9/20/2025
Trent Carter
Comprehensive Database Storage Schema for LNSP + Conceptual Interrogation PipelineThis schema supports the full pipeline from corpus ingestion through inference, with proper indexing for the 32,768 TMCD lanes that enable billion-scale concept storage while maintaining high retrieval performance.
tools needed:
bash
_# Core dependencies_
pip install langchain transformers sentence-transformers
pip install faiss-cpu numpy scikit-learn
pip install openai anthropic _# or vllm for local_
_# Database connections_
pip install psycopg2 neo4j pymongo
_# Distributed processing (optional)_
pip install ray celery redis
_# Development tools_
pip install pandas matplotlib wandb
This stack provides everything needed to build the LNSP + Conceptual Interrogation pipeline, without relying on TokenLearn which isn't suitable for semantic concept extraction.
Process Dependency Map┌─────────────────────────────────────────────────────────────────┐
│ PROCESS DEPENDENCY FLOW │
│ │
│ Foundation Layer: P1 ──► P2 ──► P3 │
│ │ │
│ Mission Layer: └────► P4 ──┬──► P14 (Batching) │
│ │ │
│ Extraction Layer: └──► P5 (LLM) │
│ │ │
│ Processing Layer: ┌────────────────────┼─────────┐ │
│ ▼ ▼ ▼ │
│ P6 + P7 ──► P8 P9 P10 │
│ │ │ │ │ │
│ Storage Layer: └───────────┼─────────┼─────────┘ │
│ ▼ ▼ │
│ P11 P12 │
│ │ │ │
│ Validation Layer: └────┬────┘ │
│ ▼ │
│ P13 │
│ │ │
│ Training Layer: └──► P15 │
│ │ │
│ Inference Layer: P16 ◄──────┘ │
│ │ │
│ └──► P17 │
└─────────────────────────────────────────────────────────────────┘
Key Insights: Resource Intensity (1-10 scale):This table format makes it easy to:
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ LNSP + TokenLearn Multi-Layer RAG System │
└─────────────────────────────────────────────────────────────────────────────────────────┘
STEP 0: Create Mission Text from Dataset Corpus:
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ MISSION TEXT GENERATION FROM RAW DATASETS │
└─────────────────────────────────────────────────────────────────────────────────────────┘
STEP 1: RAW DATASET INGESTION
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ GSM8K │ │ Dolly │ │ Synthetic │ │ C4 │ │ Wikipedia │
│ (Math) │ │ (Instruct) │ │ SFT │ │ (Web) │ │ (Facts) │
└──────┬───────┘ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘
└──────────────────┴──────────────────┴──────────────────┴──────────────────┘
│
▼
STEP 2: DOCUMENT LOADING & CHUNKING (LangChain)
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ ┌─────────────────────┐ ┌──────────────────────┐ ┌──────────────────┐ │
│ │ Document Loaders │ │ Text Splitters │ │ Chunk Metadata │ │
│ ├─────────────────────┤ ├──────────────────────┤ ├──────────────────┤ │
│ │ • TextLoader │───────►│ • CharacterSplitter │──────►│ • Source doc │ │
│ │ • JSONLoader │ │ (size=1000) │ │ • Position │ │
│ │ • CSVLoader │ │ • RecursiveCharacter │ │ • Type │ │
│ │ • UnstructuredLoader│ │ (size=500, │ │ • Dataset name │ │
│ └─────────────────────┘ │ overlap=50) │ └──────────────────┘ │
│ │ • SentenceSplitter │ │
│ │ • TokenTextSplitter │ │
│ └──────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────────────┘
│
▼
STEP 3: SEMANTIC CHUNKING & ANALYSIS
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ ┌─────────────────────┐ ┌──────────────────────┐ ┌──────────────────┐ │
│ │ Sentence Transformer│ │ Semantic Splitter │ │ Coherence Check │ │
│ │ (all-MiniLM-L6) │───────►│ │──────►│ │ │
│ ├─────────────────────┤ ├──────────────────────┤ ├──────────────────┤ │
│ │ Embed sentences │ │ • Cosine similarity │ │ • Min sentences: │ │
│ │ [384D vectors] │ │ threshold = 0.7 │ │ 2-3 │ │
