qp-vault 0.13.0

Governed knowledge store for autonomous organizations. Trust tiers, cryptographic audit trails, content-addressed storage, air-gap native.

qp-vault

The governed knowledge store for autonomous organizations.

Every document has a trust tier that weights search results. Every chunk has a SHA3-256 content ID. Every mutation is auditable. The entire vault is verifiable via Merkle tree. Air-gap native.

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The Architecture

A Vault is a governed store of knowledge resources. Each resource is chunked, hashed, trust-classified, and indexed for hybrid search. The trust tier directly affects which results surface first.

┌─────────────────────────────────────────────────────────────┐
│                          VAULT                              │
├──────────────┬──────────────────────────────────────────────┤
│  INGEST      │  Parse → Chunk → SHA3-256 CID → Embed → Store│
│  GOVERN      │  Trust tiers · Lifecycle · Data classification│
│  RETRIEVE    │  Hybrid search · Trust-weighted · Time-travel │
│  VERIFY      │  Merkle tree · CID per chunk · Proof export  │
│  AUDIT       │  Every write → VaultEvent → Capsule (opt.)   │
├──────────────┴──────────────────────────────────────────────┤
│  Trust weights: CANONICAL 1.5x · WORKING 1.0x · EPHEMERAL 0.7x│
│  SHA3-256 content IDs · Merkle root · Ed25519+ML-DSA-65 audit │
└─────────────────────────────────────────────────────────────┘

Knowledge is not static. Resources move through a lifecycle (DRAFT, REVIEW, ACTIVE, SUPERSEDED, EXPIRED, ARCHIVED), organized into memory layers (OPERATIONAL, STRATEGIC, COMPLIANCE), and verified cryptographically on every read.

Knowledge that can't be verified can't be trusted.

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Why qp-vault

Vector databases store embeddings and return them by similarity. When something goes wrong, or when a regulator asks "was this the current policy at the time?", similarity search is not enough.

qp-vault solves three problems that vector databases do not:

1. Trust-weighted retrieval. Every resource has a trust tier (CANONICAL, WORKING, EPHEMERAL, ARCHIVED) that multiplies its search relevance. A CANONICAL SOP at 0.6 similarity outranks a WORKING draft at 0.8 similarity. This is not metadata filtering; it is a scoring function that ensures authoritative knowledge surfaces first, every time, without manual curation.

2. Cryptographic content integrity. Every chunk receives a SHA3-256 content ID at ingest. Every resource receives a Merkle root over its chunk CIDs. The entire vault has a root hash. Any modification to any chunk, in any resource, changes the vault's Merkle root. Auditors can verify a single resource without downloading the vault, using an exported Merkle proof. No trust in the storage layer is required.

3. Knowledge lifecycle with temporal validity. Resources have lifecycles (DRAFT to ACTIVE to SUPERSEDED to ARCHIVED) with temporal validity windows. Point-in-time queries answer "what was our policy on March 15, 2024?" by returning only resources that were ACTIVE at that date. Supersession chains link v1 to v2 to v3 with cryptographic pointers. Expired resources auto-transition. None of this exists in any vector database.

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Content Addressing

Every chunk is hashed with SHA3-256 and assigned a content ID:

vault://sha3-256/4cb02d65a1b2c3d4e5f67890abcdef1234567890abcdef1234567890abcdef12

Every resource receives a Merkle root computed over its sorted chunk CIDs. The vault itself has a root hash over all resource hashes:

                    Vault Merkle Root
                    /               \
         Resource A Root      Resource B Root
         /          \              |
  hash(c1+c2)    hash(c3+c4)    hash(c5)
   /      \        /      \       |
CID(c1) CID(c2) CID(c3) CID(c4) CID(c5)

Identical content always produces the same CID. Modified content always produces a different root. Auditors verify a single resource via Merkle proof without downloading the vault.

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Trust Tiers

Trust is not metadata. It is a scoring function.

Tier	Weight	Freshness Half-Life	Behavior
CANONICAL	1.5x	365 days	Immutable. Official SOPs, approved policies.
WORKING	1.0x	180 days	Default. Drafts, in-progress documents.
EPHEMERAL	0.7x	30 days	Temporary. Meeting notes. Auto-archives after TTL.
ARCHIVED	0.25x	730 days	Historical. Superseded versions.

Search ranking formula:

relevance = (0.7 × vector_similarity + 0.3 × text_rank) × trust_weight × freshness_decay

Where freshness_decay = exp(-age_days / half_life × ln2). A 180-day-old WORKING document retains 50% freshness. A CANONICAL document of the same age retains 70%.

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Knowledge Lifecycle

DRAFT ──→ REVIEW ──→ ACTIVE ──→ SUPERSEDED ──→ ARCHIVED
                        │
                     EXPIRED (auto when valid_until passes)
                        │
                     ACTIVE (re-activate)

Supersession creates a linked chain. Point-in-time search returns historically correct results.

vault.transition(r.id, "review", reason="Ready for security team")
vault.transition(r.id, "active")

old, new = vault.supersede(v1_id, v2_id)        # v1 → SUPERSEDED, linked to v2
chain = vault.chain(v1_id)                        # [v1, v2, v3, ...]
results = vault.search("policy", as_of=date(2024, 3, 15))  # Time-travel
expiring = vault.expiring(days=90)                # What's about to expire?

