Rag Documentation Assistant
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unknown / python · tiny
43
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3,286
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2
Frameworks
7
Languages
Pipeline State
completedRun ID
#393156Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
35.14Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47716
Member of a group with 1 similar repo(s) — canonical #99869 view group →
Top concepts (2)
Project DescriptionWeb Backend
Repobility — the code-quality scanner for AI-generated software · https://repobility.com
AI Prompt
Create a production-ready Retrieval-Augmented Generation (RAG) system using Python. I need it to answer questions based on a document knowledge base. The system should handle document ingestion, which involves uploading files to S3, triggering a process via SQS, and using a Lambda function to download, chunk, and embed the text. For querying, it should use an API Gateway endpoint that calls a Lambda function to embed the question, search vectors in OpenSearch, and finally call the Claude API to generate an answer, citing sources. The entire system should be designed for deployment on AWS serverless architecture.
python rag aws fastapi serverless llm claude aws-s3 aws-sqs openai retrieval-augmented-generation
Generated by gemma4:latest
Catalog Information
A production-ready Retrieval-Augmented Generation (RAG) system that answers questions based on your document knowledge base. Deployed on AWS with serverless architecture.
Description
A production-ready Retrieval-Augmented Generation (RAG) system that answers questions based on your document knowledge base. Deployed on AWS with serverless architecture.
Novelty
3/10Tags
python rag aws fastapi serverless llm claude aws-s3 aws-sqs openai retrieval-augmented-generation
Technologies
anthropic fastapi gradio pydantic
Claude Models
claude-opus-4-6
Quality Score
B
80.9/100
Structure
89
Code Quality
85
Documentation
62
Testing
85
Practices
66
Security
100
Dependencies
60
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (73% 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 (C)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (3)
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
MIT
License
5.6%
Duplication
Languages
Frameworks
FastAPI pytest
Concepts (2)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Source: Repobility analyzer · https://repobility.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | A production-ready Retrieval-Augmented Generation (RAG) system that answers questions based on your document knowledge base. Deployed on AWS with serverless architecture. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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