│ └─────────────────────┘ │ • Group similar │ │ • Max sentences: │ │
│ │ sentences │ │ 5-7 │ │
│ │ • Breakpoint detect │ │ • Topic drift │ │
│ └──────────────────────┘ │ check │ │
│ └──────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────────────┘
│
▼
STEP 4: CONTENT TYPE CLASSIFICATION
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ ┌────────────────┐ ┌────────────────┐ ┌────────────────┐ ┌──────────────┐ │
│ │ Math Problem │ │ Instruction │ │ Factual │ │ Narrative │ │
│ │ Detector │ │ Detector │ │ Detector │ │ Detector │ │
│ ├────────────────┤ ├────────────────┤ ├────────────────┤ ├──────────────┤ │
│ │ • Equations? │ │ • Commands? │ │ • Definitions? │ │ • Story? │ │
│ │ • Numbers? │ │ • How-to? │ │ • Facts? │ │ • Dialogue? │ │
│ │ • Word problem?│ │ • Steps? │ │ • Data? │ │ • Events? │ │
│ └───────┬────────┘ └───────┬────────┘ └───────┬────────┘ └──────┬───────┘ │
│ └───────────────────────┴──────────────────────┴────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────┐ │
│ │ Content Type Label │ │
│ └──────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────────────┘
│
▼
STEP 5: MISSION TEXT GENERATION
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ ┌─────────────────────────────────────────────────────────────────────────────────┐ │
│ │ MISSION TEMPLATE SELECTOR │ │
│ ├─────────────────────────────────────────────────────────────────────────────────┤ │
│ │ │ │
│ │ IF content_type == "Math Problem": │ │
│ │ mission = f"Extract mathematical concepts and solution steps from: {chunk}" │ │
│ │ │ │
│ │ ELIF content_type == "Instruction": │ │
│ │ mission = f"Extract actionable steps and procedures from: {chunk}" │ │
│ │ │ │
│ │ ELIF content_type == "Factual": │ │
│ │ mission = f"Extract atomic facts and relationships from: {chunk}" │ │
│ │ │ │
│ │ ELIF content_type == "Narrative": │ │
│ │ mission = f"Extract key events and entity relationships from: {chunk}" │ │
│ │ │ │
│ │ ELSE: │ │
│ │ mission = f"Extract key concepts and their relationships from: {chunk}" │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────────────┘
│
▼
STEP 6: BATCH PROCESSING & QUEUING
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ ┌─────────────────────┐ ┌──────────────────────┐ ┌──────────────────┐ │
│ │ Mission Queue │ │ Priority Scorer │ │ Batch Creator │ │
│ ├─────────────────────┤ ├──────────────────────┤ ├──────────────────┤ │
│ │ { │ │ • Information density│ │ • Group by type │ │
│ │ "mission": "...", │───────►│ • Uniqueness score │──────►│ • Batch size: 50 │ │
│ │ "chunk": "...", │ │ • Domain importance │ │ • Similar TMD │ │
│ │ "metadata": {...}, │ │ • Length appropriate │ │ • Send to LLM │ │
│ │ "priority": 0.8 │ │ │ │ │ │
│ │ } │ └──────────────────────┘ └──────────────────┘ │
│ └─────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────────────┘
EXAMPLE OUTPUTS:
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ GSM8K Chunk: │
│ "Sarah has 5 apples. She gives 2 to her friend. How many apples does she have left?" │
│ ↓ │
│ Mission: "Extract mathematical concepts and solution steps from: Sarah has 5 apples..."│
│ │
│ C4 Web Chunk: │
│ "The Pacific Ocean is the largest ocean on Earth, covering about 63 million sq miles" │
│ ↓ │
│ Mission: "Extract atomic facts and relationships from: The Pacific Ocean is..." │
│ │
│ Dolly Instruction: │
│ "To make coffee: 1) Boil water 2) Add grounds 3) Pour water 4) Wait 4 minutes" │
│ ↓ │
│ Mission: "Extract actionable steps and procedures from: To make coffee..." │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────┘
IMPLEMENTATION EXAMPLE (Python/LangChain):
┌─────────────────────────────────────────────────────────────────────────────────────────┐
