Aumai Confidentialrag
B+ 90 completed
Ai Ml
cli / python · tiny
22
Files
1,514
LOC
1
Frameworks
4
Languages
Pipeline State
completedRun ID
#303993Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
43.05Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47628
Member of a group with 1 similar repo(s) — canonical #27307 view group →
Top concepts (4)
Project DescriptiontestingTestingTesting
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🧪 Code Distillation
Browse all specs →Sample distilled functions (click for full spec)
_extract_snippetExtracts a text snippet centered around the earliest occurrence of any provided search term within the input text. It accepts the main text, a list of search terms, and a maximum desired length for the output. The function returns a string, which is the extracted snippet, potentially padded with ell
_tfidf_scoreCalculates a normalized relevance score based on term frequency for a given set of query terms against a document. It accepts a list of strings representing query terms and a single string representing the document content. The function returns a floating-point number between 0.0 and 1.0, representi
ConfidentialIndex.get_documentRetrieves a specific document object using a provided string identifier. It accepts one string argument, the document ID, and returns the corresponding ConfidentialDocument object if found. If no document matches the given ID, the function raises a KeyError exception.
AI Prompt
Create a command-line interface (CLI) tool in Python that implements a TEE-based, privacy-preserving RAG pipeline. The tool should be structured for secure processing of sensitive data. I need it to be testable, so please include pytest setup, and the project structure should support documentation generation, following best practices for a Python package.
python cli rag tee privacy ai testing command-line
Generated by gemma4:latest
Catalog Information
This project provides a TEE-based (Trusted Execution Environment) privacy-preserving RAG (Recurrent Attention Generator) pipeline, designed for confidential and secure processing of sensitive data.
Description
The aumai-confidentialrag project is a TEE-based implementation of a Recurrent Attention Generator (RAG) pipeline. This pipeline is designed to process sensitive data in a confidential manner, ensuring the privacy of the input information. The project utilizes Trusted Execution Environments to isolate and protect the processing of sensitive data from potential threats.
الوصف
هذا المشروع يقدم خادم تكنولوجيا التشفير (Trusted Execution Environment) لخطوط إنتاج RAG (Recurrent Attention Generator) ، مصممًا للتعامل مع البيانات الحساسة بسرعة وخصوصية. يستخدم هذا المشروع بيئات تنفيذ موثوقة لتوفير حماية وتحكم في التعامل مع البيانات الحساسة من أي تهديدات محتملة.
Novelty
9/10Tags
privacy-preserving confidential-data-processing trusted-execution-environment recurrent-attention-generator sensitive-data-protection
Technologies
click pydantic
Claude Models
claude-opus-4.6
Quality Score
B+
89.9/100
Structure
95
Code Quality
100
Documentation
85
Testing
85
Practices
70
Security
100
Dependencies
90
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (60% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
- Properly licensed project
Security & Health
4.1h
Tech Debt (D)
High
DORA Rating
A
OWASP (100%)
Repobility · code-quality intelligence platform · https://repobility.com
PASS
Quality Gate
A
Risk (6)
Apache-2.0
License
2.9%
Duplication
Languages
Frameworks
pytest
Symbols
variable12
method11
function8
class6
constant1
Concepts (4)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Same scanner, your repo: https://repobility.com — Repobility | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | > TEE-based privacy-preserving RAG pipeline | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | testing | Detected testing layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Testing | testing | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Testing | Detected from 3 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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