LLM

LLM Architecture (Flagship)

What We Aim to Build

A multi-perspective, source-grounded LLM specialized in Jainism that:

• Cites specific sources for every claim ("Tattvartha Sutra 5.21 states..." not "Jainism says...")
• Tags every claim with epistemic status (empirical, doctrinal, disputed, etc.)
• Uses Jain epistemological frameworks (Anekantavada, Syadvada, Nayavada) as core reasoning

Three-Tier Architecture

TIER 1 - RAG Foundation (80% of value)
├── Local open-source 70B parameter LLM
├── Vector database with Jain knowledge base
├── Retrieval orchestration + system prompt
└── Source-grounded retrieval with metadata filtering

TIER 2 - Fine-Tuned Reasoning (15%)
├── Parameter-efficient fine-tuning on 70B model
├── 500-1000 gold-standard Q&A training pairs
└── Epistemic tagging + multi-perspective behavior trained in

TIER 3 - Full Knowledge Engine (5%)
├── Knowledge graph (concepts, texts, typed relationships)
├── Two-pass verification pipeline (citation fact-checking)
└── Syadvada/Saptabhangi qualified truth framework

No cloud dependency. Corpus stays on-machine.

Truth-Grounding

Knowledge Base

Markdown vault with structured metadata, version-controlled. Modified Johnny Decimal:

10-19 Canonical Texts    
20-29 Commentaries       
30-39 Doctrinal Topics
40-49 Comparative        
50-59 Historical          
60-69 Training Data
90-99 Meta (taxonomy, authority definitions, scripts)

Phase 3: LLM Foundation - Knowledge Base & RAG - 5-6 weeks | INR 3,50,000 - 4,50,000

Knowledge Vault & Architecture US

Knowledge Base Population JOINT

Ingestion Pipeline US

RAG System US

Q&A Demo JOINT

Checklist:

• Schema validation passes 100%
• Ingestion processes full vault in <5 minutes
• 10 questions return sourced multi-perspective answers
• Retrieval latency <5 seconds on 8B model
• Domain expert sign-off

Client Dependency: Domain expert 15-20 hrs/week.

Phase 4: LLM Intelligence - Epistemic & Knowledge Graph - 10-12 weeks | INR 5,75,000 - 6,50,000

Epistemic Tagging JOINT

Source Authority US

Knowledge Graph US

Hybrid Retrieval US

Evaluation Framework JOINT

Checklist:

• Every response includes epistemic tags
• 5 Digambara/Shvetambara disputes - both views surfaced
• 200+ nodes, 500+ edges
• Hybrid outperforms vector-only
• Baseline score documented

Client Dependency: Domain expert 15-18 hrs/week.

Phase 6: LLM Fine-Tuning & Verification - 8-12 weeks | INR 3,50,000 - 4,50,000

Training Data JOINT

Fine-Tuning US

Verification Pipeline US

Syadvada Framework US

Checklist:

• Fine-tuned 70B scores 20%+ higher than base
• Verification catches 80%+ seeded hallucinations
• Never "Jainism says..." without specific source
• Inference <15 seconds for 70B
• Syadvada expert-approved

Client Dependency: Domain expert 10-15 hrs/week. Most client-intensive phase.

Phase 7: Githarth Ganga R&D AI & Integrations - 4-6 weeks | INR 4,75,000 - 5,75,000

Scripture Research Tool US + JOINT

Document Purpose

Full knowledge export from architecture planning session. Covers vision, architecture, hardware, tech stack, truth-grounding methodology, knowledge organization, and implementation roadmap.

1. PROJECT VISION

What We're Building

A multi-perspective, source-grounded LLM system specialized in Jainism that:

• Answers questions from multiple epistemological perspectives (Jain, scientific, philosophical, etc.)
• Cites specific sources for every claim (not "Jainism says..." but "Tattvartha Sutra 5.21 states...")
• Tags every claim with its epistemic status (empirical, doctrinal, disputed, etc.)
• Uses Jain epistemological frameworks (Anekantavada, Syadvada, Nayavada) as core reasoning architecture
• Is grounded in truth through retrieval-verified source attribution

Example Output Format

User: "Is the Earth a globe?"

System Response:

• From Jain cosmological perspective (Doctrinal/Scriptural): Jain cosmology as described in the Tiloyapannatti and Jambudvipa Prajnapti describes a fundamentally different cosmological structure with Jambudvipa as a flat circular continent at the center of Middle World (Madhya Loka). [Source: Tiloyapannatti, X.XX]
• From scientific perspective (Empirically Verified): Modern astrophysics and direct observation confirm Earth is an oblate spheroid. [Source: NASA, direct satellite imagery]
• Epistemological note: These operate in different knowledge systems making different types of claims. Jain cosmology is a doctrinal framework; scientific cosmology is an empirical framework. (Syad asti — in some respect both hold truth within their respective domains.)