│ │
│ from langchain.text_splitter import RecursiveCharacterTextSplitter │
│ from langchain.embeddings import HuggingFaceEmbeddings │
│ from transformers import pipeline │
│ │
│ # 1. Load and chunk │
│ splitter = RecursiveCharacterTextSplitter( │
│ chunk_size=500, │
│ chunk_overlap=50, │
│ separators=["\n\n", "\n", ".", "!", "?", " "] │
│ ) │
│ │
│ # 2. Semantic analysis │
│ embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") │
│ │
│ # 3. Content classification │
│ classifier = pipeline("zero-shot-classification") │
│ labels = ["math", "instruction", "factual", "narrative"] │
│ │
│ # 4. Generate mission │
│ def create_mission(chunk, content_type): │
│ templates = { │
│ "math": "Extract mathematical concepts and solution steps from:", │
│ "instruction": "Extract actionable steps and procedures from:", │
│ "factual": "Extract atomic facts and relationships from:", │
│ "narrative": "Extract key events and entity relationships from:" │
│ } │
│ return f"{templates.get(content_type, templates['factual'])} {chunk[:100]}..." │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────┘
STEP 1: TEACHER LLM GENERATES EVERYTHING POST MISSION TEXT
┌─────────────────┐ ┌──────────────────────────────────────┐
│ Teacher LLM │ │ Mission: "Extract 10 core scientific │
│ (LLaMA 3.1-70B)│◄────────┤ concepts about photosynthesis" │
└────────┬────────┘ └──────────────────────────────────────┘
│
├─► Concept (C): "Light-dependent reactions split water"
├─► Probe (P): "What process in photosynthesis splits water?"
├─► Expected (E): "Photolysis of water"
├─► Domain: Science (4 bits)
├─► Task: Fact Retrieval (5 bits)
├─► Modifier: Biochemical (6 bits)
└─► Relationships: "causes→oxygen_production", "requires→sunlight"
STEP 2: MULTI-MODAL PROCESSING
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Python TMD │ │ GTR-T5/Stella │ │ Relationship │
│ Generator │ │ Embedder │ │ Extractor │
├─────────────────┤ ├─────────────────┤ ├─────────────────┤
│ Domain = 0001 │ │ Input: Concept │ │ Subject: concept│
│ Task = 00101 │ │ Output: [768D] │ │ Predicate: causes│
│ Modifier= 000011│ │ vector │ │ Object: O2_prod │
│ ───────────────│ └────────┬────────┘ └────────┬────────┘
│ TMD = [16D] │ │ │
└────────┬────────┘ │ │
│ │ │
└───────────┬───────────────┘ │
▼ ▼
┌───────────────────┐ ┌─────────────────────┐
│ [16D] + [768D] = │ │ Graph Triples: │
│ [784D] vector │ │ (C1)-[causes]->(O2) │
└───────────────────┘ └─────────────────────┘
STEP 3: CORE RAG TRIPLE STORAGE
┌──────────────────────────────────────────────────────────────────────────────────┐
│ CORE RAG │
├──────────────────────┬────────────────────────┬──────────────────────────────────┤
│ TEXT DATABASE │ VECTOR DATABASE │ GRAPH DATABASE │
├──────────────────────┼────────────────────────┼──────────────────────────────────┤
│ ID: C_001 │ ID: C_001 │ Nodes: │
│ Mission: "Extract..."│ Vector: [784D] │ - C_001: "Light reactions..." │
│ Concept: "Light..." │ TMD_lane: Sci-Fact-Bio │ - O2_prod: "Oxygen production" │
│ Probe: "What..." │ Embedding: [768D part] │ Edges: │
│ Expected: "Photo..." │ Metadata: [16D part] │ - (C_001)-[causes]->(O2_prod) │
│ TMD: Sci-Fact-Bio │ │ - (C_001)-[requires]->(sunlight)│
└──────────────────────┴────────────────────────┴──────────────────────────────────┘
STEP 4: HIERARCHICAL RAG LAYERS
┌────────────────────────────────────────────────────────────────────────────────────┐
│ RAG HIERARCHY │
│ │
│ ┌──────────────────────────────────────────────────────────────────────────┐ │
│ │ CORE RAG (Global Knowledge) │ │
│ │ • Wikipedia concepts • Scientific facts • Universal relationships │ │
