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qaz159qaz159/
review-knowledge-base-tool

Name: review-knowledge-base-tool
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Review Knowledge Base

A RAG (Retrieval-Augmented Generation) system that scrapes academic paper reviews from OpenReview and builds a searchable vector database. Designed for AI agents to perform Precedent Checks — verifying whether a criticism of a paper aligns with historical review norms.

What's Inside

File	Purpose
`01_scrape_openreview.py`	Scrape submissions, decisions, meta-reviews, and reviews from OpenReview
`02_build_chromadb.py`	Build a ChromaDB vector database (embed Title + Abstract)
`03_push_to_hf.py`	Push/pull data to HuggingFace Datasets for versioning and sharing
`04_query_rag.py`	Query the database by semantic similarity (interactive or CLI)
`05_agent_integration.py`	`ReviewerRAG` class for programmatic Agent integration
`config.py`	Central configuration (venues, credentials, paths)
`run_pipeline.sh`	One-click pipeline: scrape → build → push

Supported Conferences

ICLR 2023–2026
NeurIPS 2023–2026
ICML 2023–2025
ACL ARR 2025 (monthly cycles: Feb, Apr, Jun, Aug, Oct, Dec)

Quick Start

1. Install Dependencies

bash

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. Set Credentials (Optional)

bash

export OPENREVIEW_USERNAME="your-email@example.com"
export OPENREVIEW_PASSWORD="your-password"
export HF_TOKEN="hf_xxxxxxx"

3. Run the Pipeline

bash

# Full pipeline
./run_pipeline.sh

# Or step by step
python 01_scrape_openreview.py
python 02_build_chromadb.py --require-decision --require-meta-review --rebuild

4. Query

bash

# Interactive
python 04_query_rag.py

# Single query
python 04_query_rag.py --query "using TTS synthetic data for training"

# Precedent check
python 04_query_rag.py --query "weak evaluation methodology" --check

# JSON output (for agents)
python 04_query_rag.py --query "..." --json

5. Agent Integration (Python)

python

from agent_integration import ReviewerRAG

rag = ReviewerRAG()
result = rag.check_criticism(
    paper_abstract="We propose a novel TTS model using synthetic data...",
    criticism="The paper relies on synthetic data which is unreliable."
)
print(result["is_valid_criticism"])  # True/False
print(result["suggestion"])          # How to revise the criticism

Data Schema

Each record contains:

Field	Description
`title`	Paper title
`abstract`	Paper abstract
`decision`	Accept (Poster/Spotlight/Oral) or Reject
`meta_review`	Area Chair's summary and final judgment
`reviews`	List of reviewer ratings + comments
`keywords`	Paper keywords
`venue` / `year`	Conference name and year

Using with HuggingFace

bash

# Push data
python 03_push_to_hf.py --require-decision

# Pull data on another machine
python 03_push_to_hf.py --pull

License

MIT

qaz159qaz159/review-knowledge-base-tool

Review Knowledge Base

What's Inside

Supported Conferences

Quick Start

1. Install Dependencies

2. Set Credentials (Optional)

3. Run the Pipeline

4. Query

5. Agent Integration (Python)

Data Schema

Using with HuggingFace

License

qaz159qaz159/
review-knowledge-base-tool