afterthought.
• Provenance-everywhere: Track source of every piece of information for transparency.
• Deduplication-continuous: Implement semantic deduplication as a continuous process, not just during ingestion.

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9. CI/CD, release & contributor guidelines

• Monorepo with packages under packages/ using poetry or flit.
• Pre-commit hooks (black, isort, ruff, mypy), unit tests on GitHub Actions, release automation for PyPI and DockerHub.
• CONTRIBUTING.md with code style, PR checklist, and issue template.

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10. Deliverables (file list for initial repo scaffolding)

/README.md
/pyproject.toml
/packages/core/
/packages/ingest/
/packages/parse/
/packages/split/
/packages/semantic_extract/
/packages/embeddings/
/packages/vector_store/
/packages/kg/
/packages/ontology/
/packages/cli/
/docs/
/examples/
/infra/k8s/
/helm/semanticore/
/.github/workflows/

🆕 /packages/template_manager/
🆕 /packages/seed_manager/
🆕 /packages/semantic_deduplicator/
🆕 /packages/conflict_detector/
🆕 /packages/provenance_tracker/

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11. Starter tasks (first sprint — 2 weeks)

1. Scaffold repo and package layout.
2. Implement core and a file ingestor that accepts local directories and single files.
3. Implement pdf parser using pdfplumber that returns structured blocks and simple tables.
4. Implement split with structural and sliding-window modes.
5. Implement SemanticEmbedder with local huggingface mini-embedding and persist to FAISS.
6. Add basic unit tests and a demo notebook that shows E2E from PDF -> embeddings -> search.

🆕 NEW STARTER TASKS FOR KNOWLEDGE GRAPH QUALITY

7. Implement template_manager with basic JSON schema validation.
8. Create seed_manager for importing known entities from CSV/JSON.
9. Build semantic_deduplicator using sentence transformers for similarity detection.
10. Develop conflict_detector to identify source disagreements.
11. Add provenance_tracker to link all information to source documents.

---

12. Example API usage (reference)

from semanticore import SemantiCore

core = SemantiCore(
    config_path="./config.yaml",
    llm_provider="local",
    vector_store="faiss"
)

kb = core.build_knowledge_base(["./sample_docs/"])
print(kb.search("key trends"))

🆕 NEW API EXAMPLES FOR KNOWLEDGE GRAPH QUALITY

from semanticore import SemantiCore
from semanticore.template_manager import TemplateRegistry
from semanticore.seed_manager import SeedManager
from semanticore.semantic_deduplicator import SemanticDeduplicator
from semanticore.conflict_detector import ConflictDetector

# Initialize with quality modules
core = SemantiCore(
    config_path="./config.yaml",
    template_manager=TemplateRegistry("./templates/"),
    seed_manager=SeedManager("./seed_data/"),
    deduplicator=SemanticDeduplicator(),
    conflict_detector=ConflictDetector()
)

# Load predefined template
template = core.template_manager.load_template("financial_report")

# Seed with known data
core.seed_manager.load_seed_data("departments.csv", "employees.json")

# Build knowledge base with quality controls
kb = core.build_knowledge_base(
    ["./documents/"],
    template=template,
    enable_deduplication=True,
    enable_conflict_detection=True
)

# Check for conflicts
conflicts = core.conflict_detector.get_conflicts()
for conflict in conflicts:
    print(f"Conflict: {conflict.description}")
    print(f"Sources: {conflict.sources}")
    print(f"Severity: {conflict.severity}")

# Get provenance for any entity
provenance = core.provenance_tracker.get_provenance("Q1_Sales_Report")
print(f"Source: {provenance.source_document}")
print(f"Extracted: {provenance.extraction_timestamp}")
print(f"Confidence: {provenance.confidence_score}")

---

13. Next steps / how I can help

If you want, I can:

• Expand each module into a README.md + __init__.py template for the repo scaffold.
• Generate a prioritized ticket backlog (Jira/GitHub issues format).
• Create starter code for core, file ingestor, pdf parser and a demo notebook.

🆕 NEW NEXT STEPS FOR KNOWLEDGE GRAPH QUALITY

• Create detailed specifications for the 5 new Knowledge Graph quality modules.
• Design the conflict resolution workflow and UI components.
• Develop template libraries for common domains (finance, healthcare, legal).
• Build integration examples showing how these modules work together.
• Create quality metrics and validation frameworks for Knowledge Graph trustworthiness.