│ │ • 100M-1B concepts • 32,768 TMD lanes • Dense knowledge graph │ │
│ └─────────────────────────────────┬────────────────────────────────────────┘ │
│ │ │
│ ┌──────────────────────────┴──────────────────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ DOMAIN RAG │ │ DOMAIN RAG │ │ DOMAIN RAG │ │
│ │ Science │ │ Technology │ │ Medicine │ │
│ ├──────────────┤ ├──────────────┤ ├──────────────┤ │
│ │ • Research │ │ • Code repos │ │ • Clinical │ │
│ │ • Papers │ │ • APIs │ │ • Guidelines │ │
│ │ • Protocols │ │ • Libraries │ │ • Drug data │ │
│ └───────┬──────┘ └───────┬──────┘ └───────┬──────┘ │
│ │ │ │ │
│ └──────────────────────────┴──────────────────────────────┘ │
│ │ │
│ ┌──────────────────────────┴──────────────────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ USER RAG │ │ LOCAL RAG │ │CORPORATE RAG │ │
│ │ (Personal) │ │ (Device/Edge)│ │(Organization)│ │
│ ├──────────────┤ ├──────────────┤ ├──────────────┤ │
│ │ • Preferences│ │ • Cache │ │ • Policies │ │
│ │ • History │ │ • Offline │ │ • Internal │ │
│ │ • Context │ │ • Fast access│ │ • Proprietary│ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└────────────────────────────────────────────────────────────────────────────────────┘
STEP 5: TRAINING/INFERENCE FLOW
┌────────────────────────────────────────────────────────────────────────────┐
│ │
│ Query: "How does photosynthesis work in my tomato plants?" │
│ │
│ 1. USER RAG: Check personal garden notes │
│ ↓ │
│ 2. LOCAL RAG: Recent queries about plants │
│ ↓ │
│ 3. DOMAIN RAG: Botanical/Agriculture specific │
│ ↓ │
│ 4. CORE RAG: General photosynthesis concepts │
│ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ RETRIEVAL PROCESS │ │
│ │ │ │
│ │ TMD Analysis: Agriculture-Explanation-Botanical │ │
│ │ ↓ │ │
│ │ Vector Search: Find similar [784D] in TMD lane │ │
│ │ ↓ │ │
│ │ Graph Walk: Follow relationships from retrieved concepts │ │
│ │ ↓ │ │
│ │ Text Fetch: Get full CPE entries for context │ │
│ │ │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ │
│ LNSP Processing: Vector-native reasoning across all retrieved data │
│ │
└────────────────────────────────────────────────────────────────────────────┘
ECHO LOOP VALIDATION (During Training)
┌────────────────────────────────────────────────────────────────────────────┐
│ For each concept C: │
│ 1. Retrieve using TMD-routed vector search │
│ 2. Test with probe question P │
│ 3. Compare output with expected answer E │
│ 4. Validate graph relationships still hold │
│ 5. If cosine_sim < 0.82, flag for review │
└────────────────────────────────────────────────────────────────────────────┘
Feasibility with Open Source ToolsYes, the updated LNSP using Semantic Chunking TMD CPE Pipeline described in the PDF is feasible to implement using primarily open-source tools, with minimal custom development for specialized components like the LNSP model (Mamba + MoE) and mission generation. The pipeline remains a 17-stage process (P1-P17) for corpus ingestion, semantic chunking (500-word units), content classification (e.g., math/fact), mission generation (extraction prompts), LLM interrogation for Concept-Phrase Extraction (CPE) + Task-Modifier-Domain (TMD) + relations, TMD encoding (16D vector), concept embedding (768D), vector fusion (784D), graph extraction, storage in text/vector/graph DBs, echo validation (retrieval quality via cosine similarity), batch optimization, LNSP training, multi-RAG query, and MoE inference. This aligns with your vector-native bootstrapping approach from token-based LLMs (e.g., LLaMA), as detailed in the integrated "Live-Conceptual Bootstrapping" and "Open Source Model and Interrogation" sections.
