Adivamsi
D 58 completed
Other
unknown / html · tiny
5
Files
1,325
LOC
0
Frameworks
2
Languages
Pipeline State
completedRun ID
#1540289Phase
doneProgress
0%Started
2026-04-16 20:01:26Finished
2026-04-16 20:01:26LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
11.42Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:28Deduplication 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)
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