What Makes This Different From Grok

• Grok's "truth-seeking" = fewer content filters, freer speech
• This project = actual source grounding, epistemic transparency, formal multi-valued truth framework
• Jain Saptabhangi (seven-fold predication) as a logic system for qualified truth claims
• Every claim traceable to a specific text, scholar, or empirical source

2. ARCHITECTURE OVERVIEW

Three-Tier Approach (Build Incrementally)

TIER 1 — RAG Foundation (Gets 80% of value)
├── Base LLM (local, open-source)
├── Vector database with Jain knowledge base
├── LlamaIndex orchestration
├── Well-crafted system prompt enforcing multi-perspective format
└── Source-grounded retrieval

TIER 2 — Fine-Tuned Reasoning (Next 15%)
├── LoRA/QLoRA fine-tuning on 70B model
├── 500-1000 gold-standard Q&A training pairs
├── Model learns Jain epistemological reasoning style
├── Multi-perspective response structure baked in
└── Epistemic tagging behavior trained

TIER 3 — Full Knowledge Engine (Final 5%, "another level")
├── Neo4j knowledge graph (concepts, texts, relationships)
├── Multi-agent architecture (Jain agent, Science agent, Synthesis agent)
├── RARR verification pipeline (retrieval-based fact-checking)
├── Syadvada/Saptabhangi response framework
└── Structured source hierarchy with conflict surfacing

3. HARDWARE (Available)

What This Means

• No cloud dependency needed — entire dev loop runs locally
• No API costs for inference during development
• Fine-tuning a 70B model: ~4-8 hours per QLoRA run locally
• Can run inference server + vector DB + knowledge graph simultaneously
• Corpus stays on-machine (good for sacred text sensitivity)

Actual Costs

• Software: $0 (all open-source)
• Electricity: single-digit dollars per fine-tuning run
• Optional: $50-100 API budget to benchmark against Claude/GPT-4
• Primary investment: time and domain expertise

4. TECH STACK

Core Components

Why LlamaIndex Over LangChain

• More purpose-built for knowledge-heavy retrieval
• Better handling of structured sources and metadata
• LangChain is more general-purpose and can feel over-engineered for RAG-first projects

5. KNOWLEDGE ORGANIZATION (Critical Prerequisite)

Design Principles

The knowledge base is the foundation everything else depends on. Get this wrong and no amount of fine-tuning saves you. Get this right and even basic RAG performs impressively.

The knowledge base must be:

• Human-readable and editable — you and domain experts review and add to it constantly
• Machine-parseable — ingestion pipeline extracts metadata cleanly
• Relationship-aware — Jain concepts are deeply interconnected (karma → jiva → gunasthana → moksha)

Approach: Obsidian Vault as Source of Truth

Markdown files with YAML frontmatter and [[wikilinks]]:

• Plain files on filesystem — no vendor lock-in
• Easy to version control with git
• Easy to write ingestion scripts against
• Domain experts can contribute without technical knowledge
• Obsidian's graph view gives visual exploration of concept relationships

Frontmatter Schema (Per Entry)

---
id: tattvartha-sutra-5-21
title: "Nature of Karma Bondage"
type: sutra | commentary | scholarly | modern | practice
source:
  text: "Tattvartha Sutra"
  author: "Umasvati"
  chapter: 5
  verse: 21
  tradition: both | digambara | shvetambara
  authority_level: 1  # 1=canonical, 2=classical commentary, 3=scholarly, 4=modern
  date_range: "2nd-5th century CE"
  language_original: prakrit
  translator: "Nathmal Tatia"
epistemic_tag: doctrinal | empirical | scholarly_consensus | disputed_internal | philosophical
topics: [karma, bondage, jiva, ajiva]
related:
  - "[[tattvartha-sutra-5-20]]"
  - "[[sarvarthasiddhi-ch5]]"
  - "[[karma-theory-overview]]"
counter_positions:
  - "[[digambara-view-karma-subtypes]]"
modern_parallels:
  - "[[conservation-of-energy]]"
status: draft | reviewed | verified
reviewed_by: ""
last_updated: 2026-04-06
---

(Body content: actual teaching, translation, explanation below the frontmatter)

File Structure — Modified Johnny Decimal

Categories are domain-specific with room for expansion. The numbering leaves gaps for categories discovered later.