Key libraries/tools are open-source or have open alternatives (confirmed via current knowledge up to September 20, 2025; no major changes in licensing):
Custom/open adaptations:
Challenges: Teacher LLM (P5) prefers open LLaMA over GPT-4 to avoid proprietary costs. High parallelism (✓✓✓ for most) uses Ray for distributed runs. Storage scales with +10% overhead per chunk. Overall, 85-95% open-source; custom CLI for interrogation (e.g., connected to GTR-T5) can be prototyped in <100 lines of Python. This pipeline supports your TMCD integration for overcoming DeepMind's embedding limits (e.g., partitioning into 32,768 subspaces).
Cost Estimation for 100,000 Documents (Avg. 500 Words Each)Assuming ~1 chunk per document (500-word chunks in P2), this yields ~100,000 items. Build phase (P1-P15) dominates; inference (P16-P17) is low. Costs focus on LLM interrogation (P5: ~80M tokens total, 600 input + 200 output per item for missions/CPE/TMD/relations), embeddings (P7: GPU-based), training (P15: small MoE), and storage (P10-P12: ~500GB at ~5KB/item). Other steps (e.g., chunking, classification) are CPU-bound and cheap (~$1-5 total). Estimates use 2025 pricing trends (fallen 20-40% YoY due to efficiency gains like AMD/NVIDIA optimizations and competition from providers like Deepinfra/TogetherAI).
Batched on A100 GPU (~$1.35/hr cheapest on-demand from Hyperstack/Thunder Compute; median $2-4/hr from AWS/GCP, but use efficient providers). Self-hosting (e.g., 2x RTX 4090) drops to electricity (~$0.10/hr).
Response from ChatGPT-5:
What I’m fixing (blunt + brief)-- Extensions
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
CREATE EXTENSION IF NOT EXISTS vector; -- pgvector
-- Enums
DO $$ BEGIN
CREATE TYPE content_type AS ENUM ('factual','math','instruction','narrative');
EXCEPTION WHEN duplicate_object THEN NULL; END $$;
DO $$ BEGIN
CREATE TYPE validation_status AS ENUM ('passed','failed','pending');
EXCEPTION WHEN duplicate_object THEN NULL; END $$;
-- Core table
CREATE TABLE cpe_entry (
cpe_id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
mission_text TEXT NOT NULL, -- from P4
source_chunk TEXT NOT NULL, -- P2 original
concept_text TEXT NOT NULL, -- P5
probe_question TEXT NOT NULL, -- P5
expected_answer TEXT NOT NULL, -- P5
-- Categorical labels
domain_code SMALLINT NOT NULL, -- 0..15
task_code SMALLINT NOT NULL, -- 0..31
modifier_code SMALLINT NOT NULL, -- 0..63
content_type content_type NOT NULL,
dataset_source TEXT NOT NULL, -- short string, use FK if you want
chunk_position JSONB NOT NULL, -- {doc_id, start, end}
relations_text JSONB, -- raw relations from P5
-- Quality / ops
echo_score REAL, -- float4
validation_status validation_status NOT NULL DEFAULT 'pending',
batch_id UUID,
created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
-- TMCD routing
tmd_bits SMALLINT NOT NULL, -- 16-bit packed (Domain|Task|Modifier)
tmd_lane TEXT NOT NULL, -- "Science-FactRetrieval-Biochemical"