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Install

pip install qp-vault

Command	What You Get	Dependencies
pip install qp-vault	SQLite, trust search, CAS, Merkle, lifecycle	1 (pydantic)
pip install qp-vault[postgres]	+ PostgreSQL + pgvector hybrid search	+ sqlalchemy, asyncpg, pgvector
pip install qp-vault[capsule]	+ Cryptographic audit trail	+ qp-capsule
pip install qp-vault[docling]	+ 25+ format document processing (PDF, DOCX, etc.)	+ docling
pip install qp-vault[encryption]	+ AES-256-GCM encryption at rest	+ cryptography, pynacl
pip install qp-vault[local]	+ Local embeddings (sentence-transformers, air-gap safe)	+ sentence-transformers
pip install qp-vault[openai]	+ OpenAI embeddings (cloud)	+ openai
pip install qp-vault[fastapi]	+ REST API (15+ endpoints)	+ fastapi
pip install qp-vault[cli]	+ vault command-line tool	+ typer, rich
pip install qp-vault[all]	Everything	All of the above

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Quick Start

from qp_vault import Vault

vault = Vault("./my-knowledge")

# Add with trust tiers
vault.add("Incident response: acknowledge within 15 minutes...",
          name="sop-incident.md", trust="canonical")
vault.add("Draft proposal for new onboarding process...",
          name="draft-onboard.md", trust="working")

# Trust-weighted search
results = vault.search("incident response")
# CANONICAL surfaces first (1.5x), even at lower raw similarity

# Verify integrity
result = vault.verify()
print(result.merkle_root)  # Changes if any content is modified

# Export proof for auditors
proof = vault.export_proof(resource_id)

Memory Layers

from qp_vault import MemoryLayer

ops = vault.layer(MemoryLayer.OPERATIONAL)        # SOPs, runbooks (boost=1.5x)
strategic = vault.layer(MemoryLayer.STRATEGIC)     # ADRs, decisions (trust=CANONICAL)
compliance = vault.layer(MemoryLayer.COMPLIANCE)   # Audit evidence (reads audited)

await ops.add("deploy-runbook.md")                 # Layer defaults auto-applied
await compliance.search("SOC2")                     # This search is logged

Health Scoring

score = vault.health()
# score.overall:      85.0/100
# score.freshness:    92.0  (are documents current?)
# score.uniqueness:   100.0 (no duplicates?)
# score.coherence:    80.0  (no contradictions?)
# score.connectivity: 70.0  (are docs organized?)

Plugin System

from qp_vault.plugins import embedder, parser

@embedder("my-model")
class MyEmbedder:
    dimensions = 768
    async def embed(self, texts):
        return my_model.encode(texts)

@parser("dicom")
class DicomParser:
    supported_extensions = {".dcm"}
    async def parse(self, path):
        return ParseResult(text=extract_dicom(path))

Three discovery methods: explicit registration, entry_points (pip packages), or --plugins-dir (air-gap: drop .py files, no install needed).

CLI

vault init ./knowledge
vault add report.pdf --trust canonical
vault search "revenue projections" --top-k 5
vault inspect <resource-id>
vault verify
vault health
vault status

Exit codes: 0 = pass, 1 = fail. Designed for CI: vault verify && deploy.

FastAPI

from qp_vault.integrations.fastapi_routes import create_vault_router

router = create_vault_router(vault)
app.include_router(router, prefix="/v1/vault")
# 15+ endpoints: resources CRUD, search, verify, health, lifecycle, proof

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Security

Layer	Algorithm	Standard	Purpose
Content integrity	SHA3-256	FIPS 202	Tamper-evident CIDs and Merkle roots
Audit signatures	Ed25519 + ML-DSA-65	FIPS 186-5, FIPS 204	Via qp-capsule (optional)
Encryption at rest	AES-256-GCM	FIPS 197	pip install qp-vault[encryption]
Search integrity	Parameterized SQL	--	No string interpolation, FTS5 sanitized
Input validation	Pydantic + custom	--	Enum checks, name/tag/metadata limits

No deprecated cryptography. No runtime network dependencies. Air-gapped operation supported. 100/100 security score. Full threat model in docs/security.md.

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Part of the Quantum Pipes Stack

Package	Purpose	Install
qp-capsule	Cryptographic audit protocol	pip install qp-capsule
qp-vault	Governed knowledge store	pip install qp-vault
qp-conduit	Infrastructure management	Shell toolkit
qp-tunnel	Encrypted remote access	Shell toolkit

Each independently useful. Together, the governed AI platform for autonomous organizations.

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Documentation

Document	Audience
Getting Started	Developers
Architecture	Developers, Architects
API Reference	Developers
Trust Tiers	Developers, Product
Knowledge Lifecycle	Developers, Compliance
Memory Layers	Developers, Architects
Plugin Development	SDK Authors
Security Model	CISOs, Security Teams
CLI Reference	DevOps, Developers
FastAPI Integration	Backend Developers

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Contributing

See CONTRIBUTING.md. Bug fixes with tests, new plugins (embedders, parsers, policies), and documentation improvements are welcome.

License

Apache License 2.0.

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Knowledge that can't be verified can't be trusted.

An open-source governed knowledge store

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