Aashi779 / RQ-RAG

Codes for our paper "RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation"

RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

This is the repo of our paper "RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation"

Code Refactoring in Progress

Please note that the code in this repository is currently undergoing refactoring as part of preparations for submission to a conference and is not fully tested yet.

Getting Start

---

1. Installation

Clone this repository first, and install the dependencies.

git clone [e-mail]:chanchimin/RQ-RAG.git
cd RQ-RAG
pip install -r requirements.txt

2. Construct Search-Augmented Dataset

First, set up your openai api key

export OPENAI_API_KEY="your_api_key_here"

Second, preprocess your data to the following format

# for multi-turn data， your data should contain messages format, an example:

{
    "id": ...,
    "messages":
        [
            {   
                "role":"user",
                "content": ...,
            },
            {
                "role":"assistant",
                "content": ...,
            }
        ]
}

Afterward, execute the following lines, make sure to substitute your data path.

cd ./data_curation

python main_multiturn_answer_generate.py \
--raw_data_path_multiturn  \
"your file" \
--ndocs  \
3  \
--output_path  \
"your output_path" \
--search_engine_type  \
duckduckgo  \
--openai_api_key  \
"your key" \
--overwrite_output

After gathering the intermediate results, run:

python merge_intermediate.py \
--raw_data_path \
"intermediate data path" \
--output_path \
"final data output path"

3. Train the model

cd ..
bash retrieval_lm/scripts/train/script_finetune_7b.sh

4. Inference and Sample the results

# take hotpotqa as an example
# first inference the model, all the trajectory will be saved to "your_output_dir/final_results.json"
bash retrieval_lm/scripts/inference/search_engine/hotpotqa.sh
# then do the sample
bash retrieval_lm/scripts/sample_from_tree/hotpotqa.sh