lane_index SMALLINT NOT NULL CHECK (lane_index BETWEEN 0 AND 32767)
);
-- Fast filters
CREATE INDEX cpe_lane_idx ON cpe_entry (lane_index);
CREATE INDEX cpe_ct_idx ON cpe_entry (content_type);
CREATE INDEX cpe_ds_idx ON cpe_entry (dataset_source);
CREATE INDEX cpe_created_idx ON cpe_entry (created_at DESC);
-- Optional FK tables for normalization
CREATE TABLE dataset_catalog (
name TEXT PRIMARY KEY
);
-- Then add FK if desired:
-- ALTER TABLE cpe_entry ADD CONSTRAINT fk_dataset
-- FOREIGN KEY (dataset_source) REFERENCES dataset_catalog(name);
-- pgvector sidecars (kept in Postgres for convenience; you can also keep only IDs here and store vectors in a vector DB)
CREATE TABLE cpe_vectors (
cpe_id UUID PRIMARY KEY REFERENCES cpe_entry(cpe_id) ON DELETE CASCADE,
vector_id UUID UNIQUE NOT NULL, -- = cpe_id (duplicate for interoperability)
concept_vec vector(768) NOT NULL,
tmd_dense vector(16) NOT NULL, -- learned dense TMD
fused_vec vector(784) NOT NULL,
question_vec vector(768) -- optional
);
-- Metadata to speed similarity math (cos norm caching)
ALTER TABLE cpe_vectors ADD COLUMN fused_norm REAL;
CREATE INDEX cpe_fused_norm_idx ON cpe_vectors (fused_norm);
-- pgvector ANN index (IVFFlat). Build after loading and VACUUM ANALYZE.
-- Choose lists by scale: ~sqrt(N). For 1M concepts, 1000–2000 is reasonable.
CREATE INDEX cpe_fused_ann ON cpe_vectors USING ivfflat (fused_vec vector_cosine_ops) WITH (lists = 1200);
CREATE INDEX cpe_concept_ann ON cpe_vectors USING ivfflat (concept_vec vector_cosine_ops) WITH (lists = 1200);
Note: If you keep Faiss/Weaviate as your primary vector DB, the Postgres cpe_vectors can be a mirror for governance/auditing and simple offline queries. B. Faiss (primary vector index) — recommended configsSchema (fused vectors + metadata; cosine):
{
"classes": [{
"class": "Concept",
"vectorIndexType": "hnsw",
"vectorIndexConfig": {"distance": "cosine"},
"vectorizer": "none",
"properties": [
{"name":"cpeId","dataType":["uuid"]},
{"name":"conceptText","dataType":["text"]},
{"name":"tmdLane","dataType":["text"]},
{"name":"laneIndex","dataType":["int"]},
{"name":"domainCode","dataType":["int"]},
{"name":"taskCode","dataType":["int"]},
{"name":"modifierCode","dataType":["int"]},
{"name":"tmdBits","dataType":["int"]},
{"name":"echoScore","dataType":["number"]},
{"name":"validationStatus","dataType":["text"]},
{"name":"createdAt","dataType":["date"]}
]
}]
}
// Constraints
CREATE CONSTRAINT concept_id IF NOT EXISTS
FOR (n:Concept) REQUIRE n.cpe_id IS UNIQUE;
CREATE CONSTRAINT entity_id IF NOT EXISTS
FOR (n:Entity) REQUIRE n.node_id IS UNIQUE;
// Concept nodes mirror the text DB (optionally also store vector norms if doing hybrid)
MERGE (c:Concept {cpe_id: $cpe_id})
SET c.text = $concept_text,
c.tmdBits = $tmd_bits,
c.tmdLane = $tmd_lane,
c.laneIndex = $lane_index,
c.domainCode = $domain_code,
c.taskCode = $task_code,
