Chatassistant Retail

B 82 completed
Other
cli / python · small
83
Files
16,224
LOC
2
Frameworks
7
Languages

Pipeline State

completed
Run ID
#377177
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
56.67
Framework unique
Isolation
Last stage change
2026-05-10 03:35:41
Deduplication group #50880
Member of a group with 20 similar repo(s) — canonical #102665 view group →
Top concepts (2)
Project DescriptionTesting
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/

AI Prompt

Create a production-ready, CLI-based conversational AI chatbot for the retail industry, focusing on inventory management. I need it to have a Gradio-based web UI and support multi-modal input, meaning it must handle both text and images (like PNG or JPG). The core logic should use Azure OpenAI GPT-4o-mini and incorporate a LangGraph state machine for complex workflows. It needs to feature hybrid RAG search using Azure Cognitive Search, and ideally, it should support session persistence using Redis or PostgreSQL. Please ensure it can perform image-based product lookups and provide low-stock automation recommendations.
python cli ai-chatbot retail azure-openai langgraph rag gradio multi-modal inventory-management
Generated by gemma4:latest

Catalog Information

chatassistant_retail is a production-ready conversational AI chatbot designed specifically for the retail industry, providing intelligent assistance for retail inventory management. It features a multi-modal interface (text + images) powered by Azure OpenAI GPT-4o-mini, hybrid RAG search with Az

Description

chatassistant_retail is a production-ready conversational AI chatbot designed specifically for the retail industry, providing intelligent assistance for retail inventory management. It features a multi-modal interface (text + images) powered by Azure OpenAI GPT-4o-mini, hybrid RAG search with Az

Novelty

3/10

Tags

python cli ai-chatbot retail azure-openai langgraph rag gradio multi-modal inventory-management

Technologies

gradio langchain openai pydantic sqlalchemy

Claude Models

claude-opus-4-6

Quality Score

B
81.7/100
Structure
94
Code Quality
74
Documentation
100
Testing
75
Practices
71
Security
75
Dependencies
60

Strengths

  • Well-documented README with substantial content
  • CI/CD pipeline configured (github_actions)
  • Good test coverage (41% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Properly licensed project

Weaknesses

  • 811 duplicate lines detected \u2014 consider DRY refactoring
  • 1 'god files' with >500 LOC need decomposition

Security & Health

5.3h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
MIT
License
7.7%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
53.6%
json
35.0%
markdown
10.5%
toml
0.6%
shell
0.1%
yaml
0.1%
text
0.1%

Frameworks

pytest SQLAlchemy

Concepts (2)

Findings produced by Repobility · scan your repo at https://repobility.com/scan/
CategoryNameDescriptionConfidence
Source: Repobility analyzer · https://repobility.com
auto_descriptionProject Description!PyPI version ![Documentation Status](https://chatassistantretail.readthedocs.io/en/latest/?version=latest)80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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