10-19 CANONICAL TEXTS
  11 Agamas
    11.01 Acharanga Sutra/
    11.02 Sutrakritanga/
    11.03 Uttaradhyayana Sutra/
  12 Philosophical Treatises
    12.01 Tattvartha Sutra/
      12.01-ch01-overview.md
      12.01-ch01-v01.md
      12.01-ch01-v02.md
    12.02 Samayasara/
    12.03 Pravachanasara/
  13 Cosmological Texts
    13.01 Tiloyapannatti/
    13.02 Jambudvipa Prajnapti/

20-29 COMMENTARIES
  21 Classical Commentaries
    21.01 Sarvarthasiddhi/
    21.02 Tatparya Vritti/
    21.03 Dhavalaa/
  22 Medieval Commentaries
  23 Modern Commentaries

30-39 DOCTRINAL TOPICS
  31 Metaphysics
    31.01-jiva.md
    31.02-ajiva.md
    31.03-karma-theory.md
    31.04-gunasthana.md
  32 Epistemology
    32.01-anekantavada.md
    32.02-syadvada.md
    32.03-nayavada.md
    32.04-pramana.md
  33 Ethics
    33.01-ahimsa.md
    33.02-five-vows.md
  34 Cosmology
    34.01-loka-structure.md
    34.02-kalachakra.md
  35 Practice & Path
    35.01-ratnatraya.md
    35.02-samayika.md

40-49 COMPARATIVE & MODERN
  41 Jainism vs Science
    41.01-cosmology-comparison.md
    41.02-karma-vs-physics.md
  42 Jainism vs Other Traditions
    42.01-jain-buddhist-comparison.md
    42.02-jain-hindu-comparison.md
  43 Modern Scholarship
    43.01-padmanabh-jaini/
    43.02-paul-dundas/
    43.03-john-cort/

50-59 HISTORICAL
  51 Tirthankaras
  52 Historical Figures
  53 Institutional History

60-69 TRAINING DATA
  61 Gold Standard QA Pairs/
  62 Evaluation Sets/
  63 System Prompts/

90-99 META
  91 Taxonomy & Tagging Guide
  92 Source Authority Definitions
  93 Ingestion Scripts
  94 Project Documentation

Bidirectional Linking Strategy

Links are typed so the ingestion pipeline can build a proper knowledge graph with typed edges:

## In the body of any entry, use typed links:

Commentaries: [[sarvarthasiddhi-ch5]] comments on this sutra
Related concept: [[jiva]] is the subject of this teaching
Contrasts with: [[buddhist-anatta]] for comparative context
Prerequisite: understand [[six-dravyas]] before this entry
Disputed by: [[digambara-view-karma-subtypes]] offers alternate classification
Modern parallel: [[conservation-of-energy]] as analogy (not equivalence)

When ingested into Neo4j, these become typed edges:

• COMMENTS_ON, RELATED_TO, CONTRASTS_WITH, PREREQUISITE, DISPUTED_BY, PARALLEL_TO
• Enables graph traversal during retrieval, not just vector similarity

Ingestion Pipeline (Vault → RAG System)

Obsidian Vault (markdown + YAML frontmatter)
    │
    ├──→ Parse frontmatter → structured metadata
    ├──→ Parse body → content chunks (hierarchy-aware)
    ├──→ Parse links → relationship edges
    │
    ├──→ Vector DB (Qdrant): chunks + metadata for RAG retrieval
    ├──→ Knowledge Graph (Neo4j): concepts + typed relationships
    └──→ Training data export (60-69 area): for fine-tuning

Edit in Obsidian → run pipeline to sync → LLM reads from vector DB + graph. Vault is always the canonical source. Entire retrieval layer can be rebuilt from markdown files at any time.

Chunking Strategy (Critical for Jain Texts)

Jain texts have hierarchical structure that naive chunking destroys:

Sutra (root text)
  └── Commentary (Bhashya)
       └── Sub-commentary (Tika/Churni)
            └── Modern exposition

Use parent-child chunk relationships in LlamaIndex:

• Parent chunk = full sutra + immediate commentary
• Child chunks = individual passages for granular retrieval
• Metadata on every chunk links back to full hierarchy
• Retrieval can pull the child chunk that matched, then include parent context

Practical Knowledge Organization Advice

1. Start messy, refine structure. Get 50-100 entries in with good frontmatter, test retrieval, see what metadata fields you actually query. Add fields you didn't anticipate, remove ones you never use.

2. Git init your vault immediately. You want history of how entries evolved, and branching when multiple people edit.

3. The status field is essential. Mark entries draft, reviewed, or verified. Only verified entries get high retrieval priority. This lets you add content fast without quality bottlenecks.