c.modifierCode = $modifier_code,
c.echoScore = $echo_score,
c.validationStatus = $validation_status;
// Relations (typed edges with confidence)
MATCH (src:Concept {cpe_id:$src_id})
MATCH (dst:Concept {cpe_id:$dst_id})
MERGE (src)-[r:REL {type:$rel_type}]->(dst)
SET r.confidence = $confidence,
r.properties = $properties;
// Index for fast lane filtering
CREATE INDEX concept_lane_idx IF NOT EXISTS FOR (c:Concept) ON (c.laneIndex);
Query example (RAG walk):// Start from TMD lane prefilter, then expand 1–2 hops by confidence
MATCH (c:Concept)
WHERE c.laneIndex = $lane
WITH c
MATCH (c)-[r:REL]->(n:Concept)
WHERE r.confidence >= 0.6
RETURN c, r, n
ORDER BY c.echoScore DESC, r.confidence DESC
LIMIT 64;
E. TMD packing (deterministic) + dense (learned) Bit layout (16 bits total):def pack_tmd(domain, task, modifier):
assert 0 <= domain <= 0xF
assert 0 <= task <= 0x1F
assert 0 <= modifier <= 0x3F
return (domain << 12) | (task << 7) | (modifier << 1)
def unpack_tmd(bits):
domain = (bits >> 12) & 0xF
task = (bits >> 7) & 0x1F
modifier = (bits >> 1) & 0x3F
return domain, task, modifier
Dense TMD (16D float): build as Embedding(domain_vocab=16) ⊕ Embedding(task_vocab=32) ⊕ Embedding(mod_vocab=64) → 16D (MLP or learned projection). Keep both: tmd_bits (routing/filter) and tmd_dense(fusion). F. Echo Loop guardrails (P13)from dataclasses import dataclass
from uuid import UUID
@dataclass
class CPECore:
cpe_id: UUID
mission_text: str
source_chunk: str
concept_text: str
probe_question: str
expected_answer: str
domain_code: int
task_code: int
modifier_code: int
content_type: str
dataset_source: str
chunk_position: dict # {"doc_id":..., "start":..., "end":...}
relations_text: list # [{"subj":..., "pred":..., "obj":...}]
tmd_bits: int
tmd_lane: str
lane_index: int
echo_score: float | None
validation_status: str # 'passed'|'failed'|'pending'
@dataclass
class CPEVectors:
cpe_id: UUID
concept_vec: list[float] # 768
tmd_dense: list[float] # 16
fused_vec: list[float] # 784
question_vec: list[float] | None
fused_norm: float | None
H. Retrieval recipe (P16) — precise order of opsPer concept (no compression, 1× replica):
You listed 6.3 KB because you’re counting fused+concept only; decide policy:
Total (lean): ~2 KB + ~3.1–6.2 KB + ~1.5 KB → ~6.6–9.7 KB
Total (full): ~12.9 KB. Pick and document one. Your earlier “~10 KB” is a good median.
J. Ingestion → Train wiring (ops you’ll actually run)SELECT e.cpe_id, e.concept_text
FROM cpe_entry e
JOIN cpe_vectors v USING (cpe_id)
WHERE e.lane_index = $1
ORDER BY v.fused_vec <#> $query_vec -- cosine distance operator (pgvector)
LIMIT 32;
Echo check (failed set):SELECT cpe_id, echo_score, validation_status
FROM cpe_entry
WHERE validation_status = 'failed'
ORDER BY echo_score ASC
LIMIT 50;
Graph enrichment (Neo4j): get neighbors to add to context pack (IDs only), then hydrate from Postgres. L. Concrete next steps (so this ships)If you want, I can also hand you: