ragboost 0.0.1

Boosting Retrieval-Augmented Generation with Context Reordering

RAGBoost

Getting Started

Install from Source

Python >=3.10

git clone https://github.com/SecretSettler/RAGBoost.git
cd RAGBoost
pip install -e .

Using Docker

Docker Image

docker pull seanjiang01/prompt-planner:v0.0.1
docker run -d --gpus all --name prompt-planner-container prompt-planner
docker exec -it prompt-planner-container bash

Build from scatch

git clone https://github.com/SecretSettler/RAGBoost.git
cd RAGBoost
docker build -t ragboost .
docker run -d --gpus all --name ragboost-container ragboost
docker exec -it ragboost-container bash

Note: This is slow due to building FlashAttention from scatch.

Quick usage

Offline

Build dataset and index from scratch

---

1. BM25, MultihopRAG

RECOMMENDED: Please refer to examples/construct_rag_data/multihopRAG_bm25.py

Quick running command:

docker run -d \
  --name elasticsearch \
  -p 9200:9200 \
  -p 9300:9300 \
  -e "discovery.type=single-node" \
  -e "xpack.security.enabled=false" \
  -e "xpack.security.http.ssl.enabled=false" \
  -e "xpack.security.transport.ssl.enabled=false" \
  -e "ES_JAVA_OPTS=-Xms512m -Xmx512m" \
  docker.elastic.co/elasticsearch/elasticsearch:8.18.2

python examples/construct_rag_data/multihopRAG_bm25.py

2. Faiss, MultihopRAG

RECOMMENDED: Please refer to examples/construct_rag_data/multihopRAG_faiss.py

Quick running command:

python -m sglang.launch_server \
  --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \
  --is-embedding \
  --host 0.0.0.0 \
  --port 30000

python examples/construct_rag_data/multihopRAG_faiss.py

---

Generating and selecting plan

RECOMMENDED: Please refer to examples/planner/generate_plan.py

Quick running command:

python examples/planner/generate_plan.py --prompts_path <PATH-TO-YOUR RETRIEVAL-OUTPUT> --output_path <YOUR-PLAN-SAVE-PATH>

Launch inference

RECOMMENDED: Please refer to examples/planner/sglang_inference.py

Quick running command:

python -m sglang.launch_server --model-path Qwen/Qwen3-32B --port 30000 --tp-size 4 --reasoning-parser qwen3 --enable-metrics --schedule-policy lpm

python examples/planner/sglang_inference.py --model Qwen/Qwen3-32B --plan_path <YOUR-PLAN-SAVE-PATH> --corpus_path <PATH-TO-YOUR-CORPUS-WITH-CTX-LENGTH>

Data Format:

If you have your own data, please format to the example below. Currently we only support data with jsonl format. Each json should at least contain these attributes:

{
    "qid": 0,
    "text": "Is the sky blue?",
    "answer": ["Yes", "Yes the sky is blue"],
    "top_k_doc_id": [2, 8, 1, 10]
}

This should be under the --prompts_path for plan generation and selection.

Roadmap

• Implement the Group Aware RR scheduler
• Support online inference
• Implement a faster prefix cache
• Support multi-modality models