Adivamsi

D 58 completed
Other
unknown / html · tiny
5
Files
1,325
LOC
0
Frameworks
2
Languages

Pipeline State

completed
Run ID
#1540289
Phase
done
Progress
0%
Started
2026-04-16 20:01:26
Finished
2026-04-16 20:01:26
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
11.42
Framework unique
Isolation
Last stage change
2026-05-10 03:35:28
Deduplication group #47271
Member of a group with 10,751 similar repo(s) — canonical #189445 view group →
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/

AI Prompt

Create a project that demonstrates an AI-powered insurance claim adjudication system. I need it to be a grounded retrieval service that processes PDFs. The core functionality should involve a LangGraph state machine that takes a request via a POST endpoint. This system must classify the request, retrieve relevant documents, ground the answer, and verify the quote against the context. Crucially, if the confidence is low, the similarity is low, or the quote isn't in context, the system must refuse to answer and provide a structured JSON audit trail detailing which gate tripped. The output should be structured JSON, not conversational chat.
python llm rag langgraph backend api insurance document-processing json
Generated by gemma4:latest

Catalog Information

Create a project that demonstrates an AI-powered insurance claim adjudication system. I need it to be a grounded retrieval service that processes PDFs. The core functionality should involve a LangGraph state machine that takes a request via a POST endpoint. This system must classify the request, retrieve relevant documents, ground the answer, and verify the quote against the context. Crucially, if the confidence is low, the similarity is low, or the quote isn't in context, the system must refuse

Tags

python llm rag langgraph backend api insurance document-processing json

Quality Score

D
57.6/100
Structure
32
Code Quality
100
Documentation
30
Testing
0
Practices
78
Security
100
Dependencies
50

Strengths

  • Low average code complexity — well-structured code
  • Good security practices — no major issues detected

Weaknesses

  • No LICENSE file — legal ambiguity for contributors
  • No tests found — high risk of regressions
  • No CI/CD configuration — manual testing and deployment

Recommendations

  • Add a test suite — start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Languages

html
87.9%
markdown
12.1%

Frameworks

None detected

Quality Timeline

1 quality score recorded.

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