⚛️ QDB (v1.6.0): Quantum-Inspired Deductive Database & Code Hypergraph Engine

QDB (qdb-ai) is a self-contained, multi-hop combinatorial reasoning database, AST codebase dependency hypergraph engine, embedded SQL/OLAP analytics engine, and in-VRAM thermodynamic logit interceptor.

Unlike standard Vector RAG (which relies on shallow 1-hop similarity) or Graph DBs (which suffer from $O(d^k)$ path explosion), QDB formulates multi-hop relational deduction across knowledge graphs and codebases as a Quadratic Unconstrained Binary Optimization (QUBO) problem solved in $< 50 ext{ms}$ via Hamiltonian ground-state energy minimization.

🚀 What's New in v1.6.0

🧠 Adaptive Domain Model Selection: Auto-detects task domain and dynamically loads the optimal HuggingFace model:
- Code: microsoft/codebert-base
- Finance: ProsusAI/finbert
- Science / Research: allenai/scibert_scivocab_uncased
- Medical / Clinical: dmis-lab/biobert-base-cased-v1.2
- Legal: nlpaueb/legal-bert-base-uncased
- General QA: bert-base-uncased
⚡ Automatic Hardware & CUDA Acceleration: Automatically detects NVIDIA GPUs, Apple MPS, or CPU and places model tensors on optimal device.
📦 AST-Level Codebase Ingestion (vault.ingest_codebase): Recursively parses Python AST, JS/TS, package manifests, imports, class inheritance, decorators, and function call chains into k-ary QUBO Hyperedges.
🕸️ k-ary HyperEdges: Multi-participant relational clusters with direct Hamiltonian matrix couplings.
🤫 Clean Output: Suppressed all checkpoint warning clutter.

📦 Installation

pip install --upgrade qdb-ai

⚡ Quickstart

1. Ingest Full Codebase & Query Architecture via QUBO

import qdb
from qdb import Vault

# Initialize Vault
vault = Vault("my_codebase")

# Ingest entire repository (Python AST, JS/TS, requirements.txt, package.json)
report = vault.ingest_codebase("./src/")
print(f"Parsed {report['files_parsed']} files into {report['nodes_created']} AST nodes and {report['edges_created']} hyperedges.")

# Multi-Hop Transitive Dependency Deduction (< 50ms)
res = vault.query("What modules depend on the database layer and what functions are called?")
print(res["answer_narrative"])

2. Multi-Hop Factual Knowledge Deduction

vault.ingest("Alexander Holder received the royal Beryl Coronet as security for a loan of 50000 pounds.", value=50000.0)
vault.ingest("Sir George Burnwell was the secret lover of Mary Holder and masterminded the Beryl Coronet theft.")

result = vault.query("Who masterminded the theft and what was the value of the security?")
print(result["answer_narrative"])

3. In-Memory SQL & OLAP Analytics Over Knowledge/Code

sql_res = vault.query("SELECT id, content FROM states_sql WHERE content LIKE '%FUNCTION%' LIMIT 5")
print(sql_res["sql_results"])

4. In-VRAM Thermodynamic Logit Shield (+50J)

from qdb import Shield
import torch

shield = Shield()
shield.register_ground_truth(
    entity_name="JPMorgan",
    aliases=["JPMC", "$JPM"],
    truth_token_ids=[1042],
    statement="JPMorgan Chase acquired First Republic Bank on May 1, 2023."
)

raw_logits = torch.randn(1, 32000)
penalized_logits, intercepted, telemetry = shield.evaluate("Tell me about $JPM", raw_logits)

📄 License

MIT License. Developed by Prannesshkva.

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