4. Create an Obsidian template. Every new entry gets the correct frontmatter skeleton. Consistency in metadata naming is more important than completeness — a missing field is fine, an inconsistently named field breaks your pipeline.

5. Don't over-organize before you start. The Johnny Decimal structure above is a starting framework. You'll discover categories you didn't anticipate. The numbering gaps are intentional.

6. TRUTH-GROUNDING SYSTEM (Core Innovation)

Layer 1 — Source-Level Grounding (Non-Negotiable)

Every claim traces to a specific source. Build a source authority hierarchy:

Level 1 (Highest): Agamas & canonical texts
  └── Tattvartha Sutra, Uttaradhyayana Sutra, Tiloyapannatti, etc.

Level 2: Classical commentaries
  └── Sarvarthasiddhi, Tatparya Vritti, Dhavalaa, etc.

Level 3: Modern scholarly works
  └── Padmanabh Jaini, John Cort, Paul Dundas, etc.

Level 4 (Lowest): Contemporary interpretations
  └── Modern teachers, online sources, etc.

When sources conflict → system surfaces the conflict, never hides it.

Layer 2 — Epistemic Tagging

Every claim in the knowledge base gets tagged:

System prompt and fine-tuning teach the model to always surface this tag in responses.

Layer 3 — Retrieval-Based Verification (RARR)

Two-pass verification pipeline:

1. Pass 1: Model generates response citing sources
2. Pass 2: Retrieval step checks cited sources actually say what model claims
3. Mismatch handling: System corrects itself or flags uncertainty

Implementation: Run a smaller verification model alongside main model (72GB VRAM supports this).

Layer 4 — Syadvada Response Framework

Use simplified Saptabhangi (seven-fold predication) to structure responses:

syad asti       — "in some respect, X is the case" → perspective + evidence
syad nasti      — "in some respect, X is not the case" → counter-perspective
syad avaktavya  — "in some respect, X is indescribable" → limits of the framework

This is not just philosophical decoration — it's a formal logic for qualified truth claims that makes the system genuinely more epistemologically sophisticated than binary true/false AI systems.

Handling Conflicts Between Jain Doctrine and Science

7. KNOWLEDGE BASE SCHEMA (For Vector DB)

Entry Structure (Derived from Obsidian Vault)

{
  "id": "unique-id",
  "content": "The actual text/claim/teaching",
  "source": {
    "text": "Tattvartha Sutra",
    "location": "Chapter 5, Sutra 21",
    "author": "Umasvati",
    "tradition": "accepted by both Digambara and Shvetambara",
    "date_range": "2nd-5th century CE",
    "authority_level": 1
  },
  "epistemic_tag": "DOCTRINAL",
  "related_concepts": ["karma", "jiva", "ajiva"],
  "counter_positions": [
    {
      "position": "Digambara commentary differs on...",
      "source": "Sarvarthasiddhi, Chapter 5"
    }
  ],
  "modern_parallels": [
    {
      "domain": "physics",
      "claim": "Related to conservation of energy concepts",
      "source": "...",
      "strength": "analogy, not equivalence"
    }
  ],
  "topics": ["metaphysics", "karma theory", "soul"],
  "language_original": "Prakrit",
  "translation_notes": "Translation by X, alternate reading by Y"
}

This schema is generated from the Obsidian vault via the ingestion pipeline — the vault is the source of truth, this is the derived format for the vector DB.

8. FINE-TUNING DATA REQUIREMENTS

What You Need

• 500-1000 high-quality Q&A pairs demonstrating exact reasoning style
• Each example shows: multi-perspective answer, source citations, epistemic tags, Syadvada framing

Example Training Pair

{
  "instruction": "What happens to the soul after death according to Jainism?",
  "response": "**From Jain doctrinal perspective (Doctrinal/Scriptural):**\nAccording to the Tattvartha Sutra (Ch. 2), the jiva (soul) transmigrates to a new body immediately upon death, determined by the karma particles bound to it. The specific destination — naraka (hell), tiryancha (animal/plant), manushya (human), or deva (celestial) — depends on the type and intensity of accumulated karma. Pujyapada's Sarvarthasiddhi commentary elaborates that this transition is instantaneous with no intermediate state.\n\n**From Digambara-Shvetambara comparative note (Disputed/Internal):**\nBoth traditions agree on the fundamental mechanism. Minor differences exist in classification of karma types. [Sources differ on specific sub-categories.]\n\n**From comparative religion perspective (Scholarly Consensus):**\nAs noted by Padmanabh Jaini (The Jain Path of Purification, Ch. 4), this differs from Buddhist rebirth (no permanent soul) and Hindu atman theory (soul as identical with Brahman). The Jain position is unique in treating the soul as real, individual, and eternal but bound by material karma.\n\n**Epistemological note (Syadvada framing):**\nSyad asti — the soul's continuity is affirmed within Jain metaphysics. Syad nasti — the specific identity/personality does not persist (no 'you' reborn, but your jiva continues). Syad avaktavya — the precise mechanism of karma-binding transcends ordinary description."
}

