venkatesh-hyper/paperlens

Semantic search engine for research papers — find papers by meaning, not keywords.

What's novel

Semantic search engine for research papers — find papers by meaning, not keywords.

Code Analysis

10 files read · 3 rounds

A semantic search engine for arXiv research papers that uses sentence-transformer embeddings and FAISS vector indexing to find papers by meaning, with a FastAPI backend, Streamlit dashboard, and Groq LLM-powered paper summarization.

Strengths

Clean modular architecture with clear separation of ingestion, embedding, indexing, API, and dashboard layers; comprehensive test suite covering all endpoints with edge cases; well-structured FAISS index building with benchmarking and sanity checks.

Weaknesses

The 'RAG' pipeline is misnamed—it's a simple single-prompt LLM summarization call with no retrieval-augmented generation or multi-step reasoning; the cache implementation is a basic dict with manual eviction rather than a true LRU, and the dashboard sidebar contains outdated benchmark numbers that contradict the README.

Score Breakdown

Innovation

4 (25%)

Craft

71 (35%)

Traction

6 (15%)

Scope

67 (25%)

Signal breakdown

Innovation

Not Fork+1

Code Novelty+1

Concept Novelty+1

Craft

Ci+5

Tests+5

Polish+1

Releases+1

Has License+5

Code Quality+22

Readme Quality+15

Recent Activity+7

Structure Quality+5

Commit Consistency+0

Has Dependency Mgmt+5

Traction

Forks+0

Stars+6

Hn Points+0

Watchers+0

Early Traction+0

Devto Reactions+0

Community Contribs+0

Scope

Commits+5

Languages+3

Subsystems+10

Bloat Penalty+0

Completeness+7

Contributors+5

Authored Files+12

Readme Code Match+3

Architecture Depth+5

Implementation Depth+8

Evidence

Commits

Contributors

Files

Active weeks

TestsCI/CDREADMELicenseContributing

Repository

Language

Python

Stars

Forks

License

NOASSERTION