Creation Process

• Most labor-intensive part of the project
• Requires domain expert + technical person collaborating
• Each pair must be carefully crafted
• Cover: cosmology, ethics, metaphysics, epistemology, practice, history, comparative religion

9. IMPLEMENTATION ROADMAP

Phase 0: Knowledge Organization (Weeks 1-4) ← NEW FIRST PHASE

• Set up Obsidian vault with modified Johnny Decimal structure
• git init the vault immediately
• Create Obsidian entry template with full frontmatter schema
• Write taxonomy & tagging guide (90-91 area)
• Define source authority levels (90-92 area)
• Begin ingesting canonical texts (10-19 area) — aim for 50-100 entries
• Add typed bidirectional links as you go
• Test: can a script parse all frontmatter and links cleanly?
• Refine schema based on what you actually need
• Recruit / involve domain expert for review

Phase 1: RAG Foundation (Weeks 4-7)

• Set up development environment (Python, Docker, CUDA)
• Download and run base model locally (start with Llama 3.1 8B for speed)
• Set up vLLM inference server
• Install and configure Qdrant (Docker)
• Install LlamaIndex
• Build ingestion pipeline: Obsidian vault → parsed chunks + metadata → Qdrant
• Build initial system prompt with multi-perspective format
• Test basic RAG Q&A flow
• Iterate on retrieval quality — this is where most value is created

Phase 2: Epistemic Layer (Weeks 7-9)

• Ensure epistemic tags are populated across knowledge base entries
• Build source authority hierarchy
• Add metadata filtering to retrieval (filter by authority level, tradition, epistemic tag)
• Refine system prompt to use epistemic tags in responses
• Build evaluation set (50-100 questions with expected answers)

Phase 3: Knowledge Graph (Weeks 9-11)

• Set up Neo4j Community Edition locally
• Build ingestion script: parse typed links from vault → Neo4j edges
• Map concept relationships (karma → jiva → ajiva → pudgala, etc.)
• Integrate graph queries into retrieval pipeline (hybrid: vector + graph)
• Test: does graph-augmented retrieval improve answer quality?

Phase 4: Fine-Tuning (Weeks 11-15)

• Create 500+ gold-standard Q&A training pairs (60-61 area in vault)
• Set up Axolotl / PEFT fine-tuning pipeline
• Fine-tune 8B model first (fast iteration, ~1-2 hours per run)
• Evaluate against gold-standard set
• Iterate on training data based on failure modes
• Graduate to 70B model fine-tuning once training data is validated
• QLoRA fine-tune 70B (~4-8 hours per run on your hardware)

Phase 5: Verification Pipeline (Weeks 15-18)

• Implement RARR verification pipeline (two-pass fact-checking)
• Run smaller verification model alongside main model
• Build multi-agent setup if needed (Jain agent, Science agent, Synthesizer)
• Stress-test with edge cases (controversial topics, internal disputes)

Phase 6: Production Hardening (Weeks 18-22)

• Build user-facing interface (web app or API)
• Add feedback mechanism for domain experts to flag errors
• Benchmark against Claude/GPT-4 on Jainism questions
• Document system for other contributors
• Establish ongoing vault maintenance workflow

10. KEY RISKS & MITIGATION

11. KEY CONCEPTS REFERENCED

12. PHILOSOPHICAL GROUNDING NOTE

This project is not just "chatbot + Jainism content." The core insight is that Jain epistemology — developed over 2500 years — already provides a formal framework for multi-valued truth, qualified claims, and perspective-aware reasoning. Modern AI defaulting to binary true/false is epistemologically primitive compared to Syadvada.

What we're building is, in essence, a Syadvada reasoning engine powered by modern ML infrastructure. The LLM provides language generation, RAG provides source grounding, and Jain epistemology provides the truth framework that ties it all together.

Done well, this would be genuinely novel — not just a Jainism chatbot, but a demonstration that ancient epistemological frameworks can produce more rigorous and honest AI reasoning than current approaches.

Document generated from planning session. Last updated: April 2026. Hardware: RTX 5000 Pro 72GB / Ryzen 9 / 128GB RAM